system

Abstract

We provide the system. [Solution] Data collection means, A data cleaning means for processing weather information and soil information acquired through the aforementioned data collection means, A data analysis means for analyzing the information prepared by the aforementioned data cleaning means, A decision-making support means that proposes an agricultural work plan based on the results of the data analysis means, A method for constructing a predictive model that uses past agricultural work data to predict crop yields and the occurrence of pests and diseases, A system including a user interface means for displaying information generated by the decision support means and the predictive model construction means.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Unexamined Patent Application Publication No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the agricultural field, due to aging and labor shortages, productivity decline and difficulties in farmland management have become problems. In addition, the inability to quickly respond to changes in weather and soil conditions has become a factor affecting the quality and yield of agricultural crops. Furthermore, there is a problem that it is difficult to formulate plans in agricultural management because the accuracy of predicting yields and the occurrence of pests and diseases is insufficient. It is required to solve these problems and improve agricultural productivity. 【Means for Solving the Problems】 【0005】 In this invention, weather and soil information are acquired in real time using data collection means, and noise reduction and missing value imputation are performed using data cleaning means based on this data. The prepared data is then analyzed in detail by data analysis means, and based on the results, decision support means proposes an optimal farming plan. Furthermore, past data is used to construct a predictive model that accurately predicts yield and pest and disease occurrence, and this information is provided to the user through a user interface means. In this way, the invention provides a system that realizes increased agricultural efficiency and effective farming management. 【0006】 "Data collection means" refers to devices and processes that have the function of acquiring weather information and soil information through external APIs or IoT devices. 【0007】 "Data cleaning means" refers to a device and process that has the function of removing noise contained in collected information and imputing missing values. 【0008】 "Data analysis means" refers to devices and processes that have the function of performing necessary analyses by applying statistical methods and machine learning algorithms using pre-processed information. 【0009】 "Decision-making support means" refers to devices and processes that have the function of formulating agricultural work plans based on data analysis results and proposing optimal cultivation methods and harvesting times to the user. 【0010】 "Predictive model construction means" refers to a device and process that has the function of training a model using past agricultural data to predict crop yields and the occurrence of pests and diseases. 【0011】 "User interface means" refers to devices and processes that have the function of displaying information obtained from decision support means and predictive model building means in a format that is easy for the user to understand. [Brief explanation of the drawing] 【0012】 [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] 【0013】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0014】 First, the terms used in the following description will be explained. 【0015】 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. 【0016】 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. 【0017】 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, etc. 【0018】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0019】 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." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 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. 【0023】 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). 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 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". 【0033】 This invention relates to an AI-powered agricultural support system aimed at optimizing agricultural work. This system includes a process in which a server collects and processes various data and proposes an appropriate agricultural work plan to the user. 【0034】 The server first collects weather and soil information in real time using external APIs and multiple IoT devices. This includes data such as temperature, precipitation, soil moisture content, and pH values. This data is processed on the server using data cleaning mechanisms to prepare it for analysis. 【0035】 Through data analysis tools, the server analyzes the optimal environmental conditions for crop growth based on the compiled information. For example, it evaluates the impact of temperature fluctuation patterns on crop growth rate. Based on these analysis results, decision support tools are activated, and the server proposes the optimal cultivation method and harvest time to the user. This leads to increased efficiency in agricultural management. 【0036】 Furthermore, the server utilizes historical growth data to build predictive models and forecast crop yields and pest and disease outbreaks. These predictions are transmitted via the server to the user interface and displayed on the user's terminal. Based on this visualized information, users can then develop farming strategies. 【0037】 As a concrete example, in a corn cultivation field, if the server incorporates weather information and predicts that this summer's rainfall will be less than average, the system could suggest to the user the introduction of a water-saving irrigation system. In this way, it is possible to formulate agricultural work plans that are adapted to future weather conditions. 【0038】 As described above, the present invention provides a means to achieve optimal farming practices by using AI technology to analyze farm data from multiple perspectives. 【0039】 The following describes the processing flow. 【0040】 Step 1: 【0041】 The server periodically retrieves weather and soil information from external APIs and IoT devices. This includes data such as temperature, precipitation, soil moisture content, and pH values. 【0042】 Step 2: 【0043】 The server processes the acquired data using data cleaning techniques. Specifically, it filters out noise and outliers and fills in missing data. This process improves data reliability and the accuracy of analysis. 【0044】 Step 3: 【0045】 The server analyzes the cleansed data using data analysis tools. Here, machine learning algorithms are applied to find correlations between weather and soil conditions and crop growth. 【0046】 Step 4: 【0047】 Based on the analysis results, the server utilizes decision-making support tools to calculate and propose the optimal cultivation methods and harvest times. For example, it can develop an irrigation plan based on expected weather conditions. 【0048】 Step 5: 【0049】 The server utilizes historical growth data to build predictive models and forecast future crop yields and pest and disease risks. The prediction results are regularly validated and updated to improve the accuracy of the models. 【0050】 Step 6: 【0051】 The server provides users with information derived from decision support and predictive models through a user interface. The information is visualized on the user's terminal, and the user adjusts their farming plan based on it. 【0052】 (Example 1) 【0053】 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." 【0054】 In conventional agriculture, optimizing production activities by effectively utilizing environmental and geological data has been difficult, and there has been a particular need to improve the accuracy of yield forecasts and disease outbreak predictions. Furthermore, conventional systems have not adequately acquired and immediately utilized information in real time, resulting in a decrease in the efficiency of production activity planning. 【0055】 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. 【0056】 In this invention, the server includes data collection means, information purification means, and information analysis means. This enables the immediate acquisition of environmental and geological data, and analysis based on the purified data. This leads to the optimization of production activities and the development of efficient production activity plans. 【0057】 "Data acquisition means" refers to the technical elements within a system for instantly acquiring environmental and geological data through external communication protocols and networked devices. 【0058】 "Information purification methods" are means of preparing collected data to be analyzable and ensuring consistency and quality by imputing missing values ​​and removing outliers. 【0059】 "Information analysis methods" are techniques for analyzing conditions and influences for specific purposes based on purified data, and for generating analysis results. 【0060】 A "decision-making support tool" is a function within a system that proposes the optimal production method and harvest time based on the analysis results. 【0061】 "Predictive model construction methods" refer to the process of creating and implementing mathematical models that utilize past production activity data to predict crop yields and the occurrence of diseases and pests. 【0062】 "User display means" refers to an interface for conveying information generated by decision-making support means and predictive model construction means to the user. 【0063】 A specific embodiment for carrying out this invention will be described. 【0064】 This system is an AI-assisted system aimed at optimizing agriculture, designed to automate the processes of data collection, analysis, and decision-making. The server acquires environmental and geological data in real time using external communication protocols and network-connected devices as data collection means. This data includes temperature, precipitation, soil moisture content, and acidity. 【0065】 The server formats the acquired data using data purification methods. Specifically, this includes processes such as imputing missing values, identifying and correcting outliers, and standardizing data formats. This purification enables smooth analysis while maintaining data quality. 【0066】 Next, the server uses information analysis tools to analyze the purified data and evaluate the impact on the production environment. For example, it predicts the impact of temperature fluctuations on the growth of a particular crop. Based on the analysis results, the server, through decision-making support tools, proposes the optimal production method and harvest time to the user. 【0067】 Furthermore, the server utilizes predictive model building techniques to forecast crop yields and pest outbreaks based on historical data. This provides crucial insights for planning future production activities. 【0068】 This information is provided to the user's terminal in a visualized form via a user display device. The user can use this information to develop specific strategies for production activities. 【0069】 As a concrete example, if a user inputs a prompt message into the AI ​​model for corn cultivation, such as "Please create an irrigation plan for the next week," the server will propose an optimal irrigation plan based on weather forecasts and soil data. This system enables users to achieve more efficient and sustainable farming practices. 【0070】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0071】 Step 1: 【0072】 The server acquires environmental and geological data using data collection methods. Specifically, it obtains temperature and precipitation from weather information APIs via external communication protocols and collects soil sensor data from networked devices. The input to this process is real-time data from APIs and sensors, and the output is raw data stored in a database on the server. 【0073】 Step 2: 【0074】 The server processes the collected data using data purification methods and prepares it for analysis. Specifically, it uses statistical methods to impute missing values ​​and removes or corrects outliers if detected. The input to this process is the raw data collected in step 1, and the output is purified and consistent data. 【0075】 Step 3: 【0076】 The server analyzes the prepared data using information analysis tools. Specifically, it uses numerical models to evaluate the effects of temperature fluctuations and soil conditions on crop growth. The input to this process is the data purified in step 2, and the output is the analysis results of the condition evaluation in the production environment. 【0077】 Step 4: 【0078】 The server, based on the analysis results through decision-making support tools, proposes production methods and harvest times to the user. Specifically, it takes prompt text into the generating AI model and responds to requests such as, "Please propose the optimal cultivation plan for these weather conditions." The input for this process is the analysis results from step 3, and the output is a specific production plan provided to the user. 【0079】 Step 5: 【0080】 The server utilizes predictive model building methods to forecast future production conditions, such as crop yields and disease outbreak rates, using historical data. Machine learning models are used for this forecast. The input consists of previously collected and analyzed data, while the output provides detailed information on future production forecasts. 【0081】 Step 6: 【0082】 The terminal presents information to the user through a user interface. Specifically, it visualizes production plans and forecast information sent from the server and displays it in a format that the user can easily understand. The input to this process is the plan and forecast information provided by the server, and the output is the visualized user interface. 【0083】 (Application Example 1) 【0084】 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." 【0085】 Agricultural production in urban environments still faces many challenges due to limited land and fluctuating weather conditions. In particular, the lack of methods for efficient agricultural management and productivity improvement in urban areas makes it difficult to develop appropriate work plans. Furthermore, flexible agricultural methods that can adapt to predictable weather changes are needed. Addressing these challenges is essential to enhancing the sustainability and efficiency of urban agriculture. 【0086】 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. 【0087】 In this invention, the server includes a data acquisition device, an information organization device, an information analysis device, a decision support device, a predictive model construction device, a display device, a user management device, and an environment optimization device. This enables real-time optimization of agricultural activities in urban industrial environments, providing optimal farm work plans and rapid implementation of environmental adaptation measures. 【0088】 A "data acquisition device" is a device that acquires environmental information and soil data in real time through an external information provision interface and sensor devices. 【0089】 An "information processing device" is a device that processes environmental information and soil data acquired from a data collection device and converts it into a format suitable for analysis. 【0090】 An "information analysis device" is a device that performs data analysis based on organized information to derive optimal growing conditions and work plans in agriculture. 【0091】 A "decision support device" is a device that proposes an agricultural work plan to the user based on the results of an information analysis device. 【0092】 A "predictive model building device" is a device that uses past agricultural work data to build predictive models for crop yields and the occurrence of biological disasters. 【0093】 A "display device" is a device that provides users with visually generated data analysis and predictive information, supporting them in developing concrete work strategies. 【0094】 A "user management device" is a device that manages agricultural activities in an urban industrial environment and supports users in efficiently managing their farm work. 【0095】 An "environmental optimization device" is a device that proposes optimized growing conditions, such as agricultural methods and heat retention measures, that can be applied within urban environments. 【0096】 This invention provides a system for optimizing agricultural activities in an urban industrial environment. The server consists of a data collection device, an information organization device, an information analysis device, a decision support device, a predictive model building device, a display device, a user management device, and an environment optimization device. 【0097】 The data collection device acquires environmental information and soil data in real time using an external information provision interface and sensor devices. This makes it possible to constantly monitor weather conditions and soil conditions. 【0098】 The information processing system organizes the acquired information into a database format and performs appropriate data cleaning processes to convert it into data suitable for analysis. Python and its libraries (Pandas, NumPy) are used here. 【0099】 The information analysis system performs analysis to derive optimal agricultural conditions based on the organized information. Machine learning libraries (Scikit-learn, TENSORFLOW®) are used for condition evaluation and method selection. 【0100】 The decision support device proposes a farming plan to the user based on the analysis results. Therefore, it is possible to use the generated AI model to present specific actions that the user should take. 【0101】 The predictive model building device utilizes historical agricultural data to construct models that predict crop yields and the occurrence of biological disasters, and this information supports the user's decision-making. 【0102】 The display device visualizes the calculation results and presents the information in a way that the user can intuitively understand. An interface developed using React Native makes this possible. 【0103】 The user management device manages the status of agricultural activities in urban areas and supports efficiency by providing information according to user requests. 【0104】 The environmental optimization device proposes the optimal conditions necessary for agricultural work in urban environments and provides corresponding countermeasures. This device automatically suggests appropriate cultivation methods and heat retention measures. 【0105】 For example, a server collects weather data from a cornfield and, based on the predicted precipitation pattern for the month, suggests optimized water management to the user. Furthermore, the following prompt messages can be used to clarify intentions through AI. 【0106】 Example of a prompt: 【0107】 "Based on this year's spring temperature forecast for Tokyo, please tell me what measures I can take to optimize plant growth." 【0108】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0109】 Step 1: 【0110】 The server acquires environmental and soil data using data acquisition devices. By collecting data in real time from external information provision interfaces and sensor devices, it obtains the latest weather and soil condition data as input. This data forms the basis for analysis in subsequent steps. 【0111】 Step 2: 【0112】 The server performs data cleaning on the data acquired by the information processing device. Using the Python Pandas library, it handles missing values, detects and removes outliers, and prepares the data for analysis. The output consists of cleaned environmental information and soil data. 【0113】 Step 3: 【0114】 The server evaluates environmental conditions based on data prepared using an information analysis device. It uses Scikit-learn to model the effects of factors such as temperature and precipitation, and quantifies their impact on agricultural work. The input is cleaned data, and the output is the degree of impact as a result of the analysis. 【0115】 Step 4: 【0116】 The server generates a specific farm work plan based on the analysis results using a decision support device. Using a generation AI model, it proposes the optimal work plan and improvement measures based on prompt messages. The user is provided with instructions regarding work methods, procedures, and timing. 【0117】 Step 5: 【0118】 The server uses a predictive model building system to forecast future agricultural conditions from historical data. Using TensorFlow, it predicts yields and the likelihood of disease outbreaks, generating output that can serve as a reference for future agricultural activities. The input is historical data, and the output is the prediction result. 【0119】 Step 6: 【0120】 The server visualizes analysis and prediction results through a display device. Using React Native, it provides users with an interactive dashboard to support intuitive understanding. Users can immediately see the suggested work strategy. 【0121】 Step 7: 【0122】 Users track the progress of urban farming operations using a user management device and send requests to the server. This allows for the management of further operations and data collection progress, supporting efficient work execution. Inputs are user operation information, and outputs are feedback on the execution status. 【0123】 Step 8: 【0124】 The server uses an environmental optimization device to propose agricultural measures tailored to the urban environment. Based on the automatically generated proposals, appropriate cultivation methods and heat retention measures are presented, and the user is assisted in their implementation. This enables agricultural work to be carried out under optimized environmental conditions. 【0125】 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. 【0126】 This invention relates to a system that incorporates an emotion engine into an agricultural support system, recognizes the user's emotional state, and provides suggestions for agricultural work plans and decision-making support accordingly. 【0127】 In addition to regular data collection, the server uses an emotion engine to evaluate the user's emotional state. This emotion engine analyzes the voice and text information entered by the user and can recognize emotions such as joy, surprise, anger, and anxiety. The data from the emotion engine is used in decision support systems and user interface systems. 【0128】 Specifically, the server adjusts the suggested decision-making support measures based on emotional information. For example, if a user is feeling stressed, the server can take that information into consideration and suggest a plan to reduce the burden of farm work. Furthermore, based on the analysis results of the emotional engine, the server displays encouraging or cautionary messages to the user. 【0129】 Furthermore, by utilizing sentiment data in the predictive model construction method, it is possible to flexibly adjust priorities. When a user's emotional state is unstable, measures such as emphasizing alerts for harvest plans or pest and disease outbreak predictions can be implemented. 【0130】 The user terminal displays this adjusted information in an easy-to-understand format, allowing the user to provide feedback or take direct action based on that information. For example, if the harvest time is postponed and the user is emotionally dissatisfied, they can request that the plan be adjusted again. 【0131】 In this way, by incorporating an emotion engine, we aim to create a more appropriate and user-friendly agricultural support system, thereby improving the efficiency and comfort of agricultural management. 【0132】 The following describes the processing flow. 【0133】 Step 1: 【0134】 The server uses its standard data collection functions to acquire weather and soil information from external APIs and IoT devices. Simultaneously, it analyzes user voice and text input through an emotion engine to collect emotion data. 【0135】 Step 2: 【0136】 The server processes weather and soil information acquired through data cleaning methods, removes noise, and classifies and organizes emotional information using an emotion engine. Emotional information is categorized into emotional categories such as joy, anger, and anxiety. 【0137】 Step 3: 【0138】 The server analyzes weather and soil information compiled using data analysis tools to evaluate factors that affect crop growth. This allows it to determine whether the current agricultural environment is appropriate. 【0139】 Step 4: 【0140】 The server incorporates user emotional information obtained from the emotion engine into decision-making support tools. For example, if a user is feeling anxious, the server infers the cause and adjusts the proposed farm work plan to one that is expected to reduce stress. 【0141】 Step 5: 【0142】 The server predicts crop yields and pest and disease outbreaks through predictive model construction. In this process, emotional information is used to prioritize the analysis results, presenting information tailored to the user's emotional state. 【0143】 Step 6: 【0144】 The terminal displays farm work plans and forecast information received from the server to the user via a user interface. The user interface provides information in a visually easy-to-understand format, depending on the user's emotional state. 【0145】 Step 7: 【0146】 Users implement farming plans based on the information provided and send feedback from their devices to the server. This feedback information is used to support decision-making and improve prediction accuracy in the next cycle. 【0147】 (Example 2) 【0148】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0149】 Conventional agricultural support systems propose farming plans based on weather and soil information, but they have the challenge of not being able to address the individual emotions and mental states of users. As a result, it is difficult to provide plans that take into account the user's emotional burden, and acceptance of the plan may be difficult. Therefore, there is a need for a new system that takes the user's emotional state into consideration in agricultural support. 【0150】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0151】 In this invention, the server includes data collection means, emotion recognition means, and emotion-responsive decision support means. This makes it possible to adjust the farm work plan according to the user's emotional state. 【0152】 "Data collection means" refers to devices or methods for collecting weather information, soil information, and user voice and text data. 【0153】 "Data cleaning means" refers to a device or method for processing acquired information, eliminating errors and noise, and preparing it for analysis. 【0154】 "Data analysis means" refers to a device or method that analyzes organized information to derive useful insights and recommendations related to agricultural work. 【0155】 A "decision support tool" is a device or method that proposes a farming plan to a user based on the results of data analysis. 【0156】 A "predictive model construction means" is a device or method for predicting crop yields and the occurrence of pests and diseases by utilizing past agricultural work data. 【0157】 "User interface means" refers to a device or method that presents generated information to a user and allows the user to provide feedback or perform operations on that information. 【0158】 "Emotion recognition means" refers to a device or method that analyzes voice and text data collected from a user to determine the user's emotional state, such as joy, anxiety, or anger. 【0159】 An "emotion-responsive decision-making support device" is a device or method that adjusts farm work plans based on emotions determined by an emotion recognition device and makes suggestions that take into account the user's mental burden. 【0160】 This invention relates to a system that provides a plan for agricultural support systems that takes into account the user's emotional state. This system is implemented as follows. 【0161】 The server acquires voice and text data from users using data collection methods, and further collects weather and soil information through external APIs and IoT devices. This allows for the collection of a wide range of agricultural data. Natural language processing and speech recognition technologies are used as emotion recognition methods to analyze the user's emotional state from the data. For example, an emotion analysis algorithm using a Python library could be implemented. 【0162】 The server adjusts the farm work plan through an emotion-response decision support system based on emotional data obtained by the emotion recognition system. The provided plan is optimized to reduce the user's emotional burden. For example, if the user is feeling stressed, suggestions will be made to reduce the workload or extend break times. 【0163】 The user terminal displays the plan sent from the server in an easy-to-understand format. Based on this information, the user can decide on actions or send feedback to the server. This feedback is used in predictive model building for future improvements. As an example, the prompt message is, "Recognize the user's emotional state from their voice input or text, and propose a farming plan based on that emotion." 【0164】 By incorporating an emotion engine in this way, it is expected that agricultural support will be more user-centric than with conventional systems, contributing to increased efficiency in agricultural management and improved user satisfaction. 【0165】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0166】 Step 1: 【0167】 The server acquires voice and text data from users through data collection means, as well as weather and soil information via IoT devices and external APIs. Inputs include user voice data, text messages, weather data, and soil data. Based on these inputs, the server integrates and cleans the data, outputting it in an analyzable format. Specifically, voice data is converted to text, and each piece of information is correctly formatted. 【0168】 Step 2: 【0169】 The server uses emotion recognition to analyze the user's emotional state from their voice and text data. The input is the text data organized in step 1. The server applies natural language processing and speech analysis techniques to detect emotions such as joy, anxiety, and anger, and outputs the results as emotion data. This quantitatively shows what emotions the user is experiencing. 【0170】 Step 3: 【0171】 The server takes emotional states into account and adjusts farm work plans through decision-making support mechanisms. Inputs include emotional data and weather and soil information. The server utilizes a generative AI model to optimize farm plans and output work schedules that fit the emotional state. For example, if a user is stressed, a plan with reduced workload will be generated. 【0172】 Step 4: 【0173】 The server sends a tailored farming plan and an emotion-based message to the user's terminal. The input is the farming plan generated in step 3. Along with the plan, the server outputs a message that includes an encouraging sentence such as, "Let's be flexible and adapt to the situation." 【0174】 Step 5: 【0175】 The user terminal visually displays received farming plans and messages. Input is data sent from the server. The terminal organizes the information and outputs it in an easy-to-understand format. Users can either work based on the displayed plan or provide feedback to the server. 【0176】 Step 6: 【0177】 The user provides feedback on the presented plan. The input is the data displayed on the device. If the user is satisfied with the plan, it proceeds; if not, it generates an output requesting readjustment. This feedback is used to improve future plans. 【0178】 (Application Example 2) 【0179】 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". 【0180】 In agriculture, uniform work plans that do not consider the user's emotional state can impair work efficiency and comfort. Furthermore, there is a lack of concrete means to reduce the psychological burden on users while they work, highlighting the need for more efficient agricultural management and user-friendly support. 【0181】 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. 【0182】 In this invention, the server includes a data acquisition device, a data cleaning device, a data analysis device, a decision support device, a predictive model building device, an emotion analysis device, and a message generation device. This makes it possible to adjust the farm work plan to reflect the user's emotional state and provide optimal messages. 【0183】 A "data acquisition device" is a device for acquiring weather information and land information, and its role is to collect information in real time via external APIs and Internet of Things devices. 【0184】 A "data cleanup device" is a device that organizes acquired information into an appropriate format and performs processing to correct any missing or incorrect information. 【0185】 A "data analysis device" is a device that performs detailed analysis based on well-organized information and extracts useful insights from diverse data related to agriculture. 【0186】 A "decision support device" is a device that uses the results of data analysis to propose farm work plans to users, and has the function of calculating the optimal cultivation method and harvest time. 【0187】 A "predictive model building device" is a device that uses past agricultural data to predict future crop yields and the occurrence of pests and diseases. 【0188】 A "user interface device" is a device that visually displays analysis results and plans to the user and facilitates information sharing between the user and the system. 【0189】 An "emotion analysis device" is a device that analyzes the user's emotional state from voice and text information and adjusts the farm work plan based on the results. 【0190】 A "message generation device" is a device that generates and displays appropriate messages to the user based on the analyzed emotional state of the user. 【0191】 The system for realizing this invention involves the coordinated operation of various devices and software. A server collects weather and land information in real time from external APIs and Internet of Things devices via a data acquisition device. The collected data is processed by a data cleansing device to correct any errors or missing information. Subsequently, a data analysis device analyzes the cleaned data to extract useful information related to agriculture. 【0192】 The server uses the analyzed information to propose an optimal farming plan using a decision support device. In this process, a predictive model building device uses past agricultural data to predict future crop yields and the likelihood of pest and disease outbreaks, and incorporates this information. The user's emotional state is analyzed by an emotion analysis device using voice and text information, and the farming plan is adjusted according to the results. 【0193】 The terminal displays the adjusted plans and analysis results on a user interface device, allowing users to visually confirm this information. Furthermore, a message generation device generates and displays messages tailored to the user's emotional state. This reduces the user's psychological burden and improves the efficiency and comfort of agricultural work. 【0194】 For example, if the soil condition in a home garden is deteriorating, and the system determines that the user is in an angry state, the emotion analyzer will take this state into consideration, propose a plan to reduce the burden more than usual, and display an encouraging message. In this case, an example of a prompt message might be, "When the user is feeling frustrated with farm work, what kind of work plan and message should be proposed?" 【0195】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0196】 Step 1: 【0197】 The server acquires weather and land information in real time from external APIs and Internet of Things devices via data acquisition devices. The input is data from APIs, and the acquired raw data is stored in a database for further processing in the next step. 【0198】 Step 2: 【0199】 The server corrects erroneous or missing data from the information acquired using a data cleansing device, and supplements it as needed. A cleansing process is performed here, and the output data is stored in an improved, more accurate form. 【0200】 Step 3: 【0201】 The server uses data analysis equipment to perform statistical analysis on cleaned data and extract agricultural indicators and forecast data. It takes clean data as input and outputs analytical data useful for formulating farm work plans. 【0202】 Step 4: 【0203】 The server generates an optimal farming plan for the user based on the analysis results in the decision support system. This plan aims to improve work efficiency and provides optimized output based on the input analysis data. 【0204】 Step 5: 【0205】 The server utilizes a predictive model building system to predict crop yields and pest and disease outbreaks based on historical data. It predicts future events based on the incorporated agricultural data and provides the results to decision support systems. 【0206】 Step 6: 【0207】 The server analyzes the user's emotional state based on voice and text data entered by the user using an emotion analysis device. The input is voice and text data from the user, and the output generates emotional information such as joy or anger. 【0208】 Step 7: 【0209】 The server adjusts the farming plan based on emotional information and generates and displays appropriate messages to the user using a message generator. This process takes the user's emotional state into consideration, providing further motivational and cautionary information. The output is a user-specific, tailored plan and message. 【0210】 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. 【0211】 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. 【0212】 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. 【0213】 [Second Embodiment] 【0214】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0215】 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. 【0216】 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). 【0217】 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. 【0218】 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. 【0219】 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). 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 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. 【0224】 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. 【0225】 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". 【0226】 This invention relates to an AI-powered agricultural support system aimed at optimizing agricultural work. This system includes a process in which a server collects and processes various data and proposes an appropriate agricultural work plan to the user. 【0227】 The server first collects weather and soil information in real time using external APIs and multiple IoT devices. This includes data such as temperature, precipitation, soil moisture content, and pH values. This data is processed on the server using data cleaning mechanisms to prepare it for analysis. 【0228】 Through data analysis tools, the server analyzes the optimal environmental conditions for crop growth based on the compiled information. For example, it evaluates the impact of temperature fluctuation patterns on crop growth rate. Based on these analysis results, decision support tools are activated, and the server proposes the optimal cultivation method and harvest time to the user. This leads to increased efficiency in agricultural management. 【0229】 Furthermore, the server utilizes historical growth data to build predictive models and forecast crop yields and pest and disease outbreaks. These predictions are transmitted via the server to the user interface and displayed on the user's terminal. Based on this visualized information, users can then develop farming strategies. 【0230】 As a concrete example, in a corn cultivation field, if the server incorporates weather information and predicts that this summer's rainfall will be less than average, the system could suggest to the user the introduction of a water-saving irrigation system. In this way, it is possible to formulate agricultural work plans that are adapted to future weather conditions. 【0231】 As described above, the present invention provides a means for achieving optimal farming practices by using AI technology to analyze farm data from multiple perspectives. 【0232】 The following describes the processing flow. 【0233】 Step 1: 【0234】 The server periodically retrieves weather and soil information from external APIs and IoT devices. This includes data such as temperature, precipitation, soil moisture content, and pH values. 【0235】 Step 2: 【0236】 The server processes the acquired data using data cleaning techniques. Specifically, it filters out noise and outliers and fills in missing data. This process improves data reliability and the accuracy of analysis. 【0237】 Step 3: 【0238】 The server analyzes the cleansed data using data analysis tools. Here, machine learning algorithms are applied to find correlations between weather and soil conditions and crop growth. 【0239】 Step 4: 【0240】 Based on the analysis results, the server utilizes decision support tools to calculate and propose the optimal cultivation methods and harvest times. For example, it can develop an irrigation plan based on expected weather conditions. 【0241】 Step 5: 【0242】 The server utilizes historical growth data to build predictive models and forecast future crop yields and pest / disease risks. The prediction results are regularly validated and updated to improve the model's accuracy. 【0243】 Step 6: 【0244】 The server provides users with information derived from decision support and predictive models through a user interface. The information is visualized on the user's terminal, and the user adjusts their farming plan based on it. 【0245】 (Example 1) 【0246】 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." 【0247】 In conventional agriculture, optimizing production activities by effectively utilizing environmental and geological data has been difficult, and there has been a particular need to improve the accuracy of yield forecasts and disease outbreak predictions. Furthermore, conventional systems have not adequately acquired and immediately utilized information in real time, resulting in a decrease in the efficiency of production activity planning. 【0248】 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. 【0249】 In this invention, the server includes data collection means, information purification means, and information analysis means. This enables the immediate acquisition of environmental and geological data, and analysis based on the purified data. This leads to the optimization of production activities and the development of efficient production activity plans. 【0250】 "Data acquisition means" refers to the technical elements within a system for instantly acquiring environmental and geological data through external communication protocols and networked devices. 【0251】 "Information purification methods" are means of preparing collected data to be analyzable and ensuring consistency and quality by imputing missing values ​​and removing outliers. 【0252】 "Information analysis methods" are techniques for analyzing conditions and influences according to a specific purpose, based on purified data, and for generating analysis results. 【0253】 A "decision-making support tool" is a function within a system that proposes the optimal production method and harvest time based on the analysis results. 【0254】 "Predictive model construction methods" refer to the process of creating and implementing mathematical models that utilize past production activity data to predict crop yields and the occurrence of diseases and pests. 【0255】 "User display means" refers to an interface for communicating information generated by decision-making support means and predictive model construction means to the user. 【0256】 A specific embodiment for carrying out this invention will be described. 【0257】 This system is an AI-assisted system aimed at optimizing agriculture, designed to automate the processes of data collection, analysis, and decision-making. The server acquires environmental and geological data in real time using external communication protocols and network-connected devices as data collection means. This data includes temperature, precipitation, soil moisture content, and acidity. 【0258】 The server formats the acquired data using data purification methods. Specifically, this includes processes such as imputing missing values, identifying and correcting outliers, and standardizing data formats. This purification enables smooth analysis while maintaining data quality. 【0259】 Next, the server uses information analysis tools to analyze the purified data and evaluate the impact on the production environment. For example, it predicts the impact of temperature fluctuations on the growth of a particular crop. Based on the analysis results, the server, through decision-making support tools, proposes the optimal production method and harvest time to the user. 【0260】 Furthermore, the server utilizes predictive model building techniques to forecast crop yields and pest outbreaks based on historical data. This provides crucial insights for planning future production activities. 【0261】 This information is provided to the user's terminal in a visualized form via a user display device. The user can use this information to develop specific strategies for production activities. 【0262】 As a concrete example, if a user inputs a prompt message into the AI ​​model for corn cultivation, such as "Please create an irrigation plan for the next week," the server will propose an optimal irrigation plan based on weather forecasts and soil data. This system enables users to achieve more efficient and sustainable farming practices. 【0263】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0264】 Step 1: 【0265】 The server acquires environmental and geological data using data collection methods. Specifically, it obtains temperature and precipitation from weather information APIs via external communication protocols and collects soil sensor data from networked devices. The input to this process is real-time data from APIs and sensors, and the output is raw data stored in a database on the server. 【0266】 Step 2: 【0267】 The server processes the collected data using data purification methods and prepares it for analysis. Specifically, it uses statistical methods to impute missing values ​​and removes or corrects outliers if detected. The input to this process is the raw data collected in step 1, and the output is purified and consistent data. 【0268】 Step 3: 【0269】 The server analyzes the prepared data using information analysis tools. Specifically, it uses numerical models to evaluate the effects of temperature fluctuations and soil conditions on crop growth. The input to this process is the data purified in step 2, and the output is the analysis results of the condition evaluation in the production environment. 【0270】 Step 4: 【0271】 The server, based on the analysis results through decision-making support tools, proposes production methods and harvest times to the user. Specifically, it takes prompt text into the generating AI model and responds to requests such as, "Please propose the optimal cultivation plan for these weather conditions." The input for this process is the analysis results from step 3, and the output is a specific production plan provided to the user. 【0272】 Step 5: 【0273】 The server utilizes predictive model building methods to forecast future production conditions, such as crop yields and disease outbreak rates, using historical data. Machine learning models are used for this forecast. The input consists of previously collected and analyzed data, while the output provides detailed information on future production forecasts. 【0274】 Step 6: 【0275】 The terminal presents information to the user through a user interface. Specifically, it visualizes production plans and forecast information sent from the server and displays it in a format that the user can easily understand. The input to this process is the plan and forecast information provided by the server, and the output is the visualized user interface. 【0276】 (Application Example 1) 【0277】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0278】 Agricultural production in urban environments still faces many challenges due to limited land and fluctuating weather conditions. In particular, the lack of methods for efficient agricultural management and productivity improvement in urban areas makes it difficult to develop appropriate work plans. Furthermore, flexible agricultural methods that can adapt to predictable weather changes are needed. Addressing these challenges is essential to enhancing the sustainability and efficiency of urban agriculture. 【0279】 The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means. 【0280】 In this invention, the server includes a data collection device, an information preparation device, an information analysis device, a decision support device, a prediction model construction device, a display device, a user management device, and an environment optimization device. Thereby, it is possible to optimize agricultural activities in the urban industrial environment in real time, provide an optimal farming plan, and implement rapid environmental adaptation measures. 【0281】 The "data collection device" is a device that acquires environmental information and soil data in real time through an external information providing interface and a sensor device. 【0282】 The "information preparation device" is a device that processes the environmental information and soil data acquired from the data collection device and converts them into a format suitable for analysis. 【0283】 The "information analysis device" is a device that performs data analysis based on the prepared information and derives optimal growth conditions and work plans in agriculture. 【0284】 The "decision support device" is a device that proposes a farming plan to the user based on the results of the information analysis device. 【0285】 The "prediction model construction device" is a device that constructs a prediction model for crop yields and occurrence of biological disasters by utilizing past farming data. 【0286】 The "display device" is a device that visually provides the obtained data analysis and prediction information to the user and supports a specific work strategy. 【0287】 The "user management device" is a device that manages agricultural activities in the urban industrial environment and supports efficient farming management by the user. 【0288】 An "environmental optimization device" is a device that proposes optimized growing conditions, such as agricultural methods and heat retention measures, that can be applied within urban environments. 【0289】 This invention provides a system for optimizing agricultural activities in an urban industrial environment. The server consists of a data collection device, an information organization device, an information analysis device, a decision support device, a predictive model building device, a display device, a user management device, and an environment optimization device. 【0290】 The data collection device acquires environmental information and soil data in real time using an external information provision interface and sensor devices. This makes it possible to constantly monitor weather conditions and soil conditions. 【0291】 The information processing system organizes the acquired information into a database format and performs appropriate data cleaning processes to convert it into data suitable for analysis. Python and its libraries (Pandas, NumPy) are used here. 【0292】 The information analysis system performs analysis to derive optimal agricultural conditions based on the organized information. Machine learning libraries (Scikit-learn, TensorFlow) are used for condition evaluation and method selection. 【0293】 The decision support device proposes a farming plan to the user based on the analysis results. Therefore, it is possible to use the generated AI model to present specific actions that the user should take. 【0294】 The predictive model building device utilizes historical agricultural data to construct models that predict crop yields and the occurrence of biological disasters, and this information supports the user's decision-making. 【0295】 The display device visualizes the calculation results and presents the information in a way that the user can intuitively understand. An interface developed using React Native makes this possible. 【0296】 The user management device manages the status of agricultural activities in urban areas and supports efficiency by providing information according to user requests. 【0297】 The environmental optimization device proposes the optimal conditions necessary for agricultural work in urban environments and provides corresponding countermeasures. This device automatically suggests appropriate cultivation methods and heat retention measures. 【0298】 For example, a server collects weather data from a cornfield and, based on the predicted precipitation pattern for the month, suggests optimized water management to the user. Furthermore, the following prompt messages can be used to clarify intentions through AI. 【0299】 Example of a prompt: 【0300】 "Based on this year's spring temperature forecast for Tokyo, please tell me what measures I can take to optimize plant growth." 【0301】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0302】 Step 1: 【0303】 The server acquires environmental and soil data using data acquisition devices. By collecting data in real time from external information provision interfaces and sensor devices, it obtains the latest weather and soil condition data as input. This data forms the basis for analysis in subsequent steps. 【0304】 Step 2: 【0305】 The server performs data cleaning on the data acquired by the information processing device. Using the Python Pandas library, it handles missing values, detects and removes outliers, and prepares the data for analysis. The output consists of cleaned environmental information and soil data. 【0306】 Step 3: 【0307】 The server evaluates environmental conditions based on the data refined using an information analysis device. It models the impacts such as temperature and precipitation using Scikit-learn and quantifies the impacts on agricultural operations. The input is the cleaned data, and the output is the degree of impact as the analysis result. 【0308】 Step 4: 【0309】 The server generates a specific agricultural operation plan based on the analysis result using a decision support device. It uses a generation AI model to propose an optimal operation plan and improvement measures based on the prompt text. Instructions such as the working method, procedure, and timing are provided to the user. 【0310】 Step 5: 【0311】 The server predicts the future agricultural operation environment from past data using a prediction model construction device. It predicts the yield and the possibility of disease occurrence using TensorFlow and generates an output for reference in future agricultural activities. The input is the past data, and the output is the prediction result. 【0312】 Step 6: 【0313】 The server visualizes the analysis and prediction results through a display device. It uses React Native to provide an interactive dashboard for the user and supports intuitive understanding. The user can immediately check the proposed operation strategy. 【0314】 Step 7: 【0315】 The user uses a user management device to track the implementation status of urban agriculture and send requests to the server. This manages the progress of further operations and data collection and supports the implementation of efficient work. The input is the user's operation information, and the output is the feedback on the execution status. 【0316】 Step 8: 【0317】 The server uses an environmental optimization device to propose agricultural measures tailored to the urban environment. Based on the automatically generated proposals, appropriate cultivation methods and heat retention measures are presented, and the user is assisted in their implementation. This enables agricultural work to be carried out under optimized environmental conditions. 【0318】 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. 【0319】 This invention relates to a system that incorporates an emotion engine into an agricultural support system, recognizes the user's emotional state, and provides suggestions for agricultural work plans and decision-making support accordingly. 【0320】 In addition to regular data collection, the server uses an emotion engine to evaluate the user's emotional state. This emotion engine analyzes the voice and text information entered by the user and can recognize emotions such as joy, surprise, anger, and anxiety. The data from the emotion engine is used in decision support systems and user interface systems. 【0321】 Specifically, the server adjusts the suggested decision-making support measures based on emotional information. For example, if a user is feeling stressed, the server can take that information into consideration and suggest a plan to reduce the burden of farm work. Furthermore, based on the analysis results of the emotional engine, the server displays encouraging or cautionary messages to the user. 【0322】 Furthermore, by utilizing sentiment data in the predictive model construction method, it is possible to flexibly adjust priorities. When a user's emotional state is unstable, measures such as emphasizing alerts for harvest plans or pest and disease outbreak predictions can be implemented. 【0323】 The user terminal displays this adjusted information in an easy-to-understand format, allowing the user to provide feedback or take direct action based on that information. For example, if the harvest time is postponed and the user is emotionally dissatisfied, they can request that the plan be adjusted again. 【0324】 In this way, by incorporating an emotion engine, we aim to create a more appropriate and user-friendly agricultural support system, thereby improving the efficiency and comfort of agricultural management. 【0325】 The following describes the processing flow. 【0326】 Step 1: 【0327】 The server uses its standard data collection functions to acquire weather and soil information from external APIs and IoT devices. Simultaneously, it analyzes user voice and text input through an emotion engine to collect emotion data. 【0328】 Step 2: 【0329】 The server processes weather and soil information acquired through data cleaning methods, removes noise, and classifies and organizes emotional information using an emotion engine. Emotional information is categorized into emotional categories such as joy, anger, and anxiety. 【0330】 Step 3: 【0331】 The server analyzes weather and soil information compiled using data analysis tools to evaluate factors that affect crop growth. This allows it to determine whether the current agricultural environment is appropriate. 【0332】 Step 4: 【0333】 The server incorporates user emotional information obtained from the emotion engine into decision-making support tools. For example, if a user is feeling anxious, the server infers the cause and adjusts the proposed farm work plan to one that is expected to reduce stress. 【0334】 Step 5: 【0335】 The server predicts crop yields and pest and disease outbreaks through predictive model construction. In this process, emotional information is used to prioritize the analysis results, presenting information tailored to the user's emotional state. 【0336】 Step 6: 【0337】 The terminal displays farm work plans and forecast information received from the server to the user via a user interface. The user interface provides information in a visually easy-to-understand format, depending on the user's emotional state. 【0338】 Step 7: 【0339】 Users implement farming plans based on the information provided and send feedback from their devices to the server. This feedback information is used to support decision-making and improve prediction accuracy in the next cycle. 【0340】 (Example 2) 【0341】 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". 【0342】 Conventional agricultural support systems propose farming plans based on weather and soil information, but they have the challenge of not being able to address the individual emotions and mental states of users. As a result, it is difficult to provide plans that take into account the user's emotional burden, and acceptance of the plan may be difficult. Therefore, there is a need for a new system that takes the user's emotional state into consideration in agricultural support. 【0343】 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. 【0344】 In this invention, the server includes data collection means, emotion recognition means, and emotion-responsive decision support means. This makes it possible to adjust the farm work plan according to the user's emotional state. 【0345】 "Data collection means" refers to devices or methods for collecting weather information, soil information, and user voice and text data. 【0346】 "Data cleaning means" refers to a device or method for processing acquired information, eliminating errors and noise, and preparing it for analysis. 【0347】 "Data analysis means" refers to a device or method that analyzes organized information to derive useful insights and recommendations related to agricultural work. 【0348】 A "decision support tool" is a device or method that proposes a farming plan to a user based on the results of data analysis. 【0349】 A "predictive model construction means" is a device or method for predicting crop yields and the occurrence of pests and diseases by utilizing past agricultural work data. 【0350】 "User interface means" refers to a device or method that presents generated information to a user and allows the user to provide feedback or perform operations on that information. 【0351】 "Emotion recognition means" refers to a device or method that analyzes voice and text data collected from a user to determine the user's emotional state, such as joy, anxiety, or anger. 【0352】 An "emotion-responsive decision-making support device" is a device or method that adjusts farm work plans based on emotions determined by an emotion recognition device and makes suggestions that take into account the user's mental burden. 【0353】 This invention relates to a system that provides a plan for agricultural support systems that takes into account the user's emotional state. This system is implemented as follows. 【0354】 The server acquires voice and text data from users using data collection methods, and further collects weather and soil information through external APIs and IoT devices. This allows for the collection of a wide range of agricultural data. Natural language processing and speech recognition technologies are used as emotion recognition methods to analyze the user's emotional state from the data. For example, an emotion analysis algorithm using a Python library could be implemented. 【0355】 The server adjusts the farm work plan through an emotion-response decision support system based on emotional data obtained by the emotion recognition system. The provided plan is optimized to reduce the user's emotional burden. For example, if the user is feeling stressed, suggestions will be made to reduce the workload or extend break times. 【0356】 The user terminal displays the plan sent from the server in an easy-to-understand format. Based on this information, the user can decide on actions or send feedback to the server. This feedback is used in predictive model building for future improvements. As an example, the prompt message is, "Recognize the user's emotional state from their voice input or text, and propose a farming plan based on that emotion." 【0357】 By incorporating an emotion engine in this way, it is expected that agricultural support will be more user-centric than with conventional systems, contributing to increased efficiency in agricultural management and improved user satisfaction. 【0358】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0359】 Step 1: 【0360】 The server acquires voice and text data from users through data collection means, as well as weather and soil information via IoT devices and external APIs. Inputs include user voice data, text messages, weather data, and soil data. Based on these inputs, the server integrates and cleans the data, outputting it in an analyzable format. Specifically, voice data is converted to text, and each piece of information is correctly formatted. 【0361】 Step 2: 【0362】 The server uses emotion recognition to analyze the user's emotional state from their voice and text data. The input is the text data organized in step 1. The server applies natural language processing and speech analysis techniques to detect emotions such as joy, anxiety, and anger, and outputs the results as emotion data. This quantitatively shows what emotions the user is experiencing. 【0363】 Step 3: 【0364】 The server takes emotional states into account and adjusts farm work plans through decision-making support mechanisms. Inputs include emotional data and weather and soil information. The server utilizes a generative AI model to optimize farm plans and output work schedules that fit the emotional state. For example, if a user is stressed, a plan with reduced workload will be generated. 【0365】 Step 4: 【0366】 The server sends a tailored farming plan and an emotion-based message to the user's terminal. The input is the farming plan generated in step 3. Along with the plan, the server outputs a message that includes an encouraging sentence such as, "Let's be flexible and adapt to the situation." 【0367】 Step 5: 【0368】 The user terminal visually displays received farming plans and messages. Input is data sent from the server. The terminal organizes the information and outputs it in an easy-to-understand format. Users can either work based on the displayed plan or provide feedback to the server. 【0369】 Step 6: 【0370】 The user provides feedback on the presented plan. The input is the data displayed on the device. If the user is satisfied with the plan, it proceeds; if not, it generates an output requesting readjustment. This feedback is used to improve future plans. 【0371】 (Application Example 2) 【0372】 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 as the "terminal". 【0373】 In agriculture, uniform work plans that do not consider the user's emotional state can impair work efficiency and comfort. Furthermore, there is a lack of concrete means to reduce the psychological burden on users while they work, highlighting the need for more efficient agricultural management and user-friendly support. 【0374】 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. 【0375】 In this invention, the server includes a data acquisition device, a data cleaning device, a data analysis device, a decision support device, a predictive model building device, an emotion analysis device, and a message generation device. This makes it possible to adjust the farm work plan to reflect the user's emotional state and provide optimal messages. 【0376】 A "data acquisition device" is a device for acquiring weather information and land information, and its role is to collect information in real time via external APIs and Internet of Things devices. 【0377】 A "data cleanup device" is a device that organizes acquired information into an appropriate format and performs processing to correct any missing or incorrect information. 【0378】 A "data analysis device" is a device that performs detailed analysis based on well-organized information and extracts useful insights from diverse data related to agriculture. 【0379】 A "decision support device" is a device that uses the results of data analysis to propose farm work plans to users, and has the function of calculating the optimal cultivation method and harvest time. 【0380】 A "predictive model building device" is a device that uses past agricultural data to predict future crop yields and the occurrence of pests and diseases. 【0381】 A "user interface device" is a device that visually displays analysis results and plans to the user and facilitates information sharing between the user and the system. 【0382】 An "emotion analysis device" is a device that analyzes the user's emotional state from voice and text information and adjusts the farm work plan based on the results. 【0383】 A "message generation device" is a device that generates and displays appropriate messages to the user based on the analyzed emotional state of the user. 【0384】 The system for realizing this invention involves the coordinated operation of various devices and software. A server collects weather and land information in real time from external APIs and Internet of Things devices via a data acquisition device. The collected data is processed by a data cleansing device to correct any errors or missing information. Subsequently, a data analysis device analyzes the cleaned data to extract useful information related to agriculture. 【0385】 The server uses the analyzed information to propose an optimal farming plan using a decision support device. In this process, a predictive model building device uses past agricultural data to predict future crop yields and the likelihood of pest and disease outbreaks, and incorporates this information. The user's emotional state is analyzed by an emotion analysis device using voice and text information, and the farming plan is adjusted according to the results. 【0386】 The terminal displays the adjusted plans and analysis results on a user interface device, allowing users to visually confirm this information. Furthermore, a message generation device generates and displays messages tailored to the user's emotional state. This reduces the user's psychological burden and improves the efficiency and comfort of agricultural work. 【0387】 For example, if the soil condition in a home garden is deteriorating, and the system determines that the user is in an angry state, the emotion analyzer will take this state into consideration, propose a plan to reduce the burden more than usual, and display an encouraging message. In this case, an example of a prompt message might be, "When the user is feeling frustrated with farm work, what kind of work plan and message should be proposed?" 【0388】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0389】 Step 1: 【0390】 The server acquires weather and land information in real time from external APIs and Internet of Things devices via data acquisition devices. The input is data from APIs, and the acquired raw data is stored in a database for further processing in the next step. 【0391】 Step 2: 【0392】 The server corrects erroneous or missing data from the information acquired using a data cleansing device, and supplements it as needed. A cleansing process is performed here, and the output data is stored in an improved, more accurate form. 【0393】 Step 3: 【0394】 The server uses data analysis equipment to perform statistical analysis on cleaned data and extract agricultural indicators and forecast data. It takes clean data as input and outputs analytical data useful for formulating farm work plans. 【0395】 Step 4: 【0396】 The server generates an optimal farming plan for the user based on the analysis results in the decision support system. This plan aims to improve work efficiency and provides optimized output based on the input analysis data. 【0397】 Step 5: 【0398】 The server utilizes a predictive model building system to predict crop yields and pest and disease outbreaks based on historical data. It predicts future events based on the incorporated agricultural data and provides the results to decision support systems. 【0399】 Step 6: 【0400】 The server analyzes the user's emotional state based on voice and text data entered by the user using an emotion analysis device. The input is voice and text data from the user, and the output generates emotional information such as joy or anger. 【0401】 Step 7: 【0402】 The server adjusts the farming plan based on emotional information and generates and displays appropriate messages to the user using a message generator. This process takes the user's emotional state into consideration, providing further motivational and cautionary information. The output is a user-specific, tailored plan and message. 【0403】 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. 【0404】 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. 【0405】 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. 【0406】 [Third Embodiment] 【0407】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0408】 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. 【0409】 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). 【0410】 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. 【0411】 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. 【0412】 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). 【0413】 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. 【0414】 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. 【0415】 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. 【0416】 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. 【0417】 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. 【0418】 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". 【0419】 This invention relates to an AI-powered agricultural support system aimed at optimizing agricultural work. This system includes a process in which a server collects and processes various data and proposes an appropriate agricultural work plan to the user. 【0420】 The server first collects weather and soil information in real time using external APIs and multiple IoT devices. This includes data such as temperature, precipitation, soil moisture content, and pH values. This data is processed on the server using data cleaning mechanisms to prepare it for analysis. 【0421】 Through data analysis tools, the server analyzes the optimal environmental conditions for crop growth based on the compiled information. For example, it evaluates the impact of temperature fluctuation patterns on crop growth rate. Based on these analysis results, decision support tools are activated, and the server proposes the optimal cultivation method and harvest time to the user. This leads to increased efficiency in agricultural management. 【0422】 Furthermore, the server utilizes historical growth data to build predictive models and forecast crop yields and pest and disease outbreaks. These predictions are transmitted via the server to the user interface and displayed on the user's terminal. Based on this visualized information, users can then develop farming strategies. 【0423】 As a concrete example, in a corn cultivation field, if the server incorporates weather information and predicts that this summer's rainfall will be less than average, the system could suggest to the user the introduction of a water-saving irrigation system. In this way, it is possible to formulate agricultural work plans that are adapted to future weather conditions. 【0424】 As described above, the present invention provides a means for achieving optimal farming practices by using AI technology to analyze farm data from multiple perspectives. 【0425】 The following describes the processing flow. 【0426】 Step 1: 【0427】 The server periodically retrieves weather and soil information from external APIs and IoT devices. This includes data such as temperature, precipitation, soil moisture content, and pH values. 【0428】 Step 2: 【0429】 The server processes the acquired data using data cleaning techniques. Specifically, it filters out noise and outliers and fills in missing data. This process improves data reliability and the accuracy of analysis. 【0430】 Step 3: 【0431】 The server analyzes the cleansed data using data analysis tools. Here, machine learning algorithms are applied to find correlations between weather and soil conditions and crop growth. 【0432】 Step 4: 【0433】 Based on the analysis results, the server utilizes decision support tools to calculate and propose the optimal cultivation methods and harvest times. For example, it can develop an irrigation plan based on expected weather conditions. 【0434】 Step 5: 【0435】 The server utilizes historical growth data to build predictive models and forecast future crop yields and pest / disease risks. The prediction results are regularly validated and updated to improve the model's accuracy. 【0436】 Step 6: 【0437】 The server provides users with information derived from decision support and predictive models through a user interface. The information is visualized on the user's terminal, and the user adjusts their farming plan based on it. 【0438】 (Example 1) 【0439】 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." 【0440】 In conventional agriculture, optimizing production activities by effectively utilizing environmental and geological data has been difficult, and there has been a particular need to improve the accuracy of yield forecasts and disease outbreak predictions. Furthermore, conventional systems have not adequately acquired and immediately utilized information in real time, resulting in a decrease in the efficiency of production activity planning. 【0441】 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. 【0442】 In this invention, the server includes data collection means, information purification means, and information analysis means. This enables the immediate acquisition of environmental and geological data, and analysis based on the purified data. This leads to the optimization of production activities and the development of efficient production activity plans. 【0443】 "Data acquisition means" refers to the technical elements within a system for instantly acquiring environmental and geological data through external communication protocols and networked devices. 【0444】 "Information purification methods" are means of preparing collected data to be analyzable and ensuring consistency and quality by imputing missing values ​​and removing outliers. 【0445】 "Information analysis methods" are techniques for analyzing conditions and influences according to a specific purpose, based on purified data, and for generating analysis results. 【0446】 A "decision-making support tool" is a function within a system that proposes the optimal production method and harvest time based on the analysis results. 【0447】 "Predictive model construction methods" refer to the process of creating and implementing mathematical models that utilize past production activity data to predict crop yields and the occurrence of diseases and pests. 【0448】 "User display means" refers to an interface for communicating information generated by decision-making support means and predictive model construction means to the user. 【0449】 A specific embodiment for carrying out this invention will be described. 【0450】 This system is an AI-assisted system aimed at optimizing agriculture, designed to automate the processes of data collection, analysis, and decision-making. The server acquires environmental and geological data in real time using external communication protocols and network-connected devices as data collection means. This data includes temperature, precipitation, soil moisture content, and acidity. 【0451】 The server formats the acquired data using data purification methods. Specifically, this includes processes such as imputing missing values, identifying and correcting outliers, and standardizing data formats. This purification enables smooth analysis while maintaining data quality. 【0452】 Next, the server uses information analysis tools to analyze the purified data and evaluate the impact on the production environment. For example, it predicts the impact of temperature fluctuations on the growth of a particular crop. Based on the analysis results, the server, through decision-making support tools, proposes the optimal production method and harvest time to the user. 【0453】 Furthermore, the server utilizes predictive model building techniques to forecast crop yields and pest outbreaks based on historical data. This provides crucial insights for planning future production activities. 【0454】 This information is provided to the user's terminal in a visualized form via a user display device. The user can use this information to develop specific strategies for production activities. 【0455】 As a concrete example, if a user inputs a prompt message into the AI ​​model for corn cultivation, such as "Please create an irrigation plan for the next week," the server will propose an optimal irrigation plan based on weather forecasts and soil data. This system enables users to achieve more efficient and sustainable farming practices. 【0456】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0457】 Step 1: 【0458】 The server acquires environmental and geological data using data collection methods. Specifically, it obtains temperature and precipitation from weather information APIs via external communication protocols and collects soil sensor data from networked devices. The input to this process is real-time data from APIs and sensors, and the output is raw data stored in a database on the server. 【0459】 Step 2: 【0460】 The server processes the collected data using data purification methods and prepares it for analysis. Specifically, it uses statistical methods to impute missing values ​​and removes or corrects outliers if detected. The input to this process is the raw data collected in step 1, and the output is purified and consistent data. 【0461】 Step 3: 【0462】 The server analyzes the prepared data using information analysis tools. Specifically, it uses numerical models to evaluate the effects of temperature fluctuations and soil conditions on crop growth. The input to this process is the data purified in step 2, and the output is the analysis results of the condition evaluation in the production environment. 【0463】 Step 4: 【0464】 The server, based on the analysis results through decision-making support tools, proposes production methods and harvest times to the user. Specifically, it takes prompt text into the generating AI model and responds to requests such as, "Please propose the optimal cultivation plan for these weather conditions." The input for this process is the analysis results from step 3, and the output is a specific production plan provided to the user. 【0465】 Step 5: 【0466】 The server utilizes predictive model building methods to forecast future production conditions, such as crop yields and disease outbreak rates, using historical data. Machine learning models are used for this forecast. The input consists of previously collected and analyzed data, while the output provides detailed information on future production forecasts. 【0467】 Step 6: 【0468】 The terminal presents information to the user through a user interface. Specifically, it visualizes production plans and forecast information sent from the server and displays it in a format that the user can easily understand. The input to this process is the plan and forecast information provided by the server, and the output is the visualized user interface. 【0469】 (Application Example 1) 【0470】 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." 【0471】 Agricultural production in urban environments still faces many challenges due to limited land and fluctuating weather conditions. In particular, the lack of methods for efficient agricultural management and productivity improvement in urban areas makes it difficult to develop appropriate work plans. Furthermore, flexible agricultural methods that can adapt to predictable weather changes are needed. Addressing these challenges is essential to enhancing the sustainability and efficiency of urban agriculture. 【0472】 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. 【0473】 In this invention, the server includes a data acquisition device, an information organization device, an information analysis device, a decision support device, a predictive model construction device, a display device, a user management device, and an environment optimization device. This enables real-time optimization of agricultural activities in urban industrial environments, providing optimal farm work plans and rapid implementation of environmental adaptation measures. 【0474】 A "data acquisition device" is a device that acquires environmental information and soil data in real time through an external information provision interface and sensor devices. 【0475】 An "information processing device" is a device that processes environmental information and soil data acquired from data collection devices and converts them into a format suitable for analysis. 【0476】 An "information analysis device" is a device that performs data analysis based on organized information to derive optimal growing conditions and work plans in agriculture. 【0477】 A "decision support device" is a device that proposes an agricultural work plan to the user based on the results of an information analysis device. 【0478】 A "predictive model building device" is a device that uses past agricultural work data to build predictive models for crop yields and the occurrence of biological disasters. 【0479】 A "display device" is a device that provides users with visually generated data analysis and predictive information, supporting them in developing concrete work strategies. 【0480】 A "user management device" is a device that manages agricultural activities in an urban industrial environment and supports users in efficiently managing their farm work. 【0481】 An "environmental optimization device" is a device that proposes optimized growing conditions, such as agricultural methods and heat retention measures, that can be applied within urban environments. 【0482】 This invention provides a system for optimizing agricultural activities in an urban industrial environment. The server consists of a data collection device, an information organization device, an information analysis device, a decision support device, a predictive model building device, a display device, a user management device, and an environment optimization device. 【0483】 The data collection device acquires environmental information and soil data in real time using an external information provision interface and sensor devices. This makes it possible to constantly monitor weather conditions and soil conditions. 【0484】 The information processing system organizes the acquired information into a database format and performs appropriate data cleaning processes to convert it into data suitable for analysis. Python and its libraries (Pandas, NumPy) are used here. 【0485】 The information analysis system performs analysis to derive optimal agricultural conditions based on the organized information. Machine learning libraries (Scikit-learn, TensorFlow) are used for condition evaluation and method selection. 【0486】 The decision support device proposes a farming plan to the user based on the analysis results. Therefore, it is possible to use the generated AI model to present specific actions that the user should take. 【0487】 The predictive model building device utilizes historical agricultural data to construct models that predict crop yields and the occurrence of biological disasters, and this information supports the user's decision-making. 【0488】 The display device visualizes the calculation results and presents the information in a way that the user can intuitively understand. An interface developed using React Native makes this possible. 【0489】 The user management device manages the status of agricultural activities in urban areas and supports efficiency by providing information according to user requests. 【0490】 The environmental optimization device proposes the optimal conditions necessary for agricultural work in urban environments and provides corresponding countermeasures. This device automatically suggests appropriate cultivation methods and heat retention measures. 【0491】 For example, a server collects weather data from a cornfield and, based on the predicted precipitation pattern for the month, suggests optimized water management to the user. Furthermore, the following prompt messages can be used to clarify intentions through AI. 【0492】 Example of a prompt: 【0493】 "Based on this year's spring temperature forecast for Tokyo, please tell me what measures I can take to optimize plant growth." 【0494】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0495】 Step 1: 【0496】 The server acquires environmental and soil data using data acquisition devices. By collecting data in real time from external information provision interfaces and sensor devices, it obtains the latest weather and soil condition data as input. This data forms the basis for analysis in subsequent steps. 【0497】 Step 2: 【0498】 The server performs data cleaning on the data acquired by the information processing device. Using the Python Pandas library, it handles missing values, detects and removes outliers, and prepares the data for analysis. The output consists of cleaned environmental information and soil data. 【0499】 Step 3: 【0500】 The server evaluates environmental conditions based on data prepared using an information analysis device. It uses Scikit-learn to model the effects of temperature, precipitation, and other factors, and quantifies their impact on agricultural work. The input is cleaned data, and the output is the degree of impact as a result of the analysis. 【0501】 Step 4: 【0502】 The server generates a specific farm work plan based on the analysis results from the decision support device. Using the generated AI model, it proposes the optimal work plan and improvement measures based on the prompt text. The user is provided with instructions regarding work methods, procedures, timing, etc. 【0503】 Step 5: 【0504】 The server uses a predictive model building system to forecast future agricultural conditions from historical data. Using TensorFlow, it predicts yields and the likelihood of disease outbreaks, generating output that can serve as a reference for future agricultural activities. The input is historical data, and the output is the prediction result. 【0505】 Step 6: 【0506】 The server visualizes analysis and prediction results through a display device. Using React Native, it provides users with an interactive dashboard to support intuitive understanding. Users can immediately see the suggested work strategy. 【0507】 Step 7: 【0508】 Users track the progress of urban farming operations using a user management device and send requests to the server. This allows for the management of further operations and data collection progress, supporting efficient work execution. Inputs are user operation information, and outputs are feedback on the execution status. 【0509】 Step 8: 【0510】 The server uses an environmental optimization device to propose agricultural measures tailored to the urban environment. Based on the automatically generated proposals, appropriate cultivation methods and heat retention measures are presented, and the user is assisted in their implementation. This enables agricultural work to be carried out under optimized environmental conditions. 【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 relates to a system that incorporates an emotion engine into an agricultural support system, recognizes the user's emotional state, and provides suggestions for agricultural work plans and decision-making support accordingly. 【0513】 In addition to regular data collection, the server uses an emotion engine to evaluate the user's emotional state. This emotion engine analyzes the voice and text information entered by the user and can recognize emotions such as joy, surprise, anger, and anxiety. The data from the emotion engine is used in decision support systems and user interface systems. 【0514】 Specifically, the server adjusts the suggested decision-making support measures based on emotional information. For example, if a user is feeling stressed, the server can take that information into consideration and suggest a plan to reduce the burden of farm work. Furthermore, based on the analysis results of the emotional engine, the server displays encouraging or cautionary messages to the user. 【0515】 Furthermore, by utilizing sentiment data in the predictive model construction method, it is possible to flexibly adjust priorities. When a user's emotional state is unstable, measures such as emphasizing alerts for harvest plans or pest and disease outbreak predictions can be implemented. 【0516】 The user terminal displays this adjusted information in an easy-to-understand format, allowing the user to provide feedback or take direct action based on that information. For example, if the harvest time is postponed and the user is emotionally dissatisfied, they can request that the plan be adjusted again. 【0517】 In this way, by incorporating an emotion engine, we aim to create a more appropriate and user-friendly agricultural support system, thereby improving the efficiency and comfort of agricultural management. 【0518】 The following describes the processing flow. 【0519】 Step 1: 【0520】 The server uses its standard data collection functions to acquire weather and soil information from external APIs and IoT devices. Simultaneously, it analyzes user voice and text input through an emotion engine to collect emotion data. 【0521】 Step 2: 【0522】 The server processes weather and soil information acquired through data cleaning methods, removes noise, and classifies and organizes emotional information using an emotion engine. Emotional information is categorized into emotional categories such as joy, anger, and anxiety. 【0523】 Step 3: 【0524】 The server analyzes weather and soil information compiled using data analysis tools to evaluate factors that affect crop growth. This allows it to determine whether the current agricultural environment is appropriate. 【0525】 Step 4: 【0526】 The server incorporates user emotional information obtained from the emotion engine into decision-making support tools. For example, if a user is feeling anxious, the server will infer the cause and adjust the proposed farm work plan to one that is expected to reduce stress. 【0527】 Step 5: 【0528】 The server predicts crop yields and pest and disease outbreaks through predictive model construction. In this process, emotional information is used to prioritize the analysis results, presenting information tailored to the user's emotional state. 【0529】 Step 6: 【0530】 The terminal displays farm work plans and forecast information received from the server to the user via a user interface. The user interface provides information in a visually easy-to-understand format, depending on the user's emotional state. 【0531】 Step 7: 【0532】 Users implement farming plans based on the information provided and send feedback from their terminals to the server. This feedback information is used to support decision-making and improve prediction accuracy in the next cycle. 【0533】 (Example 2) 【0534】 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." 【0535】 Conventional agricultural support systems propose farming plans based on weather and soil information, but they have the challenge of not being able to address the individual emotions and mental states of users. As a result, it is difficult to provide plans that take into account the user's emotional burden, and acceptance of the plan may be difficult. Therefore, there is a need for a new system that takes the user's emotional state into consideration in agricultural support. 【0536】 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. 【0537】 In this invention, the server includes data collection means, emotion recognition means, and emotion-responsive decision support means. This makes it possible to adjust the farm work plan according to the user's emotional state. 【0538】 "Data collection means" refers to devices or methods for collecting weather information, soil information, and user voice and text data. 【0539】 "Data cleaning means" refers to a device or method for processing acquired information, eliminating errors and noise, and preparing it for analysis. 【0540】 "Data analysis means" refers to a device or method that analyzes organized information to derive useful insights and recommendations related to agricultural work. 【0541】 A "decision support tool" is a device or method that proposes a farming plan to a user based on the results of data analysis. 【0542】 A "predictive model construction means" is a device or method for predicting crop yields and the occurrence of pests and diseases by utilizing past agricultural work data. 【0543】 "User interface means" refers to a device or method that presents generated information to a user and allows the user to provide feedback or perform operations on that information. 【0544】 "Emotion recognition means" refers to a device or method that analyzes voice and text data collected from a user to determine the user's emotional state, such as joy, anxiety, or anger. 【0545】 An "emotion-responsive decision-making support device" is a device or method that adjusts farm work plans based on emotions determined by an emotion recognition device and makes suggestions that take into account the user's mental burden. 【0546】 This invention relates to a system that provides a plan for agricultural support systems that takes into account the user's emotional state. This system is implemented as follows. 【0547】 The server acquires voice and text data from users using data collection methods, and further collects weather and soil information through external APIs and IoT devices. This allows for the collection of a wide range of agricultural data. Natural language processing and speech recognition technologies are used as emotion recognition methods to analyze the user's emotional state from the data. For example, an emotion analysis algorithm using a Python library could be implemented. 【0548】 The server adjusts the farm work plan through an emotion-response decision support system based on emotional data obtained by the emotion recognition system. The provided plan is optimized to reduce the user's emotional burden. For example, if the user is feeling stressed, suggestions will be made to reduce the workload or extend break times. 【0549】 The user terminal displays the plan sent from the server in an easy-to-understand format. Based on this information, the user can decide on actions or send feedback to the server. This feedback is used in predictive model building for future improvements. As an example, the prompt message is, "Recognize the user's emotional state from their voice input or text, and propose a farming plan based on that emotion." 【0550】 By incorporating an emotion engine in this way, it is expected that agricultural support will be more user-centric than with conventional systems, contributing to increased efficiency in agricultural management and improved user satisfaction. 【0551】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0552】 Step 1: 【0553】 The server acquires voice and text data from users through data collection means, as well as weather and soil information via IoT devices and external APIs. Inputs include user voice data, text messages, weather data, and soil data. Based on these inputs, the server integrates and cleans the data, outputting it in an analyzable format. Specifically, voice data is converted to text, and each piece of information is correctly formatted. 【0554】 Step 2: 【0555】 The server uses emotion recognition to analyze the user's emotional state from their voice and text data. The input is the text data organized in step 1. The server applies natural language processing and speech analysis techniques to detect emotions such as joy, anxiety, and anger, and outputs the results as emotion data. This quantitatively shows what emotions the user is experiencing. 【0556】 Step 3: 【0557】 The server takes emotional states into account and adjusts farm work plans through decision-making support mechanisms. Inputs include emotional data and weather and soil information. The server utilizes a generative AI model to optimize farm plans and output work schedules that fit the emotional state. For example, if a user is stressed, a plan with reduced workload will be generated. 【0558】 Step 4: 【0559】 The server sends a tailored farming plan and an emotion-based message to the user's terminal. The input is the farming plan generated in step 3. Along with the plan, the server outputs a message that includes an encouraging sentence such as, "Let's be flexible and adapt to the situation." 【0560】 Step 5: 【0561】 The user terminal visually displays received farming plans and messages. Input is data sent from the server. The terminal organizes the information and outputs it in an easy-to-understand format. Users can either work based on the displayed plan or provide feedback to the server. 【0562】 Step 6: 【0563】 The user provides feedback on the presented plan. The input is the data displayed on the device. If the user is satisfied with the plan, it proceeds; if not, it generates an output requesting readjustment. This feedback is used to improve future plans. 【0564】 (Application Example 2) 【0565】 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." 【0566】 In agriculture, uniform work plans that do not consider the user's emotional state can impair work efficiency and comfort. Furthermore, there is a lack of concrete means to reduce the psychological burden on users while they work, highlighting the need for more efficient agricultural management and user-friendly support. 【0567】 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. 【0568】 In this invention, the server includes a data acquisition device, a data cleaning device, a data analysis device, a decision support device, a predictive model building device, an emotion analysis device, and a message generation device. This makes it possible to adjust the farm work plan to reflect the user's emotional state and provide optimal messages. 【0569】 A "data acquisition device" is a device for acquiring weather information and land information, and its role is to collect information in real time via external APIs and Internet of Things devices. 【0570】 A "data cleanup device" is a device that organizes acquired information into an appropriate format and performs processing to correct any missing or incorrect information. 【0571】 A "data analysis device" is a device that performs detailed analysis based on well-organized information and extracts useful insights from diverse data related to agriculture. 【0572】 A "decision support device" is a device that uses the results of data analysis to propose farm work plans to users, and has the function of calculating the optimal cultivation method and harvest time. 【0573】 A "predictive model building device" is a device that uses past agricultural data to predict future crop yields and the occurrence of pests and diseases. 【0574】 A "user interface device" is a device that visually displays analysis results and plans to the user and facilitates information sharing between the user and the system. 【0575】 An "emotion analysis device" is a device that analyzes the user's emotional state from voice and text information and adjusts the farm work plan based on the results. 【0576】 A "message generation device" is a device that generates and displays appropriate messages to the user based on the analyzed emotional state of the user. 【0577】 The system for realizing this invention involves the coordinated operation of various devices and software. A server collects weather and land information in real time from external APIs and Internet of Things devices via a data acquisition device. The collected data is processed by a data cleansing device to correct any errors or missing information. Subsequently, a data analysis device analyzes the cleaned data to extract useful information related to agriculture. 【0578】 The server uses the analyzed information to propose an optimal farming plan using a decision support device. In this process, a predictive model building device uses past agricultural data to predict future crop yields and the likelihood of pest and disease outbreaks, and incorporates this information. The user's emotional state is analyzed by an emotion analysis device using voice and text information, and the farming plan is adjusted according to the results. 【0579】 The terminal displays the adjusted plans and analysis results on a user interface device, allowing users to visually confirm this information. Furthermore, a message generation device generates and displays messages tailored to the user's emotional state. This reduces the user's psychological burden and improves the efficiency and comfort of agricultural work. 【0580】 For example, if the soil condition in a home garden is deteriorating, and the system determines that the user is in an angry state, the emotion analyzer will take this state into consideration, propose a plan to reduce the burden more than usual, and display an encouraging message. In this case, an example of a prompt message might be, "When the user is feeling frustrated with farm work, what kind of work plan and message should be proposed?" 【0581】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0582】 Step 1: 【0583】 The server acquires weather and land information in real time from external APIs and Internet of Things devices via data acquisition devices. The input is data from APIs, and the acquired raw data is stored in a database for further processing in the next step. 【0584】 Step 2: 【0585】 The server corrects erroneous or missing data from the information acquired using a data cleansing device, and supplements it as needed. A cleansing process is performed here, and the output data is stored in an improved, more accurate form. 【0586】 Step 3: 【0587】 The server uses data analysis equipment to perform statistical analysis on cleaned data and extract agricultural indicators and forecast data. It takes clean data as input and outputs analytical data useful for formulating farm work plans. 【0588】 Step 4: 【0589】 The server generates an optimal farming plan for the user based on the analysis results in the decision support system. This plan aims to improve work efficiency and provides optimized output based on the input analysis data. 【0590】 Step 5: 【0591】 The server utilizes a predictive model building system to predict crop yields and pest and disease outbreaks based on historical data. It predicts future events based on the incorporated agricultural data and provides the results to decision support systems. 【0592】 Step 6: 【0593】 The server analyzes the user's emotional state based on voice and text data entered by the user using an emotion analysis device. The input is voice and text data from the user, and the output generates emotional information such as joy or anger. 【0594】 Step 7: 【0595】 The server adjusts the farming plan based on emotional information and generates and displays appropriate messages to the user using a message generator. This process takes the user's emotional state into consideration, providing further motivational and cautionary information. The output is a user-specific, tailored plan and message. 【0596】 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. 【0597】 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. 【0598】 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. 【0599】 [Fourth Embodiment] 【0600】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0601】 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. 【0602】 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). 【0603】 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. 【0604】 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. 【0605】 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). 【0606】 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. 【0607】 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. 【0608】 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. 【0609】 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. 【0610】 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. 【0611】 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. 【0612】 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". 【0613】 This invention relates to an AI-powered agricultural support system aimed at optimizing agricultural work. This system includes a process in which a server collects and processes various data and proposes an appropriate agricultural work plan to the user. 【0614】 The server first collects weather and soil information in real time using external APIs and multiple IoT devices. This includes data such as temperature, precipitation, soil moisture content, and pH values. This data is processed on the server using data cleaning mechanisms to prepare it for analysis. 【0615】 Through data analysis tools, the server analyzes the optimal environmental conditions for crop growth based on the compiled information. For example, it evaluates the impact of temperature fluctuation patterns on crop growth rate. Based on these analysis results, decision support tools are activated, and the server proposes the optimal cultivation method and harvest time to the user. This leads to increased efficiency in agricultural management. 【0616】 Furthermore, the server utilizes historical growth data to build predictive models and forecast crop yields and pest and disease outbreaks. These predictions are transmitted via the server to the user interface and displayed on the user's terminal. Based on this visualized information, users can then develop farming strategies. 【0617】 As a concrete example, in a corn cultivation field, if the server incorporates weather information and predicts that this summer's rainfall will be less than average, the system could suggest to the user the introduction of a water-saving irrigation system. In this way, it is possible to formulate agricultural work plans that are adapted to future weather conditions. 【0618】 As described above, the present invention provides a means for achieving optimal farming practices by using AI technology to analyze farm data from multiple perspectives. 【0619】 The following describes the processing flow. 【0620】 Step 1: 【0621】 The server periodically retrieves weather and soil information from external APIs and IoT devices. This includes data such as temperature, precipitation, soil moisture content, and pH values. 【0622】 Step 2: 【0623】 The server processes the acquired data using data cleaning techniques. Specifically, it filters out noise and outliers and fills in missing data. This process improves data reliability and the accuracy of analysis. 【0624】 Step 3: 【0625】 The server analyzes the cleansed data using data analysis tools. Here, machine learning algorithms are applied to find correlations between weather and soil conditions and crop growth. 【0626】 Step 4: 【0627】 Based on the analysis results, the server utilizes decision support tools to calculate and propose the optimal cultivation methods and harvest times. For example, it can develop an irrigation plan based on expected weather conditions. 【0628】 Step 5: 【0629】 The server utilizes historical growth data to build predictive models and forecast future crop yields and pest / disease risks. The prediction results are regularly validated and updated to improve the model's accuracy. 【0630】 Step 6: 【0631】 The server provides users with information derived from decision support and predictive models through a user interface. The information is visualized on the user's terminal, and the user adjusts their farming plan based on it. 【0632】 (Example 1) 【0633】 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". 【0634】 In conventional agriculture, optimizing production activities by effectively utilizing environmental and geological data has been difficult, and there has been a particular need to improve the accuracy of yield forecasts and disease outbreak predictions. Furthermore, conventional systems have not adequately acquired and immediately utilized information in real time, resulting in a decrease in the efficiency of production activity planning. 【0635】 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. 【0636】 In this invention, the server includes data collection means, information purification means, and information analysis means. This enables the immediate acquisition of environmental and geological data, and analysis based on the purified data. This leads to the optimization of production activities and the development of efficient production activity plans. 【0637】 "Data acquisition means" refers to the technical elements within a system for instantly acquiring environmental and geological data through external communication protocols and networked devices. 【0638】 "Information purification methods" are means of preparing collected data to be analyzable and ensuring consistency and quality by imputing missing values ​​and removing outliers. 【0639】 "Information analysis methods" are techniques for analyzing conditions and influences according to a specific purpose, based on purified data, and for generating analysis results. 【0640】 A "decision-making support tool" is a function within a system that proposes the optimal production method and harvest time based on the analysis results. 【0641】 "Predictive model construction methods" refer to the process of creating and implementing mathematical models that utilize past production activity data to predict crop yields and the occurrence of diseases and pests. 【0642】 "User display means" refers to an interface for communicating information generated by decision-making support means and predictive model construction means to the user. 【0643】 A specific embodiment for carrying out this invention will be described. 【0644】 This system is an AI-assisted system aimed at optimizing agriculture, designed to automate the processes of data collection, analysis, and decision-making. The server acquires environmental and geological data in real time using external communication protocols and network-connected devices as data collection means. This data includes temperature, precipitation, soil moisture content, and acidity. 【0645】 The server formats the acquired data using data purification methods. Specifically, this includes processes such as imputing missing values, identifying and correcting outliers, and standardizing data formats. This purification enables smooth analysis while maintaining data quality. 【0646】 Next, the server uses information analysis tools to analyze the purified data and evaluate the impact on the production environment. For example, it predicts the impact of temperature fluctuations on the growth of a particular crop. Based on the analysis results, the server, through decision-making support tools, proposes the optimal production method and harvest time to the user. 【0647】 Furthermore, the server utilizes predictive model building techniques to forecast crop yields and pest outbreaks based on historical data. This provides crucial insights for planning future production activities. 【0648】 This information is provided to the user's terminal in a visualized form via a user display device. The user can use this information to develop specific strategies for production activities. 【0649】 As a concrete example, if a user inputs a prompt message into the AI ​​model for corn cultivation, such as "Please create an irrigation plan for the next week," the server will propose an optimal irrigation plan based on weather forecasts and soil data. This system enables users to achieve more efficient and sustainable farming practices. 【0650】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0651】 Step 1: 【0652】 The server acquires environmental and geological data using data collection methods. Specifically, it obtains temperature and precipitation from weather information APIs via external communication protocols and collects soil sensor data from networked devices. The input to this process is real-time data from APIs and sensors, and the output is raw data stored in a database on the server. 【0653】 Step 2: 【0654】 The server processes the collected data using data purification methods and prepares it for analysis. Specifically, it uses statistical methods to impute missing values ​​and removes or corrects outliers if detected. The input to this process is the raw data collected in step 1, and the output is purified and consistent data. 【0655】 Step 3: 【0656】 The server analyzes the prepared data using information analysis tools. Specifically, it uses numerical models to evaluate the effects of temperature fluctuations and soil conditions on crop growth. The input to this process is the data purified in step 2, and the output is the analysis results of the condition evaluation in the production environment. 【0657】 Step 4: 【0658】 The server, based on the analysis results through decision-making support tools, proposes production methods and harvest times to the user. Specifically, it takes prompt text into the generating AI model and responds to requests such as, "Please propose the optimal cultivation plan for these weather conditions." The input for this process is the analysis results from step 3, and the output is a specific production plan provided to the user. 【0659】 Step 5: 【0660】 The server utilizes predictive model building methods to forecast future production conditions, such as crop yields and disease outbreak rates, using historical data. Machine learning models are used for this forecast. The input consists of previously collected and analyzed data, while the output provides detailed information on future production forecasts. 【0661】 Step 6: 【0662】 The terminal presents information to the user through a user interface. Specifically, it visualizes production plans and forecast information sent from the server and displays it in a format that the user can easily understand. The input to this process is the plan and forecast information provided by the server, and the output is the visualized user interface. 【0663】 (Application Example 1) 【0664】 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". 【0665】 Agricultural production in urban environments still faces many challenges due to limited land and fluctuating weather conditions. In particular, the lack of methods for efficient agricultural management and productivity improvement in urban areas makes it difficult to develop appropriate work plans. Furthermore, flexible agricultural methods that can adapt to predictable weather changes are needed. Addressing these challenges is essential to enhancing the sustainability and efficiency of urban agriculture. 【0666】 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. 【0667】 In this invention, the server includes a data acquisition device, an information organization device, an information analysis device, a decision support device, a predictive model construction device, a display device, a user management device, and an environment optimization device. This enables real-time optimization of agricultural activities in urban industrial environments, providing optimal farm work plans and rapid implementation of environmental adaptation measures. 【0668】 A "data acquisition device" is a device that acquires environmental information and soil data in real time through an external information provision interface and sensor devices. 【0669】 An "information processing device" is a device that processes environmental information and soil data acquired from data collection devices and converts them into a format suitable for analysis. 【0670】 An "information analysis device" is a device that performs data analysis based on organized information to derive optimal growing conditions and work plans in agriculture. 【0671】 A "decision support device" is a device that proposes an agricultural work plan to the user based on the results of an information analysis device. 【0672】 A "predictive model building device" is a device that uses past agricultural work data to build predictive models for crop yields and the occurrence of biological disasters. 【0673】 A "display device" is a device that provides users with visually generated data analysis and predictive information, supporting them in developing concrete work strategies. 【0674】 A "user management device" is a device that manages agricultural activities in an urban industrial environment and supports users in efficiently managing their farm work. 【0675】 An "environmental optimization device" is a device that proposes optimized growing conditions, such as agricultural methods and heat retention measures, that can be applied within urban environments. 【0676】 This invention provides a system for optimizing agricultural activities in an urban industrial environment. The server consists of a data collection device, an information organization device, an information analysis device, a decision support device, a predictive model building device, a display device, a user management device, and an environment optimization device. 【0677】 The data collection device acquires environmental information and soil data in real time using an external information provision interface and sensor devices. This makes it possible to constantly monitor weather conditions and soil conditions. 【0678】 The information processing system organizes the acquired information into a database format and performs appropriate data cleaning processes to convert it into data suitable for analysis. Python and its libraries (Pandas, NumPy) are used here. 【0679】 The information analysis system performs analysis to derive optimal agricultural conditions based on the organized information. Machine learning libraries (Scikit-learn, TensorFlow) are used for condition evaluation and method selection. 【0680】 The decision support device proposes a farming plan to the user based on the analysis results. Therefore, it is possible to use the generated AI model to present specific actions that the user should take. 【0681】 The predictive model building device utilizes historical agricultural data to construct models that predict crop yields and the occurrence of biological disasters, and this information supports the user's decision-making. 【0682】 The display device visualizes the calculation results and presents the information in a way that the user can intuitively understand. An interface developed using React Native makes this possible. 【0683】 The user management device manages the status of agricultural activities in urban areas and supports efficiency by providing information according to user requests. 【0684】 The environmental optimization device proposes the optimal conditions necessary for agricultural work in urban environments and provides corresponding countermeasures. This device automatically suggests appropriate cultivation methods and heat retention measures. 【0685】 For example, a server collects weather data from a cornfield and, based on the predicted precipitation pattern for the month, suggests optimized water management to the user. Furthermore, the following prompt messages can be used to clarify intentions through AI. 【0686】 Example of a prompt: 【0687】 "Based on this year's spring temperature forecast for Tokyo, please tell me what measures I can take to optimize plant growth." 【0688】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0689】 Step 1: 【0690】 The server acquires environmental and soil data using data acquisition devices. By collecting data in real time from external information provision interfaces and sensor devices, it obtains the latest weather and soil condition data as input. This data forms the basis for analysis in subsequent steps. 【0691】 Step 2: 【0692】 The server performs data cleaning on the data acquired by the information processing device. Using the Python Pandas library, it handles missing values, detects and removes outliers, and prepares the data for analysis. The output consists of cleaned environmental information and soil data. 【0693】 Step 3: 【0694】 The server evaluates environmental conditions based on data prepared using an information analysis device. It uses Scikit-learn to model the effects of temperature, precipitation, and other factors, and quantifies their impact on agricultural work. The input is cleaned data, and the output is the degree of impact as a result of the analysis. 【0695】 Step 4: 【0696】 The server generates a specific farm work plan based on the analysis results from the decision support device. Using the generated AI model, it proposes the optimal work plan and improvement measures based on the prompt text. The user is provided with instructions regarding work methods, procedures, timing, etc. 【0697】 Step 5: 【0698】 The server uses a predictive model building system to forecast future agricultural conditions from historical data. Using TensorFlow, it predicts yields and the likelihood of disease outbreaks, generating output that can serve as a reference for future agricultural activities. The input is historical data, and the output is the prediction result. 【0699】 Step 6: 【0700】 The server visualizes analysis and prediction results through a display device. Using React Native, it provides users with an interactive dashboard to support intuitive understanding. Users can immediately see the suggested work strategy. 【0701】 Step 7: 【0702】 Users track the progress of urban farming operations using a user management device and send requests to the server. This allows for the management of further operations and data collection progress, supporting efficient work execution. Inputs are user operation information, and outputs are feedback on the execution status. 【0703】 Step 8: 【0704】 The server uses an environmental optimization device to propose agricultural measures tailored to the urban environment. Based on the automatically generated proposals, appropriate cultivation methods and heat retention measures are presented, and the user is assisted in their implementation. This enables agricultural work to be carried out under optimized environmental conditions. 【0705】 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. 【0706】 This invention relates to a system that incorporates an emotion engine into an agricultural support system, recognizes the user's emotional state, and provides suggestions for agricultural work plans and decision-making support accordingly. 【0707】 In addition to regular data collection, the server uses an emotion engine to evaluate the user's emotional state. This emotion engine analyzes the voice and text information entered by the user and can recognize emotions such as joy, surprise, anger, and anxiety. The data from the emotion engine is used in decision support systems and user interface systems. 【0708】 Specifically, the server adjusts the suggested decision-making support measures based on emotional information. For example, if a user is feeling stressed, the server can take that information into consideration and suggest a plan to reduce the burden of farm work. Furthermore, based on the analysis results of the emotional engine, the server displays encouraging or cautionary messages to the user. 【0709】 Furthermore, by utilizing sentiment data in the predictive model construction method, it is possible to flexibly adjust priorities. When a user's emotional state is unstable, measures such as emphasizing alerts for harvest plans or pest and disease outbreak predictions can be implemented. 【0710】 The user terminal displays this adjusted information in an easy-to-understand format, allowing the user to provide feedback or take direct action based on that information. For example, if the harvest time is postponed and the user is emotionally dissatisfied, they can request that the plan be adjusted again. 【0711】 In this way, by incorporating an emotion engine, we aim to create a more appropriate and user-friendly agricultural support system, thereby improving the efficiency and comfort of agricultural management. 【0712】 The following describes the processing flow. 【0713】 Step 1: 【0714】 The server uses its standard data collection functions to acquire weather and soil information from external APIs and IoT devices. Simultaneously, it analyzes user voice and text input through an emotion engine to collect emotion data. 【0715】 Step 2: 【0716】 The server processes weather and soil information acquired through data cleaning methods, removes noise, and classifies and organizes emotional information using an emotion engine. Emotional information is categorized into emotional categories such as joy, anger, and anxiety. 【0717】 Step 3: 【0718】 The server analyzes weather and soil information compiled using data analysis tools to evaluate factors that affect crop growth. This allows it to determine whether the current agricultural environment is appropriate. 【0719】 Step 4: 【0720】 The server incorporates user emotional information obtained from the emotion engine into decision-making support tools. For example, if a user is feeling anxious, the server will infer the cause and adjust the proposed farm work plan to one that is expected to reduce stress. 【0721】 Step 5: 【0722】 The server predicts crop yields and pest and disease outbreaks through predictive model construction. In this process, emotional information is used to prioritize the analysis results, presenting information tailored to the user's emotional state. 【0723】 Step 6: 【0724】 The terminal displays farm work plans and forecast information received from the server to the user via a user interface. The user interface provides information in a visually easy-to-understand format, depending on the user's emotional state. 【0725】 Step 7: 【0726】 Users implement farming plans based on the information provided and send feedback from their terminals to the server. This feedback information is used to support decision-making and improve prediction accuracy in the next cycle. 【0727】 (Example 2) 【0728】 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". 【0729】 Conventional agricultural support systems propose farming plans based on weather and soil information, but they have the challenge of not being able to address the individual emotions and mental states of users. As a result, it is difficult to provide plans that take into account the user's emotional burden, and acceptance of the plan may be difficult. Therefore, there is a need for a new system that takes the user's emotional state into consideration in agricultural support. 【0730】 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. 【0731】 In this invention, the server includes data collection means, emotion recognition means, and emotion-responsive decision support means. This makes it possible to adjust the farm work plan according to the user's emotional state. 【0732】 "Data collection means" refers to devices or methods for collecting weather information, soil information, and user voice and text data. 【0733】 "Data cleaning means" refers to a device or method for processing acquired information, eliminating errors and noise, and preparing it for analysis. 【0734】 "Data analysis means" refers to a device or method that analyzes organized information to derive useful insights and recommendations related to agricultural work. 【0735】 A "decision support tool" is a device or method that proposes a farming plan to a user based on the results of data analysis. 【0736】 A "predictive model construction means" is a device or method for predicting crop yields and the occurrence of pests and diseases by utilizing past agricultural work data. 【0737】 "User interface means" refers to a device or method that presents generated information to a user and allows the user to provide feedback or perform operations on that information. 【0738】 "Emotion recognition means" refers to a device or method that analyzes voice and text data collected from a user to determine the user's emotional state, such as joy, anxiety, or anger. 【0739】 An "emotion-responsive decision-making support device" is a device or method that adjusts farm work plans based on emotions determined by an emotion recognition device and makes suggestions that take into account the user's mental burden. 【0740】 This invention relates to a system that provides a plan for agricultural support systems that takes into account the user's emotional state. This system is implemented as follows. 【0741】 The server acquires voice and text data from users using data collection methods, and further collects weather and soil information through external APIs and IoT devices. This allows for the collection of a wide range of agricultural data. Natural language processing and speech recognition technologies are used as emotion recognition methods to analyze the user's emotional state from the data. For example, an emotion analysis algorithm using a Python library could be implemented. 【0742】 The server adjusts the farm work plan through an emotion-response decision support system based on emotional data obtained by the emotion recognition system. The provided plan is optimized to reduce the user's emotional burden. For example, if the user is feeling stressed, suggestions will be made to reduce the workload or extend break times. 【0743】 The user terminal displays the plan sent from the server in an easy-to-understand format. Based on this information, the user can decide on actions or send feedback to the server. This feedback is used in predictive model building for future improvements. As an example, the prompt message is, "Recognize the user's emotional state from their voice input or text, and propose a farming plan based on that emotion." 【0744】 By incorporating an emotion engine in this way, it is expected that agricultural support will be more user-centric than with conventional systems, contributing to increased efficiency in agricultural management and improved user satisfaction. 【0745】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0746】 Step 1: 【0747】 The server acquires voice and text data from users through data collection means, as well as weather and soil information via IoT devices and external APIs. Inputs include user voice data, text messages, weather data, and soil data. Based on these inputs, the server integrates and cleans the data, outputting it in an analyzable format. Specifically, voice data is converted to text, and each piece of information is correctly formatted. 【0748】 Step 2: 【0749】 The server uses emotion recognition to analyze the user's emotional state from their voice and text data. The input is the text data organized in step 1. The server applies natural language processing and speech analysis techniques to detect emotions such as joy, anxiety, and anger, and outputs the results as emotion data. This quantitatively shows what emotions the user is experiencing. 【0750】 Step 3: 【0751】 The server takes emotional states into account and adjusts farm work plans through decision-making support mechanisms. Inputs include emotional data and weather and soil information. The server utilizes a generative AI model to optimize farm plans and output work schedules that fit the emotional state. For example, if a user is stressed, a plan with reduced workload will be generated. 【0752】 Step 4: 【0753】 The server sends a tailored farming plan and an emotion-based message to the user's terminal. The input is the farming plan generated in step 3. Along with the plan, the server outputs a message that includes an encouraging sentence such as, "Let's be flexible and adapt to the situation." 【0754】 Step 5: 【0755】 The user terminal visually displays received farming plans and messages. Input is data sent from the server. The terminal organizes the information and outputs it in an easy-to-understand format. Users can either work based on the displayed plan or provide feedback to the server. 【0756】 Step 6: 【0757】 The user provides feedback on the presented plan. The input is the data displayed on the device. If the user is satisfied with the plan, it proceeds; if not, it generates an output requesting readjustment. This feedback is used to improve future plans. 【0758】 (Application Example 2) 【0759】 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". 【0760】 In agriculture, uniform work plans that do not consider the user's emotional state can impair work efficiency and comfort. Furthermore, there is a lack of concrete means to reduce the psychological burden on users while they work, highlighting the need for more efficient agricultural management and user-friendly support. 【0761】 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. 【0762】 In this invention, the server includes a data acquisition device, a data cleaning device, a data analysis device, a decision support device, a predictive model building device, an emotion analysis device, and a message generation device. This makes it possible to adjust the farm work plan to reflect the user's emotional state and provide optimal messages. 【0763】 A "data acquisition device" is a device for acquiring weather information and land information, and its role is to collect information in real time via external APIs and Internet of Things devices. 【0764】 A "data cleanup device" is a device that organizes acquired information into an appropriate format and performs processing to correct any missing or incorrect information. 【0765】 A "data analysis device" is a device that performs detailed analysis based on well-organized information and extracts useful insights from diverse data related to agriculture. 【0766】 A "decision support device" is a device that uses the results of data analysis to propose farm work plans to users, and has the function of calculating the optimal cultivation method and harvest time. 【0767】 A "predictive model building device" is a device that uses past agricultural data to predict future crop yields and the occurrence of pests and diseases. 【0768】 A "user interface device" is a device that visually displays analysis results and plans to the user and facilitates information sharing between the user and the system. 【0769】 An "emotion analysis device" is a device that analyzes the user's emotional state from voice and text information and adjusts the farm work plan based on the results. 【0770】 A "message generation device" is a device that generates and displays appropriate messages to the user based on the analyzed emotional state of the user. 【0771】 The system for realizing this invention involves the coordinated operation of various devices and software. A server collects weather and land information in real time from external APIs and Internet of Things devices via a data acquisition device. The collected data is processed by a data cleansing device to correct any errors or missing information. Subsequently, a data analysis device analyzes the cleaned data to extract useful information related to agriculture. 【0772】 The server uses the analyzed information to propose an optimal farming plan using a decision support device. In this process, a predictive model building device uses past agricultural data to predict future crop yields and the likelihood of pest and disease outbreaks, and incorporates this information. The user's emotional state is analyzed by an emotion analysis device using voice and text information, and the farming plan is adjusted according to the results. 【0773】 The terminal displays the adjusted plans and analysis results on a user interface device, allowing users to visually confirm this information. Furthermore, a message generation device generates and displays messages tailored to the user's emotional state. This reduces the user's psychological burden and improves the efficiency and comfort of agricultural work. 【0774】 For example, if the soil condition in a home garden is deteriorating, and the system determines that the user is in an angry state, the emotion analyzer will take this state into consideration, propose a plan to reduce the burden more than usual, and display an encouraging message. In this case, an example of a prompt message might be, "When the user is feeling frustrated with farm work, what kind of work plan and message should be proposed?" 【0775】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0776】 Step 1: 【0777】 The server acquires weather and land information in real time from external APIs and Internet of Things devices via data acquisition devices. The input is data from APIs, and the acquired raw data is stored in a database for further processing in the next step. 【0778】 Step 2: 【0779】 The server corrects erroneous or missing data from the information acquired using a data cleansing device, and supplements it as needed. A cleansing process is performed here, and the output data is stored in an improved, more accurate form. 【0780】 Step 3: 【0781】 The server uses data analysis equipment to perform statistical analysis on cleaned data and extract agricultural indicators and forecast data. It takes clean data as input and outputs analytical data useful for formulating farm work plans. 【0782】 Step 4: 【0783】 The server generates an optimal farming plan for the user based on the analysis results in the decision support system. This plan aims to improve work efficiency and provides optimized output based on the input analysis data. 【0784】 Step 5: 【0785】 The server utilizes a predictive model building system to predict crop yields and pest and disease outbreaks based on historical data. It predicts future events based on the incorporated agricultural data and provides the results to decision support systems. 【0786】 Step 6: 【0787】 The server analyzes the user's emotional state based on voice and text data entered by the user using an emotion analysis device. The input is voice and text data from the user, and the output generates emotional information such as joy or anger. 【0788】 Step 7: 【0789】 The server adjusts the farming plan based on emotional information and generates and displays appropriate messages to the user using a message generator. This process takes the user's emotional state into consideration, providing further motivational and cautionary information. The output is a user-specific, tailored plan and message. 【0790】 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. 【0791】 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. 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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. 【0796】 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. 【0797】 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. 【0798】 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." 【0799】 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. 【0800】 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. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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. 【0809】 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. 【0810】 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. 【0811】 The following is further disclosed regarding the embodiments described above. 【0812】 (Claim 1) 【0813】 Data collection means, 【0814】 A data cleaning means for processing weather information and soil information acquired through the aforementioned data collection means, 【0815】 A data analysis means for analyzing the information prepared by the aforementioned data cleaning means, 【0816】 A decision-making support means that proposes an agricultural work plan based on the results of the data analysis means, 【0817】 A method for constructing a predictive model that uses past agricultural work data to predict crop yields and the occurrence of pests and diseases, 【0818】 A system including a user interface means for displaying information generated by the decision support means and the predictive model construction means. 【0819】 (Claim 2) 【0820】 The system according to claim 1, characterized in that the data collection means acquires weather information and soil information in real time through an external API and an IoT device. 【0821】 (Claim 3) 【0822】 The system according to claim 1, characterized in that the decision-making support means calculates the optimal cultivation method and harvest time according to the agricultural environment. 【0823】 "Example 1" 【0824】 (Claim 1) 【0825】 Data collection means, 【0826】 Information purification means for processing environmental data and geological data acquired through the aforementioned data collection means, 【0827】 Information analysis means for analyzing the information prepared by the aforementioned information purification means, 【0828】 A decision-making support means that proposes a production activity plan based on the results of the information analysis means, 【0829】 A method for constructing a predictive model that uses past production activity data to predict the yield of products and the occurrence of diseases and organisms, 【0830】 A system including a user display means for displaying information generated by the aforementioned decision-making support means and predictive model construction means. 【0831】 (Claim 2) 【0832】 The system according to claim 1, characterized in that the data collection means immediately acquires environmental data and geological data through an external communication protocol and networked devices. 【0833】 (Claim 3) 【0834】 The system according to claim 1, characterized in that the decision-making support means calculates the optimal production method and harvest time according to the production environment. 【0835】 "Application Example 1" 【0836】 (Claim 1) 【0837】 Data collection device, 【0838】 An information processing device for processing environmental information and soil data acquired through the aforementioned data collection device, 【0839】 An information analysis device that analyzes the information prepared by the aforementioned information preparation device, 【0840】 A decision support device that proposes an agricultural work plan based on the results of the aforementioned information analysis device, 【0841】 A device for constructing predictive models that use past agricultural work data to predict crop yields and the occurrence of biological disasters, 【0842】 A display device that displays information generated by the decision support device and the prediction model building device, and provides the user with a work strategy through the visualized information, 【0843】 A user management device that collects growth data in urban industrial environments in real time and provides optimal agricultural methods, 【0844】 A system including an environmental optimization device that provides growth environment improvement measures such as heat retention measures applicable in urban environments. 【0845】 (Claim 2) 【0846】 The system according to claim 1, characterized in that the data acquisition device acquires environmental information and soil data in real time through an external information provision interface and a sensor device. 【0847】 (Claim 3) 【0848】 The system according to claim 1, characterized in that the decision support device calculates the optimal cultivation method and harvest time according to the urban agricultural environment. 【0849】 "Example 2 of combining an emotion engine" 【0850】 (Claim 1) 【0851】 Data collection means, 【0852】 A data cleaning means for processing weather information and soil information acquired through the aforementioned data collection means, 【0853】 A data analysis means for analyzing the information prepared by the aforementioned data cleaning means, 【0854】 A decision-making support means that proposes an agricultural work plan based on the results of the data analysis means, 【0855】 A method for constructing a predictive model that uses past agricultural work data to predict crop yields and the occurrence of pests and diseases, 【0856】 A user interface means for displaying information generated by the aforementioned decision support means and predictive model construction means, 【0857】 An emotion recognition means that analyzes user input data and recognizes the emotional state, 【0858】 An emotion-response decision-making support means that adjusts the farm work plan based on the emotion determined by the emotion recognition means, 【0859】 User interface means that visually display the adjusted information and allow interaction for the user to provide feedback. 【0860】 A system that includes this. 【0861】 (Claim 2) 【0862】 The system according to claim 1, characterized in that the data collection means acquires weather information and soil information in real time through an external API and an IoT device, and further collects user voice and text data. 【0863】 (Claim 3) 【0864】 The system according to claim 1, characterized in that the decision-making support means calculates the optimal cultivation method and harvest time according to the agricultural environment and the user's emotional state, and generates and presents a message based on the emotional state to the user. 【0865】 "Application example 2 when combining with an emotional engine" 【0866】 (Claim 1) 【0867】 Data acquisition device, 【0868】 A data cleaning device that processes weather information and land information acquired through the aforementioned data acquisition device, 【0869】 A data analysis device that analyzes the information prepared by the aforementioned data cleaning device, 【0870】 A decision support device that proposes an agricultural work plan based on the results of the data analysis device, 【0871】 A device for constructing predictive models that use past agricultural data to predict crop yields and pest and disease outbreaks, 【0872】 A user interface device that displays information generated by the aforementioned decision support device and predictive model building device, 【0873】 An emotion analysis device that analyzes the user's emotional state and adjusts the farm work plan based on the results, 【0874】 A system including a message generation device that generates and displays messages according to the user's emotional state. 【0875】 (Claim 2) 【0876】 The system according to claim 1, characterized in that the data acquisition device acquires weather information and land information in real time through external APIs and Internet of Things devices. 【0877】 (Claim 3) 【0878】 The system according to claim 1, characterized in that the decision support device calculates the optimal cultivation method and harvest time according to the agricultural environment, and further adjusts the farm work plan taking into account the user's emotional state. [Explanation of symbols] 【0879】 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] Data collection means, A data cleaning means for processing weather information and soil information acquired through the aforementioned data collection means, A data analysis means for analyzing the information prepared by the aforementioned data cleaning means, A decision-making support means that proposes a farm work plan based on the results of the data analysis means, A method for constructing a predictive model that uses past agricultural work data to predict crop yields and the occurrence of pests and diseases, A system including a user interface means for displaying information generated by the decision support means and the predictive model construction means. [Claim 2] The system according to claim 1, characterized in that the data collection means acquires weather information and soil information in real time through an external API and an IoT device. [Claim 3] The system according to claim 1, characterized in that the decision-making support means calculates the optimal cultivation method and harvest time according to the agricultural environment.

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