system

Abstract

We provide the system. [Solution] A means of collecting local weather information, agricultural land information, harvest results, and market information, Means for preprocessing and analyzing the collected data, Based on the aforementioned analysis results, a means for selecting the optimal crops and formulating a cultivation plan, Means for providing the aforementioned cultivation plan to the user, Means for providing agricultural education programs and workshops, A means to support information exchange and community building among agricultural workers, Agricultural management systems including

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 Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 An object of the present invention is to solve problems such as reduced profits and unstable quality faced by agricultural workers due to climate change and market instability. It also aims to solve the problem that it is difficult to secure sales routes because it cannot appropriately respond to changing needs of consumers. 【Means for Solving the Problems】 【0005】 This invention provides a system that includes means for collecting local weather information, agricultural land information, harvest records, and market information, preprocesses and analyzes this data, and selects the optimal crops and formulates cultivation plans. Furthermore, it improves agricultural profitability by providing cultivation plans to users. It also includes means for improving agricultural knowledge through agricultural education programs and workshops, and has means to support information exchange and community building among agricultural workers. 【0006】 "Regional weather information" refers to data on meteorological conditions such as temperature, precipitation, and sunshine in a specific geographical area. 【0007】 "Agricultural land information" refers to data such as soil type, topography, and fertility of land used for specific agricultural purposes. 【0008】 "Harvest records" refer to records of crop production quantities and quality from past agricultural activities. 【0009】 "Market information" refers to data on market trends related to the demand, supply, and prices of agricultural products. 【0010】 "Preprocessing" refers to processes such as cleansing and transformation performed to prepare data for analysis. 【0011】 "Analysis" is the process of using collected data to interpret information and identify patterns and trends. 【0012】 "Crop selection" refers to determining the optimal crop for cultivation under specific conditions. 【0013】 A "cultivation plan" refers to specific schedules and plans for crop production, such as planting times and cultivation methods. 【0014】 "Providing to users" means presenting analysis results and suggestions to system users so that they can use them to make decisions and take action. 【0015】 An "agricultural education program" refers to learning content and lectures provided to improve agricultural technologies and knowledge. 【0016】 "Information exchange" refers to sharing knowledge and data and communicating with each other among different entities. 【0017】 "Community formation" is to construct a mechanism in which individuals or groups with the same purpose or interests gather and support each other's activities. 【Brief Explanation of Drawings】 【0018】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine. 【Mode for Carrying Out the Invention】 【0019】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0020】 First, the terms used in the following description will be explained. 【0021】 In the following embodiments, a processor with a reference numeral (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. 【0022】 In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0023】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0024】 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). 【0025】 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." 【0026】 [First Embodiment] 【0027】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0028】 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. 【0029】 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). 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 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. 【0034】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0035】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0036】 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. 【0037】 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. 【0038】 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". 【0039】 In implementing the present invention, the agricultural management system is operated with a configuration comprising multiple functional modules. Specifically, the server automatically collects local weather information, agricultural land information, harvest results, and market information from external data sources. Once the data is collected, the server performs cleansing and preprocessing to generate a dataset suitable for analysis. 【0040】 This pre-processed data is analyzed by an analysis module on the server. The analysis uses machine learning models and other tools to identify risk factors according to region and conditions, as well as to select crops that are expected to be profitable. 【0041】 The selected crops and cultivation suggestions are provided to the user's terminal in report format. The user can then send their own farm information to the server via their terminal and receive individually optimized production methods based on that information. 【0042】 Furthermore, the server provides agricultural education programs and workshops online, allowing users to access these programs using their devices and acquire skills to improve productivity. The information exchange platform facilitates the sharing of knowledge and information among other farmers within the community. 【0043】 As a concrete example, if a user considers cultivating a new crop in a specific region, the server analyzes the region's climate and market data and suggests appropriate crops and cultivation methods. This allows the user to select the optimal crop at the right time and streamline production. In this way, the present invention supports optimal agricultural production based on weather conditions and market trends. 【0044】 The following describes the processing flow. 【0045】 Step 1: 【0046】 The server retrieves weather information, agricultural land information, harvest results, and market information in real time from weather data APIs, geographic information databases, and agricultural market databases. It sends queries to each data source to retrieve the necessary datasets. 【0047】 Step 2: 【0048】 The server cleanses the acquired data, performs preprocessing by imputing missing values ​​and removing noise. Data formatting, detection, and correction of outliers are also carried out at this stage. 【0049】 Step 3: 【0050】 Based on the pre-processed data, the server uses machine learning algorithms to perform data analysis. It identifies risk factors, predicts profitable crops, and generates analysis results. 【0051】 Step 4: 【0052】 Users upload their agricultural management information (crop type, land area, resources used, etc.) to the cloud using a device. The device then sends this data to the server. 【0053】 Step 5: 【0054】 The server analyzes the user's farm data and calculates individually optimized production methods. It generates farm-specific cultivation calendars and resource management plans. 【0055】 Step 6: 【0056】 The generated suggestions and reports are provided from the server to the user's terminal. The user then reviews this information on their terminal and uses it to improve their agricultural management. 【0057】 Step 7: 【0058】 The server regularly updates content for agricultural education programs and online workshops, allowing users to participate in these educational services through their devices. 【0059】 Step 8: 【0060】 The terminal provides access to an information exchange platform, allowing users to share knowledge and information with other farmers in the same area. This fosters a collaborative environment within the local community. 【0061】 (Example 1) 【0062】 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." 【0063】 Modern agriculture requires rapid responses to climate change and market trends, as well as the development of optimal cultivation plans. However, traditional methods make it difficult to effectively collect and analyze these factors and reflect them in production plans. Furthermore, opportunities for farmers to exchange information and share the latest technologies and knowledge are limited. A system is needed to address these challenges. 【0064】 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. 【0065】 In this invention, the server includes means for collecting local weather information, agricultural land information, harvest results, and market information; means for cleansing and preprocessing the collected data; and means for analyzing the preprocessed data using a machine learning model. This makes it possible to select optimal crops and formulate cultivation plans based on local characteristics and market trends. Users receive individually optimized production methods, and through online educational programs and information exchange, improvements in agricultural technology and information sharing within the community are promoted. 【0066】 "Local weather information" refers to data about the weather in a specific region, including information such as temperature, precipitation, and wind speed. 【0067】 "Agricultural land information" refers to data about land used for cultivating crops, and includes information such as soil properties, land area, and topography. 【0068】 "Harvest records" refer to data on past crop yields and harvest times, and are used to evaluate cultivation history and harvest efficiency. 【0069】 "Market information" refers to data on the prices and demand for agricultural products, and includes information for understanding consumer trends and competitive situations. 【0070】 "Cleansing and preprocessing" refers to a series of operations performed to improve the accuracy and integrity of data, including the removal of duplicates and noise, and the imputation of missing values. 【0071】 A "machine learning model" refers to a collection of algorithms used to learn data patterns and make predictions or classifications based on specific tasks. 【0072】 A "prompt message" refers to a text-based instruction used to input instructions or questions to an AI model and obtain generated results. 【0073】 "Individually optimized production methods" refer to crop cultivation techniques and management methods that are optimized considering the characteristics and conditions of each individual user. 【0074】 An "agricultural education program" refers to online or offline learning courses or training programs offered to acquire knowledge and skills related to agriculture. 【0075】 An "information exchange platform" refers to a system that provides an online or offline space for agricultural workers to exchange knowledge and experience and to promote communication. 【0076】 One embodiment of this invention is a system that combines multiple functions to highly streamline agricultural management. This system mainly consists of a server, terminals, and users. 【0077】 The server first collects local weather information, agricultural land information, harvest records, and market information from external data sources. This process utilizes software such as APIs and web scraping tools. For example, it may use an API from a weather information service to obtain weather data. The collected data is cleansed and preprocessed using data analysis libraries such as Pandas. Specifically, this involves removing duplicate data, imputing missing values, and standardizing the format. 【0078】 Next, this preprocessed data is analyzed using a machine learning model. Platforms such as TENSORFLOW® and scikit-learn are used for the analysis to identify risk factors specific to a particular region or condition, and to select highly profitable crops. Direct instructions are given to the AI ​​model using the generated prompt sentences, and it makes predictions to questions such as, "What crop is best for the next harvest season?" 【0079】 The analysis results are generated by the server in report format and provided to the user via the terminal. On the terminal, reports in PDF or HTML format can be viewed via a dedicated application or web browser. Users also use the terminal to send information about their farm to the server. This user input is done using a dedicated app or web interface, and the information is reflected on the server in real time. 【0080】 Furthermore, the server provides online agricultural education programs and workshops, helping users learn skills and improve productivity through their devices. A community platform is established to facilitate information exchange among farmers, supporting the sharing of knowledge and experience. As a concrete example, to select highly profitable crops, a prompt such as, "What are the best crops to cultivate in this region this spring?" is generated, and AI-powered analysis is performed. 【0081】 Thus, the system of the present invention aims to improve the user's production efficiency by comprehensively covering a series of agricultural management processes, from data collection and analysis to suggestions, education, and communication. 【0082】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0083】 Step 1: 【0084】 The server collects local weather information, agricultural land information, harvest records, and market information from external data sources. Input is data obtained via APIs or web scraping, and output is raw, unprocessed data. This raw data is stored in a database for preservation. 【0085】 Step 2: 【0086】 The server cleanses and preprocesses the data acquired in Step 1. This includes removing duplicate data, imputing missing values, and standardizing the format. Specifically, it manages the data as a DataFrame using the Pandas library. The input is the raw, unprocessed data, and the output is a clean, analyzable dataset. 【0087】 Step 3: 【0088】 The server analyzes a preprocessed dataset using a machine learning model. Specifically, it trains the model using TensorFlow or scikit-learn to identify risk factors and profitable crops specific to a particular region or condition. The input is a clean dataset, and the output is the analysis results. 【0089】 Step 4: 【0090】 The server generates a report based on the analysis results. It uses Python's Matplotlib and ReportLab to create a visually easy-to-understand report. The input is the analysis results, and the output is a report in PDF or HTML format. This report is sent to the user's terminal. 【0091】 Step 5: 【0092】 Users send their farm information to the server from their terminal. This information is entered using a dedicated application. The input is the farm data entered by the user, and the output is the user data sent to the server. The server uses this information to optimize production methods. 【0093】 Step 6: 【0094】 The server generates and presents individually optimized production methods based on information from the user. Using a generated AI model, it makes suggestions including appropriate fertilizer usage and irrigation schedules. Inputs are user data and analysis results, and output is the proposed production method. 【0095】 Step 7: 【0096】 The server provides online agricultural education programs and workshops, allowing users to access them via their devices. It also provides an information exchange platform, facilitating knowledge sharing within the community. Inputs are user registration information, and outputs are learning opportunities and community information. 【0097】 (Application Example 1) 【0098】 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." 【0099】 In modern urban environments, there is a need to optimize agricultural production while achieving efficient and sustainable agricultural management. However, the lack of appropriate cultivation guidelines and information exchange forums based on urban-specific weather conditions and market trends makes it difficult to improve production efficiency and form agricultural communities. To solve this problem, an information management and support system specifically for urban agriculture is necessary. 【0100】 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. 【0101】 In this invention, the server includes means for collecting local weather data, data on agricultural land, harvest performance information, and market trend data; means for preprocessing and analyzing the collected data; and means for selecting the optimal crops to cultivate and formulating a cultivation plan based on the analysis results. This enables agricultural management suitable for urban environments, improving production efficiency and revitalizing agricultural communities. 【0102】 "Regional weather data" refers to data that shows weather conditions such as temperature, precipitation, humidity, and wind speed in a specific geographical area. 【0103】 "Agricultural land data" refers to data that shows the geographical and physical conditions that affect crop cultivation, such as soil characteristics, land area, and sunlight conditions of land where agriculture is carried out. 【0104】 "Harvest performance information" refers to historical agricultural production data such as crop yield, profit, and sales period. 【0105】 "Market trend data" refers to economic data related to the agricultural market, such as price fluctuations of agricultural products, the balance of supply and demand, and consumer preferences. 【0106】 "Means for preprocessing and analysis" refers to techniques and methods for preparing collected data into a format suitable for analysis and for interpreting the meaning of the data using statistical methods and machine learning models. 【0107】 "Means for selecting crops and formulating cultivation plans" refers to a system or process for selecting the optimal crop species and formulating efficient cultivation methods and schedules for those crops, based on collected data and the results of its analysis. 【0108】 An "information management and support system" is a collection of hardware and software that collects, processes, and analyzes data, and provides valuable information to users based on the results. 【0109】 This invention is a system for streamlining the management of urban agriculture. The server automatically collects local weather data, agricultural land data, harvest performance information, and market trend data from external data sources. The server preprocesses the collected data, preparing it for analysis. This data preprocessing is performed using software such as Python or a database management system (e.g., MySQL®). 【0110】 Next, the server uses a machine learning model to analyze the pre-processed data and formulate the optimal crop selection and cultivation plan. This analysis process utilizes machine learning libraries such as Scikit-learn. As a result, a production plan that takes weather conditions and market trends into account is formulated and provided to the user. 【0111】 The user's device receives an optimal cultivation plan and prompts them to take the necessary cultivation actions accordingly. Furthermore, the user can send their own business information and production data to the server via the device. They can then receive personalized advice based on their information. The device runs an application to display this information intuitively. 【0112】 For example, if a city farmer plans to cultivate tomatoes, the server analyzes local climate data and market trends to suggest the optimal tomato variety, planting time, and cultivation method. This improves production efficiency and enables more profitable farming. 【0113】 An example of a prompt to use with a generative AI model is as follows: "Based on local weather patterns and market demand, suggest the optimal tomato varieties and cultivation plan." 【0114】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0115】 Step 1: 【0116】 The server collects local weather data, agricultural land data, harvest performance information, and market trend data from external data sources. The input consists of various data obtained via APIs, and the output is a formatted version of this data. This process includes specific actions to retrieve data using HTTP requests. 【0117】 Step 2: 【0118】 The server preprocesses the collected data. This preprocessing includes data cleansing, such as imputing missing values ​​and removing outliers. The input is the organized data obtained in step 1, and the output is data prepared in a format suitable for analysis. Specifically, it manipulates dataframes using the Python Pandas library. 【0119】 Step 3: 【0120】 The server performs analysis using a machine learning model based on pre-processed data. The model incorporates functions for selecting profitable crops and optimizing cultivation plans. The input is the data prepared in step 2, and the output is the optimal crop selection results and cultivation plan. The specific operation includes training the model and making predictions using the Scikit-learn library. 【0121】 Step 4: 【0122】 The server sends the cultivation plan generated based on the analysis results to the user's terminal. The input is the analysis results obtained in step 3, and the output is a cultivation plan report that the user can view on their terminal. Specific operations include formatting the data into a user-friendly format and sending it using HTTP or WebSocket. 【0123】 Step 5: 【0124】 Users view cultivation plans using their terminals and utilize them to improve their own farming activities. Input is a cultivation plan report sent from the server, and output is specific action guidelines for the user's cultivation activities. The system visualizes information through a GUI, making it easy for users to refer to the plan. 【0125】 Step 6: 【0126】 Users transmit their agricultural business information to the server via a terminal. Input is the business information acquired by the user, and output is data in a format usable by the server. Specifically, information is collected using an input form on the terminal and transmitted to the server via a secure communication protocol. 【0127】 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. 【0128】 This invention provides a system configuration that incorporates an emotion engine into an agricultural management system, recognizes the user's emotions, and supports agricultural activities based on those emotions. The server collects, preprocesses, and analyzes basic agricultural data to generate optimal cultivation plans and agricultural education programs. This process uses machine learning algorithms to identify agricultural risk factors and predict profitable crops. 【0129】 Furthermore, an emotion engine is implemented on the user's device. This engine analyzes user feedback and dialogue logs to evaluate their emotional state. Based on the analysis results, the server generates information to provide appropriate advice and emotional support to the user. 【0130】 For example, when a user submits feedback about their cultivation plan through the system, the emotion engine analyzes the text information and detects signs that the user is experiencing anxiety. The server then provides the terminal with suggestions for appropriate cultivation adjustments and information to support the user's mental health. 【0131】 Furthermore, the content of the educational program is customized to reflect the results of the sentiment analysis. By including content that enhances user motivation, a more effective learning environment is provided. 【0132】 This invention goes beyond simply efficient agricultural management, enabling comprehensive support that also considers the mental health of users. This is expected to promote sustainable farming practices for agricultural workers and revitalize the entire community. 【0133】 The following describes the processing flow. 【0134】 Step 1: 【0135】 The server retrieves weather information, agricultural land information, harvest records, and market information from external data sources. This data is collected in real time via APIs and stored in a database. 【0136】 Step 2: 【0137】 The server preprocesses the acquired data, including imputing missing values ​​and correcting outliers. This prepares a clean dataset suitable for analysis. 【0138】 Step 3: 【0139】 The server uses pre-processed data to apply machine learning algorithms to select the optimal crops and develop cultivation plans. This takes into account local weather conditions and market needs. 【0140】 Step 4: 【0141】 Users transmit their agricultural management data to the server using their devices. This data includes information about the specific conditions of the farm (land size, crop types, etc.). 【0142】 Step 5: 【0143】 The server calculates a user-specific cultivation plan and production method based on agricultural management data provided by the user, and generates a report. 【0144】 Step 6: 【0145】 The generated report is sent from the server to the user's terminal, where the user can review it. This allows them to obtain a detailed cultivation schedule and resource management plan. 【0146】 Step 7: 【0147】 The user inputs feedback through an emotion engine built into the device. The engine analyzes this feedback and evaluates the user's emotional state. 【0148】 Step 8: 【0149】 Based on the evaluation obtained from the emotion engine, the server generates information, including advice and mental support tailored to the user's emotional state, and provides it to the terminal. 【0150】 Step 9: 【0151】 Taking emotional responses into account, the server adjusts the content of the agricultural education program to enhance the user's motivation to learn and delivers it as an online workshop. 【0152】 Step 10: 【0153】 The device continuously monitors user reactions through an emotion engine, sends further feedback to the server, and optimizes the overall system performance. 【0154】 (Example 2) 【0155】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0156】 Traditional agricultural management systems, while focusing on profitability and efficiency, have struggled to consider the mental health of farmers. Furthermore, they have failed to adequately address risks posed by unpredictable natural environments, as well as providing crop selection and cultivation plans suited to individual farming conditions. As a result, farmer motivation has declined, hindering sustainable agricultural operations. 【0157】 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. 【0158】 In this invention, the server includes means for collecting local environmental condition information, farmland attribute information, harvest history, and supply destination information; means for preprocessing and analyzing the collected data; and means for selecting suitable crops and formulating cultivation plans based on the analysis results. This enables comprehensive support that considers not only the efficiency of agriculture but also the mental health of agricultural workers. 【0159】 "Regional environmental conditions information" refers to data on the natural environment in a specific region, such as temperature, humidity, and precipitation. 【0160】 "Agricultural land attribute information" refers to data on the characteristics of agricultural land, such as land type, soil properties, and topography. 【0161】 "Harvest history" refers to past cultivation records and yield data. 【0162】 "Supplier information" refers to data about the markets and trading partners to which products are supplied. 【0163】 "Means of analysis" refer to the techniques and methods used to analyze collected data and derive patterns and trends. 【0164】 "Means for selecting crops and formulating cultivation plans" refers to methods for selecting suitable crops and formulating optimal cultivation methods and plans. 【0165】 "Means for analyzing and evaluating emotional states" refers to technologies that estimate emotions from user feedback and dialogue logs and determine their state. 【0166】 "Means for generating appropriate advice and emotional support" refers to methods of providing advice on agricultural activities and mental health support information based on the emotional state of the user. 【0167】 The agricultural management system of this invention has a mechanism that supports both improved agricultural efficiency and the mental health of users. The server collects and manages local environmental condition information, farmland attribute information, harvest history, and supply destination information. This data is acquired automatically using hardware such as sensors and ground observation equipment. Furthermore, the collected data is stored and processed using a cloud environment. 【0168】 The server preprocesses and analyzes data using data processing languages ​​such as Python and R. Using software that implements machine learning algorithms, it analyzes the data to identify agricultural risk factors and predict profitable crops. This enables the selection of optimal crops and the development of cultivation plans. 【0169】 The user's device has an emotion engine implemented. The device analyzes the user's input feedback and dialogue logs to evaluate their emotional state. Natural language processing tools are used in this process. Based on the results of the emotion analysis, the server provides the user with appropriate advice and emotional support. 【0170】 For example, if a user inputs feedback into the device stating, "I'm worried because my crops aren't growing as expected," the device will sense the user's anxiety. Based on this analysis, the server will generate and provide the user with advice on appropriate farming methods and support information regarding the user's mental health. 【0171】 An example of a prompt to input into the generating AI model is, "If a user is worried about crop growth, what specific advice would you offer?" Based on this prompt, the system generates the optimal solution. 【0172】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0173】 Step 1: 【0174】 The server collects local environmental condition information and farmland attribute information from sensors and ground observation equipment. The input is data acquired in real time from sensors, including temperature, humidity, and precipitation. The server preprocesses this data and removes noise to convert it into a format suitable for analysis. 【0175】 Step 2: 【0176】 The server analyzes pre-processed data and uses machine learning algorithms to identify agricultural risk factors and predict profitable crops. The input is the clean data obtained in the previous step. The server analyzes this data to generate predictive models for optimal crop selection and cultivation planning. The output is design information for specific cultivation plans and educational programs. 【0177】 Step 3: 【0178】 The emotion engine implemented in the device analyzes user feedback and dialogue logs to evaluate the user's emotional state. The input is information entered by the user in text format. The device uses natural language processing to analyze the text and evaluate the user's emotions (anxiety, joy, interest, etc.). The output is evaluation information regarding the emotional state. 【0179】 Step 4: 【0180】 The server receives the user's emotion analysis results and generates corresponding advice and emotional support information. The input is emotional state evaluation information obtained from the terminal. Based on this information, the server uses a generation AI model to create information to suggest appropriate actions to the user. The output is specific advice and support messages. 【0181】 Step 5: 【0182】 The server sends the generated information to the user's terminal and provides it to the user. The user receives advice and support from the server through their terminal and uses it to improve their agricultural activities. The input is the advice and support messages sent from the server. The output is the specific improvements to agricultural activities that the user implements based on the information received. 【0183】 (Application Example 2) 【0184】 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". 【0185】 Current agricultural management systems focus on improving production efficiency and developing optimal cultivation plans, but they lack sufficient support for the emotional state and mental health of farmers. This can lead to farmers working under stress and anxiety, potentially resulting in decreased motivation and reduced productivity. 【0186】 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. 【0187】 In this invention, the server includes means for collecting local weather information, agricultural land information, harvest results, and market information; means for analyzing the user's emotional state and providing support information that takes mental health into consideration; and means for selecting optimal crops and formulating cultivation plans. This makes it possible for agricultural workers to improve production efficiency while maintaining their mental health. 【0188】 "Local weather information" refers to data such as weather, temperature, and precipitation for a specific geographical area. 【0189】 "Information on agricultural land" refers to data on geographical and physical characteristics such as soil conditions, land area, location, and types of crops that can be grown. 【0190】 "Harvest records" refer to historical data regarding crop yield and quality over a specific period. 【0191】 "Market information" refers to data on the supply and demand of agricultural products, price trends, and consumer preferences. 【0192】 "Analyzing the user's emotional state" refers to the process of evaluating the user's emotions and psychological characteristics from text and audio, and then conducting an analysis based on that evaluation. 【0193】 "Providing support information that takes mental health into consideration" refers to providing information and advice aimed at psychological stability and stress reduction, tailored to the user's emotional state. 【0194】 "Selecting the optimal crop" refers to the process of determining the type of crop that is suitable for cultivation, taking into account profitability and growing conditions. 【0195】 "Developing a cultivation plan" refers to creating a detailed schedule of agricultural activities for selected crops, including the timing of sowing, fertilizing, and harvesting. 【0196】 The system that realizes this invention is an advanced information processing system using a server and a user's terminal. The server collects local weather information, agricultural land information, harvest records, and market information, and preprocesses and analyzes this data. Furthermore, the server uses machine learning algorithms to select crops suitable for the user and formulate a cultivation plan, which it then provides to the user. 【0197】 On the user's device, an emotion engine is implemented to analyze user feedback and dialogue logs. This evaluates the user's emotional state, and the server generates support information that takes the user's mental health into consideration, displaying it on the device. This system uses programming languages ​​such as Python and machine learning libraries (e.g., Spacy, TextBlob) to perform natural language processing and emotion analysis. 【0198】 As a concrete example, when a user inputs "I've been feeling a little tired lately," the system can analyze that emotion and provide relaxation advice in real time. An example of a prompt from the generative AI model in this process would be: "Based on the text 'I've been feeling a little tired lately,' please perform an emotion analysis and provide appropriate support advice." 【0199】 This makes it possible for agricultural workers to improve production efficiency while maintaining their mental health. Such a system is expected to lead to an overall improvement in agricultural performance. 【0200】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0201】 Step 1: 【0202】 The server collects local weather information, agricultural land information, harvest records, and market information. Input is information from external data sources, and output is an integrated dataset. The server uses multiple APIs to retrieve information in real time and stores it in a database. 【0203】 Step 2: 【0204】 The server preprocesses and analyzes the collected data. The input is an integrated dataset, and the output is the analysis results. Data cleansing and normalization are performed, and machine learning algorithms are used to identify risk factors and predict crop growth. 【0205】 Step 3: 【0206】 Based on the analysis results, the server selects suitable crops and develops a cultivation plan for the user. The input is the analysis results, and the output is a specific cultivation plan. The server uses an algorithm to generate the optimal crops and their cultivation schedules, and customizes them according to the user's requirements. 【0207】 Step 4: 【0208】 The device receives user feedback and dialogue logs and analyzes them using an emotion engine. The input is the user's feedback text, and the output is an emotion rating score. The device extracts emotions using natural language processing tools and quantifies their state. 【0209】 Step 5: 【0210】 The server provides users with appropriate mental health support information based on the results of sentiment analysis. The input is a sentiment evaluation score, and the output is support information and advice. The server uses a generative AI model to generate the most suitable support message and sends it to the terminal. 【0211】 Step 6: 【0212】 The user reviews the provided cultivation plan and support information via their terminal and provides feedback as needed. Input is information sent from the server, and output is the user's response. The user reviews the system's suggestions and plans and adjusts the next steps. 【0213】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0214】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0215】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0216】 [Second Embodiment] 【0217】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0218】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0219】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0220】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0221】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0222】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0223】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0224】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0225】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0226】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0227】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0228】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0229】 In implementing the present invention, the agricultural management system is operated with a configuration comprising multiple functional modules. Specifically, the server automatically collects local weather information, agricultural land information, harvest results, and market information from external data sources. Once the data is collected, the server performs cleansing and preprocessing to generate a dataset suitable for analysis. 【0230】 This pre-processed data is analyzed by an analysis module on the server. The analysis uses machine learning models and other tools to identify risk factors according to region and conditions, as well as to select crops that are expected to be profitable. 【0231】 The selected crops and cultivation suggestions are provided to the user's terminal in report format. The user can then send their own farm information to the server via their terminal and receive individually optimized production methods based on that information. 【0232】 Furthermore, the server provides agricultural education programs and workshops online, allowing users to access these programs using their devices and acquire skills to improve productivity. The information exchange platform facilitates the sharing of knowledge and information among other farmers within the community. 【0233】 As a concrete example, if a user considers cultivating a new crop in a specific region, the server analyzes the region's climate and market data and suggests appropriate crops and cultivation methods. This allows the user to select the optimal crop at the right time and streamline production. In this way, the present invention supports optimal agricultural production based on weather conditions and market trends. 【0234】 The following describes the processing flow. 【0235】 Step 1: 【0236】 The server retrieves weather information, agricultural land information, harvest results, and market information in real time from weather data APIs, geographic information databases, and agricultural market databases. It sends queries to each data source to retrieve the necessary datasets. 【0237】 Step 2: 【0238】 The server cleanses the acquired data, performs preprocessing by imputing missing values ​​and removing noise. Data formatting, detection, and correction of outliers are also carried out at this stage. 【0239】 Step 3: 【0240】 Based on the pre-processed data, the server uses machine learning algorithms to perform data analysis. It identifies risk factors, predicts profitable crops, and generates analysis results. 【0241】 Step 4: 【0242】 Users upload their agricultural management information (crop type, land area, resources used, etc.) to the cloud using a device. The device then sends this data to the server. 【0243】 Step 5: 【0244】 The server analyzes the user's farm data and calculates individually optimized production methods. It generates farm-specific cultivation calendars and resource management plans. 【0245】 Step 6: 【0246】 The generated suggestions and reports are provided from the server to the user's terminal. The user then reviews this information on their terminal and uses it to improve their agricultural management. 【0247】 Step 7: 【0248】 The server regularly updates content for agricultural education programs and online workshops, allowing users to participate in these educational services through their devices. 【0249】 Step 8: 【0250】 The terminal provides access to an information exchange platform, allowing users to share knowledge and information with other farmers in the same area. This fosters a collaborative environment within the local community. 【0251】 (Example 1) 【0252】 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." 【0253】 Modern agriculture requires rapid responses to climate change and market trends, as well as the development of optimal cultivation plans. However, traditional methods make it difficult to effectively collect and analyze these factors and reflect them in production plans. Furthermore, opportunities for farmers to exchange information and share the latest technologies and knowledge are limited. A system is needed to address these challenges. 【0254】 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. 【0255】 In this invention, the server includes means for collecting local weather information, agricultural land information, harvest results, and market information; means for cleansing and preprocessing the collected data; and means for analyzing the preprocessed data using a machine learning model. This makes it possible to select optimal crops and formulate cultivation plans based on local characteristics and market trends. Users receive individually optimized production methods, and through online educational programs and information exchange, improvements in agricultural technology and information sharing within the community are promoted. 【0256】 "Local weather information" refers to data about the weather in a specific region, including information such as temperature, precipitation, and wind speed. 【0257】 "Agricultural land information" refers to data about land used for cultivating crops, and includes information such as soil properties, land area, and topography. 【0258】 "Harvest records" refer to data on past crop yields and harvest times, and are used to evaluate cultivation history and harvest efficiency. 【0259】 "Market information" refers to data on the prices and demand for agricultural products, and includes information for understanding consumer trends and competitive situations. 【0260】 "Cleansing and preprocessing" refers to a series of operations performed to improve the accuracy and integrity of data, including the removal of duplicates and noise, and the imputation of missing values. 【0261】 A "machine learning model" refers to a collection of algorithms used to learn data patterns and make predictions or classifications based on specific tasks. 【0262】 A "prompt message" refers to a text-based instruction used to input instructions or questions to an AI model and obtain generated results. 【0263】 "Individually optimized production methods" refer to crop cultivation techniques and management methods that are optimized considering the characteristics and conditions of each individual user. 【0264】 An "agricultural education program" refers to online or offline learning courses or training programs offered to acquire knowledge and skills related to agriculture. 【0265】 An "information exchange platform" refers to a system that provides an online or offline space for agricultural workers to exchange knowledge and experience and to promote communication. 【0266】 One embodiment of this invention is a system that combines multiple functions to highly streamline agricultural management. This system mainly consists of a server, terminals, and users. 【0267】 The server first collects local weather information, agricultural land information, harvest records, and market information from external data sources. This process utilizes software such as APIs and web scraping tools. For example, it may use an API from a weather information service to obtain weather data. The collected data is cleansed and preprocessed using data analysis libraries such as Pandas. Specifically, this involves removing duplicate data, imputing missing values, and standardizing the format. 【0268】 Next, this preprocessed data is analyzed using a machine learning model. Platforms such as TensorFlow and scikit-learn are used for the analysis to identify risk factors specific to certain regions and conditions, and to select highly profitable crops. Generated prompts are used to directly instruct the AI ​​model, making predictions to questions such as, "What crop is best for the next harvest season?" 【0269】 The analysis results are generated by the server in report format and provided to the user via the terminal. On the terminal, reports in PDF or HTML format can be viewed via a dedicated application or web browser. Users also use the terminal to send information about their farm to the server. This user input is done using a dedicated app or web interface, and the information is reflected on the server in real time. 【0270】 Furthermore, the server provides online agricultural education programs and workshops, helping users learn skills and improve productivity through their devices. A community platform is established to facilitate information exchange among farmers, supporting the sharing of knowledge and experience. As a concrete example, to select highly profitable crops, a prompt such as, "What are the best crops to cultivate in this region this spring?" is generated, and AI-powered analysis is performed. 【0271】 Thus, the system of the present invention aims to improve the user's production efficiency by comprehensively covering a series of agricultural management processes, from data collection and analysis to suggestions, education, and communication. 【0272】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0273】 Step 1: 【0274】 The server collects local weather information, agricultural land information, harvest records, and market information from external data sources. Input is data obtained via APIs or web scraping, and output is raw, unprocessed data. This raw data is stored in a database for preservation. 【0275】 Step 2: 【0276】 The server cleanses and preprocesses the data acquired in Step 1. This includes removing duplicate data, imputing missing values, and standardizing the format. Specifically, it manages the data as a DataFrame using the Pandas library. The input is the raw, unprocessed data, and the output is a clean, analyzable dataset. 【0277】 Step 3: 【0278】 The server analyzes a preprocessed dataset using a machine learning model. Specifically, it trains the model using TensorFlow or scikit-learn to identify risk factors and profitable crops specific to a particular region or condition. The input is a clean dataset, and the output is the analysis results. 【0279】 Step 4: 【0280】 The server generates a report based on the analysis results. It uses Python's Matplotlib and ReportLab to create a visually easy-to-understand report. The input is the analysis results, and the output is a report in PDF or HTML format. This report is sent to the user's terminal. 【0281】 Step 5: 【0282】 Users send their farm information to the server from their terminal. This information is entered using a dedicated application. The input is the farm data entered by the user, and the output is the user data sent to the server. The server uses this information to optimize production methods. 【0283】 Step 6: 【0284】 Based on the information from the user, the server generates an individually optimized production method and presents it to the user. At this time, an AI model for generation is used to make proposals including the appropriate amount of fertilizer to be used and the irrigation schedule. The input is user data and analysis results, and the output is the proposed production method. 【0285】 Step 7: 【0286】 The server provides agricultural education programs and workshops online so that users can access them through their terminals. In addition, an information exchange platform is provided to promote knowledge sharing within the community. The input is the user's registration information, and the output is learning opportunities and community information. 【0287】 (Application Example 1) 【0288】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0289】 In the modern urban environment, it is required to optimize agricultural production and achieve efficient and sustainable agricultural management. However, due to the lack of appropriate cultivation guidelines and information exchange platforms based on the weather conditions and market trends specific to cities, it is difficult to improve production efficiency and form an agricultural community. To solve this problem, an information management and support system specialized for urban agriculture is required. 【0290】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0291】 In this invention, the server includes means for collecting local weather data, data on agricultural land, harvest performance information, and market trend data; means for preprocessing and analyzing the collected data; and means for selecting the optimal crops to cultivate and formulating a cultivation plan based on the analysis results. This enables agricultural management suitable for urban environments, improving production efficiency and revitalizing agricultural communities. 【0292】 "Regional weather data" refers to data that shows weather conditions such as temperature, precipitation, humidity, and wind speed in a specific geographical area. 【0293】 "Agricultural land data" refers to data that shows the geographical and physical conditions that affect crop cultivation, such as soil characteristics, land area, and sunlight conditions of land where agriculture is carried out. 【0294】 "Harvest performance information" refers to historical agricultural production data such as crop yield, profit, and sales period. 【0295】 "Market trend data" refers to economic data related to the agricultural market, such as price fluctuations of agricultural products, the balance of supply and demand, and consumer preferences. 【0296】 "Means for preprocessing and analysis" refers to techniques and methods for preparing collected data into a format suitable for analysis and for interpreting the meaning of the data using statistical methods and machine learning models. 【0297】 "Means for selecting crops and formulating cultivation plans" refers to a system or process for selecting the optimal crop species and formulating efficient cultivation methods and schedules for those crops, based on collected data and the results of its analysis. 【0298】 An "information management and support system" is a collection of hardware and software that collects, processes, and analyzes data, and provides valuable information to users based on the results. 【0299】 This invention is a system for streamlining the management of urban agriculture. The server automatically collects local weather data, agricultural land data, harvest performance information, and market trend data from external data sources. The server preprocesses the collected data, preparing it for analysis. This data preprocessing is performed using software such as Python or a database management system (e.g., MySQL). 【0300】 Next, the server uses a machine learning model to analyze the pre-processed data and formulate the optimal crop selection and cultivation plan. This analysis process utilizes machine learning libraries such as Scikit-learn. As a result, a production plan that takes weather conditions and market trends into account is formulated and provided to the user. 【0301】 The user's device receives an optimal cultivation plan and prompts them to take the necessary cultivation actions accordingly. Furthermore, the user can send their own business information and production data to the server via the device. They can then receive personalized advice based on their information. The device runs an application to display this information intuitively. 【0302】 For example, if a city farmer plans to cultivate tomatoes, the server analyzes local climate data and market trends to suggest the optimal tomato variety, planting time, and cultivation method. This improves production efficiency and enables more profitable farming. 【0303】 An example of a prompt to use with a generative AI model is as follows: "Based on local weather patterns and market demand, suggest the optimal tomato varieties and cultivation plan." 【0304】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0305】 Step 1: 【0306】 The server collects regional weather data, data related to agricultural land, harvest performance information, and data related to market trends from external data sources. The input is various data obtained through the API, and the output is the form in which these data are organized. This process includes specific operations such as using HTTP requests to obtain data. 【0307】 Step 2: 【0308】 The server preprocesses the collected data. This preprocessing includes data cleaning such as filling in missing values and removing outliers. The input is the organized data obtained in Step 1, and the output is the data arranged in a form suitable for analysis. As a specific operation, the Pandas library in Python is used to manipulate data frames. 【0309】 Step 3: 【0310】 The server performs analysis using a machine learning model based on the preprocessed data. The model incorporates functions for selecting highly profitable crops and optimizing cultivation plans. The input is the data arranged in Step 2, and the output is the selection result of the optimal crop and the cultivation plan. Specific operations include training and prediction processing of the model using the Scikit-learn library. 【0311】 Step 4: 【0312】 The server sends the cultivation plan generated based on the analysis results to the user's terminal. The input is the analysis results obtained in Step 3, and the output is a report on the cultivation plan that the user can view on the terminal. Specific operations include formatting the data in a user-friendly form and sending it using HTTP or WebSocket. 【0313】 Step 5: 【0314】 Users view cultivation plans using their terminals and utilize them to improve their own farming activities. Input is a cultivation plan report sent from the server, and output is specific action guidelines for the user's cultivation activities. The system visualizes information through a GUI, making it easy for users to refer to the plan. 【0315】 Step 6: 【0316】 Users transmit their agricultural business information to the server via a terminal. Input is the business information acquired by the user, and output is data in a format usable by the server. Specifically, information is collected using an input form on the terminal and transmitted to the server via a secure communication protocol. 【0317】 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. 【0318】 This invention provides a system configuration that incorporates an emotion engine into an agricultural management system, recognizes the user's emotions, and supports agricultural activities based on those emotions. The server collects, preprocesses, and analyzes basic agricultural data to generate optimal cultivation plans and agricultural education programs. This process uses machine learning algorithms to identify agricultural risk factors and predict profitable crops. 【0319】 Furthermore, an emotion engine is implemented on the user's device. This engine analyzes user feedback and dialogue logs to evaluate their emotional state. Based on the analysis results, the server generates information to provide appropriate advice and emotional support to the user. 【0320】 For example, when a user submits feedback about their cultivation plan through the system, the emotion engine analyzes the text information and detects signs that the user is experiencing anxiety. The server then provides the terminal with suggestions for appropriate cultivation adjustments and information to support the user's mental health. 【0321】 Furthermore, the content of the educational program is customized to reflect the results of the sentiment analysis. By including content that enhances user motivation, a more effective learning environment is provided. 【0322】 This invention goes beyond simply efficient agricultural management, enabling comprehensive support that also considers the mental health of users. This is expected to promote sustainable farming practices for agricultural workers and revitalize the entire community. 【0323】 The following describes the processing flow. 【0324】 Step 1: 【0325】 The server retrieves weather information, agricultural land information, harvest records, and market information from external data sources. This data is collected in real time via APIs and stored in a database. 【0326】 Step 2: 【0327】 The server preprocesses the acquired data, including imputing missing values ​​and correcting outliers. This prepares a clean dataset suitable for analysis. 【0328】 Step 3: 【0329】 The server uses pre-processed data to apply machine learning algorithms to select the optimal crops and develop cultivation plans. This takes into account local weather conditions and market needs. 【0330】 Step 4: 【0331】 Users transmit their agricultural management data to the server using their devices. This data includes information about the specific conditions of the farm (land size, crop types, etc.). 【0332】 Step 5: 【0333】 The server calculates a user-specific cultivation plan and production method based on agricultural management data provided by the user, and generates a report. 【0334】 Step 6: 【0335】 The generated report is sent from the server to the user's terminal, where the user can review it. This allows them to obtain a detailed cultivation schedule and resource management plan. 【0336】 Step 7: 【0337】 The user inputs feedback through an emotion engine built into the device. The engine analyzes this feedback and evaluates the user's emotional state. 【0338】 Step 8: 【0339】 Based on the evaluation obtained from the emotion engine, the server generates information, including advice and mental support tailored to the user's emotional state, and provides it to the terminal. 【0340】 Step 9: 【0341】 Taking emotional responses into account, the server adjusts the content of the agricultural education program to enhance the user's motivation to learn and delivers it as an online workshop. 【0342】 Step 10: 【0343】 The device continuously monitors user reactions through an emotion engine, sends further feedback to the server, and optimizes the overall system performance. 【0344】 (Example 2) 【0345】 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". 【0346】 Traditional agricultural management systems, while focusing on profitability and efficiency, have struggled to consider the mental health of farmers. Furthermore, they have failed to adequately address risks posed by unpredictable natural environments, as well as providing crop selection and cultivation plans suited to individual farming conditions. As a result, farmer motivation has declined, hindering sustainable agricultural operations. 【0347】 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. 【0348】 In this invention, the server includes means for collecting local environmental condition information, farmland attribute information, harvest history, and supply destination information; means for preprocessing and analyzing the collected data; and means for selecting suitable crops and formulating cultivation plans based on the analysis results. This enables comprehensive support that considers not only the efficiency of agriculture but also the mental health of agricultural workers. 【0349】 "Regional environmental conditions information" refers to data on the natural environment in a specific region, such as temperature, humidity, and precipitation. 【0350】 "Agricultural land attribute information" refers to data on the characteristics of agricultural land, such as land type, soil properties, and topography. 【0351】 "Harvest history" refers to past cultivation records and yield data. 【0352】 "Supplier information" refers to data about the markets and trading partners to which products are supplied. 【0353】 "Means of analysis" refer to the techniques and methods used to analyze collected data and derive patterns and trends. 【0354】 "Means for selecting crops and formulating cultivation plans" refers to methods for selecting suitable crops and formulating optimal cultivation methods and plans. 【0355】 "Means for analyzing and evaluating emotional states" refers to technologies that estimate emotions from user feedback and dialogue logs and determine their state. 【0356】 "Means for generating appropriate advice and emotional support" refers to methods of providing advice on agricultural activities and mental health support information based on the emotional state of the user. 【0357】 The agricultural management system of this invention has a mechanism that supports both improved agricultural efficiency and the mental health of users. The server collects and manages local environmental condition information, farmland attribute information, harvest history, and supply destination information. This data is acquired automatically using hardware such as sensors and ground observation equipment. Furthermore, the collected data is stored and processed using a cloud environment. 【0358】 The server preprocesses and analyzes data using data processing languages ​​such as Python and R. Using software that implements machine learning algorithms, it analyzes the data to identify agricultural risk factors and predict profitable crops. This enables the selection of optimal crops and the development of cultivation plans. 【0359】 The user's device has an emotion engine implemented. The device analyzes the user's input feedback and dialogue logs to evaluate their emotional state. Natural language processing tools are used in this process. Based on the results of the emotion analysis, the server provides the user with appropriate advice and emotional support. 【0360】 For example, if a user inputs feedback into the device stating, "I'm worried because my crops aren't growing as expected," the device will sense the user's anxiety. Based on this analysis, the server will generate and provide the user with advice on appropriate farming methods and support information regarding the user's mental health. 【0361】 An example of a prompt to input into the generating AI model is, "If a user is worried about crop growth, what specific advice would you offer?" Based on this prompt, the system generates the optimal solution. 【0362】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0363】 Step 1: 【0364】 The server collects local environmental condition information and farmland attribute information from sensors and ground observation equipment. The input is data acquired in real time from sensors, including temperature, humidity, and precipitation. The server preprocesses this data and removes noise to convert it into a format suitable for analysis. 【0365】 Step 2: 【0366】 The server analyzes pre-processed data and uses machine learning algorithms to identify agricultural risk factors and predict profitable crops. The input is the clean data obtained in the previous step. The server analyzes this data to generate predictive models for optimal crop selection and cultivation planning. The output is design information for specific cultivation plans and educational programs. 【0367】 Step 3: 【0368】 The emotion engine implemented in the device analyzes user feedback and dialogue logs to evaluate the user's emotional state. The input is information entered by the user in text format. The device uses natural language processing to analyze the text and evaluate the user's emotions (anxiety, joy, interest, etc.). The output is evaluation information regarding the emotional state. 【0369】 Step 4: 【0370】 The server receives the user's emotion analysis results and generates corresponding advice and emotional support information. The input is emotional state evaluation information obtained from the terminal. Based on this information, the server uses a generation AI model to create information to suggest appropriate actions to the user. The output is specific advice and support messages. 【0371】 Step 5: 【0372】 The server sends the generated information to the user's terminal and provides it to the user. The user receives advice and support from the server through their terminal and uses it to improve their agricultural activities. The input is the advice and support messages sent from the server. The output is the specific improvements to agricultural activities that the user implements based on the information received. 【0373】 (Application Example 2) 【0374】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0375】 Current agricultural management systems focus on improving production efficiency and developing optimal cultivation plans, but they lack sufficient support for the emotional state and mental health of farmers. This can lead to farmers working under stress and anxiety, potentially resulting in decreased motivation and reduced productivity. 【0376】 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. 【0377】 In this invention, the server includes means for collecting local weather information, agricultural land information, harvest results, and market information; means for analyzing the user's emotional state and providing support information that takes mental health into consideration; and means for selecting optimal crops and formulating cultivation plans. This makes it possible for agricultural workers to improve production efficiency while maintaining their mental health. 【0378】 "Local weather information" refers to data such as weather, temperature, and precipitation for a specific geographical area. 【0379】 "Information on agricultural land" refers to data on geographical and physical characteristics such as soil conditions, land area, location, and types of crops that can be grown. 【0380】 "Harvest records" refer to historical data regarding crop yield and quality over a specific period. 【0381】 "Market information" refers to data on the supply and demand of agricultural products, price trends, and consumer preferences. 【0382】 "Analyzing the user's emotional state" refers to the process of evaluating the user's emotions and psychological characteristics from text and audio, and then conducting an analysis based on that evaluation. 【0383】 "Providing support information that takes mental health into consideration" refers to providing information and advice aimed at psychological stability and stress reduction, tailored to the user's emotional state. 【0384】 "Selecting the optimal crop" refers to the process of determining the type of crop that is suitable for cultivation, taking into account profitability and growing conditions. 【0385】 "Developing a cultivation plan" refers to creating a detailed schedule of agricultural activities for selected crops, including the timing of sowing, fertilizing, and harvesting. 【0386】 The system that realizes this invention is an advanced information processing system using a server and a user's terminal. The server collects local weather information, agricultural land information, harvest records, and market information, and preprocesses and analyzes this data. Furthermore, the server uses machine learning algorithms to select crops suitable for the user and formulate a cultivation plan, which it then provides to the user. 【0387】 On the user's device, an emotion engine is implemented to analyze user feedback and dialogue logs. This evaluates the user's emotional state, and the server generates support information that takes the user's mental health into consideration, displaying it on the device. This system uses programming languages ​​such as Python and machine learning libraries (e.g., Spacy, TextBlob) to perform natural language processing and emotion analysis. 【0388】 As a concrete example, when a user inputs "I've been feeling a little tired lately," the system can analyze that emotion and provide relaxation advice in real time. An example of a prompt from the generative AI model in this process would be: "Based on the text 'I've been feeling a little tired lately,' please perform an emotion analysis and provide appropriate support advice." 【0389】 This makes it possible for agricultural workers to improve production efficiency while maintaining their mental health. Such a system is expected to lead to an overall improvement in agricultural performance. 【0390】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0391】 Step 1: 【0392】 The server collects local weather information, agricultural land information, harvest records, and market information. Input is information from external data sources, and output is an integrated dataset. The server uses multiple APIs to retrieve information in real time and stores it in a database. 【0393】 Step 2: 【0394】 The server preprocesses and analyzes the collected data. The input is an integrated dataset, and the output is the analysis results. Data cleansing and normalization are performed, and machine learning algorithms are used to identify risk factors and predict crop growth. 【0395】 Step 3: 【0396】 Based on the analysis results, the server selects suitable crops and develops a cultivation plan for the user. The input is the analysis results, and the output is a specific cultivation plan. The server uses an algorithm to generate the optimal crops and their cultivation schedules, and customizes them according to the user's requirements. 【0397】 Step 4: 【0398】 The device receives user feedback and dialogue logs and analyzes them using an emotion engine. The input is the user's feedback text, and the output is an emotion rating score. The device extracts emotions using natural language processing tools and quantifies their state. 【0399】 Step 5: 【0400】 The server provides users with appropriate mental health support information based on the results of sentiment analysis. The input is a sentiment evaluation score, and the output is support information and advice. The server uses a generative AI model to generate the most suitable support message and sends it to the terminal. 【0401】 Step 6: 【0402】 The user reviews the provided cultivation plan and support information via their terminal and provides feedback as needed. Input is information sent from the server, and output is the user's response. The user reviews the system's suggestions and plans and adjusts the next steps. 【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】 In implementing the present invention, the agricultural management system is operated with a configuration comprising multiple functional modules. Specifically, the server automatically collects local weather information, agricultural land information, harvest results, and market information from external data sources. Once the data is collected, the server performs cleansing and preprocessing to generate a dataset suitable for analysis. 【0420】 This pre-processed data is analyzed by an analysis module on the server. The analysis uses machine learning models and other tools to identify risk factors according to region and conditions, as well as to select crops that are expected to be profitable. 【0421】 The selected crops and cultivation suggestions are provided to the user's terminal in report format. The user can then send their own farm information to the server via their terminal and receive individually optimized production methods based on that information. 【0422】 Furthermore, the server provides agricultural education programs and workshops online, allowing users to access these programs using their devices and acquire skills to improve productivity. The information exchange platform facilitates the sharing of knowledge and information among other farmers within the community. 【0423】 As a concrete example, if a user considers cultivating a new crop in a specific region, the server analyzes the region's climate and market data and suggests appropriate crops and cultivation methods. This allows the user to select the optimal crop at the right time and streamline production. In this way, the present invention supports optimal agricultural production based on weather conditions and market trends. 【0424】 The following describes the processing flow. 【0425】 Step 1: 【0426】 The server retrieves weather information, agricultural land information, harvest results, and market information in real time from weather data APIs, geographic information databases, and agricultural market databases. It sends queries to each data source to retrieve the necessary datasets. 【0427】 Step 2: 【0428】 The server cleanses the acquired data, performs preprocessing by imputing missing values ​​and removing noise. Data formatting, detection, and correction of outliers are also carried out at this stage. 【0429】 Step 3: 【0430】 Based on the pre-processed data, the server uses machine learning algorithms to perform data analysis. It identifies risk factors, predicts profitable crops, and generates analysis results. 【0431】 Step 4: 【0432】 Users upload their agricultural management information (crop type, land area, resources used, etc.) to the cloud using a device. The device then sends this data to the server. 【0433】 Step 5: 【0434】 The server analyzes the user's farm data and calculates individually optimized production methods. It generates farm-specific cultivation calendars and resource management plans. 【0435】 Step 6: 【0436】 The generated suggestions and reports are provided from the server to the user's terminal. The user then reviews this information on their terminal and uses it to improve their agricultural management. 【0437】 Step 7: 【0438】 The server regularly updates content for agricultural education programs and online workshops, allowing users to participate in these educational services through their devices. 【0439】 Step 8: 【0440】 The terminal provides access to an information exchange platform, allowing users to share knowledge and information with other farmers in the same area. This fosters a collaborative environment within the local community. 【0441】 (Example 1) 【0442】 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." 【0443】 Modern agriculture requires rapid responses to climate change and market trends, as well as the development of optimal cultivation plans. However, traditional methods make it difficult to effectively collect and analyze these factors and reflect them in production plans. Furthermore, opportunities for farmers to exchange information and share the latest technologies and knowledge are limited. A system is needed to address these challenges. 【0444】 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. 【0445】 In this invention, the server includes means for collecting local weather information, agricultural land information, harvest results, and market information; means for cleansing and preprocessing the collected data; and means for analyzing the preprocessed data using a machine learning model. This makes it possible to select optimal crops and formulate cultivation plans based on local characteristics and market trends. Users receive individually optimized production methods, and through online educational programs and information exchange, improvements in agricultural technology and information sharing within the community are promoted. 【0446】 "Local weather information" refers to data about the weather in a specific region, including information such as temperature, precipitation, and wind speed. 【0447】 "Agricultural land information" refers to data about land used for cultivating crops, and includes information such as soil properties, land area, and topography. 【0448】 "Harvest records" refer to data on past crop yields and harvest times, and are used to evaluate cultivation history and harvest efficiency. 【0449】 "Market information" refers to data on the prices and demand for agricultural products, and includes information for understanding consumer trends and competitive situations. 【0450】 "Cleansing and preprocessing" refers to a series of operations performed to improve the accuracy and integrity of data, including the removal of duplicates and noise, and the imputation of missing values. 【0451】 A "machine learning model" refers to a collection of algorithms used to learn data patterns and make predictions or classifications based on specific tasks. 【0452】 A "prompt message" refers to a text-based instruction used to input instructions or questions to an AI model and obtain generated results. 【0453】 "Individually optimized production methods" refer to crop cultivation techniques and management methods that are optimized considering the characteristics and conditions of each individual user. 【0454】 An "agricultural education program" refers to online or offline learning courses or training programs offered to acquire knowledge and skills related to agriculture. 【0455】 An "information exchange platform" refers to a system that provides an online or offline space for agricultural workers to exchange knowledge and experience and to promote communication. 【0456】 One embodiment of this invention is a system that combines multiple functions to highly streamline agricultural management. This system mainly consists of a server, terminals, and users. 【0457】 The server first collects local weather information, agricultural land information, harvest records, and market information from external data sources. This process utilizes software such as APIs and web scraping tools. For example, it may use an API from a weather information service to obtain weather data. The collected data is cleansed and preprocessed using data analysis libraries such as Pandas. Specifically, this involves removing duplicate data, imputing missing values, and standardizing the format. 【0458】 Next, this preprocessed data is analyzed using a machine learning model. Platforms such as TensorFlow and scikit-learn are used for the analysis to identify risk factors specific to certain regions and conditions, and to select highly profitable crops. Generated prompts are used to directly instruct the AI ​​model, making predictions to questions such as, "What crop is best for the next harvest season?" 【0459】 The analysis results are generated by the server in report format and provided to the user via the terminal. On the terminal, reports in PDF or HTML format can be viewed via a dedicated application or web browser. Users also use the terminal to send information about their farm to the server. This user input is done using a dedicated app or web interface, and the information is reflected on the server in real time. 【0460】 Furthermore, the server provides online agricultural education programs and workshops, helping users learn skills and improve productivity through their devices. A community platform is established to facilitate information exchange among farmers, supporting the sharing of knowledge and experience. As a concrete example, to select highly profitable crops, a prompt such as, "What are the best crops to cultivate in this region this spring?" is generated, and AI-powered analysis is performed. 【0461】 Thus, the system of the present invention aims to improve the user's production efficiency by comprehensively covering a series of agricultural management processes, from data collection and analysis to suggestions, education, and communication. 【0462】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0463】 Step 1: 【0464】 The server collects local weather information, agricultural land information, harvest records, and market information from external data sources. Input is data obtained via APIs or web scraping, and output is raw, unprocessed data. This raw data is stored in a database for preservation. 【0465】 Step 2: 【0466】 The server cleanses and preprocesses the data acquired in Step 1. This includes removing duplicate data, imputing missing values, and standardizing the format. Specifically, it manages the data as a DataFrame using the Pandas library. The input is the raw, unprocessed data, and the output is a clean, analyzable dataset. 【0467】 Step 3: 【0468】 The server analyzes a preprocessed dataset using a machine learning model. Specifically, it trains the model using TensorFlow or scikit-learn to identify risk factors and profitable crops specific to a particular region or condition. The input is a clean dataset, and the output is the analysis results. 【0469】 Step 4: 【0470】 The server generates a report based on the analysis results. It uses Python's Matplotlib and ReportLab to create a visually easy-to-understand report. The input is the analysis results, and the output is a report in PDF or HTML format. This report is sent to the user's terminal. 【0471】 Step 5: 【0472】 Users send their farm information to the server from their terminal. This information is entered using a dedicated application. The input is the farm data entered by the user, and the output is the user data sent to the server. The server uses this information to optimize production methods. 【0473】 Step 6: 【0474】 The server generates and presents individually optimized production methods based on information from the user. Using a generated AI model, it makes suggestions including appropriate fertilizer usage and irrigation schedules. Inputs are user data and analysis results, and output is the proposed production method. 【0475】 Step 7: 【0476】 The server provides online agricultural education programs and workshops, allowing users to access them via their devices. It also provides an information exchange platform, facilitating knowledge sharing within the community. Inputs are user registration information, and outputs are learning opportunities and community information. 【0477】 (Application Example 1) 【0478】 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." 【0479】 In modern urban environments, there is a need to optimize agricultural production while achieving efficient and sustainable agricultural management. However, the lack of appropriate cultivation guidelines and information exchange forums based on urban-specific weather conditions and market trends makes it difficult to improve production efficiency and form agricultural communities. To solve this problem, an information management and support system specifically for urban agriculture is necessary. 【0480】 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. 【0481】 In this invention, the server includes means for collecting local weather data, data on agricultural land, harvest performance information, and market trend data; means for preprocessing and analyzing the collected data; and means for selecting the optimal crops to cultivate and formulating a cultivation plan based on the analysis results. This enables agricultural management suitable for urban environments, improving production efficiency and revitalizing agricultural communities. 【0482】 "Regional weather data" refers to data that shows weather conditions such as temperature, precipitation, humidity, and wind speed in a specific geographical area. 【0483】 "Agricultural land data" refers to data that shows the geographical and physical conditions that affect crop cultivation, such as soil characteristics, land area, and sunlight conditions of land where agriculture is carried out. 【0484】 "Harvest performance information" refers to historical agricultural production data such as crop yield, profit, and sales period. 【0485】 "Market trend data" refers to economic data related to the agricultural market, such as price fluctuations of agricultural products, the balance of supply and demand, and consumer preferences. 【0486】 "Means for preprocessing and analysis" refers to techniques and methods for preparing collected data into a format suitable for analysis and for interpreting the meaning of the data using statistical methods and machine learning models. 【0487】 "Means for selecting crops and formulating cultivation plans" refers to a system or process for selecting the optimal crop species and formulating efficient cultivation methods and schedules for those crops, based on collected data and the results of its analysis. 【0488】 An "information management and support system" is a collection of hardware and software that collects, processes, and analyzes data, and provides valuable information to users based on the results. 【0489】 This invention is a system for streamlining the management of urban agriculture. The server automatically collects local weather data, agricultural land data, harvest performance information, and market trend data from external data sources. The server preprocesses the collected data, preparing it for analysis. This data preprocessing is performed using software such as Python or a database management system (e.g., MySQL). 【0490】 Next, the server uses a machine learning model to analyze the pre-processed data and formulate the optimal crop selection and cultivation plan. This analysis process utilizes machine learning libraries such as Scikit-learn. As a result, a production plan that takes weather conditions and market trends into account is formulated and provided to the user. 【0491】 The user's device receives an optimal cultivation plan and prompts them to take the necessary cultivation actions accordingly. Furthermore, the user can send their own business information and production data to the server via the device. They can then receive personalized advice based on their information. The device runs an application to display this information intuitively. 【0492】 For example, if a city farmer plans to cultivate tomatoes, the server analyzes local climate data and market trends to suggest the optimal tomato variety, planting time, and cultivation method. This improves production efficiency and enables more profitable farming. 【0493】 An example of a prompt to use with a generative AI model is as follows: "Based on local weather patterns and market demand, suggest the optimal tomato varieties and cultivation plan." 【0494】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0495】 Step 1: 【0496】 The server collects local weather data, agricultural land data, harvest performance information, and market trend data from external data sources. The input consists of various data obtained via APIs, and the output is a formatted version of this data. This process includes specific actions to retrieve data using HTTP requests. 【0497】 Step 2: 【0498】 The server preprocesses the collected data. This preprocessing includes data cleansing, such as imputing missing values ​​and removing outliers. The input is the organized data obtained in step 1, and the output is data prepared in a format suitable for analysis. Specifically, it manipulates dataframes using the Python Pandas library. 【0499】 Step 3: 【0500】 The server performs analysis using a machine learning model based on pre-processed data. The model incorporates functions for selecting profitable crops and optimizing cultivation plans. The input is the data prepared in step 2, and the output is the optimal crop selection results and cultivation plan. The specific operation includes training the model and making predictions using the Scikit-learn library. 【0501】 Step 4: 【0502】 The server sends the cultivation plan generated based on the analysis results to the user's terminal. The input is the analysis results obtained in step 3, and the output is a cultivation plan report that the user can view on their terminal. Specific operations include formatting the data into a user-friendly format and sending it using HTTP or WebSocket. 【0503】 Step 5: 【0504】 Users view cultivation plans using their terminals and utilize them to improve their own farming activities. Input is a cultivation plan report sent from the server, and output is specific action guidelines for the user's cultivation activities. The system visualizes information through a GUI, making it easy for users to refer to the plan. 【0505】 Step 6: 【0506】 Users transmit their agricultural business information to the server via a terminal. Input is the business information acquired by the user, and output is data in a format usable by the server. Specifically, information is collected using an input form on the terminal and transmitted to the server via a secure communication protocol. 【0507】 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. 【0508】 This invention provides a system configuration that incorporates an emotion engine into an agricultural management system, recognizes the user's emotions, and supports agricultural activities based on those emotions. The server collects, preprocesses, and analyzes basic agricultural data to generate optimal cultivation plans and agricultural education programs. This process uses machine learning algorithms to identify agricultural risk factors and predict profitable crops. 【0509】 Furthermore, an emotion engine is implemented on the user's device. This engine analyzes user feedback and dialogue logs to evaluate their emotional state. Based on the analysis results, the server generates information to provide appropriate advice and emotional support to the user. 【0510】 For example, when a user submits feedback about their cultivation plan through the system, the emotion engine analyzes the text information and detects signs that the user is experiencing anxiety. The server then provides the terminal with suggestions for appropriate cultivation adjustments and information to support the user's mental health. 【0511】 Furthermore, the content of the educational program is customized to reflect the results of the sentiment analysis. By including content that enhances user motivation, a more effective learning environment is provided. 【0512】 This invention goes beyond simply efficient agricultural management, enabling comprehensive support that also considers the mental health of users. This is expected to promote sustainable farming practices for agricultural workers and revitalize the entire community. 【0513】 The following describes the processing flow. 【0514】 Step 1: 【0515】 The server retrieves weather information, agricultural land information, harvest records, and market information from external data sources. This data is collected in real time via APIs and stored in a database. 【0516】 Step 2: 【0517】 The server preprocesses the acquired data, including imputing missing values ​​and correcting outliers. This prepares a clean dataset suitable for analysis. 【0518】 Step 3: 【0519】 The server uses pre-processed data to apply machine learning algorithms to select the optimal crops and develop cultivation plans. This takes into account local weather conditions and market needs. 【0520】 Step 4: 【0521】 Users transmit their agricultural management data to the server using their devices. This data includes information about the specific conditions of the farm (land size, crop types, etc.). 【0522】 Step 5: 【0523】 The server calculates a user-specific cultivation plan and production method based on agricultural management data provided by the user, and generates a report. 【0524】 Step 6: 【0525】 The generated report is sent from the server to the user's terminal, where the user can review it. This allows them to obtain a detailed cultivation schedule and resource management plan. 【0526】 Step 7: 【0527】 The user inputs feedback through an emotion engine built into the device. The engine analyzes this feedback and evaluates the user's emotional state. 【0528】 Step 8: 【0529】 Based on the evaluation obtained from the emotion engine, the server generates information, including advice and mental support tailored to the user's emotional state, and provides it to the terminal. 【0530】 Step 9: 【0531】 Taking emotional responses into account, the server adjusts the content of the agricultural education program to enhance the user's motivation to learn and delivers it as an online workshop. 【0532】 Step 10: 【0533】 The device continuously monitors user reactions through an emotion engine, sends further feedback to the server, and optimizes the overall system performance. 【0534】 (Example 2) 【0535】 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." 【0536】 Traditional agricultural management systems, while focusing on profitability and efficiency, have struggled to consider the mental health of farmers. Furthermore, they have failed to adequately address risks posed by unpredictable natural environments, as well as providing crop selection and cultivation plans suited to individual farming conditions. As a result, farmer motivation has declined, hindering sustainable agricultural operations. 【0537】 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. 【0538】 In this invention, the server includes means for collecting local environmental condition information, farmland attribute information, harvest history, and supply destination information; means for preprocessing and analyzing the collected data; and means for selecting suitable crops and formulating cultivation plans based on the analysis results. This enables comprehensive support that considers not only the efficiency of agriculture but also the mental health of agricultural workers. 【0539】 "Regional environmental conditions information" refers to data on the natural environment in a specific region, such as temperature, humidity, and precipitation. 【0540】 "Agricultural land attribute information" refers to data on the characteristics of agricultural land, such as land type, soil properties, and topography. 【0541】 "Harvest history" refers to past cultivation records and yield data. 【0542】 "Supplier information" refers to data about the markets and trading partners to which products are supplied. 【0543】 "Means of analysis" refer to the techniques and methods used to analyze collected data and derive patterns and trends. 【0544】 "Means for selecting crops and formulating cultivation plans" refers to methods for selecting suitable crops and formulating optimal cultivation methods and plans. 【0545】 "Means for analyzing and evaluating emotional states" refers to technologies that estimate emotions from user feedback and dialogue logs and determine their state. 【0546】 "Means for generating appropriate advice and emotional support" refers to methods of providing advice on agricultural activities and mental health support information based on the emotional state of the user. 【0547】 The agricultural management system of this invention has a mechanism that supports both improved agricultural efficiency and the mental health of users. The server collects and manages local environmental condition information, farmland attribute information, harvest history, and supply destination information. This data is acquired automatically using hardware such as sensors and ground observation equipment. Furthermore, the collected data is stored and processed using a cloud environment. 【0548】 The server preprocesses and analyzes data using data processing languages ​​such as Python and R. Using software that implements machine learning algorithms, it analyzes the data to identify agricultural risk factors and predict profitable crops. This enables the selection of optimal crops and the development of cultivation plans. 【0549】 The user's device has an emotion engine implemented. The device analyzes the user's input feedback and dialogue logs to evaluate their emotional state. Natural language processing tools are used in this process. Based on the results of the emotion analysis, the server provides the user with appropriate advice and emotional support. 【0550】 For example, if a user inputs feedback into the device stating, "I'm worried because my crops aren't growing as expected," the device will sense the user's anxiety. Based on this analysis, the server will generate and provide the user with advice on appropriate farming methods and support information regarding the user's mental health. 【0551】 An example of a prompt to input into the generating AI model is, "If a user is worried about crop growth, what specific advice would you offer?" Based on this prompt, the system generates the optimal solution. 【0552】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0553】 Step 1: 【0554】 The server collects local environmental condition information and farmland attribute information from sensors and ground observation equipment. The input is data acquired in real time from sensors, including temperature, humidity, and precipitation. The server preprocesses this data and removes noise to convert it into a format suitable for analysis. 【0555】 Step 2: 【0556】 The server analyzes pre-processed data and uses machine learning algorithms to identify agricultural risk factors and predict profitable crops. The input is the clean data obtained in the previous step. The server analyzes this data to generate predictive models for optimal crop selection and cultivation planning. The output is design information for specific cultivation plans and educational programs. 【0557】 Step 3: 【0558】 The emotion engine implemented in the device analyzes user feedback and dialogue logs to evaluate the user's emotional state. The input is information entered by the user in text format. The device uses natural language processing to analyze the text and evaluate the user's emotions (anxiety, joy, interest, etc.). The output is evaluation information regarding the emotional state. 【0559】 Step 4: 【0560】 The server receives the user's emotion analysis results and generates corresponding advice and emotional support information. The input is emotional state evaluation information obtained from the terminal. Based on this information, the server uses a generation AI model to create information to suggest appropriate actions to the user. The output is specific advice and support messages. 【0561】 Step 5: 【0562】 The server sends the generated information to the user's terminal and provides it to the user. The user receives advice and support from the server through their terminal and uses it to improve their agricultural activities. The input is the advice and support messages sent from the server. The output is the specific improvements to agricultural activities that the user implements based on the information received. 【0563】 (Application Example 2) 【0564】 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." 【0565】 Current agricultural management systems focus on improving production efficiency and developing optimal cultivation plans, but they lack sufficient support for the emotional state and mental health of farmers. This can lead to farmers working under stress and anxiety, potentially resulting in decreased motivation and reduced productivity. 【0566】 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. 【0567】 In this invention, the server includes means for collecting local weather information, agricultural land information, harvest results, and market information; means for analyzing the user's emotional state and providing support information that takes mental health into consideration; and means for selecting optimal crops and formulating cultivation plans. This makes it possible for agricultural workers to improve production efficiency while maintaining their mental health. 【0568】 "Local weather information" refers to data such as weather, temperature, and precipitation for a specific geographical area. 【0569】 "Information on agricultural land" refers to data on geographical and physical characteristics such as soil conditions, land area, location, and types of crops that can be grown. 【0570】 "Harvest records" refer to historical data regarding crop yield and quality over a specific period. 【0571】 "Market information" refers to data on the supply and demand of agricultural products, price trends, and consumer preferences. 【0572】 "Analyzing the user's emotional state" refers to the process of evaluating the user's emotions and psychological characteristics from text and audio, and then conducting an analysis based on that evaluation. 【0573】 "Providing support information that takes mental health into consideration" refers to providing information and advice aimed at psychological stability and stress reduction, tailored to the user's emotional state. 【0574】 "Selecting the optimal crop" refers to the process of determining the type of crop that is suitable for cultivation, taking into account profitability and growing conditions. 【0575】 "Developing a cultivation plan" refers to creating a detailed schedule of agricultural activities for selected crops, including the timing of sowing, fertilizing, and harvesting. 【0576】 The system that realizes this invention is an advanced information processing system using a server and a user's terminal. The server collects local weather information, agricultural land information, harvest records, and market information, and preprocesses and analyzes this data. Furthermore, the server uses machine learning algorithms to select crops suitable for the user and formulate a cultivation plan, which it then provides to the user. 【0577】 On the user's device, an emotion engine is implemented to analyze user feedback and dialogue logs. This evaluates the user's emotional state, and the server generates support information that takes the user's mental health into consideration, displaying it on the device. This system uses programming languages ​​such as Python and machine learning libraries (e.g., Spacy, TextBlob) to perform natural language processing and emotion analysis. 【0578】 As a concrete example, when a user inputs "I've been feeling a little tired lately," the system can analyze that emotion and provide relaxation advice in real time. An example of a prompt from the generative AI model in this process would be: "Based on the text 'I've been feeling a little tired lately,' please perform an emotion analysis and provide appropriate support advice." 【0579】 This makes it possible for agricultural workers to improve production efficiency while maintaining their mental health. Such a system is expected to lead to an overall improvement in agricultural performance. 【0580】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0581】 Step 1: 【0582】 The server collects local weather information, agricultural land information, harvest records, and market information. Input is information from external data sources, and output is an integrated dataset. The server uses multiple APIs to retrieve information in real time and stores it in a database. 【0583】 Step 2: 【0584】 The server preprocesses and analyzes the collected data. The input is an integrated dataset, and the output is the analysis results. Data cleansing and normalization are performed, and machine learning algorithms are used to identify risk factors and predict crop growth. 【0585】 Step 3: 【0586】 Based on the analysis results, the server selects suitable crops and develops a cultivation plan for the user. The input is the analysis results, and the output is a specific cultivation plan. The server uses an algorithm to generate the optimal crops and their cultivation schedules, and customizes them according to the user's requirements. 【0587】 Step 4: 【0588】 The device receives user feedback and dialogue logs and analyzes them using an emotion engine. The input is the user's feedback text, and the output is an emotion rating score. The device extracts emotions using natural language processing tools and quantifies their state. 【0589】 Step 5: 【0590】 The server provides users with appropriate mental health support information based on the results of sentiment analysis. The input is a sentiment evaluation score, and the output is support information and advice. The server uses a generative AI model to generate the most suitable support message and sends it to the terminal. 【0591】 Step 6: 【0592】 The user reviews the provided cultivation plan and support information via their terminal and provides feedback as needed. Input is information sent from the server, and output is the user's response. The user reviews the system's suggestions and plans and adjusts the next steps. 【0593】 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. 【0594】 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. 【0595】 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. 【0596】 [Fourth Embodiment] 【0597】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0598】 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. 【0599】 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). 【0600】 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. 【0601】 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. 【0602】 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). 【0603】 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. 【0604】 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. 【0605】 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. 【0606】 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. 【0607】 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. 【0608】 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. 【0609】 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". 【0610】 In implementing the present invention, the agricultural management system is operated with a configuration comprising multiple functional modules. Specifically, the server automatically collects local weather information, agricultural land information, harvest results, and market information from external data sources. Once the data is collected, the server performs cleansing and preprocessing to generate a dataset suitable for analysis. 【0611】 This pre-processed data is analyzed by an analysis module on the server. The analysis uses machine learning models and other tools to identify risk factors according to region and conditions, as well as to select crops that are expected to be profitable. 【0612】 The selected crops and cultivation suggestions are provided to the user's terminal in report format. The user can then send their own farm information to the server via their terminal and receive individually optimized production methods based on that information. 【0613】 Furthermore, the server provides agricultural education programs and workshops online, allowing users to access these programs using their devices and acquire skills to improve productivity. The information exchange platform facilitates the sharing of knowledge and information among other farmers within the community. 【0614】 As a concrete example, if a user considers cultivating a new crop in a specific region, the server analyzes the region's climate and market data and suggests appropriate crops and cultivation methods. This allows the user to select the optimal crop at the right time and streamline production. In this way, the present invention supports optimal agricultural production based on weather conditions and market trends. 【0615】 The following describes the processing flow. 【0616】 Step 1: 【0617】 The server retrieves weather information, agricultural land information, harvest results, and market information in real time from weather data APIs, geographic information databases, and agricultural market databases. It sends queries to each data source to retrieve the necessary datasets. 【0618】 Step 2: 【0619】 The server cleanses the acquired data, performs preprocessing by imputing missing values ​​and removing noise. Data formatting, detection, and correction of outliers are also carried out at this stage. 【0620】 Step 3: 【0621】 Based on the pre-processed data, the server uses machine learning algorithms to perform data analysis. It identifies risk factors, predicts profitable crops, and generates analysis results. 【0622】 Step 4: 【0623】 Users upload their agricultural management information (crop type, land area, resources used, etc.) to the cloud using a device. The device then sends this data to the server. 【0624】 Step 5: 【0625】 The server analyzes the user's farm data and calculates individually optimized production methods. It generates farm-specific cultivation calendars and resource management plans. 【0626】 Step 6: 【0627】 The generated suggestions and reports are provided from the server to the user's terminal. The user then reviews this information on their terminal and uses it to improve their agricultural management. 【0628】 Step 7: 【0629】 The server regularly updates content for agricultural education programs and online workshops, allowing users to participate in these educational services through their devices. 【0630】 Step 8: 【0631】 The terminal provides access to an information exchange platform, allowing users to share knowledge and information with other farmers in the same area. This fosters a collaborative environment within the local community. 【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】 Modern agriculture requires rapid responses to climate change and market trends, as well as the development of optimal cultivation plans. However, traditional methods make it difficult to effectively collect and analyze these factors and reflect them in production plans. Furthermore, opportunities for farmers to exchange information and share the latest technologies and knowledge are limited. A system is needed to address these challenges. 【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 means for collecting local weather information, agricultural land information, harvest results, and market information; means for cleansing and preprocessing the collected data; and means for analyzing the preprocessed data using a machine learning model. This makes it possible to select optimal crops and formulate cultivation plans based on local characteristics and market trends. Users receive individually optimized production methods, and through online educational programs and information exchange, improvements in agricultural technology and information sharing within the community are promoted. 【0637】 "Local weather information" refers to data about the weather in a specific region, including information such as temperature, precipitation, and wind speed. 【0638】 "Agricultural land information" refers to data about land used for cultivating crops, and includes information such as soil properties, land area, and topography. 【0639】 "Harvest records" refer to data on past crop yields and harvest times, and are used to evaluate cultivation history and harvest efficiency. 【0640】 "Market information" refers to data on the prices and demand for agricultural products, and includes information for understanding consumer trends and competitive situations. 【0641】 "Cleansing and preprocessing" refers to a series of operations performed to improve the accuracy and integrity of data, including the removal of duplicates and noise, and the imputation of missing values. 【0642】 A "machine learning model" refers to a collection of algorithms used to learn data patterns and make predictions or classifications based on specific tasks. 【0643】 A "prompt message" refers to a text-based instruction used to input instructions or questions to an AI model and obtain generated results. 【0644】 "Individually optimized production methods" refer to crop cultivation techniques and management methods that are optimized considering the characteristics and conditions of each individual user. 【0645】 An "agricultural education program" refers to online or offline learning courses or training programs offered to acquire knowledge and skills related to agriculture. 【0646】 An "information exchange platform" refers to a system that provides an online or offline space for agricultural workers to exchange knowledge and experience and to promote communication. 【0647】 One embodiment of this invention is a system that combines multiple functions to highly streamline agricultural management. This system mainly consists of a server, terminals, and users. 【0648】 The server first collects local weather information, agricultural land information, harvest records, and market information from external data sources. This process utilizes software such as APIs and web scraping tools. For example, it may use an API from a weather information service to obtain weather data. The collected data is cleansed and preprocessed using data analysis libraries such as Pandas. Specifically, this involves removing duplicate data, imputing missing values, and standardizing the format. 【0649】 Next, this preprocessed data is analyzed using a machine learning model. Platforms such as TensorFlow and scikit-learn are used for the analysis to identify risk factors specific to certain regions and conditions, and to select highly profitable crops. Generated prompts are used to directly instruct the AI ​​model, making predictions to questions such as, "What crop is best for the next harvest season?" 【0650】 The analysis results are generated by the server in report format and provided to the user via the terminal. On the terminal, reports in PDF or HTML format can be viewed via a dedicated application or web browser. Users also use the terminal to send information about their farm to the server. This user input is done using a dedicated app or web interface, and the information is reflected on the server in real time. 【0651】 Furthermore, the server provides online agricultural education programs and workshops, helping users learn skills and improve productivity through their devices. A community platform is established to facilitate information exchange among farmers, supporting the sharing of knowledge and experience. As a concrete example, to select highly profitable crops, a prompt such as, "What are the best crops to cultivate in this region this spring?" is generated, and AI-powered analysis is performed. 【0652】 Thus, the system of the present invention aims to improve the user's production efficiency by comprehensively covering a series of agricultural management processes, from data collection and analysis to suggestions, education, and communication. 【0653】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0654】 Step 1: 【0655】 The server collects local weather information, agricultural land information, harvest records, and market information from external data sources. Input is data obtained via APIs or web scraping, and output is raw, unprocessed data. This raw data is stored in a database for preservation. 【0656】 Step 2: 【0657】 The server cleanses and preprocesses the data acquired in Step 1. This includes removing duplicate data, imputing missing values, and standardizing the format. Specifically, it manages the data as a DataFrame using the Pandas library. The input is the raw, unprocessed data, and the output is a clean, analyzable dataset. 【0658】 Step 3: 【0659】 The server analyzes a preprocessed dataset using a machine learning model. Specifically, it trains the model using TensorFlow or scikit-learn to identify risk factors and profitable crops specific to a particular region or condition. The input is a clean dataset, and the output is the analysis results. 【0660】 Step 4: 【0661】 The server generates a report based on the analysis results. It uses Python's Matplotlib and ReportLab to create a visually easy-to-understand report. The input is the analysis results, and the output is a report in PDF or HTML format. This report is sent to the user's terminal. 【0662】 Step 5: 【0663】 Users send their farm information to the server from their terminal. This information is entered using a dedicated application. The input is the farm data entered by the user, and the output is the user data sent to the server. The server uses this information to optimize production methods. 【0664】 Step 6: 【0665】 The server generates and presents individually optimized production methods based on information from the user. Using a generated AI model, it makes suggestions including appropriate fertilizer usage and irrigation schedules. Inputs are user data and analysis results, and output is the proposed production method. 【0666】 Step 7: 【0667】 The server provides online agricultural education programs and workshops, allowing users to access them via their devices. It also provides an information exchange platform, facilitating knowledge sharing within the community. Inputs are user registration information, and outputs are learning opportunities and community information. 【0668】 (Application Example 1) 【0669】 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". 【0670】 In modern urban environments, there is a need to optimize agricultural production while achieving efficient and sustainable agricultural management. However, the lack of appropriate cultivation guidelines and information exchange forums based on urban-specific weather conditions and market trends makes it difficult to improve production efficiency and form agricultural communities. To solve this problem, an information management and support system specifically for urban agriculture is necessary. 【0671】 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. 【0672】 In this invention, the server includes means for collecting local weather data, data on agricultural land, harvest performance information, and market trend data; means for preprocessing and analyzing the collected data; and means for selecting the optimal crops to cultivate and formulating a cultivation plan based on the analysis results. This enables agricultural management suitable for urban environments, improving production efficiency and revitalizing agricultural communities. 【0673】 "Regional weather data" refers to data that shows weather conditions such as temperature, precipitation, humidity, and wind speed in a specific geographical area. 【0674】 "Agricultural land data" refers to data that shows the geographical and physical conditions that affect crop cultivation, such as soil characteristics, land area, and sunlight conditions of land where agriculture is carried out. 【0675】 "Harvest performance information" refers to historical agricultural production data such as crop yield, profit, and sales period. 【0676】 "Market trend data" refers to economic data related to the agricultural market, such as price fluctuations of agricultural products, the balance of supply and demand, and consumer preferences. 【0677】 "Means for preprocessing and analysis" refers to techniques and methods for preparing collected data into a format suitable for analysis and for interpreting the meaning of the data using statistical methods and machine learning models. 【0678】 "Means for selecting crops and formulating cultivation plans" refers to a system or process for selecting the optimal crop species and formulating efficient cultivation methods and schedules for those crops, based on collected data and the results of its analysis. 【0679】 An "information management and support system" is a collection of hardware and software that collects, processes, and analyzes data, and provides valuable information to users based on the results. 【0680】 This invention is a system for streamlining the management of urban agriculture. The server automatically collects local weather data, agricultural land data, harvest performance information, and market trend data from external data sources. The server preprocesses the collected data, preparing it for analysis. This data preprocessing is performed using software such as Python or a database management system (e.g., MySQL). 【0681】 Next, the server uses a machine learning model to analyze the pre-processed data and formulate the optimal crop selection and cultivation plan. This analysis process utilizes machine learning libraries such as Scikit-learn. As a result, a production plan that takes weather conditions and market trends into account is formulated and provided to the user. 【0682】 The user's device receives an optimal cultivation plan and prompts them to take the necessary cultivation actions accordingly. Furthermore, the user can send their own business information and production data to the server via the device. They can then receive personalized advice based on their information. The device runs an application to display this information intuitively. 【0683】 For example, if a city farmer plans to cultivate tomatoes, the server analyzes local climate data and market trends to suggest the optimal tomato variety, planting time, and cultivation method. This improves production efficiency and enables more profitable farming. 【0684】 An example of a prompt to use with a generative AI model is as follows: "Based on local weather patterns and market demand, suggest the optimal tomato varieties and cultivation plan." 【0685】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0686】 Step 1: 【0687】 The server collects local weather data, agricultural land data, harvest performance information, and market trend data from external data sources. The input consists of various data obtained via APIs, and the output is a formatted version of this data. This process includes specific actions to retrieve data using HTTP requests. 【0688】 Step 2: 【0689】 The server preprocesses the collected data. This preprocessing includes data cleansing, such as imputing missing values ​​and removing outliers. The input is the organized data obtained in step 1, and the output is data prepared in a format suitable for analysis. Specifically, it manipulates dataframes using the Python Pandas library. 【0690】 Step 3: 【0691】 The server performs analysis using a machine learning model based on pre-processed data. The model incorporates functions for selecting profitable crops and optimizing cultivation plans. The input is the data prepared in step 2, and the output is the optimal crop selection results and cultivation plan. The specific operation includes training the model and making predictions using the Scikit-learn library. 【0692】 Step 4: 【0693】 The server sends the cultivation plan generated based on the analysis results to the user's terminal. The input is the analysis results obtained in step 3, and the output is a cultivation plan report that the user can view on their terminal. Specific operations include formatting the data into a user-friendly format and sending it using HTTP or WebSocket. 【0694】 Step 5: 【0695】 Users view cultivation plans using their terminals and utilize them to improve their own farming activities. Input is a cultivation plan report sent from the server, and output is specific action guidelines for the user's cultivation activities. The system visualizes information through a GUI, making it easy for users to refer to the plan. 【0696】 Step 6: 【0697】 Users transmit their agricultural business information to the server via a terminal. Input is the business information acquired by the user, and output is data in a format usable by the server. Specifically, information is collected using an input form on the terminal and transmitted to the server via a secure communication protocol. 【0698】 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. 【0699】 This invention provides a system configuration that incorporates an emotion engine into an agricultural management system, recognizes the user's emotions, and supports agricultural activities based on those emotions. The server collects, preprocesses, and analyzes basic agricultural data to generate optimal cultivation plans and agricultural education programs. This process uses machine learning algorithms to identify agricultural risk factors and predict profitable crops. 【0700】 Furthermore, an emotion engine is implemented on the user's device. This engine analyzes user feedback and dialogue logs to evaluate their emotional state. Based on the analysis results, the server generates information to provide appropriate advice and emotional support to the user. 【0701】 For example, when a user submits feedback about their cultivation plan through the system, the emotion engine analyzes the text information and detects signs that the user is experiencing anxiety. The server then provides the terminal with suggestions for appropriate cultivation adjustments and information to support the user's mental health. 【0702】 Furthermore, the content of the educational program is customized to reflect the results of the sentiment analysis. By including content that enhances user motivation, a more effective learning environment is provided. 【0703】 This invention goes beyond simply efficient agricultural management, enabling comprehensive support that also considers the mental health of users. This is expected to promote sustainable farming practices for agricultural workers and revitalize the entire community. 【0704】 The following describes the processing flow. 【0705】 Step 1: 【0706】 The server retrieves weather information, agricultural land information, harvest records, and market information from external data sources. This data is collected in real time via APIs and stored in a database. 【0707】 Step 2: 【0708】 The server preprocesses the acquired data, including imputing missing values ​​and correcting outliers. This prepares a clean dataset suitable for analysis. 【0709】 Step 3: 【0710】 The server uses pre-processed data to apply machine learning algorithms to select the optimal crops and develop cultivation plans. This takes into account local weather conditions and market needs. 【0711】 Step 4: 【0712】 Users transmit their agricultural management data to the server using their devices. This data includes information about the specific conditions of the farm (land size, crop types, etc.). 【0713】 Step 5: 【0714】 The server calculates a user-specific cultivation plan and production method based on agricultural management data provided by the user, and generates a report. 【0715】 Step 6: 【0716】 The generated report is sent from the server to the user's terminal, where the user can review it. This allows them to obtain a detailed cultivation schedule and resource management plan. 【0717】 Step 7: 【0718】 The user inputs feedback through an emotion engine built into the device. The engine analyzes this feedback and evaluates the user's emotional state. 【0719】 Step 8: 【0720】 Based on the evaluation obtained from the emotion engine, the server generates information, including advice and mental support tailored to the user's emotional state, and provides it to the terminal. 【0721】 Step 9: 【0722】 Taking emotional responses into account, the server adjusts the content of the agricultural education program to enhance the user's motivation to learn and delivers it as an online workshop. 【0723】 Step 10: 【0724】 The device continuously monitors user reactions through an emotion engine, sends further feedback to the server, and optimizes the overall system performance. 【0725】 (Example 2) 【0726】 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". 【0727】 Traditional agricultural management systems, while focusing on profitability and efficiency, have struggled to consider the mental health of farmers. Furthermore, they have failed to adequately address risks posed by unpredictable natural environments, as well as providing crop selection and cultivation plans suited to individual farming conditions. As a result, farmer motivation has declined, hindering sustainable agricultural operations. 【0728】 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. 【0729】 In this invention, the server includes means for collecting local environmental condition information, farmland attribute information, harvest history, and supply destination information; means for preprocessing and analyzing the collected data; and means for selecting suitable crops and formulating cultivation plans based on the analysis results. This enables comprehensive support that considers not only the efficiency of agriculture but also the mental health of agricultural workers. 【0730】 "Regional environmental conditions information" refers to data on the natural environment in a specific region, such as temperature, humidity, and precipitation. 【0731】 "Agricultural land attribute information" refers to data on the characteristics of agricultural land, such as land type, soil properties, and topography. 【0732】 "Harvest history" refers to past cultivation records and yield data. 【0733】 "Supplier information" refers to data about the markets and trading partners to which products are supplied. 【0734】 "Means of analysis" refer to the techniques and methods used to analyze collected data and derive patterns and trends. 【0735】 "Means for selecting crops and formulating cultivation plans" refers to methods for selecting suitable crops and formulating optimal cultivation methods and plans. 【0736】 "Means for analyzing and evaluating emotional states" refers to technologies that estimate emotions from user feedback and dialogue logs and determine their state. 【0737】 "Means for generating appropriate advice and emotional support" refers to methods of providing advice on agricultural activities and mental health support information based on the emotional state of the user. 【0738】 The agricultural management system of this invention has a mechanism that supports both improved agricultural efficiency and the mental health of users. The server collects and manages local environmental condition information, farmland attribute information, harvest history, and supply destination information. This data is acquired automatically using hardware such as sensors and ground observation equipment. Furthermore, the collected data is stored and processed using a cloud environment. 【0739】 The server preprocesses and analyzes data using data processing languages ​​such as Python and R. Using software that implements machine learning algorithms, it analyzes the data to identify agricultural risk factors and predict profitable crops. This enables the selection of optimal crops and the development of cultivation plans. 【0740】 The user's device has an emotion engine implemented. The device analyzes the user's input feedback and dialogue logs to evaluate their emotional state. Natural language processing tools are used in this process. Based on the results of the emotion analysis, the server provides the user with appropriate advice and emotional support. 【0741】 For example, if a user inputs feedback into the device stating, "I'm worried because my crops aren't growing as expected," the device will sense the user's anxiety. Based on this analysis, the server will generate and provide the user with advice on appropriate farming methods and support information regarding the user's mental health. 【0742】 An example of a prompt to input into the generating AI model is, "If a user is worried about crop growth, what specific advice would you offer?" Based on this prompt, the system generates the optimal solution. 【0743】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0744】 Step 1: 【0745】 The server collects local environmental condition information and farmland attribute information from sensors and ground observation equipment. The input is data acquired in real time from sensors, including temperature, humidity, and precipitation. The server preprocesses this data and removes noise to convert it into a format suitable for analysis. 【0746】 Step 2: 【0747】 The server analyzes pre-processed data and uses machine learning algorithms to identify agricultural risk factors and predict profitable crops. The input is the clean data obtained in the previous step. The server analyzes this data to generate predictive models for optimal crop selection and cultivation planning. The output is design information for specific cultivation plans and educational programs. 【0748】 Step 3: 【0749】 The emotion engine implemented in the device analyzes user feedback and dialogue logs to evaluate the user's emotional state. The input is information entered by the user in text format. The device uses natural language processing to analyze the text and evaluate the user's emotions (anxiety, joy, interest, etc.). The output is evaluation information regarding the emotional state. 【0750】 Step 4: 【0751】 The server receives the user's emotion analysis results and generates corresponding advice and emotional support information. The input is emotional state evaluation information obtained from the terminal. Based on this information, the server uses a generation AI model to create information to suggest appropriate actions to the user. The output is specific advice and support messages. 【0752】 Step 5: 【0753】 The server sends the generated information to the user's terminal and provides it to the user. The user receives advice and support from the server through their terminal and uses it to improve their agricultural activities. The input is the advice and support messages sent from the server. The output is the specific improvements to agricultural activities that the user implements based on the information received. 【0754】 (Application Example 2) 【0755】 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". 【0756】 Current agricultural management systems focus on improving production efficiency and developing optimal cultivation plans, but they lack sufficient support for the emotional state and mental health of farmers. This can lead to farmers working under stress and anxiety, potentially resulting in decreased motivation and reduced productivity. 【0757】 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. 【0758】 In this invention, the server includes means for collecting local weather information, agricultural land information, harvest results, and market information; means for analyzing the user's emotional state and providing support information that takes mental health into consideration; and means for selecting optimal crops and formulating cultivation plans. This makes it possible for agricultural workers to improve production efficiency while maintaining their mental health. 【0759】 "Local weather information" refers to data such as weather, temperature, and precipitation for a specific geographical area. 【0760】 "Information on agricultural land" refers to data on geographical and physical characteristics such as soil conditions, land area, location, and types of crops that can be grown. 【0761】 "Harvest records" refer to historical data regarding crop yield and quality over a specific period. 【0762】 "Market information" refers to data on the supply and demand of agricultural products, price trends, and consumer preferences. 【0763】 "Analyzing the user's emotional state" refers to the process of evaluating the user's emotions and psychological characteristics from text and audio, and then conducting an analysis based on that evaluation. 【0764】 "Providing support information that takes mental health into consideration" refers to providing information and advice aimed at psychological stability and stress reduction, tailored to the user's emotional state. 【0765】 "Selecting the optimal crop" refers to the process of determining the type of crop that is suitable for cultivation, taking into account profitability and growing conditions. 【0766】 "Developing a cultivation plan" refers to creating a detailed schedule of agricultural activities for selected crops, including the timing of sowing, fertilizing, and harvesting. 【0767】 The system that realizes this invention is an advanced information processing system using a server and a user's terminal. The server collects local weather information, agricultural land information, harvest records, and market information, and preprocesses and analyzes this data. Furthermore, the server uses machine learning algorithms to select crops suitable for the user and formulate a cultivation plan, which it then provides to the user. 【0768】 On the user's device, an emotion engine is implemented to analyze user feedback and dialogue logs. This evaluates the user's emotional state, and the server generates support information that takes the user's mental health into consideration, displaying it on the device. This system uses programming languages ​​such as Python and machine learning libraries (e.g., Spacy, TextBlob) to perform natural language processing and emotion analysis. 【0769】 As a concrete example, when a user inputs "I've been feeling a little tired lately," the system can analyze that emotion and provide relaxation advice in real time. An example of a prompt from the generative AI model in this process would be: "Based on the text 'I've been feeling a little tired lately,' please perform an emotion analysis and provide appropriate support advice." 【0770】 This makes it possible for agricultural workers to improve production efficiency while maintaining their mental health. Such a system is expected to lead to an overall improvement in agricultural performance. 【0771】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0772】 Step 1: 【0773】 The server collects local weather information, agricultural land information, harvest records, and market information. Input is information from external data sources, and output is an integrated dataset. The server uses multiple APIs to retrieve information in real time and stores it in a database. 【0774】 Step 2: 【0775】 The server preprocesses and analyzes the collected data. The input is an integrated dataset, and the output is the analysis results. Data cleansing and normalization are performed, and machine learning algorithms are used to identify risk factors and predict crop growth. 【0776】 Step 3: 【0777】 Based on the analysis results, the server selects suitable crops and develops a cultivation plan for the user. The input is the analysis results, and the output is a specific cultivation plan. The server uses an algorithm to generate the optimal crops and their cultivation schedules, and customizes them according to the user's requirements. 【0778】 Step 4: 【0779】 The device receives user feedback and dialogue logs and analyzes them using an emotion engine. The input is the user's feedback text, and the output is an emotion rating score. The device extracts emotions using natural language processing tools and quantifies their state. 【0780】 Step 5: 【0781】 The server provides users with appropriate mental health support information based on the results of sentiment analysis. The input is a sentiment evaluation score, and the output is support information and advice. The server uses a generative AI model to generate the most suitable support message and sends it to the terminal. 【0782】 Step 6: 【0783】 The user reviews the provided cultivation plan and support information via their terminal and provides feedback as needed. Input is information sent from the server, and output is the user's response. The user reviews the system's suggestions and plans and adjusts the next steps. 【0784】 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. 【0785】 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. 【0786】 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. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 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." 【0793】 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. 【0794】 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. 【0795】 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. 【0796】 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. 【0797】 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. 【0798】 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. 【0799】 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. 【0800】 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. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 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 as being incorporated by reference. 【0805】 The following is further disclosed regarding the embodiments described above. 【0806】 (Claim 1) 【0807】 A means of collecting local weather information, agricultural land information, harvest results, and market information, 【0808】 Means for preprocessing and analyzing the collected data, 【0809】 Based on the aforementioned analysis results, a means for selecting the optimal crops and formulating a cultivation plan, 【0810】 Means for providing the aforementioned cultivation plan to the user, 【0811】 Means for providing agricultural education programs and workshops, 【0812】 A means to support information exchange and community building among agricultural workers, 【0813】 Agricultural management systems including 【0814】 (Claim 2) 【0815】 The system according to claim 1, further comprising means for collecting user agricultural management information and transmitting it to the cloud. 【0816】 (Claim 3) 【0817】 The system according to claim 1, wherein the data analysis means includes means for identifying agricultural risk factors and predicting highly profitable crops using a machine learning algorithm. 【0818】 "Example 1" 【0819】 (Claim 1) 【0820】 A means of collecting local weather information, agricultural land information, harvest results, and market information, 【0821】 Means for cleansing and preprocessing the collected data, 【0822】 The means for analyzing the preprocessed data using a machine learning model, 【0823】 Based on the aforementioned analysis results, a means of formulating the selection of optimal crops and cultivation plans, and providing them to the user in report format, 【0824】 A means of collecting agricultural management information from users and providing individually optimized production methods based on analysis, 【0825】 Means of providing agricultural education programs and workshops online, 【0826】 A means to support communication among agricultural workers through an information exchange platform, 【0827】 A system that includes this. 【0828】 (Claim 2) 【0829】 The system according to claim 1, wherein the machine learning model includes means for analyzing using prompt statements to identify agricultural risk factors and predict highly profitable crops. 【0830】 (Claim 3) 【0831】 The system according to claim 1, which includes means for making reactive suggestions using an AI model generated based on information from the user. 【0832】 "Application Example 1" 【0833】 (Claim 1) 【0834】 A means of collecting local weather data, agricultural land data, harvest performance information, and market trend data, 【0835】 Means for preprocessing and analyzing the collected data, 【0836】 Based on the aforementioned analysis results, a means for selecting the optimal crops to cultivate and formulating a cultivation plan, 【0837】 Means for providing the aforementioned training plan to users, 【0838】 Means of providing educational programs and workshops for skill improvement, 【0839】 A means to support information exchange and community formation among those engaged in the agricultural sector, 【0840】 A means of providing growth prediction and cultivation guides for plants suitable for urban conditions, 【0841】 A management system that includes this. 【0842】 (Claim 2) 【0843】 The system according to claim 1, further comprising means for collecting user business information and transmitting it to an information processing infrastructure. 【0844】 (Claim 3) 【0845】 The system according to claim 1, wherein the data analysis means includes means for identifying risk factors in agriculture and predicting high-value crops using machine learning methods. 【0846】 "Example 2 of combining an emotion engine" 【0847】 (Claim 1) 【0848】 A means for collecting information on local environmental conditions, farmland attributes, harvest history, and supply destinations, 【0849】 Means for preprocessing and analyzing the collected data, 【0850】 Based on the aforementioned analysis results, a means for selecting suitable crops and formulating a cultivation plan, 【0851】 Means for providing the aforementioned cultivation plan to users, 【0852】 Means for providing educational programs and meetings, 【0853】 A means of analyzing and evaluating the emotional state of users, 【0854】 means for generating appropriate advice and emotional support based on the aforementioned emotional state, 【0855】 A means to promote information sharing and community building among agricultural workers, 【0856】 A system that includes this. 【0857】 (Claim 2) 【0858】 The system according to claim 1, further comprising means for acquiring user agricultural management information and transmitting it to a cloud environment infrastructure. 【0859】 (Claim 3) 【0860】 The system according to claim 1, wherein the data analysis means includes means for identifying agricultural risk factors and predicting highly profitable crops using a predictive model. 【0861】 "Application example 2 when combining with an emotional engine" 【0862】 (Claim 1) 【0863】 A means of collecting local weather information, agricultural land information, harvest results, and market information, 【0864】 Means for preprocessing and analyzing the collected data, 【0865】 Based on the aforementioned analysis results, a means for selecting the optimal crops and formulating a cultivation plan, 【0866】 Means for providing the aforementioned cultivation plan to the user, 【0867】 Means for providing agricultural education programs and workshops, 【0868】 A means to support information exchange and community building among agricultural workers, 【0869】 A means of analyzing the user's emotional state and providing support information that takes mental health into consideration, 【0870】 A system that includes this. 【0871】 (Claim 2) 【0872】 The system according to claim 1, further comprising means for collecting user agricultural management information and transmitting it to the cloud. 【0873】 (Claim 3) 【0874】 The system according to claim 1, wherein the data analysis means includes means for identifying agricultural risk factors and predicting highly profitable crops using a machine learning algorithm. [Explanation of Symbols] 【0875】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of collecting local weather information, agricultural land information, harvest results, and market information, Means for preprocessing and analyzing the collected data, Based on the aforementioned analysis results, a means for selecting the optimal crops and formulating a cultivation plan, Means for providing the aforementioned cultivation plan to the user, Means for providing agricultural education programs and workshops, A means to support information exchange and community building among agricultural workers, Agricultural management systems including [Claim 2] The system according to claim 1, further comprising means for collecting user agricultural management information and transmitting it to the cloud. [Claim 3] The system according to claim 1, wherein the data analysis means includes means for identifying agricultural risk factors and predicting highly profitable crops using a machine learning algorithm.

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