system

Abstract

We provide the system. [Solution] A server means equipped with artificial intelligence that receives soil information and analyzes crop cultivation methods based on said soil information, A plan generation means for generating an optimal cultivation schedule for crops, A control means for automatically spraying water and fertilizer using a drone or irrigation device according to the generated cultivation schedule, A forecasting method for predicting crop demand and supply based on market data, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] While the demand for sustainable and environmentally friendly organic agriculture is increasing, efficient and high-quality crop cultivation requires advanced expertise and management. Therefore, it is difficult to manage, especially for novice farmers and home garden enthusiasts, and there are also problems of cost and time. The purpose of the present invention is to solve these problems by automatically providing an optimal cultivation plan from soil data and also performing demand prediction.

Means for Solving the Problems

[0005] This invention provides a system that proposes appropriate cultivation methods by using a server equipped with artificial intelligence that receives and analyzes soil information. This automatically generates an optimal cultivation schedule for crops and automates the application of water and fertilizer as needed by controlling drones and irrigation equipment. Furthermore, it predicts supply and demand based on market data and provides advice to the user based on this prediction, thereby achieving efficient and low-cost crop production.

[0006] "Soil information" refers to data about the properties and condition of the soil, specifically including information such as pH value, moisture content, temperature, and nutrient content.

[0007] "Crop cultivation methods" refer to specific techniques and processes for efficiently producing a particular crop, encompassing a series of steps from planting to harvesting.

[0008] "Server means" refers to a computer system or its function that processes data via a network, particularly one responsible for receiving, analyzing, storing, and transmitting data.

[0009] "Plan generation means" refers to a function or algorithm for automatically creating appropriate schedules and plans based on input data.

[0010] "Control means" refers to a mechanism that includes software and hardware for managing the operation of machines and devices, particularly for performing automatically set operations.

[0011] "Predictive tools" refer to technologies and algorithms used to predict future trends and demand based on past data and survey results.

[0012] "Terminal means" refers to devices and functions that provide an interface for users to input data or receive information.

[0013] A "user interface" refers to the part of a system that provides a means for the user to interact with it, and includes software and hardware components that enable the display and input of information. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

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

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

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

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

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] The system of this invention supports efficient and sustainable cultivation in agriculture. Specifically, the server analyzes soil information, proposes the optimal cultivation method, and performs automated cultivation management based on that method.

[0036] First, the user inputs soil sample data via a dedicated terminal. The terminal has the function of sending information such as soil pH, moisture content, and nutrient content to a server. The server receives this data and uses an AI algorithm to analyze the optimal crops and conditions for cultivation. The server generates a cultivation schedule based on the analysis results. This schedule plans the appropriate amount of water and fertilizer according to the planting and growth stages of the crops.

[0037] Users can view the cultivation schedule generated by the server on their terminal. Based on the schedule, commands are sent from the terminal to drones or irrigation equipment. This automated control system ensures that the appropriate amount of water and fertilizer is applied at the right time, reducing the user's workload.

[0038] Furthermore, the server analyzes market data and forecasts crop demand. This forecast information is communicated to the user and used to help formulate harvest timings and sales plans. For example, if the server predicts an increase in tomato demand, it will notify the user via their terminal of the appropriate planting time, allowing the user to further plan their cultivation based on that information.

[0039] Thus, the system of the present invention enables proper crop cultivation based on soil information and efficient agricultural management that reflects market trends. This makes it possible for novice farmers and home garden enthusiasts to produce high-quality organic vegetables without specialized knowledge.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user uses a soil sensor to collect information such as soil pH, humidity, temperature, and nutrient content, and inputs it into a terminal. This data is automatically sent to the server.

[0043] Step 2:

[0044] The server analyzes the received soil data using an AI algorithm. Through this analysis, it understands the soil characteristics and determines which crops will grow best under those conditions. Furthermore, it determines cultivation conditions such as the amount of water and fertilizer needed.

[0045] Step 3:

[0046] The server generates an optimal cultivation schedule based on the analysis results. This schedule includes planting timing, and the amount and timing of watering and fertilizing. The schedule is then sent to the user's terminal.

[0047] Step 4:

[0048] The user's terminal displays the received cultivation schedule on the screen and prepares to issue commands to automated spraying devices and drones. This allows the user to proceed with the work according to the presented cultivation plan.

[0049] Step 5:

[0050] The server uses market data to predict crop demand and supply. This forecast information is sent to the user's terminal, and the user adjusts harvest timing and sales strategies based on it.

[0051] Step 6:

[0052] Users periodically observe the condition and health of their crops and re-enter this information into their device. The device sends the new data to the server, and the system updates the cultivation plan as needed.

[0053] This series of steps allows users to achieve effective organic farming with minimal effort.

[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] Traditional agricultural management systems often require manual collection and analysis of soil information, hindering efficient cultivation. Furthermore, insufficient supply and demand forecasting based on market trends makes it difficult for producers to develop appropriate agricultural plans. Therefore, there is a need for automated systems that enable precise and sustainable cultivation management, even for novice farmers and small-scale producers.

[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 an information processing device that receives soil information and analyzes cultivation techniques based on the soil information; a planning device that generates optimal conditions for cultivation; and a management device that automatically sprays water and nutrients using an unmanned aerial vehicle or water management device according to the generated conditions. This enables users to produce agricultural products appropriately and efficiently even if it is their first time, and enables sustainable cultivation management.

[0059] "Soil information" refers to data concerning the physical or chemical properties of soil, specifically including information such as pH, moisture content, and nutrient content.

[0060] An "information processing device" is a device that has the function of analyzing soil information and determining the optimal cultivation technique.

[0061] A "plan creation device" is a device that generates cultivation schedules and conditions based on analysis results, and is used to construct an efficient cultivation plan.

[0062] A "management device" is a device that controls unmanned aerial vehicles and water management equipment to automatically spray water and nutrients according to the generated cultivation conditions.

[0063] "Market information" refers to data related to the supply and demand of agricultural products in the market, and is information that contributes to supply and demand forecasting.

[0064] An "analytical device" is a device that analyzes market information to predict supply and demand and provides producers with useful data.

[0065] A "communication device" is a device that has an interface function to receive data from a user and provide it to a server.

[0066] A "user connection device" is a device equipped with an interface for notifying users of generated cultivation conditions and analysis results, and for directly providing information to them.

[0067] The system of this invention is designed to support efficient and sustainable cultivation in agriculture. In this system, the server, terminals, and users work together, with each component fulfilling its respective role.

[0068] Users utilize a dedicated terminal to acquire soil information. This terminal is equipped with sensors that measure data such as soil pH, moisture content, and nutrient content. The terminal collects the measurement data and transmits it to a server via its communication function. The terminal uses Wi-Fi or cellular connectivity to transmit data.

[0069] The server receives incoming data and acts as an information processing unit. In particular, it analyzes soil data using AI algorithms (for example, machine learning frameworks such as TENSORFLOW®). This analysis allows for the determination of optimal crops and conditions for cultivation. The server also retrieves historical data from a database to support more precise analysis.

[0070] Based on the analysis results, the server acts as a planning device to create a specific cultivation schedule. This schedule includes the amount of water and fertilizer needed according to the crop's growth stage, as well as the timing of fertilization.

[0071] The terminal also functions as a management device, sending instructions to unmanned aerial vehicles (drones) and irrigation systems according to the cultivation schedule sent from the server. This automatically distributes the appropriate amount of water and nutrients, reducing the user's workload.

[0072] Furthermore, the server, as part of its analytical capabilities, analyzes market information and forecasts demand for agricultural products. A generative AI model is used for this purpose. Based on the forecast information, the server notifies the user, who can then use this information to determine harvest timing and develop sales strategies.

[0073] For example, if the server performs market analysis using an AI model and predicts an increase in tomato demand, this information can be provided to the user, enabling them to adjust the cultivation timing and create a cultivation plan that maximizes sales.

[0074] An example of a prompt is, "Use the AI ​​model to predict the optimal growing conditions for tomatoes in your current location during the summer." This prompt is used for the server to suggest the optimal growing method, taking into account climate information and historical data.

[0075] Thus, the system of the present invention provides a multi-functional platform for efficiently cultivating agricultural products by integrating and utilizing soil information and market information.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user inputs soil sample data using a dedicated terminal. The terminal is connected to sensors that measure soil pH, moisture content, nutrient content, etc., and the user operates these sensors to acquire data. The entered soil information is stored in the terminal's internal memory and sent to the server in the next step.

[0079] Step 2:

[0080] The terminal transmits the acquired soil data to the server. During this process, the terminal uses a communication line (such as Wi-Fi or cellular connection) to encrypt the data before securely transmitting it to the server. As output of this process, the server receives the soil information and stores it in its database.

[0081] Step 3:

[0082] The server uses an AI algorithm to analyze the received soil information. The input data is processed using machine learning libraries such as TensorFlow to determine appropriate cultivation techniques and select the optimal crops. As an output of the analysis, the server generates recommended cultivation conditions.

[0083] Step 4:

[0084] Based on the generated cultivation conditions, the server acts as a planning device to create a specific cultivation schedule. This schedule includes detailed settings for planting time, fertilizer application, and irrigation amounts for each growth stage. This schedule is then sent to the terminal as output.

[0085] Step 5:

[0086] The terminal displays the cultivation schedule from the server in a format that the user can review. The user reviews and approves each item on the terminal screen and then initiates the automation process based on that information. This action allows the terminal, acting as a management device, to wirelessly transmit instructions for watering and fertilizing to drones and irrigation systems.

[0087] Step 6:

[0088] The server uses a generated AI model based on market information to forecast supply and demand. This analysis process takes generator data and historical trend data as input to predict increases in demand and price fluctuations. The output is notified to the user's terminal in a format they can review, helping them adjust their cultivation plan as needed.

[0089] (Application Example 1)

[0090] 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."

[0091] In today's increasingly urbanized society, securing a sustainable food supply is a critical issue. In particular, enabling urban residents to participate in local agriculture and efficiently manage cultivation in community gardens and public facilities contributes to promoting social interaction and environmental conservation, but technical and knowledge-based barriers exist. A system is needed to overcome these challenges and realize sustainable urban agriculture.

[0092] 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.

[0093] In this invention, the server includes an information processing device equipped with artificial intelligence that receives soil data and analyzes plant cultivation methods based on the data; a computation generation means that generates an optimal plant cultivation plan; and an operation means that automatically supplies water and nutrients using an unmanned aerial vehicle or a watering device according to the generated cultivation plan. This enables urban residents to participate in efficient and sustainable agricultural activities without requiring technical knowledge.

[0094] "Soil data" refers to information about soil characteristics that affect plant cultivation, such as soil pH, moisture content, and nutrient content.

[0095] "Plant cultivation methods" refer to a series of methods and procedures necessary to promote plant growth, including the process from sowing seeds to harvesting.

[0096] "Artificial intelligence" refers to algorithms and technologies that enable computer systems to mimic human intelligence and perform problem-solving and decision-making.

[0097] "Information processing device means" is a general term for hardware and software designed to receive and process data.

[0098] A "cultivation plan" is a schedule that defines the optimal conditions and schedule for effectively growing a particular plant.

[0099] "Computation generation means" refers to a set of software and hardware mechanisms for generating a specific plan based on analyzed data.

[0100] An "unmanned aerial vehicle" is a flying device that is operated remotely or autonomously without a human on board, and is mainly used in the agricultural sector for crop monitoring and material distribution.

[0101] A "watering system" is equipment that automatically sprays water or nutrient solution onto plants, and is a device that supplies the water necessary for plant growth.

[0102] "Operating means" refers to a system or interface for controlling a specific device or process.

[0103] "Socioeconomic data" refers to data that includes statistics and information related to the economic activities of a society, and is used for demand forecasting and market analysis.

[0104] "Resident participation functions" refer to the functions of a system designed to allow urban residents to actively participate, and to functions that promote joint management and participation.

[0105] To implement this invention, a system is constructed that combines an information processing device, a computation generation device, and an operation device. The information processing device receives soil data input by the user using a terminal device, and this data includes the soil's pH, moisture content, and nutrient content. This data is analyzed using an artificial intelligence algorithm to analyze plant cultivation methods.

[0106] The calculation generation means generates an optimal cultivation plan based on the analysis results obtained from the information processing device means. This plan includes a schedule for supplying water and nutrients during the cultivation period, specifying the exact timing and amount in detail.

[0107] The operating system controls autonomous unmanned aerial vehicles and irrigation equipment. This allows for the supply of the necessary amount of water and nutrients to plants based on the generated cultivation plan. This control is performed by software running on a cloud-based server, ensuring reliable agricultural management.

[0108] The server also collects socioeconomic data and uses artificial intelligence to predict supply and demand. This allows users to obtain real-time information to efficiently manage collaborative farming projects in urban areas.

[0109] For example, if a local citizens' group is jointly managing a community garden project, this system can be used to effectively manage resources and maximize yields.

[0110] Examples of prompts for the generated AI model include questions such as, "What crops are suitable for my community garden?" or "What is the best time and method for the next harvest?" This allows users to receive appropriate guidance and plan their agricultural activities systematically.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user inputs soil data using a terminal. This data includes soil pH, moisture content, and nutrient content. This data is sent from the terminal to the server, which receives it and prepares it for processing.

[0114] Step 2:

[0115] The server uses artificial intelligence algorithms based on received soil data to analyze the optimal plant cultivation methods. The AI ​​model analyzes the input data and generates suggestions for appropriate growing conditions and planting times. The output is a list of recommended cultivation conditions for each plant type.

[0116] Step 3:

[0117] The server uses the results analyzed by AI to generate an optimal cultivation plan. This plan includes a schedule for supplying water and nutrients during the cultivation period. Based on the generated cultivation plan, a detailed schedule is created to enable necessary resource management.

[0118] Step 4:

[0119] Based on the generated cultivation plan, the server sends control signals to the unmanned aerial vehicle or watering device. This enables the automatic supply of water and nutrients at predetermined times according to the plan. Specifically, the transmission of control signals includes remotely controlling the device over a network.

[0120] Step 5:

[0121] The server predicts plant demand and supply based on socioeconomic data. It uses AI models to analyze this data and perform demand forecasting. This allows for the development of cultivation strategies tailored to market conditions. The output provides users with predicted demand patterns and supply adjustment information.

[0122] Step 6:

[0123] Users review the generated cultivation plan and demand forecast information to make decisions that optimize project progress and harvest timing. This information is displayed in real time on the terminal's screen, providing an environment where users can easily make decisions. Examples of prompt input include specific questions such as, "When is the best time for the next harvest?"

[0124] 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.

[0125] This invention is a system that combines a server, a terminal, and an emotion engine to provide more personalized agricultural support. This system analyzes the user's emotions and incorporates the feedback based on that analysis into agricultural planning, enabling agricultural management that also takes psychological and emotional factors into consideration.

[0126] First, the user inputs soil data and other agricultural data into the terminal. The terminal then sends this data to the server. The server uses an AI algorithm to analyze the soil data and determine the optimal cultivation method for the crops.

[0127] Next, the device collects emotional data from the user's facial expressions and voice. The emotion engine analyzes this data to determine the user's emotional state, such as satisfaction level and stress level. Based on the emotional data, the server adjusts the cultivation schedule and market forecast. Specifically, if the user is feeling stressed, the system may simplify tasks or send encouraging messages to alleviate that stress.

[0128] For example, suppose a user is busy and stressed. In this case, the emotion engine recognizes this state, and the server automatically generates a new cultivation schedule that reduces the workload. Then, a supportive message such as "You've worked hard, watering is all you need to do today" is sent to the user through their device.

[0129] Furthermore, the emotion engine analyzes long-term emotional data and understands user trends, which contributes to improving the system itself. For example, if a user consistently shows high satisfaction with a particular task, that method can be applied to other cultivation plans.

[0130] Through this process, the system provides personalized agricultural support that also takes into account the user's emotions. By utilizing the emotion engine, agricultural work can not only meet physical requirements but also enhance psychological satisfaction. Implementing it in this way improves the quality of agricultural support and enables more comprehensive agricultural management.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses a soil sensor to collect data such as soil pH, humidity, temperature, and nutrient content, and inputs it into a terminal. The terminal then transmits this data to a server.

[0134] Step 2:

[0135] The server analyzes the received soil data using an AI algorithm to determine the optimal crop selection and cultivation method. Following this, it generates a specific cultivation schedule.

[0136] Step 3:

[0137] The user's device collects emotional data through its facial recognition camera and voice recognition microphone. This data is then transmitted from the device to the emotion engine.

[0138] Step 4:

[0139] The emotion engine analyzes collected data to recognize the user's current emotional state. For example, it determines stress levels, fatigue, satisfaction levels, and so on.

[0140] Step 5:

[0141] The server receives the sentiment analysis results from the emotion engine and adjusts the cultivation schedule accordingly. If necessary, it generates a new schedule that includes reduced workload and encouraging messages.

[0142] Step 6:

[0143] The device notifies the user of the adjusted cultivation schedule and support messages. For example, it might display a message such as, "You seem tired today, so please take adequate rest while doing only a little work."

[0144] Step 7:

[0145] The emotion engine analyzes emotional data collected over a long period to evaluate the user's emotional tendencies. Based on these results, it makes additional adjustments to improve the quality of cultivation support provided by the server.

[0146] Through this series of steps, the system can provide personalized agricultural support that responds to the user's emotional state.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] Traditional agricultural support systems primarily determined crop cultivation methods based on physical conditions, making it difficult to consider the psychological or emotional factors of those involved in agriculture. As a result, while work efficiency was optimized, there was a challenge in adequately reducing worker satisfaction and stress.

[0150] 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.

[0151] In this invention, the server includes an information processing means equipped with artificial intelligence that receives soil data and emotional data and analyzes crop cultivation methods based on the data; a plan generation means that generates an optimal crop cultivation schedule and adjusts the schedule based on the user's emotional state; and an emotional analysis means that acquires emotional information from the user's facial expressions and voice and performs emotional analysis. As a result, the agricultural support system can propose cultivation methods that take into account not only physical conditions but also psychological satisfaction.

[0152] "Soil data" refers to information that indicates the condition of the soil in crop cultivation, and includes factors such as humidity, temperature, and pH value.

[0153] "Emotional data" refers to information that indicates a user's psychological and emotional state, obtained by analyzing the user's facial expressions and voice.

[0154] "Artificial intelligence" is a technology in which computers mimic human intellectual activity and perform analysis and decision-making to solve specific problems.

[0155] "Information processing means" refers to a process or device for analyzing received data and generating output according to a specific purpose.

[0156] A "plan generation means" is a process or device that has the function of constructing an optimal activity schedule based on data analysis.

[0157] "Emotional analysis tools" are technologies that evaluate a user's emotional state and express it as specific numerical values ​​or categories.

[0158] "Predictive means" refers to a process or device that predicts future changes in demand, supply, etc., based on market information and environmental conditions.

[0159] This invention is an agricultural support system that combines a server, a terminal, and an emotion engine. A key feature of this system is that it manages agricultural operations while taking into account the user's psychological state.

[0160] Specifically, users input soil data using a terminal. The terminal sends this data to a server via the internet. The server analyzes the received data using algorithms such as Python's SciKit-Learn to determine the optimal crop cultivation method. In this process, the server also refers to historical data to improve the accuracy of the analysis.

[0161] The device acquires emotional data by collecting the user's facial expressions and voice using the camera and microphone. This enables real-time data collection. The emotion engine analyzes the collected emotional data to identify the user's satisfaction level and stress level.

[0162] Based on the analysis results of the emotion engine, the server adjusts the cultivation schedule. For example, if the user is feeling stressed, the server generates a simplified schedule and sends a message to the user via the terminal saying, "Today, just watering is enough."

[0163] A concrete example of a prompt message could be, "If the user is experiencing stress, suggest what kind of agricultural support should be provided." By sending this to a generative AI model, more personalized agricultural support can be achieved.

[0164] The system of the present invention enables users to perform agricultural management that not only adheres to numerical requirements but also provides a sense of psychological satisfaction. This improves the quality of agricultural support and increases worker satisfaction.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user uses a terminal to input soil data. The terminal collects data automatically acquired from soil sensors and data manually entered by the user. The entered data includes soil moisture, temperature, pH value, etc. The terminal organizes this data and prepares it for transmission to the server.

[0168] Step 2:

[0169] The terminal transmits the collected soil data to the server via the internet. The data is securely transmitted using the HTTPS protocol. The server stores the received data in a database for analysis and prepares it for analysis.

[0170] Step 3:

[0171] The server analyzes the received soil data using libraries such as Python's SciKit-Learn library. Specifically, it uses machine learning algorithms to analyze the characteristics of the soil data and determine the optimal cultivation method based on that analysis. As output, cultivation guidelines such as fertilizer application and irrigation amounts are generated.

[0172] Step 4:

[0173] The device collects the user's facial expressions and voice through its camera and microphone. The input data represents the user's emotions, and the device collects this data in real time, preparing to send it to a server as emotion data.

[0174] Step 5:

[0175] The server uses an emotion engine to analyze emotional data received from the terminal. Based on the input data, it uses text analysis and speech analysis technologies to determine the user's emotional state. As a result of this analysis, the user's satisfaction level and stress level are output.

[0176] Step 6:

[0177] The server adjusts the cultivation schedule based on the analysis of emotional data. For example, if a user is experiencing high stress levels, the schedule may be simplified. This adjusted information is then sent to the terminal.

[0178] Step 7:

[0179] The terminal notifies the user of the adjusted cultivation schedule received from the server. Furthermore, it displays encouraging and instructional messages tailored to the user's situation. For example, a message such as, "Today, just watering is sufficient," might be displayed.

[0180] Step 8:

[0181] The server analyzes long-term emotional data to understand users' emotional tendencies and adjusts the system to utilize this information for future agricultural support. This improves the quality of agricultural support and increases users' psychological satisfaction.

[0182] (Application Example 2)

[0183] 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".

[0184] Traditional agricultural support systems focus on optimizing crop cultivation based on physical conditions, but rarely consider the emotional state of the user. Therefore, there is a need for new approaches to reduce the psychological burden on workers and improve the efficiency and satisfaction of agricultural work.

[0185] 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.

[0186] In this invention, the server includes an information processing device equipped with artificial intelligence that receives soil information and analyzes crop cultivation methods, a plan generation device that generates an optimal crop cultivation schedule, and an emotion analysis device that collects user emotion data and adjusts the cultivation schedule based on said emotion data. This enables agricultural management that is in line with the user's psychological state, reducing the psychological burden on workers while improving work efficiency and satisfaction.

[0187] "Soil information" refers to data about the condition and characteristics of the soil that affect crop cultivation.

[0188] An "information processing device equipped with artificial intelligence" is an information processing device that has the ability to analyze crop cultivation methods based on acquired data.

[0189] A "plan generation means" is a means that has the function of creating an optimal crop cultivation schedule based on the analyzed data.

[0190] An "emotion analysis tool" is a tool that evaluates a user's emotional state from data such as facial expressions and voice, and reflects that in agricultural management.

[0191] An "unmanned aircraft" is a flying device that performs tasks remotely or automatically, and is responsible for tasks such as spraying water and fertilizer.

[0192] A "water supply system" is a device that supplies water to crops at predetermined times and in predetermined amounts.

[0193] "Market information" refers to data and forecasts regarding supply and demand related to the buying and selling of crops.

[0194] A "message generation means" is a means that has the function of generating work instructions and encouraging messages based on the user's psychological state.

[0195] "User information communication means" refers to a means of informing users about crop cultivation schedules and market forecast results.

[0196] The system for realizing this invention consists of a user, a server, an information processing device, and an unmanned aerial vehicle or water supply device. First, the user collects soil information using a terminal equipped with sensors. This soil information is analyzed by artificial intelligence in the information processing device to determine the optimal cultivation method.

[0197] Next, to analyze the user's emotional state, the device captures the user's facial expressions and voice through its camera and microphone. This data is sent to a server, which uses emotion analysis tools to determine the user's emotional state. Based on this data, the plan generation tool adjusts the cultivation schedule. By making adjustments according to the user's stress and satisfaction levels, the psychological burden is reduced.

[0198] Watering and fertilizing are automatically performed by drones or watering systems based on a pre-adjusted cultivation schedule. This process is designed to minimize the burden and stress faced by users, based on the results of sentiment analysis.

[0199] As a concrete example, in a home garden where tomatoes are grown, if the system determines that the user is busy and stressed on the weekend, it will only instruct the user to water the plants and send a supportive message such as, "That's all you need to do today."

[0200] The introduction of this system will reduce physical and psychological burdens on users, allowing them to enjoy a more efficient and satisfying agricultural experience.

[0201] An example of a prompt from a generated AI model is: "Considering the user's emotional state, suggest a work schedule that a home farming robot should adapt to. Based on recent soil data and the user's stress level, what tasks would be optimal?"

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user collects soil information using a terminal. The terminal, which has a built-in sensor, acquires data such as soil moisture, pH, and temperature, and transmits this information to the server in a digital format. The input is soil sensor data, and the output is digital soil information that has been prepared for analysis.

[0205] Step 2:

[0206] The server analyzes the soil information received by the information processing unit. Artificial intelligence receives the soil information as input data and performs data calculations to determine the optimal cultivation method for each crop. The output is cultivation guidelines.

[0207] Step 3:

[0208] The device collects the user's emotional state. It acquires the user's facial expressions and voice data through the camera and microphone, and sends this to a server where an emotion analysis system operates. The input is the user's emotional data, and the output is the emotion analysis result.

[0209] Step 4:

[0210] The server analyzes the user's emotional data using emotion analysis tools. It evaluates the user's psychological state using facial recognition and voice analysis technologies, quantifying stress and satisfaction levels. This output data represents the emotional state.

[0211] Step 5:

[0212] The server provides the analysis results to the planning generation system, which adjusts the cultivation schedule based on emotional state and soil information. Data processing optimizes the cultivation tasks so that the user has a desirable experience. The output is the adjusted cultivation schedule.

[0213] Step 6:

[0214] The terminal controls unmanned aerial vehicles or watering systems to spray water and fertilizer based on a pre-configured cultivation schedule. The input is the pre-configured cultivation schedule, and the output is the execution of the automated spraying process.

[0215] Step 7:

[0216] The user receives support messages sent from the server. A message generation system creates encouragement and instructions tailored to the user's emotional state and delivers them to the user via voice or text. The inputs are the emotion analysis results and the cultivation schedule, and the output is a support message for the user.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] The system of this invention supports efficient and sustainable cultivation in agriculture. Specifically, the server analyzes soil information, proposes the optimal cultivation method, and performs automated cultivation management based on that method.

[0234] First, the user inputs soil sample data via a dedicated terminal. The terminal has the function of sending information such as soil pH, moisture content, and nutrient content to a server. The server receives this data and uses an AI algorithm to analyze the optimal crops and conditions for cultivation. The server generates a cultivation schedule based on the analysis results. This schedule plans the appropriate amount of water and fertilizer according to the planting and growth stages of the crops.

[0235] Users can view the cultivation schedule generated by the server on their terminal. Based on the schedule, commands are sent from the terminal to drones or irrigation equipment. This automated control system ensures that the appropriate amount of water and fertilizer is applied at the right time, reducing the user's workload.

[0236] Furthermore, the server analyzes market data and forecasts crop demand. This forecast information is communicated to the user and used to help formulate harvest timings and sales plans. For example, if the server predicts an increase in tomato demand, it will notify the user via their terminal of the appropriate planting time, allowing the user to further plan their cultivation based on that information.

[0237] Thus, the system of the present invention enables proper crop cultivation based on soil information and efficient agricultural management that reflects market trends. This makes it possible for novice farmers and home garden enthusiasts to produce high-quality organic vegetables without specialized knowledge.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user uses a soil sensor to collect information such as soil pH, humidity, temperature, and nutrient content, and inputs it into a terminal. This data is automatically sent to the server.

[0241] Step 2:

[0242] The server analyzes the received soil data using an AI algorithm. Through this analysis, it understands the soil characteristics and determines which crops will grow best under those conditions. Furthermore, it determines cultivation conditions such as the amount of water and fertilizer needed.

[0243] Step 3:

[0244] The server generates an optimal cultivation schedule based on the analysis results. This schedule includes planting timing, and the amount and timing of watering and fertilizing. The schedule is then sent to the user's terminal.

[0245] Step 4:

[0246] The user's terminal displays the received cultivation schedule on the screen and prepares to issue commands to automated spraying devices and drones. This allows the user to proceed with the work according to the presented cultivation plan.

[0247] Step 5:

[0248] The server uses market data to predict crop demand and supply. This forecast information is sent to the user's terminal, and the user adjusts harvest timing and sales strategies based on it.

[0249] Step 6:

[0250] Users periodically observe the condition and health of their crops and re-enter this information into their device. The device sends the new data to the server, and the system updates the cultivation plan as needed.

[0251] This series of steps allows users to achieve effective organic farming with minimal effort.

[0252] (Example 1)

[0253] 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."

[0254] Traditional agricultural management systems often require manual collection and analysis of soil information, hindering efficient cultivation. Furthermore, insufficient supply and demand forecasting based on market trends makes it difficult for producers to develop appropriate agricultural plans. Therefore, there is a need for automated systems that enable precise and sustainable cultivation management, even for novice farmers and small-scale producers.

[0255] 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.

[0256] In this invention, the server includes an information processing device that receives soil information and analyzes cultivation techniques based on the soil information; a planning device that generates optimal conditions for cultivation; and a management device that automatically sprays water and nutrients using an unmanned aerial vehicle or water management device according to the generated conditions. This enables users to produce agricultural products appropriately and efficiently even if it is their first time, and enables sustainable cultivation management.

[0257] "Soil information" refers to data concerning the physical or chemical properties of soil, specifically including information such as pH, moisture content, and nutrient content.

[0258] An "information processing device" is a device that has the function of analyzing soil information and determining the optimal cultivation technique.

[0259] A "plan creation device" is a device that generates cultivation schedules and conditions based on analysis results, and is used to construct an efficient cultivation plan.

[0260] A "management device" is a device that controls unmanned aerial vehicles and water management equipment to automatically spray water and nutrients according to the generated cultivation conditions.

[0261] "Market information" refers to data related to the supply and demand of agricultural products in the market, and is information that contributes to supply and demand forecasting.

[0262] An "analytical device" is a device that analyzes market information to predict supply and demand and provides producers with useful data.

[0263] A "communication device" is a device that has an interface function to receive data from a user and provide it to a server.

[0264] A "user connection device" is a device equipped with an interface for notifying users of generated cultivation conditions and analysis results, and for directly providing information to them.

[0265] The system of this invention is designed to support efficient and sustainable cultivation in agriculture. In this system, the server, terminals, and users work together, with each component fulfilling its respective role.

[0266] Users utilize a dedicated terminal to acquire soil information. This terminal is equipped with sensors that measure data such as soil pH, moisture content, and nutrient content. The terminal collects the measurement data and transmits it to a server via its communication function. The terminal uses Wi-Fi or cellular connectivity to transmit data.

[0267] The server receives incoming data and acts as an information processing unit. In particular, it analyzes soil data using AI algorithms (for example, machine learning frameworks such as TensorFlow). This analysis allows for the determination of optimal crops and conditions for cultivation. The server also retrieves historical data from a database to support more precise analysis.

[0268] Based on the analysis results, the server acts as a planning device to create a specific cultivation schedule. This schedule includes the amount of water and fertilizer needed according to the crop's growth stage, as well as the timing of fertilization.

[0269] The terminal also functions as a management device, sending instructions to unmanned aerial vehicles (drones) and irrigation systems according to the cultivation schedule sent from the server. This automatically distributes the appropriate amount of water and nutrients, reducing the user's workload.

[0270] Furthermore, the server, as part of its analytical capabilities, analyzes market information and forecasts demand for agricultural products. A generative AI model is used for this purpose. Based on the forecast information, the server notifies the user, who can then use this information to determine harvest timing and develop sales strategies.

[0271] For example, if the server performs market analysis using an AI model and predicts an increase in tomato demand, this information can be provided to the user, enabling them to adjust the cultivation timing and create a cultivation plan that maximizes sales.

[0272] An example of a prompt is, "Use the AI ​​model to predict the optimal growing conditions for tomatoes in your current location during the summer." This prompt is used for the server to suggest the optimal growing method, taking into account climate information and historical data.

[0273] Thus, the system of the present invention provides a multi-functional platform for efficiently cultivating agricultural products by integrating and utilizing soil information and market information.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The user inputs soil sample data using a dedicated terminal. The terminal is connected to sensors that measure soil pH, moisture content, nutrient content, etc., and the user operates these sensors to acquire data. The entered soil information is stored in the terminal's internal memory and sent to the server in the next step.

[0277] Step 2:

[0278] The terminal transmits the acquired soil data to the server. At this time, the terminal uses a communication line (such as Wi-Fi or cellular connection), encrypts the data, and then performs a secure transmission to the server. As the output of this process, the server receives the soil information and stores it in the database.

[0279] Step 3:

[0280] The server analyzes the received soil information using an AI algorithm. The input data is processed using a machine learning library such as TensorFlow, and appropriate cultivation techniques and the selection of optimal crops are carried out. As the output result of the analysis, the server generates recommended cultivation conditions.

[0281] Step 4:

[0282] Based on the generated cultivation conditions, the server creates a specific cultivation schedule as a planning device. In this schedule, the planting time, the amount of fertilizer and irrigation for each growth stage are set in detail. As the output, this schedule is transmitted to the terminal.

[0283] Step 5:

[0284] The terminal displays the cultivation schedule from the server in a format that can be confirmed by the user. The user checks and approves each item on the terminal screen and performs an operation to start automation based on the content. By this operation, the terminal, as a management device, wirelessly transmits instructions for spraying water and fertilizer to the drone and irrigation system.

[0285] Step 6:

[0286] The server performs supply and demand forecasting using the AI model generated based on market information. In this analysis process, generator data and past trend data are input, and the increase in demand and price fluctuations are predicted. The output result is notified to the terminal in a form that can be confirmed by the user, and helps to adjust the cultivation plan as needed.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0289] In modern society where urbanization is progressing, ensuring sustainable food supply is an important issue. In particular, for urban residents to participate in local agriculture and efficiently manage cultivation in community gardens and public facilities contributes to social interaction promotion and environmental conservation, but there are technical and knowledge barriers. There is a need for a system to solve these problems and realize sustainable urban agriculture.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes an information processing device means with artificial intelligence that receives soil data and analyzes the plant cultivation method based on the data, a calculation generation means that generates an optimal growth plan for the plant, and an operation means that automatically supplies water and nutrients by an unmanned aircraft or a watering device according to the generated growth plan. As a result, urban residents can participate in efficient and sustainable agricultural activities without technical knowledge.

[0292] "Soil data" refers to the characteristic information of the soil that affects plant cultivation, such as the pH of the soil, the moisture content, and the nutrient content.

[0293] "Plant cultivation method" refers to a series of methods and procedures necessary to promote plant growth, including the process from sowing to harvesting.

[0294] "Artificial intelligence" refers to algorithms and technologies for a computer system to imitate human intelligence and perform problem-solving and decision-making.

[0295] "Information processing device means" is a general term for hardware and software designed to receive and process data.

[0296] A "cultivation plan" is a schedule that defines the optimal conditions and schedule for effectively growing a particular plant.

[0297] "Computation generation means" refers to a set of software and hardware mechanisms for generating a specific plan based on analyzed data.

[0298] An "unmanned aerial vehicle" is a flying device that is operated remotely or autonomously without a human on board, and is mainly used in the agricultural sector for crop monitoring and material distribution.

[0299] A "watering system" is equipment that automatically sprays water or nutrient solution onto plants, and is a device that supplies the water necessary for plant growth.

[0300] "Operating means" refers to a system or interface for controlling a specific device or process.

[0301] "Socioeconomic data" refers to data that includes statistics and information related to the economic activities of a society, and is used for demand forecasting and market analysis.

[0302] "Resident participation functions" refer to the functions of a system designed to allow urban residents to actively participate, and to functions that promote joint management and participation.

[0303] To implement this invention, a system is constructed that combines an information processing device, a computation generation device, and an operation device. The information processing device receives soil data input by the user using a terminal device, and this data includes the soil's pH, moisture content, and nutrient content. This data is analyzed using an artificial intelligence algorithm to analyze plant cultivation methods.

[0304] Based on the analysis results obtained from the information processing device means, the calculation and generation means generates an optimal cultivation plan. This plan includes the supply schedule of water and nutrients during the cultivation period, showing the specific timing and quantity in detail.

[0305] The operation means controls autonomous unmanned aircraft and watering devices. As a result, based on the generated cultivation plan, it becomes possible to supply the necessary amounts of water and nutrients to the plants. This control is performed by software operating on a cloud-based server, realizing highly reliable agricultural management.

[0306] In addition, the server collects socio-economic data and uses artificial intelligence to predict demand and supply. As a result, the user can obtain in real time the information for efficiently managing a co-cultivation project in an urban area.

[0307] As a specific example, when a citizen group in a certain area is jointly operating a community garden project, using this system can effectively manage resources and maximize the harvest.

[0308] Examples of prompt sentences for the generation AI model include questions such as "What crops are suitable for my community garden?" and "Please tell me the optimal timing and method for the next harvest." As a result, the user can obtain appropriate guidance and proceed with agricultural activities in a planned manner.

[0309] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The user inputs soil data using a terminal. The data to be input includes the pH, moisture content, and nutrient content of the soil. This data is transmitted from the terminal to the server. The server receives this and prepares for data processing.

[0312] Step 2:

[0313] The server uses artificial intelligence algorithms based on received soil data to analyze the optimal plant cultivation methods. The AI ​​model analyzes the input data and generates suggestions for appropriate growing conditions and planting times. The output is a list of recommended cultivation conditions for each plant type.

[0314] Step 3:

[0315] The server uses the results analyzed by AI to generate an optimal cultivation plan. This plan includes a schedule for supplying water and nutrients during the cultivation period. Based on the generated cultivation plan, a detailed schedule is created to enable necessary resource management.

[0316] Step 4:

[0317] Based on the generated cultivation plan, the server sends control signals to the unmanned aerial vehicle or watering device. This enables the automatic supply of water and nutrients at predetermined times according to the plan. Specifically, the transmission of control signals includes remotely controlling the device over a network.

[0318] Step 5:

[0319] The server predicts plant demand and supply based on socioeconomic data. It uses AI models to analyze this data and perform demand forecasting. This allows for the development of cultivation strategies tailored to market conditions. The output provides users with predicted demand patterns and supply adjustment information.

[0320] Step 6:

[0321] Users review the generated cultivation plan and demand forecast information to make decisions that optimize project progress and harvest timing. This information is displayed in real time on the terminal's screen, providing an environment where users can easily make decisions. Examples of prompt input include specific questions such as, "When is the best time for the next harvest?"

[0322] 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.

[0323] This invention is a system that combines a server, a terminal, and an emotion engine to provide more personalized agricultural support. This system analyzes the user's emotions and incorporates the feedback based on that analysis into agricultural planning, enabling agricultural management that also takes psychological and emotional factors into consideration.

[0324] First, the user inputs soil data and other agricultural data into the terminal. The terminal then sends this data to the server. The server uses an AI algorithm to analyze the soil data and determine the optimal cultivation method for the crops.

[0325] Next, the device collects emotional data from the user's facial expressions and voice. The emotion engine analyzes this data to determine the user's emotional state, such as satisfaction level and stress level. Based on the emotional data, the server adjusts the cultivation schedule and market forecast. Specifically, if the user is feeling stressed, the system may simplify tasks or send encouraging messages to alleviate that stress.

[0326] For example, suppose a user is busy and stressed. In this case, the emotion engine recognizes this state, and the server automatically generates a new cultivation schedule that reduces the workload. Then, a supportive message such as "You've worked hard, watering is all you need to do today" is sent to the user through their device.

[0327] Furthermore, the emotion engine analyzes long-term emotional data and understands user trends, which contributes to improving the system itself. For example, if a user consistently shows high satisfaction with a particular task, that method can be applied to other cultivation plans.

[0328] Through this process, the system provides personalized agricultural support that also takes into account the user's emotions. By utilizing the emotion engine, agricultural work can not only meet physical requirements but also enhance psychological satisfaction. Implementing it in this way improves the quality of agricultural support and enables more comprehensive agricultural management.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The user uses a soil sensor to collect data such as soil pH, humidity, temperature, and nutrient content, and inputs it into a terminal. The terminal then transmits this data to a server.

[0332] Step 2:

[0333] The server analyzes the received soil data using an AI algorithm to determine the optimal crop selection and cultivation method. Following this, it generates a specific cultivation schedule.

[0334] Step 3:

[0335] The user's device collects emotional data through its facial recognition camera and voice recognition microphone. This data is then transmitted from the device to the emotion engine.

[0336] Step 4:

[0337] The emotion engine analyzes collected data to recognize the user's current emotional state. For example, it determines stress levels, fatigue, satisfaction levels, and so on.

[0338] Step 5:

[0339] The server receives the sentiment analysis results from the emotion engine and adjusts the cultivation schedule accordingly. If necessary, it generates a new schedule that includes reduced workload and encouraging messages.

[0340] Step 6:

[0341] The device notifies the user of the adjusted cultivation schedule and support messages. For example, it might display a message such as, "You seem tired today, so please take adequate rest while doing only a little work."

[0342] Step 7:

[0343] The emotion engine analyzes emotional data collected over a long period to evaluate the user's emotional tendencies. Based on these results, it makes additional adjustments to improve the quality of cultivation support provided by the server.

[0344] Through this series of steps, the system can provide personalized agricultural support that responds to the user's emotional state.

[0345] (Example 2)

[0346] 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 glasses 214 will be referred to as the "terminal".

[0347] Traditional agricultural support systems primarily determined crop cultivation methods based on physical conditions, making it difficult to consider the psychological or emotional factors of those involved in agriculture. As a result, while work efficiency was optimized, there was a challenge in adequately reducing worker satisfaction and stress.

[0348] 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.

[0349] In this invention, the server includes an information processing means equipped with artificial intelligence that receives soil data and emotional data and analyzes crop cultivation methods based on the data; a plan generation means that generates an optimal crop cultivation schedule and adjusts the schedule based on the user's emotional state; and an emotional analysis means that acquires emotional information from the user's facial expressions and voice and performs emotional analysis. As a result, the agricultural support system can propose cultivation methods that take into account not only physical conditions but also psychological satisfaction.

[0350] "Soil data" refers to information that indicates the condition of the soil in crop cultivation, and includes factors such as humidity, temperature, and pH value.

[0351] "Emotional data" refers to information that indicates a user's psychological and emotional state, obtained by analyzing the user's facial expressions and voice.

[0352] "Artificial intelligence" is a technology in which computers mimic human intellectual activity and perform analysis and decision-making to solve specific problems.

[0353] "Information processing means" refers to a process or device for analyzing received data and generating output according to a specific purpose.

[0354] A "plan generation means" is a process or device that has the function of constructing an optimal activity schedule based on data analysis.

[0355] "Emotional analysis tools" are technologies that evaluate a user's emotional state and express it as specific numerical values ​​or categories.

[0356] "Predictive means" refers to a process or device that predicts future changes in demand, supply, etc., based on market information and environmental conditions.

[0357] This invention is an agricultural support system that combines a server, a terminal, and an emotion engine. A key feature of this system is that it manages agricultural operations while taking into account the user's psychological state.

[0358] Specifically, users input soil data using a terminal. The terminal sends this data to a server via the internet. The server analyzes the received data using algorithms such as Python's SciKit-Learn to determine the optimal crop cultivation method. In this process, the server also refers to historical data to improve the accuracy of the analysis.

[0359] The device acquires emotional data by collecting the user's facial expressions and voice using the camera and microphone. This enables real-time data collection. The emotion engine analyzes the collected emotional data to identify the user's satisfaction level and stress level.

[0360] Based on the analysis results of the emotion engine, the server adjusts the cultivation schedule. For example, if the user is feeling stressed, the server generates a simplified schedule and sends a message to the user via the terminal saying, "Today, just watering is enough."

[0361] A concrete example of a prompt message could be, "If the user is experiencing stress, suggest what kind of agricultural support should be provided." By sending this to a generative AI model, more personalized agricultural support can be achieved.

[0362] The system of the present invention enables users to perform agricultural management that not only adheres to numerical requirements but also provides a sense of psychological satisfaction. This improves the quality of agricultural support and increases worker satisfaction.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] The user uses a terminal to input soil data. The terminal collects data automatically acquired from soil sensors and data manually entered by the user. The entered data includes soil moisture, temperature, pH value, etc. The terminal organizes this data and prepares it for transmission to the server.

[0366] Step 2:

[0367] The terminal transmits the collected soil data to the server via the internet. The data is securely transmitted using the HTTPS protocol. The server stores the received data in a database for analysis and prepares it for analysis.

[0368] Step 3:

[0369] The server analyzes the received soil data using libraries such as Python's SciKit-Learn library. Specifically, it uses machine learning algorithms to analyze the characteristics of the soil data and determine the optimal cultivation method based on that analysis. As output, cultivation guidelines such as fertilizer application and irrigation amounts are generated.

[0370] Step 4:

[0371] The device collects the user's facial expressions and voice through its camera and microphone. The input data represents the user's emotions, and the device collects this data in real time, preparing to send it to a server as emotion data.

[0372] Step 5:

[0373] The server uses an emotion engine to analyze emotional data received from the terminal. Based on the input data, it uses text analysis and speech analysis technologies to determine the user's emotional state. As a result of this analysis, the user's satisfaction level and stress level are output.

[0374] Step 6:

[0375] The server adjusts the cultivation schedule based on the analysis of emotional data. For example, if a user is experiencing high stress levels, the schedule may be simplified. This adjusted information is then sent to the terminal.

[0376] Step 7:

[0377] The terminal notifies the user of the adjusted cultivation schedule received from the server. Furthermore, it displays encouraging and instructional messages tailored to the user's situation. For example, a message such as, "Today, just watering is sufficient," might be displayed.

[0378] Step 8:

[0379] The server analyzes long-term emotional data to understand users' emotional tendencies and adjusts the system to utilize this information for future agricultural support. This improves the quality of agricultural support and increases users' psychological satisfaction.

[0380] (Application Example 2)

[0381] 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."

[0382] Traditional agricultural support systems focus on optimizing crop cultivation based on physical conditions, but rarely consider the emotional state of the user. Therefore, there is a need for new approaches to reduce the psychological burden on workers and improve the efficiency and satisfaction of agricultural work.

[0383] 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.

[0384] In this invention, the server includes an information processing device equipped with artificial intelligence that receives soil information and analyzes crop cultivation methods, a plan generation device that generates an optimal crop cultivation schedule, and an emotion analysis device that collects user emotion data and adjusts the cultivation schedule based on said emotion data. This enables agricultural management that is in line with the user's psychological state, reducing the psychological burden on workers while improving work efficiency and satisfaction.

[0385] "Soil information" refers to data about the condition and characteristics of the soil that affect crop cultivation.

[0386] An "information processing device equipped with artificial intelligence" is an information processing device that has the ability to analyze crop cultivation methods based on acquired data.

[0387] A "plan generation means" is a means that has the function of creating an optimal crop cultivation schedule based on the analyzed data.

[0388] An "emotion analysis tool" is a tool that evaluates a user's emotional state from data such as facial expressions and voice, and reflects that in agricultural management.

[0389] An "unmanned aircraft" is a flying device that performs tasks remotely or automatically, and is responsible for tasks such as spraying water and fertilizer.

[0390] A "water supply system" is a device that supplies water to crops at predetermined times and in predetermined amounts.

[0391] "Market information" refers to data and forecasts regarding supply and demand related to the buying and selling of crops.

[0392] A "message generation means" is a means that has the function of generating work instructions and encouraging messages based on the user's psychological state.

[0393] "User information communication means" refers to a means of informing users about crop cultivation schedules and market forecast results.

[0394] The system for realizing this invention consists of a user, a server, an information processing device, and an unmanned aerial vehicle or water supply device. First, the user collects soil information using a terminal equipped with sensors. This soil information is analyzed by artificial intelligence in the information processing device to determine the optimal cultivation method.

[0395] Next, to analyze the user's emotional state, the device captures the user's facial expressions and voice through its camera and microphone. This data is sent to a server, which uses emotion analysis tools to determine the user's emotional state. Based on this data, the plan generation tool adjusts the cultivation schedule. By making adjustments according to the user's stress and satisfaction levels, the psychological burden is reduced.

[0396] Watering and fertilizing are automatically performed by drones or watering systems based on a pre-adjusted cultivation schedule. This process is designed to minimize the burden and stress faced by users, based on the results of sentiment analysis.

[0397] As a concrete example, in a home garden where tomatoes are grown, if the system determines that the user is busy and stressed on the weekend, it will only instruct the user to water the plants and send a supportive message such as, "That's all you need to do today."

[0398] The introduction of this system will reduce physical and psychological burdens on users, allowing them to enjoy a more efficient and satisfying agricultural experience.

[0399] An example of a prompt from a generated AI model is: "Considering the user's emotional state, suggest a work schedule that a home farming robot should adapt to. Based on recent soil data and the user's stress level, what tasks would be optimal?"

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The user collects soil information using a terminal. The terminal, which has a built-in sensor, acquires data such as soil moisture, pH, and temperature, and transmits this information to the server in a digital format. The input is soil sensor data, and the output is digital soil information that has been prepared for analysis.

[0403] Step 2:

[0404] The server analyzes the soil information received by the information processing unit. Artificial intelligence receives the soil information as input data and performs data calculations to determine the optimal cultivation method for each crop. The output is cultivation guidelines.

[0405] Step 3:

[0406] The device collects the user's emotional state. It acquires the user's facial expressions and voice data through the camera and microphone, and sends this to a server where an emotion analysis system operates. The input is the user's emotional data, and the output is the emotion analysis result.

[0407] Step 4:

[0408] The server analyzes the user's emotional data using emotion analysis tools. It evaluates the user's psychological state using facial recognition and voice analysis technologies, quantifying stress and satisfaction levels. This output data represents the emotional state.

[0409] Step 5:

[0410] The server provides the analysis results to the planning generation system, which adjusts the cultivation schedule based on emotional state and soil information. Data processing optimizes the cultivation tasks so that the user has a desirable experience. The output is the adjusted cultivation schedule.

[0411] Step 6:

[0412] The terminal controls unmanned aerial vehicles or watering systems to spray water and fertilizer based on a pre-configured cultivation schedule. The input is the pre-configured cultivation schedule, and the output is the execution of the automated spraying process.

[0413] Step 7:

[0414] The user receives support messages sent from the server. A message generation system creates encouragement and instructions tailored to the user's emotional state and delivers them to the user via voice or text. The inputs are the emotion analysis results and the cultivation schedule, and the output is a support message for the user.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] The system of this invention supports efficient and sustainable cultivation in agriculture. Specifically, the server analyzes soil information, proposes the optimal cultivation method, and performs automated cultivation management based on that method.

[0432] First, the user inputs soil sample data via a dedicated terminal. The terminal has the function of sending information such as soil pH, moisture content, and nutrient content to a server. The server receives this data and uses an AI algorithm to analyze the optimal crops and conditions for cultivation. The server generates a cultivation schedule based on the analysis results. This schedule plans the appropriate amount of water and fertilizer according to the planting and growth stages of the crops.

[0433] Users can view the cultivation schedule generated by the server on their terminal. Based on the schedule, commands are sent from the terminal to drones or irrigation equipment. This automated control system ensures that the appropriate amount of water and fertilizer is applied at the right time, reducing the user's workload.

[0434] Furthermore, the server analyzes market data and forecasts crop demand. This forecast information is communicated to the user and used to help formulate harvest timings and sales plans. For example, if the server predicts an increase in tomato demand, it will notify the user via their terminal of the appropriate planting time, allowing the user to further plan their cultivation based on that information.

[0435] Thus, the system of the present invention enables proper crop cultivation based on soil information and efficient agricultural management that reflects market trends. This makes it possible for novice farmers and home garden enthusiasts to produce high-quality organic vegetables without specialized knowledge.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The user uses a soil sensor to collect information such as soil pH, humidity, temperature, and nutrient content, and inputs it into a terminal. This data is automatically sent to the server.

[0439] Step 2:

[0440] The server analyzes the received soil data using an AI algorithm. Through this analysis, it understands the soil characteristics and determines which crops will grow best under those conditions. Furthermore, it determines cultivation conditions such as the amount of water and fertilizer needed.

[0441] Step 3:

[0442] The server generates an optimal cultivation schedule based on the analysis results. This schedule includes planting timing, and the amount and timing of watering and fertilizing. The schedule is then sent to the user's terminal.

[0443] Step 4:

[0444] The user's terminal displays the received cultivation schedule on the screen and prepares to issue commands to automated spraying devices and drones. This allows the user to proceed with the work according to the presented cultivation plan.

[0445] Step 5:

[0446] The server uses market data to predict crop demand and supply. This forecast information is sent to the user's terminal, and the user adjusts harvest timing and sales strategies based on it.

[0447] Step 6:

[0448] Users periodically observe the condition and health of their crops and re-enter this information into their device. The device sends the new data to the server, and the system updates the cultivation plan as needed.

[0449] This series of steps allows users to achieve effective organic farming with minimal effort.

[0450] (Example 1)

[0451] 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."

[0452] Traditional agricultural management systems often require manual collection and analysis of soil information, hindering efficient cultivation. Furthermore, insufficient supply and demand forecasting based on market trends makes it difficult for producers to develop appropriate agricultural plans. Therefore, there is a need for automated systems that enable precise and sustainable cultivation management, even for novice farmers and small-scale producers.

[0453] 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.

[0454] In this invention, the server includes an information processing device that receives soil information and analyzes cultivation techniques based on the soil information; a planning device that generates optimal conditions for cultivation; and a management device that automatically sprays water and nutrients using an unmanned aerial vehicle or water management device according to the generated conditions. This enables users to produce agricultural products appropriately and efficiently even if it is their first time, and enables sustainable cultivation management.

[0455] "Soil information" refers to data concerning the physical or chemical properties of soil, specifically including information such as pH, moisture content, and nutrient content.

[0456] An "information processing device" is a device that has the function of analyzing soil information and determining the optimal cultivation technique.

[0457] A "plan creation device" is a device that generates cultivation schedules and conditions based on analysis results, and is used to construct an efficient cultivation plan.

[0458] A "management device" is a device that controls unmanned aerial vehicles and water management equipment to automatically spray water and nutrients according to the generated cultivation conditions.

[0459] "Market information" refers to data related to the supply and demand of agricultural products in the market, and is information that contributes to supply and demand forecasting.

[0460] An "analytical device" is a device that analyzes market information to predict supply and demand and provides producers with useful data.

[0461] A "communication device" is a device that has an interface function to receive data from a user and provide it to a server.

[0462] A "user connection device" is a device equipped with an interface for notifying users of generated cultivation conditions and analysis results, and for directly providing information to them.

[0463] The system of this invention is designed to support efficient and sustainable cultivation in agriculture. In this system, the server, terminals, and users work together, with each component fulfilling its respective role.

[0464] Users utilize a dedicated terminal to acquire soil information. This terminal is equipped with sensors that measure data such as soil pH, moisture content, and nutrient content. The terminal collects the measurement data and transmits it to a server via its communication function. The terminal uses Wi-Fi or cellular connectivity to transmit data.

[0465] The server receives incoming data and acts as an information processing unit. In particular, it analyzes soil data using AI algorithms (for example, machine learning frameworks such as TensorFlow). This analysis allows for the determination of optimal crops and conditions for cultivation. The server also retrieves historical data from a database to support more precise analysis.

[0466] Based on the analysis results, the server acts as a planning device to create a specific cultivation schedule. This schedule includes the amount of water and fertilizer needed according to the crop's growth stage, as well as the timing of fertilization.

[0467] The terminal also functions as a management device, sending instructions to unmanned aerial vehicles (drones) and irrigation systems according to the cultivation schedule sent from the server. This automatically distributes the appropriate amount of water and nutrients, reducing the user's workload.

[0468] Furthermore, the server, as part of its analytical capabilities, analyzes market information and forecasts demand for agricultural products. A generative AI model is used for this purpose. Based on the forecast information, the server notifies the user, who can then use this information to determine harvest timing and develop sales strategies.

[0469] For example, if the server performs market analysis using an AI model and predicts an increase in tomato demand, this information can be provided to the user, enabling them to adjust the cultivation timing and create a cultivation plan that maximizes sales.

[0470] An example of a prompt is, "Use the AI ​​model to predict the optimal growing conditions for tomatoes in your current location during the summer." This prompt is used for the server to suggest the optimal growing method, taking into account climate information and historical data.

[0471] Thus, the system of the present invention provides a multi-functional platform for efficiently cultivating agricultural products by integrating and utilizing soil information and market information.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The user inputs soil sample data using a dedicated terminal. The terminal is connected to sensors that measure soil pH, moisture content, nutrient content, etc., and the user operates these sensors to acquire data. The entered soil information is stored in the terminal's internal memory and sent to the server in the next step.

[0475] Step 2:

[0476] The terminal transmits the acquired soil data to the server. During this process, the terminal uses a communication line (such as Wi-Fi or cellular connection) to encrypt the data before securely transmitting it to the server. As output of this process, the server receives the soil information and stores it in its database.

[0477] Step 3:

[0478] The server uses an AI algorithm to analyze the received soil information. The input data is processed using machine learning libraries such as TensorFlow to determine appropriate cultivation techniques and select the optimal crops. As an output of the analysis, the server generates recommended cultivation conditions.

[0479] Step 4:

[0480] Based on the generated cultivation conditions, the server acts as a planning device to create a specific cultivation schedule. This schedule includes detailed settings for planting time, fertilizer application, and irrigation amounts for each growth stage. This schedule is then sent to the terminal as output.

[0481] Step 5:

[0482] The terminal displays the cultivation schedule from the server in a format that the user can review. The user reviews and approves each item on the terminal screen and then initiates the automation process based on that information. This action allows the terminal, acting as a management device, to wirelessly transmit instructions for watering and fertilizing to drones and irrigation systems.

[0483] Step 6:

[0484] The server uses a generated AI model based on market information to forecast supply and demand. This analysis process takes generator data and historical trend data as input to predict increases in demand and price fluctuations. The output is notified to the user's terminal in a format they can review, helping them adjust their cultivation plan as needed.

[0485] (Application Example 1)

[0486] 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."

[0487] In today's increasingly urbanized society, securing a sustainable food supply is a critical issue. In particular, enabling urban residents to participate in local agriculture and efficiently manage cultivation in community gardens and public facilities contributes to promoting social interaction and environmental conservation, but technical and knowledge-based barriers exist. A system is needed to overcome these challenges and realize sustainable urban agriculture.

[0488] 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.

[0489] In this invention, the server includes an information processing device equipped with artificial intelligence that receives soil data and analyzes plant cultivation methods based on the data; a computation generation means that generates an optimal plant cultivation plan; and an operation means that automatically supplies water and nutrients using an unmanned aerial vehicle or a watering device according to the generated cultivation plan. This enables urban residents to participate in efficient and sustainable agricultural activities without requiring technical knowledge.

[0490] "Soil data" refers to information about soil characteristics that affect plant cultivation, such as soil pH, moisture content, and nutrient content.

[0491] "Plant cultivation methods" refer to a series of methods and procedures necessary to promote plant growth, including the process from sowing seeds to harvesting.

[0492] "Artificial intelligence" refers to algorithms and technologies that enable computer systems to mimic human intelligence and perform problem-solving and decision-making.

[0493] "Information processing device means" is a general term for hardware and software designed to receive and process data.

[0494] A "cultivation plan" is a schedule that defines the optimal conditions and schedule for effectively growing a particular plant.

[0495] "Computation generation means" refers to a set of software and hardware mechanisms for generating a specific plan based on analyzed data.

[0496] An "unmanned aerial vehicle" is a flying device that is operated remotely or autonomously without a human on board, and is mainly used in the agricultural sector for crop monitoring and material distribution.

[0497] A "watering system" is equipment that automatically sprays water or nutrient solution onto plants, and is a device that supplies the water necessary for plant growth.

[0498] "Operating means" refers to a system or interface for controlling a specific device or process.

[0499] "Socioeconomic data" refers to data that includes statistics and information related to the economic activities of a society, and is used for demand forecasting and market analysis.

[0500] "Resident participation functions" refer to the functions of a system designed to allow urban residents to actively participate, and to functions that promote joint management and participation.

[0501] To implement this invention, a system is constructed that combines an information processing device, a computation generation device, and an operation device. The information processing device receives soil data input by the user using a terminal device, and this data includes the soil's pH, moisture content, and nutrient content. This data is analyzed using an artificial intelligence algorithm to analyze plant cultivation methods.

[0502] The calculation generation means generates an optimal cultivation plan based on the analysis results obtained from the information processing device means. This plan includes a schedule for supplying water and nutrients during the cultivation period, specifying the exact timing and amount in detail.

[0503] The operating system controls autonomous unmanned aerial vehicles and irrigation equipment. This allows for the supply of the necessary amount of water and nutrients to plants based on the generated cultivation plan. This control is performed by software running on a cloud-based server, ensuring reliable agricultural management.

[0504] The server also collects socioeconomic data and uses artificial intelligence to predict supply and demand. This allows users to obtain real-time information to efficiently manage collaborative farming projects in urban areas.

[0505] For example, if a local citizens' group is jointly managing a community garden project, this system can be used to effectively manage resources and maximize yields.

[0506] Examples of prompts for the generated AI model include questions such as, "What crops are suitable for my community garden?" or "What is the best time and method for the next harvest?" This allows users to receive appropriate guidance and plan their agricultural activities systematically.

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The user inputs soil data using a terminal. This data includes soil pH, moisture content, and nutrient content. This data is sent from the terminal to the server, which receives it and prepares it for processing.

[0510] Step 2:

[0511] The server uses artificial intelligence algorithms based on received soil data to analyze the optimal plant cultivation methods. The AI ​​model analyzes the input data and generates suggestions for appropriate growing conditions and planting times. The output is a list of recommended cultivation conditions for each plant type.

[0512] Step 3:

[0513] The server uses the results analyzed by AI to generate an optimal cultivation plan. This plan includes a schedule for supplying water and nutrients during the cultivation period. Based on the generated cultivation plan, a detailed schedule is created to enable necessary resource management.

[0514] Step 4:

[0515] Based on the generated cultivation plan, the server sends control signals to the unmanned aerial vehicle or watering device. This enables the automatic supply of water and nutrients at predetermined times according to the plan. Specifically, the transmission of control signals includes remotely controlling the device over a network.

[0516] Step 5:

[0517] The server predicts plant demand and supply based on socioeconomic data. It uses AI models to analyze this data and perform demand forecasting. This allows for the development of cultivation strategies tailored to market conditions. The output provides users with predicted demand patterns and supply adjustment information.

[0518] Step 6:

[0519] Users review the generated cultivation plan and demand forecast information to make decisions that optimize project progress and harvest timing. This information is displayed in real time on the terminal's screen, providing an environment where users can easily make decisions. Examples of prompt input include specific questions such as, "When is the best time for the next harvest?"

[0520] 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.

[0521] This invention is a system that combines a server, a terminal, and an emotion engine to provide more personalized agricultural support. This system analyzes the user's emotions and incorporates the feedback based on that analysis into agricultural planning, enabling agricultural management that also takes psychological and emotional factors into consideration.

[0522] First, the user inputs soil data and other agricultural data into the terminal. The terminal then sends this data to the server. The server uses an AI algorithm to analyze the soil data and determine the optimal cultivation method for the crops.

[0523] Next, the device collects emotional data from the user's facial expressions and voice. The emotion engine analyzes this data to determine the user's emotional state, such as satisfaction level and stress level. Based on the emotional data, the server adjusts the cultivation schedule and market forecast. Specifically, if the user is feeling stressed, the system may simplify tasks or send encouraging messages to alleviate that stress.

[0524] For example, suppose a user is busy and stressed. In this case, the emotion engine recognizes this state, and the server automatically generates a new cultivation schedule that reduces the workload. Then, a supportive message such as "You've worked hard, watering is all you need to do today" is sent to the user through their device.

[0525] Furthermore, the emotion engine analyzes long-term emotional data and understands user trends, which contributes to improving the system itself. For example, if a user consistently shows high satisfaction with a particular task, that method can be applied to other cultivation plans.

[0526] Through this process, the system provides personalized agricultural support that also takes into account the user's emotions. By utilizing the emotion engine, agricultural work can not only meet physical requirements but also enhance psychological satisfaction. Implementing it in this way improves the quality of agricultural support and enables more comprehensive agricultural management.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The user uses a soil sensor to collect data such as soil pH, humidity, temperature, and nutrient content, and inputs it into a terminal. The terminal then transmits this data to a server.

[0530] Step 2:

[0531] The server analyzes the received soil data using an AI algorithm to determine the optimal crop selection and cultivation method. Following this, it generates a specific cultivation schedule.

[0532] Step 3:

[0533] The user's device collects emotional data through its facial recognition camera and voice recognition microphone. This data is then transmitted from the device to the emotion engine.

[0534] Step 4:

[0535] The emotion engine analyzes collected data to recognize the user's current emotional state. For example, it determines stress levels, fatigue, satisfaction levels, and so on.

[0536] Step 5:

[0537] The server receives the sentiment analysis results from the emotion engine and adjusts the cultivation schedule accordingly. If necessary, it generates a new schedule that includes reduced workload and encouraging messages.

[0538] Step 6:

[0539] The device notifies the user of the adjusted cultivation schedule and support messages. For example, it might display a message such as, "You seem tired today, so please take adequate rest while doing only a little work."

[0540] Step 7:

[0541] The emotion engine analyzes emotional data collected over a long period to evaluate the user's emotional tendencies. Based on these results, it makes additional adjustments to improve the quality of cultivation support provided by the server.

[0542] Through this series of steps, the system can provide personalized agricultural support that responds to the user's emotional state.

[0543] (Example 2)

[0544] 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."

[0545] Traditional agricultural support systems primarily determined crop cultivation methods based on physical conditions, making it difficult to consider the psychological or emotional factors of those involved in agriculture. As a result, while work efficiency was optimized, there was a challenge in adequately reducing worker satisfaction and stress.

[0546] 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.

[0547] In this invention, the server includes an information processing means equipped with artificial intelligence that receives soil data and emotional data and analyzes crop cultivation methods based on the data; a plan generation means that generates an optimal crop cultivation schedule and adjusts the schedule based on the user's emotional state; and an emotional analysis means that acquires emotional information from the user's facial expressions and voice and performs emotional analysis. As a result, the agricultural support system can propose cultivation methods that take into account not only physical conditions but also psychological satisfaction.

[0548] "Soil data" refers to information that indicates the condition of the soil in crop cultivation, and includes factors such as humidity, temperature, and pH value.

[0549] "Emotional data" refers to information that indicates a user's psychological and emotional state, obtained by analyzing the user's facial expressions and voice.

[0550] "Artificial intelligence" is a technology in which computers mimic human intellectual activity and perform analysis and decision-making to solve specific problems.

[0551] "Information processing means" refers to a process or device for analyzing received data and generating output according to a specific purpose.

[0552] A "plan generation means" is a process or device that has the function of constructing an optimal activity schedule based on data analysis.

[0553] "Emotional analysis tools" are technologies that evaluate a user's emotional state and express it as specific numerical values ​​or categories.

[0554] "Predictive means" refers to a process or device that predicts future changes in demand, supply, etc., based on market information and environmental conditions.

[0555] This invention is an agricultural support system that combines a server, a terminal, and an emotion engine. A key feature of this system is that it manages agricultural operations while taking into account the user's psychological state.

[0556] Specifically, users input soil data using a terminal. The terminal sends this data to a server via the internet. The server analyzes the received data using algorithms such as Python's SciKit-Learn to determine the optimal crop cultivation method. In this process, the server also refers to historical data to improve the accuracy of the analysis.

[0557] The device acquires emotional data by collecting the user's facial expressions and voice using the camera and microphone. This enables real-time data collection. The emotion engine analyzes the collected emotional data to identify the user's satisfaction level and stress level.

[0558] Based on the analysis results of the emotion engine, the server adjusts the cultivation schedule. For example, if the user is feeling stressed, the server generates a simplified schedule and sends a message to the user via the terminal saying, "Today, just watering is enough."

[0559] A concrete example of a prompt message could be, "If the user is experiencing stress, suggest what kind of agricultural support should be provided." By sending this to a generative AI model, more personalized agricultural support can be achieved.

[0560] The system of the present invention enables users to perform agricultural management that not only adheres to numerical requirements but also provides a sense of psychological satisfaction. This improves the quality of agricultural support and increases worker satisfaction.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The user uses a terminal to input soil data. The terminal collects data automatically acquired from soil sensors and data manually entered by the user. The entered data includes soil moisture, temperature, pH value, etc. The terminal organizes this data and prepares it for transmission to the server.

[0564] Step 2:

[0565] The terminal transmits the collected soil data to the server via the internet. The data is securely transmitted using the HTTPS protocol. The server stores the received data in a database for analysis and prepares it for analysis.

[0566] Step 3:

[0567] The server analyzes the received soil data using libraries such as Python's SciKit-Learn library. Specifically, it uses machine learning algorithms to analyze the characteristics of the soil data and determine the optimal cultivation method based on that analysis. As output, cultivation guidelines such as fertilizer application and irrigation amounts are generated.

[0568] Step 4:

[0569] The device collects the user's facial expressions and voice through its camera and microphone. The input data represents the user's emotions, and the device collects this data in real time, preparing to send it to a server as emotion data.

[0570] Step 5:

[0571] The server uses an emotion engine to analyze emotional data received from the terminal. Based on the input data, it uses text analysis and speech analysis technologies to determine the user's emotional state. As a result of this analysis, the user's satisfaction level and stress level are output.

[0572] Step 6:

[0573] The server adjusts the cultivation schedule based on the analysis of emotional data. For example, if a user is experiencing high stress levels, the schedule may be simplified. This adjusted information is then sent to the terminal.

[0574] Step 7:

[0575] The terminal notifies the user of the adjusted cultivation schedule received from the server. Furthermore, it displays encouraging and instructional messages tailored to the user's situation. For example, a message such as, "Today, just watering is sufficient," might be displayed.

[0576] Step 8:

[0577] The server analyzes long-term emotional data to understand users' emotional tendencies and adjusts the system to utilize this information for future agricultural support. This improves the quality of agricultural support and increases users' psychological satisfaction.

[0578] (Application Example 2)

[0579] 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."

[0580] Traditional agricultural support systems focus on optimizing crop cultivation based on physical conditions, but rarely consider the emotional state of the user. Therefore, there is a need for new approaches to reduce the psychological burden on workers and improve the efficiency and satisfaction of agricultural work.

[0581] 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.

[0582] In this invention, the server includes an information processing device equipped with artificial intelligence that receives soil information and analyzes crop cultivation methods, a plan generation device that generates an optimal crop cultivation schedule, and an emotion analysis device that collects user emotion data and adjusts the cultivation schedule based on said emotion data. This enables agricultural management that is in line with the user's psychological state, reducing the psychological burden on workers while improving work efficiency and satisfaction.

[0583] "Soil information" refers to data about the condition and characteristics of the soil that affect crop cultivation.

[0584] An "information processing device equipped with artificial intelligence" is an information processing device that has the ability to analyze crop cultivation methods based on acquired data.

[0585] A "plan generation means" is a means that has the function of creating an optimal crop cultivation schedule based on the analyzed data.

[0586] An "emotion analysis tool" is a tool that evaluates a user's emotional state from data such as facial expressions and voice, and reflects that in agricultural management.

[0587] An "unmanned aircraft" is a flying device that performs tasks remotely or automatically, and is responsible for tasks such as spraying water and fertilizer.

[0588] A "water supply system" is a device that supplies water to crops at predetermined times and in predetermined amounts.

[0589] "Market information" refers to data and forecasts regarding supply and demand related to the buying and selling of crops.

[0590] A "message generation means" is a means that has the function of generating work instructions and encouraging messages based on the user's psychological state.

[0591] "User information communication means" refers to a means of informing users about crop cultivation schedules and market forecast results.

[0592] The system for realizing this invention consists of a user, a server, an information processing device, and an unmanned aerial vehicle or water supply device. First, the user collects soil information using a terminal equipped with sensors. This soil information is analyzed by artificial intelligence in the information processing device to determine the optimal cultivation method.

[0593] Next, to analyze the user's emotional state, the device captures the user's facial expressions and voice through its camera and microphone. This data is sent to a server, which uses emotion analysis tools to determine the user's emotional state. Based on this data, the plan generation tool adjusts the cultivation schedule. By making adjustments according to the user's stress and satisfaction levels, the psychological burden is reduced.

[0594] Watering and fertilizing are automatically performed by drones or watering systems based on a pre-adjusted cultivation schedule. This process is designed to minimize the burden and stress faced by users, based on the results of sentiment analysis.

[0595] As a concrete example, in a home garden where tomatoes are grown, if the system determines that the user is busy and stressed on the weekend, it will only instruct the user to water the plants and send a supportive message such as, "That's all you need to do today."

[0596] The introduction of this system will reduce physical and psychological burdens on users, allowing them to enjoy a more efficient and satisfying agricultural experience.

[0597] An example of a prompt from a generated AI model is: "Considering the user's emotional state, suggest a work schedule that a home farming robot should adapt to. Based on recent soil data and the user's stress level, what tasks would be optimal?"

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The user collects soil information using a terminal. The terminal, which has a built-in sensor, acquires data such as soil moisture, pH, and temperature, and transmits this information to the server in a digital format. The input is soil sensor data, and the output is digital soil information that has been prepared for analysis.

[0601] Step 2:

[0602] The server analyzes the soil information received by the information processing unit. Artificial intelligence receives the soil information as input data and performs data calculations to determine the optimal cultivation method for each crop. The output is cultivation guidelines.

[0603] Step 3:

[0604] The device collects the user's emotional state. It acquires the user's facial expressions and voice data through the camera and microphone, and sends this to a server where an emotion analysis system operates. The input is the user's emotional data, and the output is the emotion analysis result.

[0605] Step 4:

[0606] The server analyzes the user's emotional data using emotion analysis tools. It evaluates the user's psychological state using facial recognition and voice analysis technologies, quantifying stress and satisfaction levels. This output data represents the emotional state.

[0607] Step 5:

[0608] The server provides the analysis results to the planning generation system, which adjusts the cultivation schedule based on emotional state and soil information. Data processing optimizes the cultivation tasks so that the user has a desirable experience. The output is the adjusted cultivation schedule.

[0609] Step 6:

[0610] The terminal controls unmanned aerial vehicles or watering systems to spray water and fertilizer based on a pre-configured cultivation schedule. The input is the pre-configured cultivation schedule, and the output is the execution of the automated spraying process.

[0611] Step 7:

[0612] The user receives support messages sent from the server. A message generation system creates encouragement and instructions tailored to the user's emotional state and delivers them to the user via voice or text. The inputs are the emotion analysis results and the cultivation schedule, and the output is a support message for the user.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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".

[0630] The system of this invention supports efficient and sustainable cultivation in agriculture. Specifically, the server analyzes soil information, proposes the optimal cultivation method, and performs automated cultivation management based on that method.

[0631] First, the user inputs soil sample data via a dedicated terminal. The terminal has the function of sending information such as soil pH, moisture content, and nutrient content to a server. The server receives this data and uses an AI algorithm to analyze the optimal crops and conditions for cultivation. The server generates a cultivation schedule based on the analysis results. This schedule plans the appropriate amount of water and fertilizer according to the planting and growth stages of the crops.

[0632] Users can view the cultivation schedule generated by the server on their terminal. Based on the schedule, commands are sent from the terminal to drones or irrigation equipment. This automated control system ensures that the appropriate amount of water and fertilizer is applied at the right time, reducing the user's workload.

[0633] Furthermore, the server analyzes market data and forecasts crop demand. This forecast information is communicated to the user and used to help formulate harvest timings and sales plans. For example, if the server predicts an increase in tomato demand, it will notify the user via their terminal of the appropriate planting time, allowing the user to further plan their cultivation based on that information.

[0634] Thus, the system of the present invention enables proper crop cultivation based on soil information and efficient agricultural management that reflects market trends. This makes it possible for novice farmers and home garden enthusiasts to produce high-quality organic vegetables without specialized knowledge.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The user uses a soil sensor to collect information such as soil pH, humidity, temperature, and nutrient content, and inputs it into a terminal. This data is automatically sent to the server.

[0638] Step 2:

[0639] The server analyzes the received soil data using an AI algorithm. Through this analysis, it understands the soil characteristics and determines which crops will grow best under those conditions. Furthermore, it determines cultivation conditions such as the amount of water and fertilizer needed.

[0640] Step 3:

[0641] The server generates an optimal cultivation schedule based on the analysis results. This schedule includes planting timing, and the amount and timing of watering and fertilizing. The schedule is then sent to the user's terminal.

[0642] Step 4:

[0643] The user's terminal displays the received cultivation schedule on the screen and prepares to issue commands to automated spraying devices and drones. This allows the user to proceed with the work according to the presented cultivation plan.

[0644] Step 5:

[0645] The server uses market data to predict crop demand and supply. This forecast information is sent to the user's terminal, and the user adjusts harvest timing and sales strategies based on it.

[0646] Step 6:

[0647] Users periodically observe the condition and health of their crops and re-enter this information into their device. The device sends the new data to the server, and the system updates the cultivation plan as needed.

[0648] This series of steps allows users to achieve effective organic farming with minimal effort.

[0649] (Example 1)

[0650] 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".

[0651] Traditional agricultural management systems often require manual collection and analysis of soil information, hindering efficient cultivation. Furthermore, insufficient supply and demand forecasting based on market trends makes it difficult for producers to develop appropriate agricultural plans. Therefore, there is a need for automated systems that enable precise and sustainable cultivation management, even for novice farmers and small-scale producers.

[0652] 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.

[0653] In this invention, the server includes an information processing device that receives soil information and analyzes cultivation techniques based on the soil information; a planning device that generates optimal conditions for cultivation; and a management device that automatically sprays water and nutrients using an unmanned aerial vehicle or water management device according to the generated conditions. This enables users to produce agricultural products appropriately and efficiently even if it is their first time, and enables sustainable cultivation management.

[0654] "Soil information" refers to data concerning the physical or chemical properties of soil, specifically including information such as pH, moisture content, and nutrient content.

[0655] An "information processing device" is a device that has the function of analyzing soil information and determining the optimal cultivation technique.

[0656] A "plan creation device" is a device that generates cultivation schedules and conditions based on analysis results, and is used to construct an efficient cultivation plan.

[0657] A "management device" is a device that controls unmanned aerial vehicles and water management equipment to automatically spray water and nutrients according to the generated cultivation conditions.

[0658] "Market information" refers to data related to the supply and demand of agricultural products in the market, and is information that contributes to supply and demand forecasting.

[0659] An "analytical device" is a device that analyzes market information to predict supply and demand and provides producers with useful data.

[0660] A "communication device" is a device that has an interface function to receive data from a user and provide it to a server.

[0661] A "user connection device" is a device equipped with an interface for notifying users of generated cultivation conditions and analysis results, and for directly providing information to them.

[0662] The system of this invention is designed to support efficient and sustainable cultivation in agriculture. In this system, the server, terminals, and users work together, with each component fulfilling its respective role.

[0663] Users utilize a dedicated terminal to acquire soil information. This terminal is equipped with sensors that measure data such as soil pH, moisture content, and nutrient content. The terminal collects the measurement data and transmits it to a server via its communication function. The terminal uses Wi-Fi or cellular connectivity to transmit data.

[0664] The server receives incoming data and acts as an information processing unit. In particular, it analyzes soil data using AI algorithms (for example, machine learning frameworks such as TensorFlow). This analysis allows for the determination of optimal crops and conditions for cultivation. The server also retrieves historical data from a database to support more precise analysis.

[0665] Based on the analysis results, the server acts as a planning device to create a specific cultivation schedule. This schedule includes the amount of water and fertilizer needed according to the crop's growth stage, as well as the timing of fertilization.

[0666] The terminal also functions as a management device, sending instructions to unmanned aerial vehicles (drones) and irrigation systems according to the cultivation schedule sent from the server. This automatically distributes the appropriate amount of water and nutrients, reducing the user's workload.

[0667] Furthermore, the server, as part of its analytical capabilities, analyzes market information and forecasts demand for agricultural products. A generative AI model is used for this purpose. Based on the forecast information, the server notifies the user, who can then use this information to determine harvest timing and develop sales strategies.

[0668] For example, if the server performs market analysis using an AI model and predicts an increase in tomato demand, this information can be provided to the user, enabling them to adjust the cultivation timing and create a cultivation plan that maximizes sales.

[0669] An example of a prompt is, "Use the AI ​​model to predict the optimal growing conditions for tomatoes in your current location during the summer." This prompt is used for the server to suggest the optimal growing method, taking into account climate information and historical data.

[0670] Thus, the system of the present invention provides a multi-functional platform for efficiently cultivating agricultural products by integrating and utilizing soil information and market information.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The user inputs soil sample data using a dedicated terminal. The terminal is connected to sensors that measure soil pH, moisture content, nutrient content, etc., and the user operates these sensors to acquire data. The entered soil information is stored in the terminal's internal memory and sent to the server in the next step.

[0674] Step 2:

[0675] The terminal transmits the acquired soil data to the server. During this process, the terminal uses a communication line (such as Wi-Fi or cellular connection) to encrypt the data before securely transmitting it to the server. As output of this process, the server receives the soil information and stores it in its database.

[0676] Step 3:

[0677] The server uses an AI algorithm to analyze the received soil information. The input data is processed using machine learning libraries such as TensorFlow to determine appropriate cultivation techniques and select the optimal crops. As an output of the analysis, the server generates recommended cultivation conditions.

[0678] Step 4:

[0679] Based on the generated cultivation conditions, the server acts as a planning device to create a specific cultivation schedule. This schedule includes detailed settings for planting time, fertilizer application, and irrigation amounts for each growth stage. This schedule is then sent to the terminal as output.

[0680] Step 5:

[0681] The terminal displays the cultivation schedule from the server in a format that the user can review. The user reviews and approves each item on the terminal screen and then initiates the automation process based on that information. This action allows the terminal, acting as a management device, to wirelessly transmit instructions for watering and fertilizing to drones and irrigation systems.

[0682] Step 6:

[0683] The server uses a generated AI model based on market information to forecast supply and demand. This analysis process takes generator data and historical trend data as input to predict increases in demand and price fluctuations. The output is notified to the user's terminal in a format they can review, helping them adjust their cultivation plan as needed.

[0684] (Application Example 1)

[0685] 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".

[0686] In today's increasingly urbanized society, securing a sustainable food supply is a critical issue. In particular, enabling urban residents to participate in local agriculture and efficiently manage cultivation in community gardens and public facilities contributes to promoting social interaction and environmental conservation, but technical and knowledge-based barriers exist. A system is needed to overcome these challenges and realize sustainable urban agriculture.

[0687] 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.

[0688] In this invention, the server includes an information processing device equipped with artificial intelligence that receives soil data and analyzes plant cultivation methods based on the data; a computation generation means that generates an optimal plant cultivation plan; and an operation means that automatically supplies water and nutrients using an unmanned aerial vehicle or a watering device according to the generated cultivation plan. This enables urban residents to participate in efficient and sustainable agricultural activities without requiring technical knowledge.

[0689] "Soil data" refers to information about soil characteristics that affect plant cultivation, such as soil pH, moisture content, and nutrient content.

[0690] "Plant cultivation methods" refer to a series of methods and procedures necessary to promote plant growth, including the process from sowing seeds to harvesting.

[0691] "Artificial intelligence" refers to algorithms and technologies that enable computer systems to mimic human intelligence and perform problem-solving and decision-making.

[0692] "Information processing device means" is a general term for hardware and software designed to receive and process data.

[0693] A "cultivation plan" is a schedule that defines the optimal conditions and schedule for effectively growing a particular plant.

[0694] "Computation generation means" refers to a set of software and hardware mechanisms for generating a specific plan based on analyzed data.

[0695] An "unmanned aerial vehicle" is a flying device that is operated remotely or autonomously without a human on board, and is mainly used in the agricultural sector for crop monitoring and material distribution.

[0696] A "watering system" is equipment that automatically sprays water or nutrient solution onto plants, and is a device that supplies the water necessary for plant growth.

[0697] "Operating means" refers to a system or interface for controlling a specific device or process.

[0698] "Socioeconomic data" refers to data that includes statistics and information related to the economic activities of a society, and is used for demand forecasting and market analysis.

[0699] "Resident participation functions" refer to the functions of a system designed to allow urban residents to actively participate, and to functions that promote joint management and participation.

[0700] To implement this invention, a system is constructed that combines an information processing device, a computation generation device, and an operation device. The information processing device receives soil data input by the user using a terminal device, and this data includes the soil's pH, moisture content, and nutrient content. This data is analyzed using an artificial intelligence algorithm to analyze plant cultivation methods.

[0701] The calculation generation means generates an optimal cultivation plan based on the analysis results obtained from the information processing device means. This plan includes a schedule for supplying water and nutrients during the cultivation period, specifying the exact timing and amount in detail.

[0702] The operating system controls autonomous unmanned aerial vehicles and irrigation equipment. This allows for the supply of the necessary amount of water and nutrients to plants based on the generated cultivation plan. This control is performed by software running on a cloud-based server, ensuring reliable agricultural management.

[0703] The server also collects socioeconomic data and uses artificial intelligence to predict supply and demand. This allows users to obtain real-time information to efficiently manage collaborative farming projects in urban areas.

[0704] For example, if a local citizens' group is jointly managing a community garden project, this system can be used to effectively manage resources and maximize yields.

[0705] Examples of prompts for the generated AI model include questions such as, "What crops are suitable for my community garden?" or "What is the best time and method for the next harvest?" This allows users to receive appropriate guidance and plan their agricultural activities systematically.

[0706] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0707] Step 1:

[0708] The user inputs soil data using a terminal. This data includes soil pH, moisture content, and nutrient content. This data is sent from the terminal to the server, which receives it and prepares it for processing.

[0709] Step 2:

[0710] The server uses artificial intelligence algorithms based on received soil data to analyze the optimal plant cultivation methods. The AI ​​model analyzes the input data and generates suggestions for appropriate growing conditions and planting times. The output is a list of recommended cultivation conditions for each plant type.

[0711] Step 3:

[0712] The server uses the results analyzed by AI to generate an optimal cultivation plan. This plan includes a schedule for supplying water and nutrients during the cultivation period. Based on the generated cultivation plan, a detailed schedule is created to enable necessary resource management.

[0713] Step 4:

[0714] Based on the generated cultivation plan, the server sends control signals to the unmanned aerial vehicle or watering device. This enables the automatic supply of water and nutrients at predetermined times according to the plan. Specifically, the transmission of control signals includes remotely controlling the device over a network.

[0715] Step 5:

[0716] The server predicts plant demand and supply based on socioeconomic data. It uses AI models to analyze this data and perform demand forecasting. This allows for the development of cultivation strategies tailored to market conditions. The output provides users with predicted demand patterns and supply adjustment information.

[0717] Step 6:

[0718] Users review the generated cultivation plan and demand forecast information to make decisions that optimize project progress and harvest timing. This information is displayed in real time on the terminal's screen, providing an environment where users can easily make decisions. Examples of prompt input include specific questions such as, "When is the best time for the next harvest?"

[0719] 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.

[0720] This invention is a system that combines a server, a terminal, and an emotion engine to provide more personalized agricultural support. This system analyzes the user's emotions and incorporates the feedback based on that analysis into agricultural planning, enabling agricultural management that also takes psychological and emotional factors into consideration.

[0721] First, the user inputs soil data and other agricultural data into the terminal. The terminal then sends this data to the server. The server uses an AI algorithm to analyze the soil data and determine the optimal cultivation method for the crops.

[0722] Next, the device collects emotional data from the user's facial expressions and voice. The emotion engine analyzes this data to determine the user's emotional state, such as satisfaction level and stress level. Based on the emotional data, the server adjusts the cultivation schedule and market forecast. Specifically, if the user is feeling stressed, the system may simplify tasks or send encouraging messages to alleviate that stress.

[0723] For example, suppose a user is busy and stressed. In this case, the emotion engine recognizes this state, and the server automatically generates a new cultivation schedule that reduces the workload. Then, a supportive message such as "You've worked hard, watering is all you need to do today" is sent to the user through their device.

[0724] Furthermore, the emotion engine analyzes long-term emotional data and understands user trends, which contributes to improving the system itself. For example, if a user consistently shows high satisfaction with a particular task, that method can be applied to other cultivation plans.

[0725] Through this process, the system provides personalized agricultural support that also takes into account the user's emotions. By utilizing the emotion engine, agricultural work can not only meet physical requirements but also enhance psychological satisfaction. Implementing it in this way improves the quality of agricultural support and enables more comprehensive agricultural management.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The user uses a soil sensor to collect data such as soil pH, humidity, temperature, and nutrient content, and inputs it into a terminal. The terminal then transmits this data to a server.

[0729] Step 2:

[0730] The server analyzes the received soil data using an AI algorithm to determine the optimal crop selection and cultivation method. Following this, it generates a specific cultivation schedule.

[0731] Step 3:

[0732] The user's device collects emotional data through its facial recognition camera and voice recognition microphone. This data is then transmitted from the device to the emotion engine.

[0733] Step 4:

[0734] The emotion engine analyzes collected data to recognize the user's current emotional state. For example, it determines stress levels, fatigue, satisfaction levels, and so on.

[0735] Step 5:

[0736] The server receives the sentiment analysis results from the emotion engine and adjusts the cultivation schedule accordingly. If necessary, it generates a new schedule that includes reduced workload and encouraging messages.

[0737] Step 6:

[0738] The device notifies the user of the adjusted cultivation schedule and support messages. For example, it might display a message such as, "You seem tired today, so please take adequate rest while doing only a little work."

[0739] Step 7:

[0740] The emotion engine analyzes emotional data collected over a long period to evaluate the user's emotional tendencies. Based on these results, it makes additional adjustments to improve the quality of cultivation support provided by the server.

[0741] Through this series of steps, the system can provide personalized agricultural support that responds to the user's emotional state.

[0742] (Example 2)

[0743] 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".

[0744] Traditional agricultural support systems primarily determined crop cultivation methods based on physical conditions, making it difficult to consider the psychological or emotional factors of those involved in agriculture. As a result, while work efficiency was optimized, there was a challenge in adequately reducing worker satisfaction and stress.

[0745] 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.

[0746] In this invention, the server includes an information processing means equipped with artificial intelligence that receives soil data and emotional data and analyzes crop cultivation methods based on the data; a plan generation means that generates an optimal crop cultivation schedule and adjusts the schedule based on the user's emotional state; and an emotional analysis means that acquires emotional information from the user's facial expressions and voice and performs emotional analysis. As a result, the agricultural support system can propose cultivation methods that take into account not only physical conditions but also psychological satisfaction.

[0747] "Soil data" refers to information that indicates the condition of the soil in crop cultivation, and includes factors such as humidity, temperature, and pH value.

[0748] "Emotional data" refers to information that indicates a user's psychological and emotional state, obtained by analyzing the user's facial expressions and voice.

[0749] "Artificial intelligence" is a technology in which computers mimic human intellectual activity and perform analysis and decision-making to solve specific problems.

[0750] "Information processing means" refers to a process or device for analyzing received data and generating output according to a specific purpose.

[0751] A "plan generation means" is a process or device that has the function of constructing an optimal activity schedule based on data analysis.

[0752] "Emotional analysis tools" are technologies that evaluate a user's emotional state and express it as specific numerical values ​​or categories.

[0753] "Predictive means" refers to a process or device that predicts future changes in demand, supply, etc., based on market information and environmental conditions.

[0754] This invention is an agricultural support system that combines a server, a terminal, and an emotion engine. A key feature of this system is that it manages agricultural operations while taking into account the user's psychological state.

[0755] Specifically, users input soil data using a terminal. The terminal sends this data to a server via the internet. The server analyzes the received data using algorithms such as Python's SciKit-Learn to determine the optimal crop cultivation method. In this process, the server also refers to historical data to improve the accuracy of the analysis.

[0756] The device acquires emotional data by collecting the user's facial expressions and voice using the camera and microphone. This enables real-time data collection. The emotion engine analyzes the collected emotional data to identify the user's satisfaction level and stress level.

[0757] Based on the analysis results of the emotion engine, the server adjusts the cultivation schedule. For example, if the user is feeling stressed, the server generates a simplified schedule and sends a message to the user via the terminal saying, "Today, just watering is enough."

[0758] A concrete example of a prompt message could be, "If the user is experiencing stress, suggest what kind of agricultural support should be provided." By sending this to a generative AI model, more personalized agricultural support can be achieved.

[0759] The system of the present invention enables users to perform agricultural management that not only adheres to numerical requirements but also provides a sense of psychological satisfaction. This improves the quality of agricultural support and increases worker satisfaction.

[0760] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0761] Step 1:

[0762] The user uses a terminal to input soil data. The terminal collects data automatically acquired from soil sensors and data manually entered by the user. The entered data includes soil moisture, temperature, pH value, etc. The terminal organizes this data and prepares it for transmission to the server.

[0763] Step 2:

[0764] The terminal transmits the collected soil data to the server via the internet. The data is securely transmitted using the HTTPS protocol. The server stores the received data in a database for analysis and prepares it for analysis.

[0765] Step 3:

[0766] The server analyzes the received soil data using libraries such as Python's SciKit-Learn library. Specifically, it uses machine learning algorithms to analyze the characteristics of the soil data and determine the optimal cultivation method based on that analysis. As output, cultivation guidelines such as fertilizer application and irrigation amounts are generated.

[0767] Step 4:

[0768] The device collects the user's facial expressions and voice through its camera and microphone. The input data represents the user's emotions, and the device collects this data in real time, preparing to send it to a server as emotion data.

[0769] Step 5:

[0770] The server uses an emotion engine to analyze emotional data received from the terminal. Based on the input data, it uses text analysis and speech analysis technologies to determine the user's emotional state. As a result of this analysis, the user's satisfaction level and stress level are output.

[0771] Step 6:

[0772] The server adjusts the cultivation schedule based on the analysis of emotional data. For example, if a user is experiencing high stress levels, the schedule may be simplified. This adjusted information is then sent to the terminal.

[0773] Step 7:

[0774] The terminal notifies the user of the adjusted cultivation schedule received from the server. Furthermore, it displays encouraging and instructional messages tailored to the user's situation. For example, a message such as, "Today, just watering is sufficient," might be displayed.

[0775] Step 8:

[0776] The server analyzes long-term emotional data to understand users' emotional tendencies and adjusts the system to utilize this information for future agricultural support. This improves the quality of agricultural support and increases users' psychological satisfaction.

[0777] (Application Example 2)

[0778] 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".

[0779] Traditional agricultural support systems focus on optimizing crop cultivation based on physical conditions, but rarely consider the emotional state of the user. Therefore, there is a need for new approaches to reduce the psychological burden on workers and improve the efficiency and satisfaction of agricultural work.

[0780] 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.

[0781] In this invention, the server includes an information processing device equipped with artificial intelligence that receives soil information and analyzes crop cultivation methods, a plan generation device that generates an optimal crop cultivation schedule, and an emotion analysis device that collects user emotion data and adjusts the cultivation schedule based on said emotion data. This enables agricultural management that is in line with the user's psychological state, reducing the psychological burden on workers while improving work efficiency and satisfaction.

[0782] "Soil information" refers to data about the condition and characteristics of the soil that affect crop cultivation.

[0783] An "information processing device equipped with artificial intelligence" is an information processing device that has the ability to analyze crop cultivation methods based on acquired data.

[0784] A "plan generation means" is a means that has the function of creating an optimal crop cultivation schedule based on the analyzed data.

[0785] An "emotion analysis tool" is a tool that evaluates a user's emotional state from data such as facial expressions and voice, and reflects that in agricultural management.

[0786] An "unmanned aircraft" is a flying device that performs tasks remotely or automatically, and is responsible for tasks such as spraying water and fertilizer.

[0787] A "water supply system" is a device that supplies water to crops at predetermined times and in predetermined amounts.

[0788] "Market information" refers to data and forecasts regarding supply and demand related to the buying and selling of crops.

[0789] A "message generation means" is a means that has the function of generating work instructions and encouraging messages based on the user's psychological state.

[0790] "User information communication means" refers to a means of informing users about crop cultivation schedules and market forecast results.

[0791] The system for realizing this invention consists of a user, a server, an information processing device, and an unmanned aerial vehicle or water supply device. First, the user collects soil information using a terminal equipped with sensors. This soil information is analyzed by artificial intelligence in the information processing device to determine the optimal cultivation method.

[0792] Next, to analyze the user's emotional state, the device captures the user's facial expressions and voice through its camera and microphone. This data is sent to a server, which uses emotion analysis tools to determine the user's emotional state. Based on this data, the plan generation tool adjusts the cultivation schedule. By making adjustments according to the user's stress and satisfaction levels, the psychological burden is reduced.

[0793] Watering and fertilizing are automatically performed by drones or watering systems based on a pre-adjusted cultivation schedule. This process is designed to minimize the burden and stress faced by users, based on the results of sentiment analysis.

[0794] As a concrete example, in a home garden where tomatoes are grown, if the system determines that the user is busy and stressed on the weekend, it will only instruct the user to water the plants and send a supportive message such as, "That's all you need to do today."

[0795] The introduction of this system will reduce physical and psychological burdens on users, allowing them to enjoy a more efficient and satisfying agricultural experience.

[0796] An example of a prompt from a generated AI model is: "Considering the user's emotional state, suggest a work schedule that a home farming robot should adapt to. Based on recent soil data and the user's stress level, what tasks would be optimal?"

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0798] Step 1:

[0799] The user collects soil information using a terminal. The terminal, which has a built-in sensor, acquires data such as soil moisture, pH, and temperature, and transmits this information to the server in a digital format. The input is soil sensor data, and the output is digital soil information that has been prepared for analysis.

[0800] Step 2:

[0801] The server analyzes the soil information received by the information processing unit. Artificial intelligence receives the soil information as input data and performs data calculations to determine the optimal cultivation method for each crop. The output is cultivation guidelines.

[0802] Step 3:

[0803] The device collects the user's emotional state. It acquires the user's facial expressions and voice data through the camera and microphone, and sends this to a server where an emotion analysis system operates. The input is the user's emotional data, and the output is the emotion analysis result.

[0804] Step 4:

[0805] The server analyzes the user's emotional data using emotion analysis tools. It evaluates the user's psychological state using facial recognition and voice analysis technologies, quantifying stress and satisfaction levels. This output data represents the emotional state.

[0806] Step 5:

[0807] The server provides the analysis results to the planning generation system, which adjusts the cultivation schedule based on emotional state and soil information. Data processing optimizes the cultivation tasks so that the user has a desirable experience. The output is the adjusted cultivation schedule.

[0808] Step 6:

[0809] The terminal controls unmanned aerial vehicles or watering systems to spray water and fertilizer based on a pre-configured cultivation schedule. The input is the pre-configured cultivation schedule, and the output is the execution of the automated spraying process.

[0810] Step 7:

[0811] The user receives support messages sent from the server. A message generation system creates encouragement and instructions tailored to the user's emotional state and delivers them to the user via voice or text. The inputs are the emotion analysis results and the cultivation schedule, and the output is a support message for the user.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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."

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A server means equipped with artificial intelligence that receives soil information and analyzes crop cultivation methods based on said soil information,

[0836] A plan generation means for generating an optimal cultivation schedule for crops,

[0837] A control means for automatically spraying water and fertilizer using a drone or irrigation device according to the generated cultivation schedule,

[0838] A forecasting method for predicting crop demand and supply based on market data,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, further comprising terminal means for receiving data from a user and providing said data to a server means.

[0842] (Claim 3)

[0843] The system according to claim 1, further comprising a user interface that notifies the user based on the generated cultivation schedule and prediction results.

[0844] "Example 1"

[0845] (Claim 1)

[0846] An information processing device that receives soil information and analyzes cultivation techniques based on said soil information,

[0847] A planning device that generates optimal conditions for cultivation,

[0848] A control device that automatically sprays water and nutrients using an unmanned aerial vehicle or water management device according to the generated conditions,

[0849] An analytical device that predicts the demand and supply of agricultural products based on market information,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, further comprising a communication device that receives information from a user and provides said information to an information processing device.

[0853] (Claim 3)

[0854] The system according to claim 1, further comprising a user connection device that notifies the user based on the generated cultivation conditions and analysis results.

[0855] "Application Example 1"

[0856] (Claim 1)

[0857] An information processing device equipped with artificial intelligence that receives soil data and analyzes plant cultivation methods based on said data,

[0858] A calculation and generation means for generating an optimal plant cultivation plan,

[0859] An operating means for automatically supplying water and nutrients by an unmanned aerial vehicle or watering device according to the generated cultivation plan,

[0860] A measurement means for predicting the demand and supply of agricultural products based on socioeconomic data,

[0861] Means including resident participation functions to support participation in collaborative cultivation projects in urban areas,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising a terminal device means for acquiring information from a user and providing said information to an information processing device means.

[0865] (Claim 3)

[0866] The system according to claim 1, further comprising a display device that provides information to the user based on the generated growth plan and prediction results.

[0867] "Example 2 of combining an emotion engine"

[0868] (Claim 1)

[0869] An information processing means equipped with artificial intelligence that receives soil data and sentiment data and analyzes crop cultivation methods based on said data,

[0870] A plan generation means that generates an optimal cultivation schedule for crops and adjusts the schedule based on the user's emotional state,

[0871] An emotion analysis means that acquires emotional information from the user's facial expressions and voice and performs emotion analysis,

[0872] A forecasting method for predicting the demand and supply of crops based on market information,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, further comprising an emotion information providing means that provides user emotion information to an emotion analysis means using a terminal device.

[0876] (Claim 3)

[0877] The system according to claim 1, further comprising a user interface that notifies the user of a message based on the generated cultivation schedule and prediction results, and provides a response that takes into account the user's psychological state.

[0878] "Application example 2 when combining with an emotional engine"

[0879] (Claim 1)

[0880] An information processing device equipped with artificial intelligence that receives soil information and analyzes crop cultivation methods based on said soil information,

[0881] A plan generation means for generating an optimal cultivation schedule for crops,

[0882] A means for analyzing user emotions, which collects user emotion data and adjusts the cultivation schedule based on said emotion data,

[0883] A control means for automatically spraying water and fertilizer using an unmanned aircraft or watering device according to the generated cultivation schedule,

[0884] A forecasting method for predicting the demand and supply of crops based on market information,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, further comprising message generation means for generating work instructions and support messages according to the user's emotional state.

[0888] (Claim 3)

[0889] The system according to claim 1, further comprising a user information communication means for informing the user based on the generated cultivation schedule and prediction results. [Explanation of symbols]

[0890] 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 server means equipped with artificial intelligence that receives soil information and analyzes crop cultivation methods based on said soil information, A plan generation means for generating an optimal cultivation schedule for crops, A control means for automatically spraying water and fertilizer using a drone or irrigation device according to the generated cultivation schedule, A forecasting method for predicting crop demand and supply based on market data, A system that includes this.

2. The system according to claim 1, further comprising terminal means for receiving data from a user and providing said data to a server means.

3. The system according to claim 1, further comprising a user interface that notifies the user based on the generated cultivation schedule and prediction results.

Images