system

Abstract

We provide the system. [Solution] A data collection method that acquires weather, soil, and crop conditions in real time from various sensors installed in the agricultural environment, An analytical tool that performs weather forecasting, soil analysis, and crop growth evaluation based on acquired data, A work flow generation means that generates an optimal work flow based on the analysis results and distributes work instructions to individual machines, A task execution means in which individual machines perform tasks according to work instructions and report on their progress, A status reporting system that reports the work performed and changes in the agricultural environment to the user in natural language. A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In modern agriculture, due to the decrease in the labor population, aging, population migration to urban areas, etc., the shortage of labor has become a serious problem, and the production efficiency has decreased and the anxiety about sustainable food supply has increased. In addition, it is difficult for conventional agricultural technologies to quickly respond to environmental changes. In response to such problems, there is a demand for providing means for efficiently and autonomously managing farms. 【Means for Solving the Problems】 【0005】 This invention includes data collection means for acquiring weather, soil, and crop conditions in real time using various sensors installed in the agricultural environment. It also includes analysis means for performing weather forecasting, soil analysis, and crop growth evaluation based on the acquired data. Furthermore, it includes work flow generation means for generating an optimal work flow based on the analysis results and distributing work instructions to individual machines. These problems are solved by including task execution means for individual machines to perform tasks according to the work instructions and report progress, and status reporting means for reporting the performed work content and changes in the agricultural environment to the user in natural language. 【0006】 "Data collection means" refers to devices and systems that have the function of acquiring weather, soil, and crop conditions in real time from various sensors installed in the agricultural environment. 【0007】 "Analysis methods" refer to processes and technologies that perform weather forecasting, soil analysis, and crop growth evaluation based on data acquired through data collection methods. 【0008】 A "workflow generation means" refers to a system that has the function of improving work efficiency by formulating the optimal work procedure based on the results of an analysis means and distributing specific work instructions to each machine. 【0009】 "Task execution means" refers to a device or system that has the function of having individual machines actually perform tasks according to work instructions distributed from a work flow generation means, and to report the progress of the work. 【0010】 A "situation reporting system" refers to a system that has the function of conveying generated reports in natural language to inform users about the content of the work performed and changes in the agricultural environment. [Brief explanation of the drawing] 【0011】 [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 the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0012】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0013】 First, the terms used in the following description will be explained. 【0014】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0015】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0016】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0017】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like. 【0018】 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." 【0019】 [First Embodiment] 【0020】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0021】 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. 【0022】 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). 【0023】 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. 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0028】 As shown in Figure 2, in the data processing device 12, 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. 【0029】 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. 【0030】 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. 【0031】 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". 【0032】 The present invention provides a system for efficiently and autonomously managing farms in an agricultural environment. This system includes data collection means, analysis means, work flow generation means, task execution means, and status reporting means. 【0033】 Data collection and processing 【0034】 Server: Collects data such as temperature, humidity, soil moisture content, and crop image data from agricultural environments equipped with various sensors. This data is aggregated on the server via 5G communication and made available in real time. 【0035】 Data Analysis 【0036】 Server: Uses received data to forecast weather, analyze soil, and evaluate crop growth processes. Generative AI is used in the analysis to detect insufficient soil moisture or signs of pests and diseases in crops. 【0037】 Workflow generation 【0038】 Server: Based on the analysis results, it plans the optimal work procedure and assigns work instructions to individual machines accordingly. For example, if rain is expected the next day, it adjusts the plan to carry out fertilization in advance. 【0039】 Execution of the task 【0040】 Terminals (robots and drones): They receive instructions from the server and perform various agricultural tasks. For example, drones scan fields from above, and ground robots manage and irrigate designated furrows. 【0041】 Situation report 【0042】 Server: Based on feedback information from each terminal, it reports the current status of the farm and the progress of the work to the user in natural language. 【0043】 User: Receive reports, check the system's operational status, and provide additional instructions as needed. 【0044】 For example, if a sensor detects abnormally low soil moisture in a particular field, the generated AI will determine the need for irrigation and immediately instruct an irrigation robot to do so. This prevents stress on the crops and helps maintain optimal growth conditions. 【0045】 In this way, this system highly automates farm management, alleviating labor shortages while improving production efficiency. 【0046】 The following describes the processing flow. 【0047】 Step 1: 【0048】 Server: Collects temperature, humidity, soil moisture, and crop image data in real time from various sensors using 5G communication. The data is stored in a database on the server. 【0049】 Step 2: 【0050】 Server: Based on the collected data, the generating AI analyzes weather forecasts and soil conditions. It also evaluates crop health from image data and identifies necessary actions. 【0051】 Step 3: 【0052】 Server: Plans the optimal workflow based on the analysis results. For example, it calculates the timing of watering based on weather forecasts and determines fertilizer application according to the crop's growth stage. 【0053】 Step 4: 【0054】 Server: Transmits individual work instructions to each machine via wireless communication. These instructions include specific work details and schedules. 【0055】 Step 5: 【0056】 Terminals (robots and drones): They perform designated farming tasks according to instructions received from the server. Robots manage the furrows, and drones conduct aerial reconnaissance. 【0057】 Step 6: 【0058】 Terminal: Reports the progress and status of the work as feedback to the server. Also reports any new problems or necessary adjustments. 【0059】 Step 7: 【0060】 Server: Aggregates feedback from terminals and reports processing results to the user in natural language. The report to the user includes the completion status of the task and the next action to take. 【0061】 Step 8: 【0062】 User: Review the report and, if necessary, instruct the server on new goals or adjustments. Also, prepare for the next work plan. 【0063】 (Example 1) 【0064】 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." 【0065】 In agriculture, there is a need to accurately assess environmental changes and growth conditions, and to perform appropriate tasks efficiently and automatically. Conventional methods lack the accuracy and speed of data collection and analysis, as well as the ability to respond to subtle changes and address labor shortages. 【0066】 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. 【0067】 In this invention, the server includes information gathering means, information analysis means, and work procedure generation means. This makes it possible to collect and analyze agricultural environment data in real time and efficiently perform appropriate tasks. 【0068】 "Information gathering means" refers to a mechanism that acquires environmental conditions, ground surface information, and plant conditions in real time from various measuring devices installed in the agricultural environment. 【0069】 "Information analysis means" refers to the process of performing weather forecasting, surface analysis, and plant growth evaluation based on acquired information, and then analyzing the results using a generated AI model. 【0070】 A "work procedure generation means" is a system that creates the optimal work procedure based on the analysis results and distributes work instructions to various machines. 【0071】 "Task execution means" refers to the function of various machines performing activities according to work instructions and reporting their progress. 【0072】 A "situation reporting method" is a method of communicating the details of the activities carried out and changes in the agricultural environment to users in natural language. 【0073】 This invention is a system that supports efficient and autonomous management in agriculture. This system consists of a server, terminals (robots and drones), and users working together. 【0074】 The server collects environmental data, ground surface information, and crop image data in real time from various sensors installed in agricultural fields. The hardware used includes thermometers, soil sensors, and cameras, and 5G networks are used for communication. This allows the server to collect data quickly. 【0075】 In data analysis, the server utilizes generative AI models to analyze weather forecasts, soil dryness, and crop health. Based on the analysis results, it formulates an optimal work plan and sends instructions to terminals. For example, if image analysis of crops detects signs of disease or pest infestation, the server instructs drones to spray pesticides in specific areas. 【0076】 The terminals perform specific tasks based on instructions received from the server. Drones scan the farm while flying through the air, and ground robots handle precise irrigation and weeding. This makes farm work more efficient. 【0077】 Furthermore, through the status reporting system, the server integrates feedback from various terminals and communicates the current status and progress of the farm to the user. This report is generated in natural language, and the user can use it to give additional instructions. 【0078】 For example, when a sensor detects a dry area, the server uses a generated AI model to determine the need for irrigation and issues instructions to an irrigation robot. This enables efficient farm management while maintaining crop health. 【0079】 An example of a prompt would be, "If the weather forecast for tomorrow is rain, plan the optimal workflow for farm management." This prompt allows the generative AI model to suggest appropriate work procedures. 【0080】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0081】 Step 1: 【0082】 The server acquires temperature, humidity, soil moisture content, and crop image data from multiple sensors installed in agricultural fields. This data is aggregated on the server via 5G communication. The input is raw data from the sensors, and the output is an integrated environmental dataset. This step involves data collection and its real-time updating. 【0083】 Step 2: 【0084】 The server receives the collected dataset and performs analysis using a generative AI model. Specifically, it performs weather forecasting, soil dryness analysis, and crop health assessment using images. The input data is the environmental dataset obtained in the previous step, and the output is the analysis results. Here, predictive and diagnostic models based on the data are executed, which identifies the issues that need to be addressed. 【0085】 Step 3: 【0086】 The server creates an optimal workflow based on the analysis results. For example, it instructs irrigation in zones experiencing severe drought and pesticide application in areas where signs of disease or pest infestation are found. The analysis results are used as input, and specific work instructions are generated as output. Here, a flexible and efficient farming plan is created that responds to real-time conditions. 【0087】 Step 4: 【0088】 The terminal devices, such as robots and drones, perform actual farm work based on work instructions received from the server. Drones scan the farm while moving through the air, and robots perform irrigation and weed removal in designated areas. The input is work instructions, and the output is the results of various farm work. Here, practical activities are carried out based on instructions from the server, and farm management is made more efficient. 【0089】 Step 5: 【0090】 The server collects work completion reports and feedback data from each terminal. This information is aggregated and used to understand the current state of the farm. The input is feedback data from the terminals, and the output is an updated farm status report. In this step, the actual work results are reported to the server, and the information is prepared for the next action. 【0091】 (Application Example 1) 【0092】 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." 【0093】 Labor shortages and a lack of efficient management in the agricultural environment are becoming increasingly serious, necessitating innovative solutions. In particular, performing appropriate tasks according to plant growth stages and monitoring and managing agricultural activities remotely are difficult. To address these challenges, there is a need for systems that autonomously manage operations and maximize efficiency. 【0094】 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. 【0095】 In this invention, the server includes information gathering means for acquiring environmental information and plant conditions in real time from various detectors installed in the agricultural environment; analysis means for performing environmental prediction, soil condition analysis, and plant growth evaluation based on the acquired information; and generation AI analysis means for performing plant growth prediction and anomaly detection using a generation AI model and proposing optimal agricultural activities. This makes it possible to efficiently and autonomously manage the agricultural environment from a remote location and economically distribute optimal work instructions. 【0096】 "Information gathering means" refers to a mechanism for acquiring environmental information and plant conditions in real time from detectors installed in agricultural environments. 【0097】 "Analytical methods" refer to the processes used to perform environmental predictions, soil condition analysis, and plant growth evaluations based on collected information. 【0098】 A "task generation means" is used to generate optimal tasks based on analysis results and distribute task instructions to individual devices. 【0099】 "Business execution means" refers to the function by which individual devices execute tasks according to business instructions and report on their progress. 【0100】 A "situation reporting method" is a system that reports the details of the work performed and changes in the agricultural environment to users in natural language. 【0101】 The "generative AI analysis means" is a function that uses a generative AI model to predict plant growth and detect anomalies, and to propose optimal agricultural activities. 【0102】 The server acquires environmental information and plant conditions in real time from various detectors through information gathering devices installed in the agricultural environment. The collected data is rapidly transmitted to the server using 5G communication technology. This server uses analytical tools to perform environmental predictions, soil condition analysis, and plant growth evaluations based on the data. Generative AI models such as TENSORFLOW® are used in this process to predict plant growth and detect anomalies. 【0103】 Based on the analysis results, the server generates optimal tasks and distributes instructions to individual devices using the task generation means. Terminals equipped with task execution means (such as drones and ground robots) then perform specific agricultural tasks based on these instructions. For example, during harvest season, a drone scans the field from above to perform harvesting at the predicted optimal timing. 【0104】 Feedback from the terminal is collected by a status reporting mechanism on the server and reported to the user using natural language processing (NLP). The report includes the current state of the agricultural environment, completed tasks, and future predictions. Based on this report, the user can send further instructions to the server. 【0105】 A concrete example is a scenario where, if a sensor detects abnormally low soil moisture, the generated AI model determines the need for irrigation and immediately issues instructions to an irrigation robot. An example of a prompt message in this case would be, "Generate suggestions for optimal farm work based on recent weather forecasts and soil analysis." This would minimize stress on crops and maintain optimal growth conditions. 【0106】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0107】 Step 1: 【0108】 The server acquires environmental information and plant condition data collected from various detectors through information gathering means. This data is transmitted from the sensor devices via 5G communication. The inputs are temperature, humidity, soil moisture content, and plant image data, which are then used for analysis in the next step. 【0109】 Step 2: 【0110】 The server uses analytical tools to perform environmental predictions and soil condition analyses based on the collected data. It uses a generative AI model (e.g., TensorFlow) to perform anomaly detection and growth prediction processes. The input is the environmental data acquired in step 1, and the output is the predicted environmental conditions and anomaly alerts. 【0111】 Step 3: 【0112】 The server plans the optimal work tasks using a task generation system based on the analysis results and distributes work instructions to individual terminals (robots and drones). The input is the prediction results and analysis data from step 2, and the output is specific work instructions for each terminal. 【0113】 Step 4: 【0114】 The terminals perform agricultural tasks based on the work instructions they receive. For example, a drone performs an aerial scan, and an irrigation robot waters designated furrows. The input is the work instructions from the server, and the output is a completion report of the agricultural tasks that have been performed. 【0115】 Step 5: 【0116】 The server receives feedback from the terminal and reports to the user the work performed and changes in the agricultural environment using a status reporting mechanism. This report is generated in natural language and formatted in a way that is easily understandable to the user. The input is feedback data from the terminal, and the output is a natural language report for the user. 【0117】 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. 【0118】 This invention integrates an emotion engine that recognizes user emotions and enables operations and information provision accordingly, in addition to an automated system for agricultural environments. This system includes data collection means, analysis means, workflow generation means, task execution means, status reporting means, and emotion sensing means. 【0119】 Data collection and analysis 【0120】 Server: Analyzes environmental data collected through various sensors in real time. Generating AI uses the data to predict weather and evaluate soil conditions. Simultaneously, this information is also provided to users. 【0121】 Emotion recognition by an emotion engine 【0122】 Server: The emotion engine analyzes the user's emotions from their voice and text input. Based on the emotion analysis, it reflects this in the user's requests and instructions. For example, if the user is feeling stressed, it suggests simplifying the system's operation procedures. 【0123】 Optimizing and executing workflows 【0124】 Server: Generates the optimal work schedule and plan based on the analysis results and distributes it to each machine. It also takes into account the user's emotional state and adjusts the work procedure if there is dissatisfaction, for example. 【0125】 Terminals (robots and drones): They receive instructions from the server and perform designated farming tasks. An emotion engine prioritizes important tasks to ensure the user's emotional well-being. 【0126】 Situation reporting and coordination 【0127】 Terminal: Reports the progress of the work to the server and readjusts the server's instructions as needed. This also includes reporting the status of the work progress and any new anomalies that may occur. 【0128】 Server: Using an integrated emotion engine, it translates reported information into natural language in a format that is sensitive to the user's emotions and provides it accordingly. For example, if the user is agitated, it will provide explanations that help them calm down and continue the operation. 【0129】 User: Receives reports from the system and sends decisions and new instructions to the system based on the situation at hand. Considers emotionally-driven suggestions and makes decisions to improve work efficiency and comfort. 【0130】 For example, if a user feels fatigued during their morning work session, the emotion engine detects this, and the server automatically changes the task priorities, performing less demanding tasks first. As a result, the user can continue working while maintaining comfort. 【0131】 In this way, the present invention provides human-centered agricultural management that takes into account the feelings of agricultural workers, enabling efficient production activities. 【0132】 The following describes the processing flow. 【0133】 Step 1: 【0134】 Server: Collects temperature, humidity, soil moisture content, and crop growth status in real time from various sensors installed in the agricultural environment and stores them in a database via 5G communication. 【0135】 Step 2: 【0136】 Server: Based on the collected data, it uses generative AI to predict weather, analyze soil conditions, and assess crop health. This analysis identifies the next necessary agricultural tasks. 【0137】 Step 3: 【0138】 Server: The emotion engine analyzes voice and text input from the user to identify the user's emotional state. This information also influences the optimization of the workflow. 【0139】 Step 4: 【0140】 Server: Based on the results and analysis of the emotion engine, it optimizes the workflow and generates work instructions for individual machines. If a user is experiencing stress, it simplifies the operating procedure or adjusts the reporting method. 【0141】 Step 5: 【0142】 Terminals (robots and drones): These perform agricultural tasks according to work instructions received from the server. For example, a furrowing robot prepares the field, or an irrigation robot supplies the appropriate amount of water. 【0143】 Step 6: 【0144】 Terminal: Provides feedback to the server regarding work progress and any newly discovered anomalies. Real-time reporting enables immediate responses tailored to the situation. 【0145】 Step 7: 【0146】 Server: Receives feedback from the terminal and updates instructions as needed. It also reports the processing results to the user in natural language and explains the situation in an emotionally sensitive manner. 【0147】 Step 8: 【0148】 User: Review reports from the server and make new instructions or adjustments based on system suggestions. If the user is relaxed, they will receive normal reports; if they are agitated, they will be instructed to calm down. 【0149】 This processing flow enables efficient farm management that responds to user emotions, thereby improving human comfort. 【0150】 (Example 2) 【0151】 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". 【0152】 Agricultural work heavily relies on weather and soil conditions, making efficient forecasting and planning crucial. However, the emotional state of the farm workers also impacts work efficiency. Traditional agricultural systems often prioritized mechanical command execution over flexible work management that considered the feelings of the users. This can lead to increased worker stress and fatigue, potentially resulting in decreased productivity. Therefore, there is a need for systems that consider not only environmental data-based work planning but also the emotions of the users. 【0153】 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. 【0154】 In this invention, the server includes information gathering means for acquiring weather, land, and plant conditions in real time from various detectors installed in agricultural areas; analysis means for performing weather forecasting, land analysis, and plant growth evaluation based on the acquired information; and emotion recognition means for recognizing the user's emotions using language and voice analysis and reflecting them in work processes and information provision. This enables not only the generation of optimal work plans based on environmental information, but also flexible work management that takes the user's emotions into consideration. 【0155】 "Information gathering means" refers to a device or method for acquiring data on weather, land, and plant conditions in real time using various detectors installed in agricultural areas. 【0156】 "Analysis means" refers to a device or method for performing weather forecasting, land analysis, and plant growth evaluation based on data obtained by information gathering means. 【0157】 "Work process generation means" refers to a device or method for generating an optimal work plan based on analysis results and distributing work instructions to individual devices. 【0158】 "Means of performing work" refers to devices or methods for which individual devices perform agricultural work in accordance with work instructions and report on its progress. 【0159】 A "situation reporting means" is a device or method for reporting to the user, in natural language, the details of the work performed and changes in the environment of the agricultural land. 【0160】 "Emotion recognition means" refers to a device or method for recognizing a user's emotions using language and speech analysis and reflecting them in work processes and information provision. 【0161】 This invention is an automated system for agricultural use that enables work optimization while taking user emotions into consideration. The following describes specific embodiments for carrying out this invention. 【0162】 The system uses various sensors as means of collecting information. These include sensors that measure temperature, humidity, and soil moisture. These sensors transmit data to a server in real time, and the server stores this information in a database. 【0163】 The server uses a generative AI model as an analytical tool to analyze the collected data. Specifically, it predicts weather by comparing it with past data and evaluates the condition of the land. The generative AI model used in this process includes machine learning algorithms to improve the accuracy of weather forecasts. 【0164】 Furthermore, the server uses emotion recognition technology to understand the user's emotions. It analyzes the user's voice and text input to determine their emotional state, such as stress or fatigue. This information is used to adjust the work plan. 【0165】 The robots and drones deployed as terminal devices will operate according to work commands from the server. Each terminal will autonomously perform tasks such as pesticide spraying and harvesting, and will periodically report the progress of its work to the server. 【0166】 For example, if a user asks, "What's the weather forecast for today?", the server will activate a generative AI model, analyze the relevant data, and provide it to the user. Furthermore, if the emotion recognition system determines that the user is tired, the server can offer suggestions such as, "We recommend you stop your current task and take a break." 【0167】 An example of a prompt message for a generative AI model would be, "Optimize today's and tomorrow's work schedule, and adjust it if necessary." 【0168】 In this way, the present invention realizes efficient and comfortable agricultural operations through work management that takes environmental data and user emotions into consideration. 【0169】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0170】 Step 1: 【0171】 The server acquires data from sensors installed in agricultural areas using information gathering methods. It receives sensor data such as temperature, humidity, and soil moisture as input, and processes this data by storing it in a database. This stored data is then used for subsequent analysis. 【0172】 Step 2: 【0173】 The server uses a generative AI model to analyze the acquired sensor data. It reads the previously stored sensor data as input and performs weather forecasting and soil condition assessment. The output provides predicted weather information and soil health status. Specifically, it uses statistical analysis and machine learning algorithms to calculate future weather patterns. 【0174】 Step 3: 【0175】 The server uses emotion recognition technology to analyze emotions from voice and text input by the user. It receives user voice commands and text messages in digital format as input and performs emotion analysis. The output is emotional indicators such as the user's stress level and fatigue level. This allows the system to understand the user's mental state. 【0176】 Step 4: 【0177】 Based on these analysis results, the server uses a work process generation mechanism to construct an optimal work plan. It considers weather information, soil conditions, and user sentiment indicators as inputs, and generates a specific work schedule as output. For example, if rain is expected or if the user is experiencing stress, a flexible plan can be created that readjusts the work order. 【0178】 Step 5: 【0179】 The terminal performs agricultural tasks based on work instructions received from the server. It receives a work schedule from the server as input and then operates the actual agricultural machinery (e.g., pesticide spraying with a drone). The output is a work completion report, providing feedback on progress to the server. 【0180】 Step 6: 【0181】 The server receives progress reports from terminals and provides information to users using status reporting mechanisms. It takes work progress data from terminals as input and creates a report adjusted based on sentiment indicators, then outputs this report to the user in natural language. For example, it might display a message such as, "Work is progressing smoothly and is expected to be completed ahead of schedule," to help users continue working with confidence. 【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】 While automated systems in agricultural environments provide efficient work management through the collection and analysis of environmental data, they face the challenge of not being able to flexibly adapt to the emotional state of users. Furthermore, when users are experiencing stress or fatigue, it is necessary to appropriately optimize the work content and information provided. Solving this challenge will enable users to utilize the system more comfortably and improve agricultural efficiency. 【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 a work sequence generation means for distributing work instructions to individual devices, an emotion engine integration means for evaluating the user's emotions using emotion recognition means and reflecting the results in adjusting the work sequence, and an information provision means for providing information and suggestions optimized according to the user's emotional state. This makes it possible to manage work and provide information while taking the user's emotions into consideration. 【0187】 "Information gathering means" refers to devices and technologies for acquiring weather, soil, and crop conditions in an agricultural environment in real time. 【0188】 "Interpretation methods" refer to methods and techniques for performing weather forecasting, soil analysis, and crop growth evaluation based on acquired information. 【0189】 A "work sequence generation means" is a method or system for generating the optimal work sequence based on the interpretation results and distributing work instructions to individual devices. 【0190】 "Emotion recognition means" refers to technology that evaluates the emotions of users and uses the results to improve the operation of the system. 【0191】 The "emotion engine integration means" refers to a technical measure that reflects the results of the emotion recognition means in adjusting the work sequence. 【0192】 "Information provision methods" refer to methods for providing information and suggestions that are optimized according to the user's emotional state. 【0193】 A "task execution means" is a system in which individual devices perform tasks according to work instructions and report their progress. 【0194】 A "situation reporting system" is a system for reporting the details of completed work and changes in the agricultural environment to users in natural language. 【0195】 To implement this invention, a server, a user terminal, and various sensors and machines are required. 【0196】 The server collects environmental data such as weather, soil, and crop conditions in real time from various sensors. This data is then interpreted to perform weather forecasting, soil analysis, and crop growth evaluation. Dedicated analysis programs and artificial intelligence models (e.g., generative AI models) are used to analyze the collected data. Based on the analysis results, an optimal work sequence is generated, and instructions are transmitted wirelessly to individual devices. 【0197】 Emotion recognition tools analyze the emotions from the user's input voice and text. The server takes the user's emotional state into consideration and adjusts the order of operations as needed. This emotion recognition is performed using speech recognition APIs (e.g., Google® Speech-to-Text) and sentiment analysis APIs (e.g., IBM Watson® Natural Language Understanding). 【0198】 Based on information received from the server, the user terminal provides the user with optimal information and suggestions tailored to their emotional state. For example, if the user is feeling stressed, it might suggest taking a break or simplifying work procedures. 【0199】 Ultimately, the technologies and functions used will enable comfortable and efficient agricultural management that takes user emotions into consideration. A specific example of a prompt is given below: "Design a program that analyzes the user's emotional state and adjusts the work sequence based on that analysis." 【0200】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0201】 Step 1: 【0202】 The server acquires environmental data from various sensors. It receives real-time weather, soil, and crop condition data as input. Based on this data, it uses a generative AI model to perform weather forecasting, soil analysis, and crop growth evaluation, and outputs the analysis results. 【0203】 Step 2: 【0204】 The server generates the optimal work sequence based on the acquired analysis results. It takes the analysis results as input and uses the work sequence generation mechanism to output the optimal work plan. This output includes work instructions that are distributed to each device via wireless communication. 【0205】 Step 3: 【0206】 The server processes user input (voice and text) using emotion recognition mechanisms. It receives voice and text data as input and evaluates the emotional state using speech recognition APIs and emotion analysis APIs. The evaluation results are output and used to adjust the workflow as needed. 【0207】 Step 4: 【0208】 The terminal receives work instructions sent from the server and performs the specified tasks according to those instructions. It receives work instructions as input and reports the progress of the performed tasks to the server. It provides the progress of the tasks as output and accepts new instructions as needed. 【0209】 Step 5: 【0210】 The user reviews information and suggestions received from the server via their terminal. They receive advice based on work details and emotions reported in natural language as input, and make decisions to improve work efficiency. The system generates feedback from the user and new instructions as output. 【0211】 Step 6: 【0212】 The server receives feedback and new instructions from users and adjusts system operations accordingly. It takes feedback information as input and outputs new work plans and optimized information provision, improving the overall flexibility of the system. 【0213】 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. 【0214】 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. 【0215】 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. 【0216】 [Second Embodiment] 【0217】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0218】 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. 【0219】 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). 【0220】 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. 【0221】 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. 【0222】 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). 【0223】 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. 【0224】 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. 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 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". 【0229】 The present invention provides a system for efficiently and autonomously managing farms in an agricultural environment. This system includes data collection means, analysis means, work flow generation means, task execution means, and status reporting means. 【0230】 Data collection and processing 【0231】 Server: Collects data such as temperature, humidity, soil moisture content, and crop image data from agricultural environments equipped with various sensors. This data is aggregated on the server via 5G communication and made available in real time. 【0232】 Data analysis 【0233】 Server: Uses received data to forecast weather, analyze soil, and evaluate crop growth processes. Generative AI is used in the analysis to detect insufficient soil moisture or signs of pests and diseases in crops. 【0234】 Workflow generation 【0235】 Server: Based on the analysis results, it plans the optimal work procedure and assigns work instructions to individual machines accordingly. For example, if rain is expected the next day, it adjusts the plan to carry out fertilization in advance. 【0236】 Execution of the task 【0237】 Terminals (robots and drones): They receive instructions from the server and perform various farming tasks. For example, drones scan fields from above, and ground robots manage and irrigate designated furrows. 【0238】 Situation report 【0239】 Server: Based on feedback information from each terminal, it reports the current status of the farm and the progress of the work to the user in natural language. 【0240】 User: Receive reports, check the system's operational status, and provide additional instructions as needed. 【0241】 For example, if a sensor detects abnormally low soil moisture in a particular field, the generated AI will determine the need for irrigation and immediately instruct an irrigation robot to do so. This prevents stress on the crops and helps maintain optimal growth conditions. 【0242】 In this way, this system highly automates farm management, alleviating labor shortages while improving production efficiency. 【0243】 The following describes the processing flow. 【0244】 Step 1: 【0245】 Server: Collects temperature, humidity, soil moisture, and crop image data in real time from various sensors using 5G communication. The data is stored in a database on the server. 【0246】 Step 2: 【0247】 Server: Based on the collected data, the generating AI analyzes weather forecasts and soil conditions. It also evaluates crop health from image data and identifies necessary actions. 【0248】 Step 3: 【0249】 Server: Plans the optimal workflow based on the analysis results. For example, it calculates the timing of watering based on weather forecasts and determines fertilizer application according to the crop's growth stage. 【0250】 Step 4: 【0251】 Server: Transmits individual work instructions to each machine via wireless communication. These instructions include specific work details and schedules. 【0252】 Step 5: 【0253】 Terminals (robots and drones): They perform designated farming tasks according to instructions received from the server. Robots manage the furrows, and drones conduct aerial reconnaissance. 【0254】 Step 6: 【0255】 Terminal: Reports the progress and status of the work as feedback to the server. Also reports any new problems or necessary adjustments. 【0256】 Step 7: 【0257】 Server: Aggregates feedback from terminals and reports processing results to the user in natural language. The report to the user includes the completion status of the task and the next action to take. 【0258】 Step 8: 【0259】 User: Review the report and, if necessary, instruct the server on new goals or adjustments. Also, prepare for the next work plan. 【0260】 (Example 1) 【0261】 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." 【0262】 In agriculture, there is a need to accurately assess environmental changes and growth conditions, and to perform appropriate tasks efficiently and automatically. Conventional methods lack the accuracy and speed of data collection and analysis, as well as the ability to respond to subtle changes and address labor shortages. 【0263】 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. 【0264】 In this invention, the server includes information gathering means, information analysis means, and work procedure generation means. This makes it possible to collect and analyze agricultural environment data in real time and efficiently perform appropriate tasks. 【0265】 "Information gathering means" refers to a mechanism that acquires environmental conditions, ground surface information, and plant conditions in real time from various measuring devices installed in the agricultural environment. 【0266】 "Information analysis means" refers to the process of performing weather forecasting, surface analysis, and plant growth evaluation based on acquired information, and then analyzing the results using a generated AI model. 【0267】 A "work procedure generation means" is a system that creates the optimal work procedure based on the analysis results and distributes work instructions to various machines. 【0268】 "Task execution means" refers to the function of various machines performing activities according to work instructions and reporting their progress. 【0269】 A "situation reporting method" is a method of communicating the details of the activities carried out and changes in the agricultural environment to users in natural language. 【0270】 This invention is a system that supports efficient and autonomous management in agriculture. This system consists of a server, terminals (robots and drones), and users working together. 【0271】 The server collects environmental data, ground surface information, and crop image data in real time from various sensors installed in agricultural fields. The hardware used includes thermometers, soil sensors, and cameras, and 5G networks are used for communication. This allows the server to collect data quickly. 【0272】 In data analysis, the server utilizes generative AI models to analyze weather forecasts, soil dryness, and crop health. Based on the analysis results, it formulates an optimal work plan and sends instructions to terminals. For example, if image analysis of crops detects signs of disease or pest infestation, the server instructs drones to spray pesticides in specific areas. 【0273】 The terminals perform specific tasks based on instructions received from the server. Drones scan the farm while flying through the air, and ground robots handle precise irrigation and weeding. This makes farm work more efficient. 【0274】 Furthermore, through the status reporting system, the server integrates feedback from various terminals and communicates the current status and progress of the farm to the user. This report is generated in natural language, and the user can use it to give additional instructions. 【0275】 For example, when a sensor detects a dry area, the server uses a generated AI model to determine the need for irrigation and issues instructions to an irrigation robot. This enables efficient farm management while maintaining crop health. 【0276】 An example of a prompt would be, "If the weather forecast for tomorrow is rain, plan the optimal workflow for farm management." This prompt allows the generative AI model to suggest appropriate work procedures. 【0277】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0278】 Step 1: 【0279】 The server acquires temperature, humidity, soil moisture content, and crop image data from multiple sensors installed in agricultural fields. This data is aggregated on the server via 5G communication. The input is raw data from the sensors, and the output is an integrated environmental dataset. This step involves data collection and its real-time updating. 【0280】 Step 2: 【0281】 The server receives the collected dataset and performs analysis using a generative AI model. Specifically, it performs weather forecasting, soil dryness analysis, and crop health assessment using images. The input data is the environmental dataset obtained in the previous step, and the output is the analysis results. Here, predictive and diagnostic models based on the data are executed, which identifies the issues that need to be addressed. 【0282】 Step 3: 【0283】 The server creates an optimal work flow based on the analysis results. For example, it instructs irrigation in the zones where drying is progressing and pesticide spraying in the parts where signs of pests and diseases are found. The analysis results are used as the input, and specific work instructions are generated as the output. Here, a flexible and efficient farming operation plan according to the real-time situation is created. 【0284】 Step 4: 【0285】 Robots and drones as terminals perform actual farming operations based on the work instructions received from the server. Drones scan the farm while moving in the air, and robots perform irrigation and weed removal in the designated areas. The work instructions are the input, and the execution results of various farming operations are obtained as the output. Here, practical activities based on the server's instructions are carried out, and farm management is made more efficient. 【0286】 Step 5: 【0287】 The server collects work completion reports and feedback data from each terminal. These pieces of information are aggregated and used to grasp the current situation of the farm. The input is the feedback data from the terminal, and the output is an updated farm situation report. In this step, the actual work results are reported to the server, and information is prepared for the next action. 【0288】 (Application Example 1) 【0289】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0290】 The shortage of labor and the lack of efficient management in the agricultural environment are becoming serious, and improvements are needed. In particular, it is difficult to carry out appropriate work according to the growth status of plants and to monitor and manage agricultural activities from remote locations. To solve these problems, the realization of a system that autonomously manages work and maximizes efficiency is required. 【0291】 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. 【0292】 In this invention, the server includes information gathering means for acquiring environmental information and plant conditions in real time from various detectors installed in the agricultural environment; analysis means for performing environmental prediction, soil condition analysis, and plant growth evaluation based on the acquired information; and generation AI analysis means for performing plant growth prediction and anomaly detection using a generation AI model and proposing optimal agricultural activities. This makes it possible to efficiently and autonomously manage the agricultural environment from a remote location and economically distribute optimal work instructions. 【0293】 "Information gathering means" refers to a mechanism for acquiring environmental information and plant conditions in real time from detectors installed in agricultural environments. 【0294】 "Analytical methods" refer to the processes used to perform environmental predictions, soil condition analysis, and plant growth evaluations based on collected information. 【0295】 A "task generation means" is used to generate optimal tasks based on analysis results and distribute task instructions to individual devices. 【0296】 "Business execution means" refers to the function by which individual devices execute tasks according to business instructions and report on their progress. 【0297】 A "situation reporting method" is a system that reports the details of the work performed and changes in the agricultural environment to users in natural language. 【0298】 The "generative AI analysis means" is a function that uses a generative AI model to predict plant growth and detect anomalies, and to propose optimal agricultural activities. 【0299】 The server acquires environmental information and plant conditions in real time from various detectors through information gathering devices installed in the agricultural environment. The collected data is rapidly transmitted to the server using 5G communication technology. This server uses analytical tools to perform environmental predictions, soil condition analysis, and plant growth evaluations based on the data. Generative AI models such as TensorFlow are used in this process to predict plant growth and detect anomalies. 【0300】 Based on the analysis results, the server generates optimal tasks and distributes instructions to individual devices using the task generation means. Terminals equipped with task execution means (such as drones and ground robots) then perform specific agricultural tasks based on these instructions. For example, during harvest season, a drone scans the field from above to perform harvesting at the predicted optimal timing. 【0301】 Feedback from the terminal is collected by a status reporting mechanism on the server and reported to the user using natural language processing (NLP). The report includes the current state of the agricultural environment, completed tasks, and future predictions. Based on this report, the user can send further instructions to the server. 【0302】 A concrete example is a scenario where, if a sensor detects abnormally low soil moisture, the generated AI model determines the need for irrigation and immediately issues instructions to an irrigation robot. An example of a prompt message in this case would be, "Generate suggestions for optimal farm work based on recent weather forecasts and soil analysis." This would minimize stress on crops and maintain optimal growth conditions. 【0303】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0304】 Step 1: 【0305】 The server acquires environmental information and plant status data collected from various detectors through information collection means. This data is transmitted from the sensor device via 5G communication. The inputs are temperature, humidity, soil moisture content, and plant image data, based on which analysis is performed in the next step. 【0306】 Step 2: 【0307】 The server performs environmental prediction and soil condition analysis using analysis means based on the collected data. An AI model (e.g., TensorFlow) is used to execute anomaly detection and growth prediction processes. The input is the environmental data obtained in Step 1, and the output is the prediction result of the environmental situation and an alert for anomaly occurrence. 【0308】 Step 3: 【0309】 The server plans optimal work tasks using work task generation means based on the analysis results and distributes work instructions to individual terminals (robots and drones). The input is the prediction result and analysis data of Step 2, and the output is specific work instructions for each terminal. 【0310】 Step 4: 【0311】 The terminal performs agricultural operations based on the received work instructions. For example, the drone conducts scans from the air, and the irrigation robot waters the designated fields. The input is the work instruction from the server, and the output is a completion report of the implemented agricultural operations. 【0312】 Step 5: 【0313】 The server receives feedback from the terminal and reports the implemented work content and changes in the agricultural environment to the user using the status reporting means. This report is generated in natural language and presented in a format that is easily understandable by the user. The input is the feedback data from the terminal, and the output is a report in natural language for the user. 【0314】 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. 【0315】 This invention integrates an emotion engine that recognizes user emotions and enables operations and information provision accordingly, in addition to an automated system for agricultural environments. This system includes data collection means, analysis means, workflow generation means, task execution means, status reporting means, and emotion sensing means. 【0316】 Data collection and analysis 【0317】 Server: Analyzes environmental data collected through various sensors in real time. Generating AI uses the data to predict weather and evaluate soil conditions. Simultaneously, this information is also provided to users. 【0318】 Emotion recognition by an emotion engine 【0319】 Server: The emotion engine analyzes the user's emotions from their voice and text input. Based on the emotion analysis, it reflects this in the user's requests and instructions. For example, if the user is feeling stressed, it suggests simplifying the system's operation procedures. 【0320】 Optimizing and executing workflows 【0321】 Server: Generates the optimal work schedule and plan based on the analysis results and distributes it to each machine. It also takes into account the user's emotional state and adjusts the work procedure if there is dissatisfaction, for example. 【0322】 Terminals (robots and drones): They receive instructions from the server and perform designated farming tasks. An emotion engine prioritizes important tasks to ensure the user's emotional well-being. 【0323】 Situation reporting and coordination 【0324】 Terminal: Reports the progress of the work to the server and readjusts the server's instructions as needed. This also includes reporting the status of the work progress and any new anomalies that may occur. 【0325】 Server: Using an integrated emotion engine, it translates reported information into natural language in a format that is sensitive to the user's emotions and provides it accordingly. For example, if the user is agitated, it will provide explanations that help them calm down and continue the operation. 【0326】 User: Receives reports from the system and sends decisions and new instructions to the system based on the situation at hand. Considers emotionally-driven suggestions and makes decisions to improve work efficiency and comfort. 【0327】 For example, if a user feels fatigued during their morning work session, the emotion engine detects this, and the server automatically changes the task priorities, performing less demanding tasks first. As a result, the user can continue working while maintaining comfort. 【0328】 In this way, the present invention provides human-centered agricultural management that takes into account the feelings of agricultural workers, enabling efficient production activities. 【0329】 The following describes the processing flow. 【0330】 Step 1: 【0331】 Server: Collects temperature, humidity, soil moisture content, and crop growth status in real time from various sensors installed in the agricultural environment and stores them in a database via 5G communication. 【0332】 Step 2: 【0333】 Server: Based on the collected data, it uses generative AI to predict weather, analyze soil conditions, and assess crop health. This analysis identifies the next necessary agricultural tasks. 【0334】 Step 3: 【0335】 Server: The emotion engine analyzes voice and text input from the user to identify the user's emotional state. This information also influences the optimization of the workflow. 【0336】 Step 4: 【0337】 Server: Based on the results and analysis of the emotion engine, it optimizes the workflow and generates work instructions for individual machines. If a user is experiencing stress, it simplifies the operating procedure or adjusts the reporting method. 【0338】 Step 5: 【0339】 Terminals (robots and drones): These perform agricultural tasks according to work instructions received from the server. For example, a furrowing robot prepares the field, or an irrigation robot supplies the appropriate amount of water. 【0340】 Step 6: 【0341】 Terminal: Provides feedback to the server regarding work progress and any newly discovered anomalies. Real-time reporting enables immediate responses tailored to the situation. 【0342】 Step 7: 【0343】 Server: Receives feedback from the terminal and updates instructions as needed. It also reports the processing results to the user in natural language and explains the situation in an emotionally sensitive manner. 【0344】 Step 8: 【0345】 User: Review reports from the server and make new instructions or adjustments based on system suggestions. If the user is relaxed, they will receive normal reports; if they are agitated, they will be instructed to calm down. 【0346】 This processing flow enables efficient farm management that responds to user emotions, thereby improving human comfort. 【0347】 (Example 2) 【0348】 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". 【0349】 Agricultural work heavily relies on weather and soil conditions, making efficient forecasting and planning crucial. However, the emotional state of the farm workers also impacts work efficiency. Traditional agricultural systems often prioritized mechanical command execution over flexible work management that considered the feelings of the users. This can lead to increased worker stress and fatigue, potentially resulting in decreased productivity. Therefore, there is a need for systems that consider not only environmental data-based work planning but also the emotions of the users. 【0350】 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. 【0351】 In this invention, the server includes information gathering means for acquiring weather, land, and plant conditions in real time from various detectors installed in agricultural areas; analysis means for performing weather forecasting, land analysis, and plant growth evaluation based on the acquired information; and emotion recognition means for recognizing the user's emotions using language and voice analysis and reflecting them in work processes and information provision. This enables not only the generation of optimal work plans based on environmental information, but also flexible work management that takes the user's emotions into consideration. 【0352】 "Information gathering means" refers to a device or method for acquiring data on weather, land, and plant conditions in real time using various detectors installed in agricultural areas. 【0353】 "Analysis means" refers to devices or methods for performing weather forecasting, land analysis, and plant growth evaluation based on data obtained through information gathering means. 【0354】 "Work process generation means" refers to a device or method for generating an optimal work plan based on analysis results and distributing work instructions to individual devices. 【0355】 "Means of performing work" refers to devices or methods for which individual devices perform agricultural work in accordance with work instructions and report on its progress. 【0356】 A "situation reporting means" is a device or method for reporting to the user, in natural language, the details of the work performed and changes in the environment of the agricultural land. 【0357】 "Emotion recognition means" refers to a device or method for recognizing a user's emotions using language and speech analysis and reflecting them in work processes and information provision. 【0358】 This invention is an automated system for agricultural use that enables work optimization while taking user emotions into consideration. The following describes specific embodiments for carrying out this invention. 【0359】 The system uses various sensors as means of collecting information. These include sensors that measure temperature, humidity, and soil moisture. These sensors transmit data to a server in real time, and the server stores this information in a database. 【0360】 The server uses a generative AI model as an analytical tool to analyze the collected data. Specifically, it predicts weather by comparing it with past data and evaluates the condition of the land. The generative AI model used in this process includes machine learning algorithms to improve the accuracy of weather forecasts. 【0361】 Furthermore, the server uses emotion recognition technology to understand the user's emotions. It analyzes the user's voice and text input to determine their emotional state, such as stress or fatigue. This information is used to adjust the work plan. 【0362】 The robots and drones deployed as terminal devices will operate according to work commands from the server. Each terminal will autonomously perform tasks such as pesticide spraying and harvesting, and will periodically report the progress of its work to the server. 【0363】 For example, if a user asks, "What's the weather forecast for today?", the server will activate a generative AI model, analyze the relevant data, and provide it to the user. Furthermore, if the emotion recognition system determines that the user is tired, the server can offer suggestions such as, "We recommend you stop your current task and take a break." 【0364】 An example of a prompt message for a generative AI model would be, "Optimize today's and tomorrow's work schedule, and adjust it if necessary." 【0365】 In this way, the present invention realizes efficient and comfortable agricultural operations through work management that takes environmental data and user emotions into consideration. 【0366】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0367】 Step 1: 【0368】 The server acquires data from sensors installed in agricultural areas using information gathering methods. It receives sensor data such as temperature, humidity, and soil moisture as input, and processes this data by storing it in a database. This stored data is then used for subsequent analysis. 【0369】 Step 2: 【0370】 The server uses a generative AI model to analyze the acquired sensor data. It reads the previously stored sensor data as input and performs weather forecasting and soil condition assessment. The output provides predicted weather information and soil health status. Specifically, it uses statistical analysis and machine learning algorithms to calculate future weather patterns. 【0371】 Step 3: 【0372】 The server uses emotion recognition technology to analyze emotions from voice and text input by the user. It receives user voice commands and text messages in digital format as input and performs emotion analysis. The output is emotional indicators such as the user's stress level and fatigue level. This allows the system to understand the user's mental state. 【0373】 Step 4: 【0374】 Based on these analysis results, the server uses a work process generation mechanism to construct an optimal work plan. It considers weather information, soil conditions, and user sentiment indicators as inputs, and generates a specific work schedule as output. For example, if rain is expected or if the user is experiencing stress, a flexible plan can be created that readjusts the work order. 【0375】 Step 5: 【0376】 The terminal performs agricultural tasks based on work instructions received from the server. It receives a work schedule from the server as input and then operates the actual agricultural machinery (e.g., pesticide spraying with a drone). The output is a work completion report, which provides feedback on the progress to the server. 【0377】 Step 6: 【0378】 The server receives progress reports from terminals and provides information to users using status reporting mechanisms. It takes work progress data from terminals as input and creates a report adjusted based on sentiment indicators, then outputs this report to the user in natural language. For example, it might display a message such as, "Work is progressing smoothly and is expected to be completed ahead of schedule," to help users continue working with confidence. 【0379】 (Application Example 2) 【0380】 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." 【0381】 While automated systems in agricultural environments provide efficient work management through the collection and analysis of environmental data, they face the challenge of not being able to flexibly adapt to the emotional state of users. Furthermore, when users are experiencing stress or fatigue, it is necessary to appropriately optimize the work content and information provided. Solving this challenge will enable users to utilize the system more comfortably and improve agricultural efficiency. 【0382】 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. 【0383】 In this invention, the server includes a work sequence generation means for distributing work instructions to individual devices, an emotion engine integration means for evaluating the user's emotions using emotion recognition means and reflecting the results in adjusting the work sequence, and an information provision means for providing information and suggestions optimized according to the user's emotional state. This makes it possible to manage work and provide information while taking the user's emotions into consideration. 【0384】 "Information gathering means" refers to devices and technologies for acquiring weather, soil, and crop conditions in an agricultural environment in real time. 【0385】 "Interpretation methods" refer to methods and techniques for performing weather forecasting, soil analysis, and crop growth evaluation based on acquired information. 【0386】 A "work sequence generation means" is a method or system for generating the optimal work sequence based on the interpretation results and distributing work instructions to individual devices. 【0387】 "Emotion recognition means" refers to technology that evaluates the emotions of users and uses the results to improve the operation of the system. 【0388】 The "emotion engine integration means" refers to a technical measure that reflects the results of the emotion recognition means in adjusting the work sequence. 【0389】 "Information provision methods" refer to methods for providing information and suggestions that are optimized according to the user's emotional state. 【0390】 A "task execution means" is a system in which individual devices perform tasks according to work instructions and report their progress. 【0391】 A "situation reporting system" is a system for reporting the details of completed work and changes in the agricultural environment to users in natural language. 【0392】 To implement this invention, a server, a user terminal, and various sensors and machines are required. 【0393】 The server collects environmental data such as weather, soil, and crop conditions in real time from various sensors. This data is then interpreted to perform weather forecasting, soil analysis, and crop growth evaluation. Dedicated analysis programs and artificial intelligence models (e.g., generative AI models) are used to analyze the collected data. Based on the analysis results, an optimal work sequence is generated, and instructions are transmitted wirelessly to individual devices. 【0394】 Emotion recognition tools analyze the emotions expressed in the user's voice and text input. The server considers the user's emotional state and adjusts the order of operations as needed. This emotion recognition is performed using speech recognition APIs (e.g., Google Speech-to-Text) and sentiment analysis APIs (e.g., IBM Watson Natural Language Understanding). 【0395】 Based on information received from the server, the user terminal provides the user with optimal information and suggestions tailored to their emotional state. For example, if the user is feeling stressed, it might suggest taking a break or simplifying work procedures. 【0396】 Ultimately, the technologies and functions used will enable comfortable and efficient agricultural management that takes user emotions into consideration. A specific example of a prompt is given below: "Design a program that analyzes the user's emotional state and adjusts the work sequence based on that analysis." 【0397】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0398】 Step 1: 【0399】 The server acquires environmental data from various sensors. It receives real-time weather, soil, and crop condition data as input. Based on this data, it uses a generative AI model to perform weather forecasting, soil analysis, and crop growth evaluation, and outputs the analysis results. 【0400】 Step 2: 【0401】 The server generates the optimal work sequence based on the acquired analysis results. It takes the analysis results as input and uses the work sequence generation mechanism to output the optimal work plan. This output includes work instructions that are distributed to each device via wireless communication. 【0402】 Step 3: 【0403】 The server processes user input (voice and text) using emotion recognition mechanisms. It receives voice and text data as input and evaluates the emotional state using speech recognition APIs and emotion analysis APIs. The evaluation results are output and used to adjust the workflow as needed. 【0404】 Step 4: 【0405】 The terminal receives work instructions sent from the server and performs the specified tasks according to those instructions. It receives work instructions as input and reports the progress of the performed tasks to the server. It provides the progress of the tasks as output and accepts new instructions as needed. 【0406】 Step 5: 【0407】 The user reviews information and suggestions received from the server via their terminal. They receive advice based on work details and emotions reported in natural language as input, and make decisions to improve work efficiency. The system generates feedback from the user and new instructions as output. 【0408】 Step 6: 【0409】 The server receives feedback and new instructions from users and adjusts system operations accordingly. It takes feedback information as input and outputs new work plans and optimized information provision, improving the overall flexibility of the system. 【0410】 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. 【0411】 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. 【0412】 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. 【0413】 [Third Embodiment] 【0414】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0415】 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. 【0416】 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). 【0417】 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. 【0418】 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. 【0419】 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). 【0420】 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. 【0421】 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. 【0422】 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. 【0423】 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. 【0424】 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. 【0425】 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". 【0426】 The present invention provides a system for efficiently and autonomously managing farms in an agricultural environment. This system includes data collection means, analysis means, work flow generation means, task execution means, and status reporting means. 【0427】 Data collection and processing 【0428】 Server: Collects data such as temperature, humidity, soil moisture content, and crop image data from agricultural environments equipped with various sensors. This data is aggregated on the server via 5G communication and made available in real time. 【0429】 Data analysis 【0430】 Server: Uses received data to forecast weather, analyze soil, and evaluate crop growth processes. Generative AI is used in the analysis to detect insufficient soil moisture or signs of pests and diseases in crops. 【0431】 Workflow generation 【0432】 Server: Based on the analysis results, it plans the optimal work procedure and assigns work instructions to individual machines accordingly. For example, if rain is expected the next day, it adjusts the plan to carry out fertilization in advance. 【0433】 Execution of the task 【0434】 Terminals (robots and drones): They receive instructions from the server and perform various farming tasks. For example, drones scan fields from above, and ground robots manage and irrigate designated furrows. 【0435】 Situation report 【0436】 Server: Based on feedback information from each terminal, it reports the current status of the farm and the progress of the work to the user in natural language. 【0437】 User: Receive reports, check the system's operational status, and provide additional instructions as needed. 【0438】 For example, if a sensor detects abnormally low soil moisture in a particular field, the generated AI will determine the need for irrigation and immediately instruct an irrigation robot to do so. This prevents stress on the crops and helps maintain optimal growth conditions. 【0439】 In this way, this system highly automates farm management, alleviating labor shortages while improving production efficiency. 【0440】 The following describes the processing flow. 【0441】 Step 1: 【0442】 Server: Collects temperature, humidity, soil moisture, and crop image data in real time from various sensors using 5G communication. The data is stored in a database on the server. 【0443】 Step 2: 【0444】 Server: Based on the collected data, the generating AI analyzes weather forecasts and soil conditions. It also evaluates crop health from image data and identifies necessary actions. 【0445】 Step 3: 【0446】 Server: Plans the optimal workflow based on the analysis results. For example, it calculates the timing of watering based on weather forecasts and determines fertilizer application according to the crop's growth stage. 【0447】 Step 4: 【0448】 Server: Transmits individual work instructions to each machine via wireless communication. These instructions include specific work details and schedules. 【0449】 Step 5: 【0450】 Terminals (robots and drones): They perform designated farming tasks according to instructions received from the server. Robots manage the furrows, and drones conduct aerial reconnaissance. 【0451】 Step 6: 【0452】 Terminal: Reports the progress and status of the work as feedback to the server. Also reports any new problems or necessary adjustments. 【0453】 Step 7: 【0454】 Server: Aggregates feedback from terminals and reports processing results to the user in natural language. The report to the user includes the completion status of the task and the next action to take. 【0455】 Step 8: 【0456】 User: Review the report and, if necessary, instruct the server on new goals or adjustments. Also, prepare for the next work plan. 【0457】 (Example 1) 【0458】 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." 【0459】 In agriculture, there is a need to accurately assess environmental changes and growth conditions, and to perform appropriate tasks efficiently and automatically. Conventional methods lack the accuracy and speed of data collection and analysis, as well as the ability to respond to subtle changes and address labor shortages. 【0460】 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. 【0461】 In this invention, the server includes information gathering means, information analysis means, and work procedure generation means. This makes it possible to collect and analyze agricultural environment data in real time and efficiently perform appropriate tasks. 【0462】 "Information gathering means" refers to a mechanism that acquires environmental conditions, ground surface information, and plant conditions in real time from various measuring devices installed in the agricultural environment. 【0463】 "Information analysis means" refers to the process of performing weather forecasting, surface analysis, and plant growth evaluation based on acquired information, and then analyzing the results using a generated AI model. 【0464】 A "work procedure generation means" is a system that creates the optimal work procedure based on the analysis results and distributes work instructions to various machines. 【0465】 "Task execution means" refers to the function of various machines performing activities according to work instructions and reporting their progress. 【0466】 A "situation reporting method" is a method of communicating the details of the activities carried out and changes in the agricultural environment to users in natural language. 【0467】 This invention is a system that supports efficient and autonomous management in agriculture. This system consists of a server, terminals (robots and drones), and users working together. 【0468】 The server collects environmental data, ground surface information, and crop image data in real time from various sensors installed in agricultural fields. The hardware used includes thermometers, soil sensors, and cameras, and 5G networks are used for communication. This allows the server to collect data quickly. 【0469】 In data analysis, the server utilizes generative AI models to analyze weather forecasts, soil dryness, and crop health. Based on the analysis results, it formulates an optimal work plan and sends instructions to terminals. For example, if image analysis of crops detects signs of disease or pest infestation, the server instructs drones to spray pesticides in specific areas. 【0470】 The terminals perform specific tasks based on instructions received from the server. Drones scan the farm while flying through the air, and ground robots handle precise irrigation and weeding. This makes farm work more efficient. 【0471】 Furthermore, through the status reporting system, the server integrates feedback from various terminals and communicates the current status and progress of the farm to the user. This report is generated in natural language, and the user can use it to give additional instructions. 【0472】 For example, when a sensor detects a dry area, the server uses a generated AI model to determine the need for irrigation and issues instructions to an irrigation robot. This enables efficient farm management while maintaining crop health. 【0473】 An example of a prompt would be, "If the weather forecast for tomorrow is rain, plan the optimal workflow for farm management." This prompt allows the generative AI model to suggest appropriate work procedures. 【0474】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0475】 Step 1: 【0476】 The server acquires temperature, humidity, soil moisture content, and crop image data from multiple sensors installed in agricultural fields. This data is aggregated on the server via 5G communication. The input is raw data from the sensors, and the output is an integrated environmental dataset. This step involves data collection and its real-time updating. 【0477】 Step 2: 【0478】 The server receives the collected dataset and performs analysis using a generative AI model. Specifically, it performs weather forecasting, soil dryness analysis, and crop health assessment using images. The input data is the environmental dataset obtained in the previous step, and the output is the analysis results. Here, predictive and diagnostic models based on the data are executed, which identifies the issues that need to be addressed. 【0479】 Step 3: 【0480】 The server creates an optimal workflow based on the analysis results. For example, it instructs irrigation in zones experiencing severe drought and pesticide application in areas where signs of disease or pest infestation are found. The analysis results are used as input, and specific work instructions are generated as output. Here, a flexible and efficient farming plan is created that responds to real-time conditions. 【0481】 Step 4: 【0482】 The terminal devices, such as robots and drones, perform actual farm work based on work instructions received from the server. Drones scan the farm while moving through the air, and robots perform irrigation and weed removal in designated areas. The input is work instructions, and the output is the results of various farm work. Here, practical activities are carried out based on instructions from the server, and farm management is made more efficient. 【0483】 Step 5: 【0484】 The server collects work completion reports and feedback data from each terminal. This information is aggregated and used to understand the current state of the farm. The input is feedback data from the terminals, and the output is an updated farm status report. In this step, the actual work results are reported to the server, and the information is prepared for the next action. 【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】 Labor shortages and a lack of efficient management in the agricultural environment are becoming increasingly serious, necessitating innovative solutions. In particular, performing appropriate tasks according to plant growth stages and monitoring and managing agricultural activities remotely are difficult. To address these challenges, there is a need for systems that autonomously manage operations and maximize efficiency. 【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 information gathering means for acquiring environmental information and plant conditions in real time from various detectors installed in the agricultural environment; analysis means for performing environmental prediction, soil condition analysis, and plant growth evaluation based on the acquired information; and generation AI analysis means for performing plant growth prediction and anomaly detection using a generation AI model and proposing optimal agricultural activities. This makes it possible to efficiently and autonomously manage the agricultural environment from a remote location and economically distribute optimal work instructions. 【0490】 "Information gathering means" refers to a mechanism for acquiring environmental information and plant conditions in real time from detectors installed in agricultural environments. 【0491】 "Analytical methods" refer to the processes used to perform environmental predictions, soil condition analysis, and plant growth evaluations based on collected information. 【0492】 A "task generation means" is used to generate optimal tasks based on analysis results and distribute task instructions to individual devices. 【0493】 "Business execution means" refers to the function by which individual devices execute tasks according to business instructions and report on their progress. 【0494】 A "situation reporting method" is a system that reports the details of the work performed and changes in the agricultural environment to users in natural language. 【0495】 The "generative AI analysis means" is a function that uses a generative AI model to predict plant growth and detect anomalies, and to propose optimal agricultural activities. 【0496】 The server acquires environmental information and plant conditions in real time from various detectors through information gathering devices installed in the agricultural environment. The collected data is rapidly transmitted to the server using 5G communication technology. This server uses analytical tools to perform environmental predictions, soil condition analysis, and plant growth evaluations based on the data. Generative AI models such as TensorFlow are used in this process to predict plant growth and detect anomalies. 【0497】 Based on the analysis results, the server generates optimal tasks and distributes instructions to individual devices using the task generation means. Terminals equipped with task execution means (such as drones and ground robots) then perform specific agricultural tasks based on these instructions. For example, during harvest season, a drone scans the field from above to perform harvesting at the predicted optimal timing. 【0498】 Feedback from the terminal is collected by a status reporting mechanism on the server and reported to the user using natural language processing (NLP). The report includes the current state of the agricultural environment, completed tasks, and future predictions. Based on this report, the user can send further instructions to the server. 【0499】 A concrete example is a scenario where, if a sensor detects abnormally low soil moisture, the generated AI model determines the need for irrigation and immediately issues instructions to an irrigation robot. An example of a prompt message in this case would be, "Generate suggestions for optimal farm work based on recent weather forecasts and soil analysis." This would minimize stress on crops and maintain optimal growth conditions. 【0500】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0501】 Step 1: 【0502】 The server acquires environmental information and plant condition data collected from various detectors through information gathering means. This data is transmitted from the sensor devices via 5G communication. The inputs are temperature, humidity, soil moisture content, and plant image data, which are then used for analysis in the next step. 【0503】 Step 2: 【0504】 The server uses analytical tools to perform environmental predictions and soil condition analyses based on the collected data. It uses a generative AI model (e.g., TensorFlow) to perform anomaly detection and growth prediction processes. The input is the environmental data acquired in step 1, and the output is the predicted environmental conditions and anomaly alerts. 【0505】 Step 3: 【0506】 The server plans the optimal work tasks using a task generation system based on the analysis results and distributes work instructions to individual terminals (robots and drones). The input is the prediction results and analysis data from step 2, and the output is specific work instructions for each terminal. 【0507】 Step 4: 【0508】 The terminals perform agricultural tasks based on the work instructions they receive. For example, a drone performs an aerial scan, and an irrigation robot waters designated furrows. The input is the work instructions from the server, and the output is a completion report of the agricultural tasks that have been performed. 【0509】 Step 5: 【0510】 The server receives feedback from the terminal and reports to the user the work performed and changes in the agricultural environment using a status reporting mechanism. This report is generated in natural language and formatted in a way that is easily understandable to the user. The input is feedback data from the terminal, and the output is a natural language report for the user. 【0511】 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. 【0512】 This invention integrates an emotion engine that recognizes user emotions and enables operations and information provision accordingly, in addition to an automated system for agricultural environments. This system includes data collection means, analysis means, workflow generation means, task execution means, status reporting means, and emotion sensing means. 【0513】 Data collection and analysis 【0514】 Server: Analyzes environmental data collected through various sensors in real time. Generating AI uses the data to predict weather and evaluate soil conditions. Simultaneously, this information is also provided to users. 【0515】 Emotion recognition by an emotion engine 【0516】 Server: The emotion engine analyzes the user's emotions from their voice and text input. Based on the emotion analysis, it reflects this in the user's requests and instructions. For example, if the user is feeling stressed, it suggests simplifying the system's operation procedures. 【0517】 Optimizing and executing workflows 【0518】 Server: Generates the optimal work schedule and plan based on the analysis results and distributes it to each machine. It also takes into account the user's emotional state and adjusts the work procedure if there is dissatisfaction, for example. 【0519】 Terminals (robots and drones): They receive instructions from the server and perform designated farming tasks. An emotion engine prioritizes important tasks to ensure the user's emotional well-being. 【0520】 Situation reporting and coordination 【0521】 Terminal: Reports the progress of the work to the server and readjusts the server's instructions as needed. This also includes reporting the status of the work progress and any new anomalies that may occur. 【0522】 Server: Using an integrated emotion engine, it translates reported information into natural language in a format that is sensitive to the user's emotions and provides it accordingly. For example, if the user is agitated, it will provide explanations that help them calm down and continue the operation. 【0523】 User: Receives reports from the system and sends decisions and new instructions to the system based on the situation at hand. Considers emotionally-driven suggestions and makes decisions to improve work efficiency and comfort. 【0524】 For example, if a user feels fatigued during their morning work session, the emotion engine detects this, and the server automatically changes the task priorities, performing less demanding tasks first. As a result, the user can continue working while maintaining comfort. 【0525】 In this way, the present invention provides human-centered agricultural management that takes into account the feelings of agricultural workers, enabling efficient production activities. 【0526】 The following describes the processing flow. 【0527】 Step 1: 【0528】 Server: Collects temperature, humidity, soil moisture content, and crop growth status in real time from various sensors installed in the agricultural environment and stores them in a database via 5G communication. 【0529】 Step 2: 【0530】 Server: Based on the collected data, it uses generative AI to predict weather, analyze soil conditions, and assess crop health. This analysis identifies the next necessary agricultural tasks. 【0531】 Step 3: 【0532】 Server: The emotion engine analyzes voice and text input from the user to identify the user's emotional state. This information also influences the optimization of the workflow. 【0533】 Step 4: 【0534】 Server: Based on the results and analysis of the emotion engine, it optimizes the workflow and generates work instructions for individual machines. If a user is experiencing stress, it simplifies the operating procedure or adjusts the reporting method. 【0535】 Step 5: 【0536】 Terminals (robots and drones): These perform agricultural tasks according to work instructions received from the server. For example, a furrowing robot prepares the field, or an irrigation robot supplies the appropriate amount of water. 【0537】 Step 6: 【0538】 Terminal: Provides feedback to the server regarding work progress and any newly discovered anomalies. Real-time reporting enables immediate responses tailored to the situation. 【0539】 Step 7: 【0540】 Server: Receives feedback from the terminal and updates instructions as needed. It also reports the processing results to the user in natural language and explains the situation in an emotionally sensitive manner. 【0541】 Step 8: 【0542】 User: Review reports from the server and make new instructions or adjustments based on system suggestions. If the user is relaxed, they will receive normal reports; if they are agitated, they will be instructed to calm down. 【0543】 This processing flow enables efficient farm management that responds to user emotions, thereby improving human comfort. 【0544】 (Example 2) 【0545】 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." 【0546】 Agricultural work heavily relies on weather and soil conditions, making efficient forecasting and planning crucial. However, the emotional state of the farm workers also impacts work efficiency. Traditional agricultural systems often prioritized mechanical command execution over flexible work management that considered the feelings of the users. This can lead to increased worker stress and fatigue, potentially resulting in decreased productivity. Therefore, there is a need for systems that consider not only environmental data-based work planning but also the emotions of the users. 【0547】 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. 【0548】 In this invention, the server includes information gathering means for acquiring weather, land, and plant conditions in real time from various detectors installed in agricultural areas; analysis means for performing weather forecasting, land analysis, and plant growth evaluation based on the acquired information; and emotion recognition means for recognizing the user's emotions using language and voice analysis and reflecting them in work processes and information provision. This enables not only the generation of optimal work plans based on environmental information, but also flexible work management that takes the user's emotions into consideration. 【0549】 "Information gathering means" refers to a device or method for acquiring data on weather, land, and plant conditions in real time using various detectors installed in agricultural areas. 【0550】 "Analysis means" refers to devices or methods for performing weather forecasting, land analysis, and plant growth evaluation based on data obtained through information gathering means. 【0551】 "Work process generation means" refers to a device or method for generating an optimal work plan based on analysis results and distributing work instructions to individual devices. 【0552】 "Means of performing work" refers to devices or methods for which individual devices perform agricultural work in accordance with work instructions and report on its progress. 【0553】 A "situation reporting means" is a device or method for reporting to the user, in natural language, the details of the work performed and changes in the environment of the agricultural land. 【0554】 "Emotion recognition means" refers to a device or method for recognizing a user's emotions using language and speech analysis and reflecting them in work processes and information provision. 【0555】 This invention is an automated system for agricultural use that enables work optimization while taking user emotions into consideration. The following describes specific embodiments for carrying out this invention. 【0556】 The system uses various sensors as means of collecting information. These include sensors that measure temperature, humidity, and soil moisture. These sensors transmit data to a server in real time, and the server stores this information in a database. 【0557】 The server uses a generative AI model as an analytical tool to analyze the collected data. Specifically, it predicts weather by comparing it with past data and evaluates the condition of the land. The generative AI model used in this process includes machine learning algorithms to improve the accuracy of weather forecasts. 【0558】 Furthermore, the server uses emotion recognition technology to understand the user's emotions. It analyzes the user's voice and text input to determine their emotional state, such as stress or fatigue. This information is used to adjust the work plan. 【0559】 The robots and drones deployed as terminal devices will operate according to work commands from the server. Each terminal will autonomously perform tasks such as pesticide spraying and harvesting, and will periodically report the progress of its work to the server. 【0560】 For example, if a user asks, "What's the weather forecast for today?", the server will activate a generative AI model, analyze the relevant data, and provide it to the user. Furthermore, if the emotion recognition system determines that the user is tired, the server can offer suggestions such as, "We recommend you stop your current task and take a break." 【0561】 An example of a prompt message for a generative AI model would be, "Optimize today's and tomorrow's work schedule, and adjust it if necessary." 【0562】 In this way, the present invention realizes efficient and comfortable agricultural operations through work management that takes environmental data and user emotions into consideration. 【0563】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0564】 Step 1: 【0565】 The server acquires data from sensors installed in agricultural areas using information gathering methods. It receives sensor data such as temperature, humidity, and soil moisture as input, and processes this data by storing it in a database. This stored data is then used for subsequent analysis. 【0566】 Step 2: 【0567】 The server uses a generative AI model to analyze the acquired sensor data. It reads the previously stored sensor data as input and performs weather forecasting and soil condition assessment. The output provides predicted weather information and soil health status. Specifically, it uses statistical analysis and machine learning algorithms to calculate future weather patterns. 【0568】 Step 3: 【0569】 The server uses emotion recognition technology to analyze emotions from voice and text input by the user. It receives user voice commands and text messages in digital format as input and performs emotion analysis. The output is emotional indicators such as the user's stress level and fatigue level. This allows the system to understand the user's mental state. 【0570】 Step 4: 【0571】 Based on these analysis results, the server uses a work process generation mechanism to construct an optimal work plan. It considers weather information, soil conditions, and user sentiment indicators as inputs, and generates a specific work schedule as output. For example, if rain is expected or if the user is experiencing stress, a flexible plan can be created that readjusts the work order. 【0572】 Step 5: 【0573】 The terminal performs agricultural tasks based on work instructions received from the server. It receives a work schedule from the server as input and then operates the actual agricultural machinery (e.g., pesticide spraying with a drone). The output is a work completion report, which provides feedback on the progress to the server. 【0574】 Step 6: 【0575】 The server receives progress reports from terminals and provides information to users using status reporting mechanisms. It takes work progress data from terminals as input and creates a report adjusted based on sentiment indicators, then outputs this report to the user in natural language. For example, it might display a message such as, "Work is progressing smoothly and is expected to be completed ahead of schedule," to help users continue working with confidence. 【0576】 (Application Example 2) 【0577】 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." 【0578】 While automated systems in agricultural environments provide efficient work management through the collection and analysis of environmental data, they face the challenge of not being able to flexibly adapt to the emotional state of users. Furthermore, when users are experiencing stress or fatigue, it is necessary to appropriately optimize the work content and information provided. Solving this challenge will enable users to utilize the system more comfortably and improve agricultural efficiency. 【0579】 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. 【0580】 In this invention, the server includes a work sequence generation means for distributing work instructions to individual devices, an emotion engine integration means for evaluating the user's emotions using emotion recognition means and reflecting the results in adjusting the work sequence, and an information provision means for providing information and suggestions optimized according to the user's emotional state. This makes it possible to manage work and provide information while taking the user's emotions into consideration. 【0581】 "Information gathering means" refers to devices and technologies for acquiring weather, soil, and crop conditions in an agricultural environment in real time. 【0582】 "Interpretation methods" refer to methods and techniques for performing weather forecasting, soil analysis, and crop growth evaluation based on acquired information. 【0583】 A "work sequence generation means" is a method or system for generating the optimal work sequence based on the interpretation results and distributing work instructions to individual devices. 【0584】 "Emotion recognition means" refers to technology that evaluates the emotions of users and uses the results to improve the operation of the system. 【0585】 The "emotion engine integration means" refers to a technical measure that reflects the results of the emotion recognition means in adjusting the work sequence. 【0586】 "Information provision methods" refer to methods for providing information and suggestions that are optimized according to the user's emotional state. 【0587】 A "task execution means" is a system in which individual devices perform tasks according to work instructions and report their progress. 【0588】 A "situation reporting system" is a system for reporting the details of completed work and changes in the agricultural environment to users in natural language. 【0589】 To implement this invention, a server, a user terminal, and various sensors and machines are required. 【0590】 The server collects environmental data such as weather, soil, and crop conditions in real time from various sensors. This data is then interpreted to perform weather forecasting, soil analysis, and crop growth evaluation. Dedicated analysis programs and artificial intelligence models (e.g., generative AI models) are used to analyze the collected data. Based on the analysis results, an optimal work sequence is generated, and instructions are transmitted wirelessly to individual devices. 【0591】 Emotion recognition tools analyze the emotions expressed in the user's voice and text input. The server considers the user's emotional state and adjusts the order of operations as needed. This emotion recognition is performed using speech recognition APIs (e.g., Google Speech-to-Text) and sentiment analysis APIs (e.g., IBM Watson Natural Language Understanding). 【0592】 Based on information received from the server, the user terminal provides the user with optimal information and suggestions tailored to their emotional state. For example, if the user is feeling stressed, it might suggest taking a break or simplifying work procedures. 【0593】 Ultimately, the technologies and functions used will enable comfortable and efficient agricultural management that takes user emotions into consideration. A specific example of a prompt is given below: "Design a program that analyzes the user's emotional state and adjusts the work sequence based on that analysis." 【0594】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0595】 Step 1: 【0596】 The server acquires environmental data from various sensors. It receives real-time weather, soil, and crop condition data as input. Based on this data, it uses a generative AI model to perform weather forecasting, soil analysis, and crop growth evaluation, and outputs the analysis results. 【0597】 Step 2: 【0598】 The server generates the optimal work sequence based on the acquired analysis results. It takes the analysis results as input and uses the work sequence generation mechanism to output the optimal work plan. This output includes work instructions that are distributed to each device via wireless communication. 【0599】 Step 3: 【0600】 The server processes user input (voice and text) using emotion recognition mechanisms. It receives voice and text data as input and evaluates the emotional state using speech recognition APIs and emotion analysis APIs. The evaluation results are output and used to adjust the workflow as needed. 【0601】 Step 4: 【0602】 The terminal receives work instructions sent from the server and performs the specified tasks according to those instructions. It receives work instructions as input and reports the progress of the performed tasks to the server. It provides the progress of the tasks as output and accepts new instructions as needed. 【0603】 Step 5: 【0604】 The user reviews information and suggestions received from the server via their terminal. They receive advice based on work details and emotions reported in natural language as input, and make decisions to improve work efficiency. The system generates feedback from the user and new instructions as output. 【0605】 Step 6: 【0606】 The server receives feedback and new instructions from users and adjusts system operations accordingly. It takes feedback information as input and outputs new work plans and optimized information provision, improving the overall flexibility of the system. 【0607】 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. 【0608】 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. 【0609】 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. 【0610】 [Fourth Embodiment] 【0611】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0612】 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. 【0613】 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). 【0614】 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. 【0615】 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. 【0616】 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). 【0617】 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. 【0618】 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. 【0619】 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. 【0620】 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. 【0621】 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. 【0622】 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. 【0623】 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". 【0624】 The present invention provides a system for efficiently and autonomously managing farms in an agricultural environment. This system includes data collection means, analysis means, work flow generation means, task execution means, and status reporting means. 【0625】 Data collection and processing 【0626】 Server: Collects data such as temperature, humidity, soil moisture content, and crop image data from agricultural environments equipped with various sensors. This data is aggregated on the server via 5G communication and made available in real time. 【0627】 Data analysis 【0628】 Server: Uses received data to forecast weather, analyze soil, and evaluate crop growth processes. Generative AI is used in the analysis to detect insufficient soil moisture or signs of pests and diseases in crops. 【0629】 Workflow generation 【0630】 Server: Based on the analysis results, it plans the optimal work procedure and assigns work instructions to individual machines accordingly. For example, if rain is expected the next day, it adjusts the plan to carry out fertilization in advance. 【0631】 Execution of the task 【0632】 Terminals (robots and drones): They receive instructions from the server and perform various farming tasks. For example, drones scan fields from above, and ground robots manage and irrigate designated furrows. 【0633】 Situation report 【0634】 Server: Based on feedback information from each terminal, it reports the current status of the farm and the progress of the work to the user in natural language. 【0635】 User: Receive reports, check the system's operational status, and provide additional instructions as needed. 【0636】 For example, if a sensor detects abnormally low soil moisture in a particular field, the generated AI will determine the need for irrigation and immediately instruct an irrigation robot to do so. This prevents stress on the crops and helps maintain optimal growth conditions. 【0637】 In this way, this system highly automates farm management, alleviating labor shortages while improving production efficiency. 【0638】 The following describes the processing flow. 【0639】 Step 1: 【0640】 Server: Collects temperature, humidity, soil moisture, and crop image data in real time from various sensors using 5G communication. The data is stored in a database on the server. 【0641】 Step 2: 【0642】 Server: Based on the collected data, the generating AI analyzes weather forecasts and soil conditions. It also evaluates crop health from image data and identifies necessary actions. 【0643】 Step 3: 【0644】 Server: Plans the optimal workflow based on the analysis results. For example, it calculates the timing of watering based on weather forecasts and determines fertilizer application according to the crop's growth stage. 【0645】 Step 4: 【0646】 Server: Transmits individual work instructions to each machine via wireless communication. These instructions include specific work details and schedules. 【0647】 Step 5: 【0648】 Terminals (robots and drones): They perform designated farming tasks according to instructions received from the server. Robots manage the furrows, and drones conduct aerial reconnaissance. 【0649】 Step 6: 【0650】 Terminal: Reports the progress and status of the work as feedback to the server. Also reports any new problems or necessary adjustments. 【0651】 Step 7: 【0652】 Server: Aggregates feedback from terminals and reports processing results to the user in natural language. The report to the user includes the completion status of the task and the next action to take. 【0653】 Step 8: 【0654】 User: Review the report and, if necessary, instruct the server on new goals or adjustments. Also, prepare for the next work plan. 【0655】 (Example 1) 【0656】 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". 【0657】 In agriculture, there is a need to accurately assess environmental changes and growth conditions, and to perform appropriate tasks efficiently and automatically. Conventional methods lack the accuracy and speed of data collection and analysis, as well as the ability to respond to subtle changes and address labor shortages. 【0658】 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. 【0659】 In this invention, the server includes information gathering means, information analysis means, and work procedure generation means. This makes it possible to collect and analyze agricultural environment data in real time and efficiently perform appropriate tasks. 【0660】 "Information gathering means" refers to a mechanism that acquires environmental conditions, ground surface information, and plant conditions in real time from various measuring devices installed in the agricultural environment. 【0661】 "Information analysis means" refers to the process of performing weather forecasting, surface analysis, and plant growth evaluation based on acquired information, and then analyzing the results using a generated AI model. 【0662】 A "work procedure generation means" is a system that creates the optimal work procedure based on the analysis results and distributes work instructions to various machines. 【0663】 "Task execution means" refers to the function of various machines performing activities according to work instructions and reporting their progress. 【0664】 A "situation reporting method" is a method of communicating the details of the activities carried out and changes in the agricultural environment to users in natural language. 【0665】 This invention is a system that supports efficient and autonomous management in agriculture. This system consists of a server, terminals (robots and drones), and users working together. 【0666】 The server collects environmental data, ground surface information, and crop image data in real time from various sensors installed in agricultural fields. The hardware used includes thermometers, soil sensors, and cameras, and 5G networks are used for communication. This allows the server to collect data quickly. 【0667】 In data analysis, the server utilizes generative AI models to analyze weather forecasts, soil dryness, and crop health. Based on the analysis results, it formulates an optimal work plan and sends instructions to terminals. For example, if image analysis of crops detects signs of disease or pest infestation, the server instructs drones to spray pesticides in specific areas. 【0668】 The terminals perform specific tasks based on instructions received from the server. Drones scan the farm while flying through the air, and ground robots handle precise irrigation and weeding. This makes farm work more efficient. 【0669】 Furthermore, through the status reporting system, the server integrates feedback from various terminals and communicates the current status and progress of the farm to the user. This report is generated in natural language, and the user can use it to give additional instructions. 【0670】 For example, when a sensor detects a dry area, the server uses a generated AI model to determine the need for irrigation and issues instructions to an irrigation robot. This enables efficient farm management while maintaining crop health. 【0671】 An example of a prompt would be, "If the weather forecast for tomorrow is rain, plan the optimal workflow for farm management." This prompt allows the generative AI model to suggest appropriate work procedures. 【0672】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0673】 Step 1: 【0674】 The server acquires temperature, humidity, soil moisture content, and crop image data from multiple sensors installed in agricultural fields. This data is aggregated on the server via 5G communication. The input is raw data from the sensors, and the output is an integrated environmental dataset. This step involves data collection and its real-time updating. 【0675】 Step 2: 【0676】 The server receives the collected dataset and performs analysis using a generative AI model. Specifically, it performs weather forecasting, soil dryness analysis, and crop health assessment using images. The input data is the environmental dataset obtained in the previous step, and the output is the analysis results. Here, predictive and diagnostic models based on the data are executed, which identifies the issues that need to be addressed. 【0677】 Step 3: 【0678】 The server creates an optimal workflow based on the analysis results. For example, it instructs irrigation in zones experiencing severe drought and pesticide application in areas where signs of disease or pest infestation are found. The analysis results are used as input, and specific work instructions are generated as output. Here, a flexible and efficient farming plan is created that responds to real-time conditions. 【0679】 Step 4: 【0680】 The terminal devices, such as robots and drones, perform actual farm work based on work instructions received from the server. Drones scan the farm while moving through the air, and robots perform irrigation and weed removal in designated areas. The input is work instructions, and the output is the results of various farm work. Here, practical activities are carried out based on instructions from the server, and farm management is made more efficient. 【0681】 Step 5: 【0682】 The server collects work completion reports and feedback data from each terminal. This information is aggregated and used to understand the current state of the farm. The input is feedback data from the terminals, and the output is an updated farm status report. In this step, the actual work results are reported to the server, and the information is prepared for the next action. 【0683】 (Application Example 1) 【0684】 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". 【0685】 Labor shortages and a lack of efficient management in the agricultural environment are becoming increasingly serious, necessitating innovative solutions. In particular, performing appropriate tasks according to plant growth stages and monitoring and managing agricultural activities remotely are difficult. To address these challenges, there is a need for systems that autonomously manage operations and maximize efficiency. 【0686】 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. 【0687】 In this invention, the server includes information gathering means for acquiring environmental information and plant conditions in real time from various detectors installed in the agricultural environment; analysis means for performing environmental prediction, soil condition analysis, and plant growth evaluation based on the acquired information; and generation AI analysis means for performing plant growth prediction and anomaly detection using a generation AI model and proposing optimal agricultural activities. This makes it possible to efficiently and autonomously manage the agricultural environment from a remote location and economically distribute optimal work instructions. 【0688】 "Information gathering means" refers to a mechanism for acquiring environmental information and plant conditions in real time from detectors installed in agricultural environments. 【0689】 "Analytical methods" refer to the processes used to perform environmental predictions, soil condition analysis, and plant growth evaluations based on collected information. 【0690】 A "task generation means" is used to generate optimal tasks based on analysis results and distribute task instructions to individual devices. 【0691】 "Business execution means" refers to the function by which individual devices execute tasks according to business instructions and report on their progress. 【0692】 A "situation reporting method" is a system that reports the details of the work performed and changes in the agricultural environment to users in natural language. 【0693】 The "generative AI analysis means" is a function that uses a generative AI model to predict plant growth and detect anomalies, and to propose optimal agricultural activities. 【0694】 The server acquires environmental information and plant conditions in real time from various detectors through information gathering devices installed in the agricultural environment. The collected data is rapidly transmitted to the server using 5G communication technology. This server uses analytical tools to perform environmental predictions, soil condition analysis, and plant growth evaluations based on the data. Generative AI models such as TensorFlow are used in this process to predict plant growth and detect anomalies. 【0695】 Based on the analysis results, the server generates optimal tasks and distributes instructions to individual devices using the task generation means. Terminals equipped with task execution means (such as drones and ground robots) then perform specific agricultural tasks based on these instructions. For example, during harvest season, a drone scans the field from above to perform harvesting at the predicted optimal timing. 【0696】 Feedback from the terminal is collected by a status reporting mechanism on the server and reported to the user using natural language processing (NLP). The report includes the current state of the agricultural environment, completed tasks, and future predictions. Based on this report, the user can send further instructions to the server. 【0697】 A concrete example is a scenario where, if a sensor detects abnormally low soil moisture, the generated AI model determines the need for irrigation and immediately issues instructions to an irrigation robot. An example of a prompt message in this case would be, "Generate suggestions for optimal farm work based on recent weather forecasts and soil analysis." This would minimize stress on crops and maintain optimal growth conditions. 【0698】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0699】 Step 1: 【0700】 The server acquires environmental information and plant condition data collected from various detectors through information gathering means. This data is transmitted from the sensor devices via 5G communication. The inputs are temperature, humidity, soil moisture content, and plant image data, which are then used for analysis in the next step. 【0701】 Step 2: 【0702】 The server uses analytical tools to perform environmental predictions and soil condition analyses based on the collected data. It uses a generative AI model (e.g., TensorFlow) to perform anomaly detection and growth prediction processes. The input is the environmental data acquired in step 1, and the output is the predicted environmental conditions and anomaly alerts. 【0703】 Step 3: 【0704】 The server plans the optimal work tasks using a task generation system based on the analysis results and distributes work instructions to individual terminals (robots and drones). The input is the prediction results and analysis data from step 2, and the output is specific work instructions for each terminal. 【0705】 Step 4: 【0706】 The terminals perform agricultural tasks based on the work instructions they receive. For example, a drone performs an aerial scan, and an irrigation robot waters designated furrows. The input is the work instructions from the server, and the output is a completion report of the agricultural tasks that have been performed. 【0707】 Step 5: 【0708】 The server receives feedback from the terminal and reports to the user the work performed and changes in the agricultural environment using a status reporting mechanism. This report is generated in natural language and formatted in a way that is easily understandable to the user. The input is feedback data from the terminal, and the output is a natural language report for the user. 【0709】 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. 【0710】 This invention integrates an emotion engine that recognizes user emotions and enables operations and information provision accordingly, in addition to an automated system for agricultural environments. This system includes data collection means, analysis means, workflow generation means, task execution means, status reporting means, and emotion sensing means. 【0711】 Data collection and analysis 【0712】 Server: Analyzes environmental data collected through various sensors in real time. Generating AI uses the data to predict weather and evaluate soil conditions. Simultaneously, this information is also provided to users. 【0713】 Emotion recognition by an emotion engine 【0714】 Server: The emotion engine analyzes the user's emotions from their voice and text input. Based on the emotion analysis, it reflects this in the user's requests and instructions. For example, if the user is feeling stressed, it suggests simplifying the system's operation procedures. 【0715】 Optimizing and executing workflows 【0716】 Server: Generates the optimal work schedule and plan based on the analysis results and distributes it to each machine. It also takes into account the user's emotional state and adjusts the work procedure if there is dissatisfaction, for example. 【0717】 Terminals (robots and drones): They receive instructions from the server and perform designated farming tasks. An emotion engine prioritizes important tasks to ensure the user's emotional well-being. 【0718】 Situation reporting and coordination 【0719】 Terminal: Reports the progress of the work to the server and readjusts the server's instructions as needed. This also includes reporting the status of the work progress and any new anomalies that may occur. 【0720】 Server: Using an integrated emotion engine, it translates reported information into natural language in a format that is sensitive to the user's emotions and provides it accordingly. For example, if the user is agitated, it will provide explanations that help them calm down and continue the operation. 【0721】 User: Receives reports from the system and sends decisions and new instructions to the system based on the situation at hand. Considers emotionally-driven suggestions and makes decisions to improve work efficiency and comfort. 【0722】 For example, if a user feels fatigued during their morning work session, the emotion engine detects this, and the server automatically changes the task priorities, performing less demanding tasks first. As a result, the user can continue working while maintaining comfort. 【0723】 In this way, the present invention provides human-centered agricultural management that takes into account the feelings of agricultural workers, enabling efficient production activities. 【0724】 The following describes the processing flow. 【0725】 Step 1: 【0726】 Server: Collects temperature, humidity, soil moisture content, and crop growth status in real time from various sensors installed in the agricultural environment and stores them in a database via 5G communication. 【0727】 Step 2: 【0728】 Server: Based on the collected data, it uses generative AI to predict weather, analyze soil conditions, and assess crop health. This analysis identifies the next necessary agricultural tasks. 【0729】 Step 3: 【0730】 Server: The emotion engine analyzes voice and text input from the user to identify the user's emotional state. This information also influences the optimization of the workflow. 【0731】 Step 4: 【0732】 Server: Based on the results and analysis of the emotion engine, it optimizes the workflow and generates work instructions for individual machines. If a user is experiencing stress, it simplifies the operating procedure or adjusts the reporting method. 【0733】 Step 5: 【0734】 Terminals (robots and drones): These perform agricultural tasks according to work instructions received from the server. For example, a furrowing robot prepares the field, or an irrigation robot supplies the appropriate amount of water. 【0735】 Step 6: 【0736】 Terminal: Provides feedback to the server regarding work progress and any newly discovered anomalies. Real-time reporting enables immediate responses tailored to the situation. 【0737】 Step 7: 【0738】 Server: Receives feedback from the terminal and updates instructions as needed. It also reports the processing results to the user in natural language and explains the situation in an emotionally sensitive manner. 【0739】 Step 8: 【0740】 User: Review reports from the server and make new instructions or adjustments based on system suggestions. If the user is relaxed, they will receive normal reports; if they are agitated, they will be instructed to calm down. 【0741】 This processing flow enables efficient farm management that responds to user emotions, thereby improving human comfort. 【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】 Agricultural work heavily relies on weather and soil conditions, making efficient forecasting and planning crucial. However, the emotional state of the farm workers also impacts work efficiency. Traditional agricultural systems often prioritized mechanical command execution over flexible work management that considered the feelings of the users. This can lead to increased worker stress and fatigue, potentially resulting in decreased productivity. Therefore, there is a need for systems that consider not only environmental data-based work planning but also the emotions of the users. 【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 information gathering means for acquiring weather, land, and plant conditions in real time from various detectors installed in agricultural areas; analysis means for performing weather forecasting, land analysis, and plant growth evaluation based on the acquired information; and emotion recognition means for recognizing the user's emotions using language and voice analysis and reflecting them in work processes and information provision. This enables not only the generation of optimal work plans based on environmental information, but also flexible work management that takes the user's emotions into consideration. 【0747】 "Information gathering means" refers to a device or method for acquiring data on weather, land, and plant conditions in real time using various detectors installed in agricultural areas. 【0748】 "Analysis means" refers to devices or methods for performing weather forecasting, land analysis, and plant growth evaluation based on data obtained through information gathering means. 【0749】 "Work process generation means" refers to a device or method for generating an optimal work plan based on analysis results and distributing work instructions to individual devices. 【0750】 "Means of performing work" refers to devices or methods for which individual devices perform agricultural work in accordance with work instructions and report on its progress. 【0751】 A "situation reporting means" is a device or method for reporting to the user, in natural language, the details of the work performed and changes in the environment of the agricultural land. 【0752】 "Emotion recognition means" refers to a device or method for recognizing a user's emotions using language and speech analysis and reflecting them in work processes and information provision. 【0753】 This invention is an automated system for agricultural use that enables work optimization while taking user emotions into consideration. The following describes specific embodiments for carrying out this invention. 【0754】 The system uses various sensors as means of collecting information. These include sensors that measure temperature, humidity, and soil moisture. These sensors transmit data to a server in real time, and the server stores this information in a database. 【0755】 The server uses a generative AI model as an analytical tool to analyze the collected data. Specifically, it predicts weather by comparing it with past data and evaluates the condition of the land. The generative AI model used in this process includes machine learning algorithms to improve the accuracy of weather forecasts. 【0756】 Furthermore, the server uses emotion recognition technology to understand the user's emotions. It analyzes the user's voice and text input to determine their emotional state, such as stress or fatigue. This information is used to adjust the work plan. 【0757】 The robots and drones deployed as terminal devices will operate according to work commands from the server. Each terminal will autonomously perform tasks such as pesticide spraying and harvesting, and will periodically report the progress of its work to the server. 【0758】 For example, if a user asks, "What's the weather forecast for today?", the server will activate a generative AI model, analyze the relevant data, and provide it to the user. Furthermore, if the emotion recognition system determines that the user is tired, the server can offer suggestions such as, "We recommend you stop your current task and take a break." 【0759】 An example of a prompt message for a generative AI model would be, "Optimize today's and tomorrow's work schedule, and adjust it if necessary." 【0760】 In this way, the present invention realizes efficient and comfortable agricultural operations through work management that takes environmental data and user emotions into consideration. 【0761】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0762】 Step 1: 【0763】 The server acquires data from sensors installed in agricultural areas using information gathering methods. It receives sensor data such as temperature, humidity, and soil moisture as input, and processes this data by storing it in a database. This stored data is then used for subsequent analysis. 【0764】 Step 2: 【0765】 The server uses a generative AI model to analyze the acquired sensor data. It reads the previously stored sensor data as input and performs weather forecasting and soil condition assessment. The output provides predicted weather information and soil health status. Specifically, it uses statistical analysis and machine learning algorithms to calculate future weather patterns. 【0766】 Step 3: 【0767】 The server uses emotion recognition technology to analyze emotions from voice and text input by the user. It receives user voice commands and text messages in digital format as input and performs emotion analysis. The output is emotional indicators such as the user's stress level and fatigue level. This allows the system to understand the user's mental state. 【0768】 Step 4: 【0769】 Based on these analysis results, the server uses a work process generation mechanism to construct an optimal work plan. It considers weather information, soil conditions, and user sentiment indicators as inputs, and generates a specific work schedule as output. For example, if rain is expected or if the user is experiencing stress, a flexible plan can be created that readjusts the work order. 【0770】 Step 5: 【0771】 The terminal performs agricultural tasks based on work instructions received from the server. It receives a work schedule from the server as input and then operates the actual agricultural machinery (e.g., pesticide spraying with a drone). The output is a work completion report, which provides feedback on the progress to the server. 【0772】 Step 6: 【0773】 The server receives progress reports from terminals and provides information to users using status reporting mechanisms. It takes work progress data from terminals as input and creates a report adjusted based on sentiment indicators, then outputs this report to the user in natural language. For example, it might display a message such as, "Work is progressing smoothly and is expected to be completed ahead of schedule," to help users continue working with confidence. 【0774】 (Application Example 2) 【0775】 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". 【0776】 While automated systems in agricultural environments provide efficient work management through the collection and analysis of environmental data, they face the challenge of not being able to flexibly adapt to the emotional state of users. Furthermore, when users are experiencing stress or fatigue, it is necessary to appropriately optimize the work content and information provided. Solving this challenge will enable users to utilize the system more comfortably and improve agricultural efficiency. 【0777】 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. 【0778】 In this invention, the server includes a work sequence generation means for distributing work instructions to individual devices, an emotion engine integration means for evaluating the user's emotions using emotion recognition means and reflecting the results in adjusting the work sequence, and an information provision means for providing information and suggestions optimized according to the user's emotional state. This makes it possible to manage work and provide information while taking the user's emotions into consideration. 【0779】 "Information gathering means" refers to devices and technologies for acquiring weather, soil, and crop conditions in an agricultural environment in real time. 【0780】 "Interpretation methods" refer to methods and techniques for performing weather forecasting, soil analysis, and crop growth evaluation based on acquired information. 【0781】 A "work sequence generation means" is a method or system for generating the optimal work sequence based on the interpretation results and distributing work instructions to individual devices. 【0782】 "Emotion recognition means" refers to technology that evaluates the emotions of users and uses the results to improve the operation of the system. 【0783】 The "emotion engine integration means" refers to a technical measure that reflects the results of the emotion recognition means in adjusting the work sequence. 【0784】 "Information provision methods" refer to methods for providing information and suggestions that are optimized according to the user's emotional state. 【0785】 A "task execution means" is a system in which individual devices perform tasks according to work instructions and report their progress. 【0786】 A "situation reporting system" is a system for reporting the details of completed work and changes in the agricultural environment to users in natural language. 【0787】 To implement this invention, a server, a user terminal, and various sensors and machines are required. 【0788】 The server collects environmental data such as weather, soil, and crop conditions in real time from various sensors. This data is then interpreted to perform weather forecasting, soil analysis, and crop growth evaluation. Dedicated analysis programs and artificial intelligence models (e.g., generative AI models) are used to analyze the collected data. Based on the analysis results, an optimal work sequence is generated, and instructions are transmitted wirelessly to individual devices. 【0789】 Emotion recognition tools analyze the emotions expressed in the user's voice and text input. The server considers the user's emotional state and adjusts the order of operations as needed. This emotion recognition is performed using speech recognition APIs (e.g., Google Speech-to-Text) and sentiment analysis APIs (e.g., IBM Watson Natural Language Understanding). 【0790】 Based on information received from the server, the user terminal provides the user with optimal information and suggestions tailored to their emotional state. For example, if the user is feeling stressed, it might suggest taking a break or simplifying work procedures. 【0791】 Ultimately, the technologies and functions used will enable comfortable and efficient agricultural management that takes user emotions into consideration. A specific example of a prompt is given below: "Design a program that analyzes the user's emotional state and adjusts the work sequence based on that analysis." 【0792】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0793】 Step 1: 【0794】 The server acquires environmental data from various sensors. It receives real-time weather, soil, and crop condition data as input. Based on this data, it uses a generative AI model to perform weather forecasting, soil analysis, and crop growth evaluation, and outputs the analysis results. 【0795】 Step 2: 【0796】 The server generates the optimal work sequence based on the acquired analysis results. It takes the analysis results as input and uses the work sequence generation mechanism to output the optimal work plan. This output includes work instructions that are distributed to each device via wireless communication. 【0797】 Step 3: 【0798】 The server processes user input (voice and text) using emotion recognition mechanisms. It receives voice and text data as input and evaluates the emotional state using speech recognition APIs and emotion analysis APIs. The evaluation results are output and used to adjust the workflow as needed. 【0799】 Step 4: 【0800】 The terminal receives work instructions sent from the server and performs the specified tasks according to those instructions. It receives work instructions as input and reports the progress of the performed tasks to the server. It provides the progress of the tasks as output and accepts new instructions as needed. 【0801】 Step 5: 【0802】 The user reviews information and suggestions received from the server via their terminal. They receive advice based on work details and emotions reported in natural language as input, and make decisions to improve work efficiency. The system generates feedback from the user and new instructions as output. 【0803】 Step 6: 【0804】 The server receives feedback and new instructions from users and adjusts system operations accordingly. It takes feedback information as input and outputs new work plans and optimized information provision, improving the overall flexibility of the system. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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. 【0809】 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. 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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." 【0814】 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. 【0815】 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. 【0816】 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. 【0817】 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. 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 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. 【0822】 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. 【0823】 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. 【0824】 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. 【0825】 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. 【0826】 The following is further disclosed regarding the embodiments described above. 【0827】 (Claim 1) 【0828】 A data collection method that acquires weather, soil, and crop conditions in real time from various sensors installed in the agricultural environment, 【0829】 An analytical tool that performs weather forecasting, soil analysis, and crop growth evaluation based on acquired data, 【0830】 A work flow generation means that generates an optimal work flow based on the analysis results and distributes work instructions to individual machines, 【0831】 A task execution means in which individual machines perform tasks according to work instructions and report on their progress, 【0832】 A status reporting system that reports the work performed and changes in the agricultural environment to the user in natural language. 【0833】 A system that includes this. 【0834】 (Claim 2) 【0835】 The system according to claim 1, wherein data acquisition and analysis are performed remotely in real time via communication means. 【0836】 (Claim 3) 【0837】 The system according to claim 1, wherein a workflow based on the analysis results is transmitted to each machine using wireless communication technology. 【0838】 "Example 1" 【0839】 (Claim 1) 【0840】 An information gathering means that acquires environmental conditions, ground surface information, and plant conditions in real time from various measuring devices placed in the agricultural environment, 【0841】 Based on the acquired information, weather forecasting, surface analysis, and plant growth evaluation are performed, and the information analysis is performed using a generative AI model. 【0842】 A work procedure generation means that creates the optimal work procedure based on the analysis results and distributes work instructions to various machines, 【0843】 Task execution means that various machines carry out activities according to work instructions and report on their progress, 【0844】 A status reporting system that communicates the details of activities performed and changes in the agricultural environment to users in natural language. 【0845】 A system that includes this. 【0846】 (Claim 2) 【0847】 The system according to claim 1, wherein information acquisition and analysis are performed in real time from a remote location via a transmission means. 【0848】 (Claim 3) 【0849】 The system according to claim 1, wherein work procedures based on analysis results are transmitted to various machines using wireless communication technology. 【0850】 "Application Example 1" 【0851】 (Claim 1) 【0852】 An information collection means that acquires environmental information and plant conditions in real time from various detectors installed in the agricultural environment, 【0853】 Analytical tools that perform environmental prediction, soil condition analysis, and plant growth evaluation based on acquired information, 【0854】 A work task generation means that generates optimal work tasks based on the analysis results and distributes work instructions to individual devices, 【0855】 A means of executing tasks in which individual devices perform tasks according to work instructions and report on their progress, 【0856】 A status reporting method that reports the details of the work performed and changes in the agricultural environment to users in natural language, 【0857】 A generative AI analysis method that uses generative AI models to predict plant growth, detect anomalies, and propose optimal agricultural activities. 【0858】 A system that includes this. 【0859】 (Claim 2) 【0860】 The system according to claim 1, wherein information acquisition and analysis are performed remotely in real time via information and communication technology. 【0861】 (Claim 3) 【0862】 The system according to claim 1, wherein work tasks based on analysis results are transmitted to each device using wireless communication technology. 【0863】 "Example 2 of combining an emotion engine" 【0864】 (Claim 1) 【0865】 An information gathering method that acquires weather, land, and plant conditions in real time from various detectors installed in agricultural areas, 【0866】 Analytical tools that perform weather forecasting, land analysis, and plant growth evaluation based on acquired information, 【0867】 A work process generation means that generates an optimal work process based on the analysis results and distributes work commands to individual devices, 【0868】 A work execution means in which individual devices perform work according to work instructions and report progress, 【0869】 A status reporting method that reports the completed work and changes in agricultural land use to the user in natural language, 【0870】 Emotion recognition means that recognizes the user's emotions using language and voice analysis and reflects them in work processes and information provision. 【0871】 A system that includes this. 【0872】 (Claim 2) 【0873】 The system according to claim 1, wherein information acquisition and analysis are performed remotely in real time using communication technology. 【0874】 (Claim 3) 【0875】 The system according to claim 1, wherein the work process based on the analysis results is transmitted to each device using wireless communication technology. 【0876】 "Application example 2 when combining with an emotional engine" 【0877】 (Claim 1) 【0878】 Information gathering means for acquiring weather, soil, and crop conditions in real time from various sensors installed in the agricultural environment, 【0879】 Interpretation tools for weather forecasting, soil analysis, and crop growth evaluation based on acquired information, 【0880】 A work sequence generation means that generates the optimal work sequence based on the interpretation results and distributes work instructions to individual devices, 【0881】 An emotion engine integration means that evaluates the user's emotions using emotion recognition means and reflects the results in adjusting the work sequence, 【0882】 An information provision method that provides information and suggestions optimized according to the user's emotional state, 【0883】 A task execution means in which individual devices perform tasks according to work instructions and report progress, 【0884】 A status reporting system that reports the completed work and changes in the agricultural environment to users in natural language. 【0885】 A system that includes this. 【0886】 (Claim 2) 【0887】 The system according to claim 1, wherein information is acquired and interpreted in real time from a remote location via communication technology. 【0888】 (Claim 3) 【0889】 The system according to claim 1, wherein the work sequence based on the interpretation result is transmitted to each device using wireless communication technology. [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

[Claim 1] A data collection method that acquires weather, soil, and crop conditions in real time from various sensors installed in the agricultural environment, An analytical tool that performs weather forecasting, soil analysis, and crop growth evaluation based on acquired data, A work flow generation means that generates an optimal work flow based on the analysis results and distributes work instructions to individual machines, A task execution means in which individual machines perform tasks according to work instructions and report on their progress, A status reporting system that reports the work performed and changes in the agricultural environment to the user in natural language. A system that includes this. [Claim 2] The system according to claim 1, wherein data acquisition and analysis are performed remotely in real time via communication means. [Claim 3] The system according to claim 1, wherein a workflow based on the analysis results is transmitted to each machine using wireless communication technology.

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