system

Abstract

We provide the system. [Solution] Data collection means for collecting environmental data, Analysis means for analyzing environmental data collected by the aforementioned data collection means, A cultivation plan generation means for generating an optimal cultivation plan based on the analysis results of the aforementioned analysis means, A notification means for informing the user of the generated cultivation plan, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 For agricultural workers, it is difficult to manually perform optimal cultivation management according to soil and climate, and advanced and efficient management is required to stabilize quality and yield. Conventionally, since it takes time and specialized knowledge to acquire and analyze environmental data, it has been difficult for small and medium-sized agricultural operators to perform efficiently, resulting in problems such as an increase in work burden and a decrease in productivity. 【Means for Solving the Problems】 【0005】 This invention provides a system that collects environmental data, analyzes that data, and generates an optimal cultivation plan. Specifically, the system includes data collection means for collecting environmental data using temperature sensors, humidity sensors, and soil condition sensors; cultivation plan generation means for analyzing the collected data and generating a cultivation plan using a machine learning model based on historical data and regional climate; and notification means for notifying the user of the generated plan. This system enables farmers to automate efficient and advanced cultivation management, reducing their workload while improving yield and quality. 【0006】 "Environmental data" is a general term for information about external environmental factors that affect crop growth in agriculture, such as temperature, humidity, and soil conditions. 【0007】 "Data collection means" refers to a function or device that acquires environmental data using sensors or devices and transmits it to a system. 【0008】 "Analysis means" refers to a function or device used to analyze collected environmental data and determine the optimal growth state of crops. 【0009】 A "cultivation plan generation means" is a function or device for constructing a crop cultivation plan based on the results of an analysis means and planning the necessary cultivation work. 【0010】 "Notification means" refers to a communication function or device for delivering generated cultivation plans and work instructions to the user. 【0011】 A "temperature sensor" is an electronic device that measures the ambient temperature and outputs it as data. 【0012】 A "humidity sensor" is a device used to measure the humidity in the air and output that data. 【0013】 A "soil condition sensor" is a device used to acquire data related to soil properties, such as soil moisture, pH, and salinity. 【0014】 A "machine learning model" is an algorithm or mathematical model used to learn patterns from past data and predict future events. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Mode for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0017】 First, the terms used in the following description will be explained. 【0018】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc. 【0019】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc. 【0021】 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). 【0022】 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." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 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. 【0026】 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). 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 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. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 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. 【0033】 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. 【0034】 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. 【0035】 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". 【0036】 This invention provides an information processing system for streamlining the crop cultivation process, and in particular aims to automate a series of processes such as collecting and analyzing environmental data, generating cultivation plans, and notifying users. 【0037】 The system uses multiple sensor devices to collect environmental data such as temperature, humidity, and soil conditions within the farm. These sensor devices are placed around the crops and have the function of periodically measuring data. The obtained data is transmitted to a central server via a wireless network. 【0038】 The server is responsible for analyzing the received environmental data. This analysis process includes filtering the data and imputing missing values. The appropriately pre-processed data is then fed into a machine learning model to generate an optimal crop cultivation plan. This model has learned from existing data and can make advanced predictions based on climate and growth patterns. 【0039】 The generated cultivation plan includes specific details of agricultural tasks such as watering, fertilizing, and harvesting timing. Once this plan is complete, the server notifies the user's device of the information. 【0040】 The user's device, such as a smartphone or tablet, receives the cultivation plan sent from the server. This allows the user to see the next steps to be taken on the farm. For example, they might be notified that they need to water the plants early in the morning based on a forecast of high temperatures the following day. The user can then manually perform the necessary tasks according to the instructions on their device. 【0041】 Furthermore, the system can be integrated with IoT-enabled automation tools, allowing for the automation of watering and fertilizing operations as needed. This feature further reduces the user's workload by accurately and efficiently executing tasks as instructed. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 The terminal uses temperature, humidity, and soil condition sensors placed on the farm to acquire environmental data at regular intervals. The acquired data is temporarily stored in the terminal and waits until the next communication timing. 【0045】 Step 2: 【0046】 The terminal transmits collected environmental data to the server via a wireless communication network. Transmission is performed in batches, with data sent according to availability and communication conditions. 【0047】 Step 3: 【0048】 The server receives data from the terminal and performs a format check. If any data abnormalities or missing data are detected, the server logs the problem and performs corrective processing to proceed to the next processing step. 【0049】 Step 4: 【0050】 The server preprocesses the received dataset. Specifically, it performs missing value imputation, filters out large outliers, and normalizes the data as needed. This prepares the data for optimal input to machine learning models. 【0051】 Step 5: 【0052】 The server feeds pre-processed data to a machine learning model and begins the analysis. Based on historical and current environmental data, the model predicts crop growth and nutrient demand and generates an optimal cultivation plan. 【0053】 Step 6: 【0054】 The server begins preparing to notify the user of the generated cultivation plan. It converts the plan, including specific farming details, into a push notification format and sends it to the user's device. 【0055】 Step 7: 【0056】 Users check their cultivation plan on their devices. They review the planned watering and fertilizing schedules and perform farming tasks accordingly if manual work is required. 【0057】 Step 8: 【0058】 If a user has automated IoT devices, they can send plan-based instructions via a server to control the devices. This allows for automated watering and fertilizing. 【0059】 (Example 1) 【0060】 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." 【0061】 In the crop cultivation process, there is a need for the automated generation and implementation of efficient cultivation plans based on environmental information. However, the conventional manual collection and analysis of environmental information is time-consuming and labor-intensive, and it is difficult to quickly formulate appropriate cultivation plans. Therefore, a system is needed that reduces the burden on farmers while providing optimal cultivation conditions. 【0062】 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. 【0063】 In this invention, the server includes data collection means for collecting environmental information, data processing means for preprocessing the environmental information collected by the data collection means, and plan generation means for generating an optimal cultivation plan using a machine learning algorithm based on the results of the data processing means. This makes it possible to quickly and automatically create an optimal cultivation plan based on the environmental conditions of the farm and notify the user. 【0064】 "Environmental information" is a general term for information related to weather and ground conditions on a farm, such as temperature, humidity, and soil conditions. 【0065】 "Data collection methods" refer to devices and technologies for acquiring environmental information, and mainly include temperature measuring devices, humidity measuring devices, and soil condition measuring devices. 【0066】 "Data processing means" refers to technologies and processes for organizing collected environmental information, removing unnecessary data, and imputing missing values. 【0067】 "Plan generation means" refers to a function that automatically creates an optimal cultivation plan using a machine learning algorithm based on pre-processed data. 【0068】 "Notification means" refers to a mechanism for informing users of the generated cultivation plan, and includes the process of sending information using email or a dedicated application. 【0069】 A "machine learning algorithm" is a series of computational methods that automatically learn from past data and perform pattern recognition and prediction. 【0070】 This invention provides an information processing system for streamlining the crop cultivation process. Specifically, it is a system that integrates the collection of environmental information, automatic analysis, generation of cultivation plans, and notification into a single workflow. 【0071】 First, the server collects environmental information about the farm using data collection devices such as temperature measuring devices, humidity measuring devices, and soil condition measuring devices installed on the farm. These sensor devices are placed in appropriate locations and are configured to measure collected data at a specified frequency and transmit it to the server via a wireless network. 【0072】 Next, the server preprocesses the received data using data processing techniques such as noise filtering and missing value imputation. The preprocessed data is then analyzed by a planning generation method using a generative AI model, and an optimal cultivation plan is automatically generated. Here, machine learning algorithms based on past weather patterns and crop growth data are used, and for example, a fertilization schedule is created that takes into account weather forecasts such as "continuous rain in the middle of next week." 【0073】 The generated cultivation plan is transmitted from the server to the user's terminal via a notification system. The user's terminal is assumed to be a smartphone or tablet-type information processing terminal. The user can check the specific steps of the next farming task on the terminal screen. For example, instructions such as "The temperature will drop tomorrow morning, so please take measures to protect against the cold" will be notified. 【0074】 Furthermore, this system can integrate with IoT technology to enable automated watering and fertilization, thereby increasing work efficiency. An example of a prompt message input to the AI ​​model would be, "Based on the temperature, humidity, and soil condition data for the past three days, please suggest the optimal next steps for tomato cultivation." Based on this prompt, the AI ​​model would recommend the next steps to be taken, which the user can then view on their device. 【0075】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0076】 Step 1: 【0077】 The server collects environmental information through various measuring devices placed on the farm. The input consists of raw data from temperature, humidity, and soil condition sensors. This data is collected in real time or at set time intervals and transmitted to the server via a wireless network. Specifically, each sensor records a specific measurement and sends it to the server as digital data. 【0078】 Step 2: 【0079】 The server preprocesses the collected environmental information using data processing tools. The input is raw data sent from the sensors. Data processing involves removing outliers through noise filtering and ensuring data integrity through missing value imputation. The output becomes clean data suitable for analysis and input to machine learning models. In this process, for example, missing data is imputed using the most recent normal value. 【0080】 Step 3: 【0081】 The server inputs pre-processed data into a generating AI model to create an optimal cultivation plan. The input for this step is clean environmental data, which is then supplied to the AI ​​model. The AI ​​model uses historical data and regional climate data to predict weather patterns, appropriate fertilization schedules, harvest times, and more. The output is set as a specific cultivation plan. For example, it calculates the appropriate timing for fertilization and watering based on the weather conditions expected for the following week. 【0082】 Step 4: 【0083】 The server notifies the user's terminal of the generated cultivation plan. The input is the cultivation plan generated by the AI ​​model. This plan is sent to the user's terminal via a notification system and made available for display. The output is the farming procedures and precautions displayed on the terminal. For example, an instruction such as "A drop in temperature is expected the following day, so take measures to protect against the cold" is sent. 【0084】 Step 5: 【0085】 The user performs the necessary tasks on the farm based on the cultivation plan instructions received on the terminal. The input is the content of the cultivation plan displayed on the terminal. The user can refer to this and perform tasks such as watering and fertilizing manually, or have them performed automatically using IoT devices. The output is the farm work that has been performed. This process allows the user to perform farm work efficiently. 【0086】 (Application Example 1) 【0087】 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." 【0088】 In urban areas and community farms, crop cultivation management is generally done manually, which presents challenges in terms of efficiency and accuracy. In particular, timely watering and fertilization are difficult due to fluctuations in climate and soil conditions, increasing the burden on workers. There is a need for a system that can solve these problems and realize efficient and accurate crop cultivation. 【0089】 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. 【0090】 In this invention, the server includes an information gathering means for collecting environmental information, an analysis means for analyzing the environmental information collected by the information gathering means, and a plan generation means for generating an optimal cultivation plan based on the analysis results of the analysis means. This enables the automation and efficiency of agricultural management in urban areas. 【0091】 "Environmental information" refers to natural conditions and weather data that affect crop cultivation, such as temperature, humidity, and soil conditions. 【0092】 "Information gathering means" refers to devices or systems that have the function of acquiring and collecting environmental information using sensor devices. 【0093】 "Analysis means" refers to devices or programs that have the function of processing and analyzing collected environmental information to obtain useful insights regarding cultivation. 【0094】 A "plan generation means" refers to a device or system that has the function of creating the most suitable schedule and method for cultivating crops based on information obtained by an analysis means. 【0095】 "Notification means" refers to devices or applications that have the function of communicating the generated cultivation plan to the user. 【0096】 "Automation methods" refer to devices and systems that have the function of performing agricultural work by machine or system rather than manually. 【0097】 This invention provides a system for efficiently managing crop cultivation in urban areas. The system includes information gathering means, analysis means, plan generation means, notification means, and automation means. 【0098】 The server uses sensor devices to collect environmental information. These devices are installed in agricultural fields and periodically measure data such as temperature, humidity, and soil conditions. The collected data is transmitted to the server via a wireless network. 【0099】 The server analyzes the received environmental information. The analysis process includes data filtering and imputation of missing values. Then, based on the data preprocessed by the analysis, a learning model is used to generate an optimal cultivation plan. This learning model can learn from existing information and make advanced predictions that take environmental fluctuations into account. 【0100】 The generated plan is sent to the user's device via a notification system. The user can review the plan through their device and understand the next steps in the farming process. If necessary, IoT-enabled automated equipment will execute the tasks. 【0101】 For example, in a community garden within an urban area, if the weekend forecast indicates rising temperatures, users will be notified that additional watering is necessary. Users can then use the app to instruct automated watering systems to do so. 【0102】 An example of a prompt given to a generating AI model would be, "Based on historical temperature and humidity data, please generate the optimal fertilization schedule for next week." 【0103】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0104】 Step 1: 【0105】 The server collects environmental information from sensor devices. This information includes temperature, humidity, and soil conditions, and is transmitted to the server periodically via a wireless network. The input is measurement data from the sensor devices, and the output is recorded in the server. 【0106】 Step 2: 【0107】 The server analyzes the collected environmental information. It removes noise through filtering, imputes missing data values, and cleans the data using statistical methods. The input is the data recorded in step 1, and the output is the clean data after analysis. 【0108】 Step 3: 【0109】 The server generates an optimal cultivation plan using a generative AI model based on the analyzed environmental information. It makes predictions about cultivation, taking into account historical data and regional climate patterns, and formulates watering and fertilization schedules. The input is the clean data from step 2, and the output is a specific cultivation plan. 【0110】 Step 4: 【0111】 The server notifies the terminal of the generated cultivation plan. The notified plan is displayed to the user through the app, clearly indicating the next steps of the farming work to be done. The input is the cultivation plan from step 3, and the output is the user-oriented instruction information displayed on the terminal. 【0112】 Step 5: 【0113】 Users can view information received through their devices and operate IoT-enabled automation devices. The automation devices automatically perform tasks such as watering and fertilizing based on specific instructions. The input is the user's instructed operation, and the output is the performed agricultural work. 【0114】 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. 【0115】 This invention provides a system that collects and analyzes environmental data to improve the efficiency of crop cultivation processes, generates an optimal cultivation plan, and notifies the user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the user experience can be enhanced. 【0116】 The system acquires environmental data from the farm using temperature, humidity, and soil condition sensors. This data is transmitted to a server via a terminal, where it is analyzed. The analysis uses machine learning models based on historical data and regional climate. This model leverages the environmental data to predict optimal growing conditions. 【0117】 Next, the server uses this analysis to generate a cultivation plan to promote optimal crop growth and notifies the user. This is where the emotion engine plays a crucial role. The emotion engine evaluates how the user interacts with the system and customizes the content of notifications and suggestions according to the user's emotions. For example, if the user is feeling stressed, the suggestions can be made more concise or encouraging messages can be added. 【0118】 Users can receive notifications and view plan details on their devices. Because an emotion engine is built in, the content of the notifications is optimized for each individual user. For example, an excited user can be provided with additional information about a new fertilization method to facilitate a deeper understanding. 【0119】 This invention enables efficient and user-friendly cultivation management by combining advanced analysis based on environmental data with appropriate responses based on user emotions. Specific application scenarios include appropriate water management during the rapid growth phase of crops and optimization of harvest timing. This simultaneously improves crop growth and enhances the work efficiency of agricultural workers. 【0120】 The following describes the processing flow. 【0121】 Step 1: 【0122】 The terminal periodically collects environmental data using temperature, humidity, and soil condition sensors placed throughout the farm. This data is collected in real time from the sensors and temporarily stored on the terminal. 【0123】 Step 2: 【0124】 The terminal transmits the collected environmental data to the server at regular intervals. The transmitted data includes identification information and timestamps for each sensor. 【0125】 Step 3: 【0126】 The server receives data sent from the terminal and verifies its integrity and completeness. If an anomaly is detected, the data is logged and excluded from analysis. 【0127】 Step 4: 【0128】 The server preprocesses the received data. Specifically, it performs tasks such as imputing missing values, filtering outliers, and removing noise to prepare the data for analysis. 【0129】 Step 5: 【0130】 The server inputs pre-processed data into machine learning algorithms based on historical data and regional climate models to predict crop growth. Based on the analysis results, it develops an optimal cultivation plan. 【0131】 Step 6: 【0132】 The server sends the generated cultivation plan to the emotion engine and adjusts the notification content based on the user's current emotional state. It also refers to the user's past response history to customize how the plan is presented and what it contains. 【0133】 Step 7: 【0134】 The server notifies the user's device of the customized cultivation plan. At this stage, depending on the user's emotional state, it may include encouraging messages or additional explanatory information. 【0135】 Step 8: 【0136】 Users check notifications received on their devices and perform necessary cultivation tasks manually or through automated tools. After completing the tasks, users send feedback to the server via their devices, contributing to improving the accuracy of the emotion engine. 【0137】 This series of processes enables efficient cultivation management tailored to the condition of the crops, improving the user experience. 【0138】 (Example 2) 【0139】 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." 【0140】 Efficient crop cultivation requires proper environmental management and rapid response, but conventional technologies are not sufficiently efficient in collecting and analyzing environmental data, making it time-consuming to generate optimal cultivation plans. Furthermore, information is not provided in a way that takes into account the user's emotional state, indicating room for improvement in the user experience. 【0141】 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. 【0142】 In this invention, the server includes information gathering means for collecting environmental information, analysis means for analyzing the collected environmental information, plan generation means for generating an optimal cultivation plan based on the analysis results, and emotion recognition means. This enables efficient processing of environmental data and the provision of an optimal cultivation plan that responds to the user's emotions. 【0143】 "Environmental information" refers to information that indicates external factors related to crop cultivation, including temperature, humidity, and soil conditions. 【0144】 "Information gathering means" is a general term for devices and technologies used to acquire environmental information, and includes data acquisition devices such as sensors. 【0145】 "Analysis methods" is a general term for technologies and devices used to analyze collected environmental information and derive useful insights and predictions. 【0146】 "Plan generation means" is a general term for technologies and systems that create an execution plan for optimal crop cultivation based on analysis results. 【0147】 "Notification means" is a general term for methods and devices used to communicate generated plans and information to users. 【0148】 "Emotion recognition means" is a general term for technologies and devices that evaluate a user's emotional state and enable the provision of adapted information based on that feedback. 【0149】 This invention is a system that supports the efficient cultivation of crops by collecting and analyzing environmental information and providing the user with an optimal cultivation plan. The main components of the system include information collection means, analysis means, plan generation means, notification means, and emotion recognition means. 【0150】 The server uses devices such as temperature sensors, humidity sensors, and soil condition sensors to collect environmental information. These devices are placed on the farm and acquire information in real time. The terminal then transmits this environmental information to the server. 【0151】 The server uses machine learning models based on historical data and regional climate to analyze the collected data. These models are built using machine learning libraries such as TENSORFLOW® and PyTorch, enabling them to predict changes in environmental conditions. Based on these analysis results, the server generates an optimal cultivation plan. 【0152】 The server's plan generation mechanism uses this analysis result to plan suitable growth conditions for the crop, proposing things like irrigation schedules and fertilization timings. The generated plan is sent to the user's terminal via a notification mechanism. 【0153】 Emotion recognition tools evaluate user feedback and reactions and customize notifications accordingly. If a user is feeling stressed, suggestions can be made more concise, or encouraging messages can be added. For example, if a user is agitated, additional information on a new fertilization method can be provided to deepen their understanding. 【0154】 As an example of a prompt, the generating AI model could be asked a question such as, "If the user is feeling stressed, what encouraging message should be added to the cultivation plan notification?" This improves the system's user experience and enables more efficient and effective crop cultivation. 【0155】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0156】 Step 1: 【0157】 The server collects environmental information from the farm using temperature, humidity, and soil condition sensors. Terminals periodically transmit data from these sensors to the server. Inputs are real-time data from each sensor, while outputs are time-series data stored in a database. This data is accumulated for future analysis. 【0158】 Step 2: 【0159】 The server analyzes the collected environmental information. The input is accumulated historical environmental data, and the output is the analysis results indicating the optimal cultivation conditions. Specifically, the server processes the data using a machine learning model. The model predicts future environmental conditions based on past climate patterns and plant growth history. 【0160】 Step 3: 【0161】 The server generates an optimal cultivation plan based on the analysis results. The input is the analysis results obtained in step 2, and the output is a detailed cultivation plan. For example, it generates irrigation timing and fertilization schedules. This plan is optimized to maximize plant growth. 【0162】 Step 4: 【0163】 The server sends the generated cultivation plan to the user via a notification system. The input is the cultivation plan generated in step 3, and the output is a notification of the plan displayed on the user's terminal. The user can receive the notification using their terminal and check the details. 【0164】 Step 5: 【0165】 Emotion recognition systems collect user feedback and evaluate the user's emotional state. Input is the user's feedback and responses, while output is customized advice and supplementary information tailored to the user's emotions. For example, if a user is experiencing stress, the system can adjust the notification content and add encouraging messages. 【0166】 (Application Example 2) 【0167】 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". 【0168】 In today's increasingly urbanized world, many individuals and communities are showing interest in urban gardening. However, novice gardeners often face difficulties in proper environmental management and cultivation planning, as well as a lack of flexible advice tailored to their individual needs. In this context, there is a need for a system that enables efficient and optimal growth management tailored to individual circumstances. 【0169】 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. 【0170】 In this invention, the server includes information gathering means for collecting environmental information, analysis means for analyzing the environmental information collected by the information gathering means, and plan generation means for generating an optimal growth plan based on the analysis results of the analysis means. This makes it possible to provide each user with an optimal growth plan based on the information, and further adjust the content of notifications according to the user's emotions. 【0171】 "Environmental information" refers to data about the surrounding conditions that affect the growth of crops, and includes temperature, humidity, soil conditions, and so on. 【0172】 "Information gathering means" is a general term for equipment and sensors used to acquire environmental information, and includes temperature sensors, humidity sensors, soil condition sensors, etc. 【0173】 "Analytical means" refers to the processes and methods used to analyze collected environmental information and derive meaningful conclusions from the results. 【0174】 A "growth plan" is a specific guideline, generated through analytical methods, aimed at the effective cultivation of crops. 【0175】 "Plan generation means" refers to devices or software used to create growth plans based on the results of analysis means. 【0176】 "Notification means" refers to methods and means for informing users of the generated growth plan, and includes information terminals. 【0177】 "Emotional state" refers to the user's current psychological condition, and includes stress, anxiety, excitement, etc. 【0178】 "Emotional evaluation means" refers to technologies and devices used to measure and evaluate a user's emotional state. 【0179】 "Adjustment means" refers to a device or process for changing the content or method of notifications based on the user's emotional state. 【0180】 This system is designed to optimize crop growth and includes sensors for collecting environmental information, a server for analyzing the information, and terminals for notifying users. 【0181】 The server first receives environmental information from temperature sensors, humidity sensors, and soil condition sensors connected to the information gathering system. These sensors are installed at different points on the farmland and acquire data in real time. Next, the server processes the collected information using an analysis system. The analysis system incorporates a machine learning algorithm that has learned from historical data and local climate conditions, and uses a Python framework (e.g., TensorFlow). Based on this processing, the server generates a growth plan. 【0182】 Once a growth plan is generated, the server notifies the user's device. The notification is sent via a digital device such as a smartphone or tablet, and the notification content is adjusted based on the user's current emotional state using an emotion evaluation tool. The emotion evaluation tool uses an API (e.g., Azure® Emotion API) that analyzes the user's voice and input data, and the optimal notification content is determined based on the results. 【0183】 For example, suppose a user is managing a vegetable garden in a smart city, and based on data from sensors, it is determined that the garden is not being watered sufficiently. In this case, the server generates a notification recommending watering. At the same time, if the notification receives an emotional assessment indicating that the user is feeling anxious, it will also include an encouraging message such as, "It's dry, but don't panic, let's deal with it." Other examples of prompts include, "It looks like it's going to rain, do I need to water the plants?" or "How can I grow healthier tomato seedlings?" 【0184】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0185】 Step 1: 【0186】 The server receives environmental information from temperature sensors, humidity sensors, and soil condition sensors. The sensors transmit data in real time from their respective points, and the server integrates and preprocesses this data. For example, it imputes missing values ​​and detects and removes outliers. 【0187】 Step 2: 【0188】 The server feeds pre-processed environmental information into a machine learning algorithm. Using the Python TensorFlow framework, it applies a model trained on historical data and regional climate conditions to perform the analysis. Based on the input information, it calculates plant growth predictions and generates an optimal growth plan. 【0189】 Step 3: 【0190】 After the server generates a growth plan, it sends a summary of the plan to the terminal. This growth plan includes specific measures (e.g., watering frequency and fertilizer amount). The plan data is encoded and compressed before transmission, minimizing communication load. 【0191】 Step 4: 【0192】 The terminal presents the user with a growth plan received from the server. The user can review recommended measures on the screen and understand the specific steps for implementation. In this process, an intuitive user interface is crucial, including visual guidelines. 【0193】 Step 5: 【0194】 The device analyzes the user's voice data and touch input to estimate their emotional state for emotion assessment. It uses the Azure Emotion API to send the acquired emotional state data as feedback to the server. 【0195】 Step 6: 【0196】 The server adjusts the notification content based on the emotional state data it receives. For example, if the user is feeling anxious, it adds an encouraging message to the growth plan notification. The adjusted notification message is then sent to the device. 【0197】 Step 7: 【0198】 The device presents users with messages tailored to their final growth plan and emotions. This allows users to work with confidence and provide feedback to the system as needed. 【0199】 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. 【0200】 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. 【0201】 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. 【0202】 [Second Embodiment] 【0203】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0204】 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. 【0205】 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). 【0206】 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. 【0207】 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. 【0208】 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). 【0209】 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. 【0210】 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. 【0211】 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. 【0212】 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. 【0213】 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. 【0214】 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". 【0215】 This invention provides an information processing system for streamlining the crop cultivation process, and in particular aims to automate a series of processes such as collecting and analyzing environmental data, generating cultivation plans, and notifying users. 【0216】 The system uses multiple sensor devices to collect environmental data such as temperature, humidity, and soil conditions within the farm. These sensor devices are placed around the crops and have the function of periodically measuring data. The obtained data is transmitted to a central server via a wireless network. 【0217】 The server is responsible for analyzing the received environmental data. This analysis process includes filtering the data and imputing missing values. The appropriately pre-processed data is then fed into a machine learning model to generate an optimal crop cultivation plan. This model has learned from existing data and can make advanced predictions based on climate and growth patterns. 【0218】 The generated cultivation plan includes specific details of agricultural tasks such as watering, fertilizing, and harvesting timing. Once this plan is complete, the server notifies the user's device of the information. 【0219】 The user's device, such as a smartphone or tablet, receives the cultivation plan sent from the server. This allows the user to see the next steps to be taken on the farm. For example, they might be notified that they need to water the plants early in the morning based on a forecast of high temperatures the following day. The user can then manually perform the necessary tasks according to the instructions on their device. 【0220】 Furthermore, the system can be integrated with IoT-enabled automation tools, allowing for the automation of watering and fertilizing operations as needed. This feature further reduces the user's workload by accurately and efficiently executing tasks as instructed. 【0221】 The following describes the processing flow. 【0222】 Step 1: 【0223】 The terminal uses temperature, humidity, and soil condition sensors placed on the farm to acquire environmental data at regular intervals. The acquired data is temporarily stored in the terminal and waits until the next communication timing. 【0224】 Step 2: 【0225】 The terminal transmits collected environmental data to the server via a wireless communication network. Transmission is performed in batches, with data sent according to availability and communication conditions. 【0226】 Step 3: 【0227】 The server receives data from the terminal and performs a format check. If any data abnormalities or missing data are detected, the server logs the problem and performs corrective processing to proceed to the next processing step. 【0228】 Step 4: 【0229】 The server preprocesses the received dataset. Specifically, it performs missing value imputation, filters out large outliers, and normalizes the data as needed. This prepares the data for optimal input to machine learning models. 【0230】 Step 5: 【0231】 The server feeds pre-processed data to a machine learning model and begins the analysis. Based on historical and current environmental data, the model predicts crop growth and nutrient demand and generates an optimal cultivation plan. 【0232】 Step 6: 【0233】 The server begins preparing to notify the user of the generated cultivation plan. It converts the plan, including specific farming details, into a push notification format and sends it to the user's device. 【0234】 Step 7: 【0235】 Users check their cultivation plan on their devices. They review the planned watering and fertilizing schedules and perform farming tasks accordingly if manual work is required. 【0236】 Step 8: 【0237】 If a user has automated IoT devices, they can send plan-based instructions via a server to control the devices. This allows for automated watering and fertilizing. 【0238】 (Example 1) 【0239】 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." 【0240】 In the crop cultivation process, there is a need for the automated generation and implementation of efficient cultivation plans based on environmental information. However, the conventional manual collection and analysis of environmental information is time-consuming and labor-intensive, and it is difficult to quickly formulate appropriate cultivation plans. Therefore, a system is needed that reduces the burden on farmers while providing optimal cultivation conditions. 【0241】 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. 【0242】 In this invention, the server includes data collection means for collecting environmental information, data processing means for preprocessing the environmental information collected by the data collection means, and plan generation means for generating an optimal cultivation plan using a machine learning algorithm based on the results of the data processing means. This makes it possible to quickly and automatically create an optimal cultivation plan based on the environmental conditions of the farm and notify the user. 【0243】 "Environmental information" is a general term for information related to weather and ground conditions on a farm, such as temperature, humidity, and soil conditions. 【0244】 "Data collection methods" refer to devices and technologies for acquiring environmental information, and mainly include temperature measuring devices, humidity measuring devices, and soil condition measuring devices. 【0245】 "Data processing means" refers to technologies and processes for organizing collected environmental information, removing unnecessary data, and imputing missing values. 【0246】 "Plan generation means" refers to a function that automatically creates an optimal cultivation plan using a machine learning algorithm based on pre-processed data. 【0247】 "Notification means" refers to a mechanism for informing users of the generated cultivation plan, and includes the process of sending information using email or a dedicated application. 【0248】 A "machine learning algorithm" is a series of computational methods that automatically learn from past data and perform pattern recognition and prediction. 【0249】 This invention provides an information processing system for streamlining the crop cultivation process. Specifically, it is a system that integrates the collection of environmental information, automatic analysis, generation of cultivation plans, and notification into a single workflow. 【0250】 First, the server collects environmental information about the farm using data collection devices such as temperature measuring devices, humidity measuring devices, and soil condition measuring devices installed on the farm. These sensor devices are placed in appropriate locations and are configured to measure collected data at a specified frequency and transmit it to the server via a wireless network. 【0251】 Next, the server preprocesses the received data using data processing techniques such as noise filtering and missing value imputation. The preprocessed data is then analyzed by a planning generation method using a generative AI model, and an optimal cultivation plan is automatically generated. Here, machine learning algorithms based on past weather patterns and crop growth data are used, and for example, a fertilization schedule is created that takes into account weather forecasts such as "continuous rain in the middle of next week." 【0252】 The generated cultivation plan is transmitted from the server to the user's terminal via a notification system. The user's terminal is assumed to be a smartphone or tablet-type information processing terminal. The user can check the specific steps of the next farming task on the terminal screen. For example, instructions such as "The temperature will drop tomorrow morning, so please take measures to protect against the cold" will be notified. 【0253】 Furthermore, this system can integrate with IoT technology to enable automated watering and fertilization, thereby increasing work efficiency. An example of a prompt message input to the AI ​​model would be, "Based on the temperature, humidity, and soil condition data for the past three days, please suggest the optimal next steps for tomato cultivation." Based on this prompt, the AI ​​model would recommend the next steps to be taken, which the user can then view on their device. 【0254】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0255】 Step 1: 【0256】 The server collects environmental information through various measuring devices placed on the farm. The input consists of raw data from temperature, humidity, and soil condition sensors. This data is collected in real time or at set time intervals and transmitted to the server via a wireless network. Specifically, each sensor records a specific measurement and sends it to the server as digital data. 【0257】 Step 2: 【0258】 The server preprocesses the collected environmental information using data processing tools. The input is raw data sent from the sensors. Data processing involves removing outliers through noise filtering and ensuring data integrity through missing value imputation. The output becomes clean data suitable for analysis and input to machine learning models. In this process, for example, missing data is imputed using the most recent normal value. 【0259】 Step 3: 【0260】 The server inputs pre-processed data into a generating AI model to create an optimal cultivation plan. The input for this step is clean environmental data, which is then supplied to the AI ​​model. The AI ​​model uses historical data and regional climate data to predict weather patterns, appropriate fertilization schedules, harvest times, and more. The output is set as a specific cultivation plan. For example, it calculates the appropriate timing for fertilization and watering based on the weather conditions expected for the following week. 【0261】 Step 4: 【0262】 The server notifies the user's terminal of the generated cultivation plan. The input is the cultivation plan generated by the AI ​​model. This plan is sent to the user's terminal via a notification system and made available for display. The output is the farming procedures and precautions displayed on the terminal. For example, an instruction such as "A drop in temperature is expected the following day, so take measures to protect against the cold" is sent. 【0263】 Step 5: 【0264】 The user performs the necessary tasks on the farm based on the cultivation plan instructions received on the terminal. The input is the content of the cultivation plan displayed on the terminal. The user can refer to this and perform tasks such as watering and fertilizing manually, or have them performed automatically using IoT devices. The output is the farm work that has been performed. This process allows the user to perform farm work efficiently. 【0265】 (Application Example 1) 【0266】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0267】 In urban areas and community farms, crop cultivation management is generally done manually, which presents challenges in terms of efficiency and accuracy. In particular, timely watering and fertilization are difficult due to fluctuations in climate and soil conditions, increasing the burden on workers. There is a need for a system that can solve these problems and realize efficient and accurate crop cultivation. 【0268】 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. 【0269】 In this invention, the server includes an information gathering means for collecting environmental information, an analysis means for analyzing the environmental information collected by the information gathering means, and a plan generation means for generating an optimal cultivation plan based on the analysis results of the analysis means. This enables the automation and efficiency of agricultural management in urban areas. 【0270】 "Environmental information" refers to natural conditions and weather data that affect crop cultivation, such as temperature, humidity, and soil conditions. 【0271】 "Information gathering means" refers to devices or systems that have the function of acquiring and collecting environmental information using sensor devices. 【0272】 "Analysis means" refers to devices or programs that have the function of processing and analyzing collected environmental information to obtain useful insights regarding cultivation. 【0273】 A "plan generation means" refers to a device or system that has the function of creating the most suitable schedule and method for cultivating crops based on information obtained by an analysis means. 【0274】 "Notification means" refers to devices or applications that have the function of communicating the generated cultivation plan to the user. 【0275】 "Automation methods" refer to devices and systems that have the function of performing agricultural work by machine or system rather than manually. 【0276】 This invention provides a system for efficiently managing crop cultivation in urban areas. The system includes information gathering means, analysis means, plan generation means, notification means, and automation means. 【0277】 The server uses sensor devices to collect environmental information. These devices are installed in agricultural fields and periodically measure data such as temperature, humidity, and soil conditions. The collected data is transmitted to the server via a wireless network. 【0278】 The server analyzes the received environmental information. The analysis process includes data filtering and imputation of missing values. Then, based on the data preprocessed by the analysis, a learning model is used to generate an optimal cultivation plan. This learning model can learn from existing information and make advanced predictions that take environmental fluctuations into account. 【0279】 The generated plan is sent to the user's device via a notification system. The user can review the plan through their device and understand the next steps in the farming process. If necessary, IoT-enabled automated equipment will execute the tasks. 【0280】 For example, in a community garden within an urban area, if the weekend forecast indicates rising temperatures, users will be notified that additional watering is necessary. Users can then use the app to instruct automated watering systems to do so. 【0281】 An example of a prompt given to a generating AI model would be, "Based on historical temperature and humidity data, please generate the optimal fertilization schedule for next week." 【0282】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0283】 Step 1: 【0284】 The server collects environmental information from the sensor device. This information includes temperature, humidity, soil conditions, etc., and is periodically transmitted to the server via a wireless network. The input is the measurement data from the sensor device, and the output is recorded in the server. 【0285】 Step 2: 【0286】 The server analyzes the collected environmental information. It removes noise through filtering, performs missing value imputation on the data, and cleans the data using statistical methods. The input is the data recorded in Step 1, and the output is the cleaned data after analysis. 【0287】 Step 3: 【0288】 Based on the analyzed environmental information, the server uses a generative AI model to generate an optimal cultivation plan. It makes predictions regarding cultivation while considering past data and regional climate patterns, and formulates schedules for irrigation and fertilization. The input is the clean data from Step 2, and the output is a specific cultivation plan. 【0289】 Step 4: 【0290】 The server notifies the terminal of the generated cultivation plan. The notified plan is displayed to the user through an app, clearly indicating the steps of the farming operations to be performed next. The input is the cultivation plan from Step 3, and the output is the instruction information for the user displayed on the terminal. 【0291】 Step 5: 【0292】 The user can check the information received through the terminal and operate IoT-enabled automation means. The automation device automatically performs irrigation and fertilization based on specific instructions. The input is the operation instructed by the user, and the output is the farming operations executed. 【0293】 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. 【0294】 This invention provides a system that collects and analyzes environmental data to improve the efficiency of crop cultivation processes, generates an optimal cultivation plan, and notifies the user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the user experience can be enhanced. 【0295】 The system acquires environmental data from the farm using temperature, humidity, and soil condition sensors. This data is transmitted to a server via a terminal, where it is analyzed. The analysis uses machine learning models based on historical data and regional climate. This model leverages the environmental data to predict optimal growing conditions. 【0296】 Next, the server uses this analysis to generate a cultivation plan to promote optimal crop growth and notifies the user. This is where the emotion engine plays a crucial role. The emotion engine evaluates how the user interacts with the system and customizes the content of notifications and suggestions according to the user's emotions. For example, if the user is feeling stressed, the suggestions can be made more concise or encouraging messages can be added. 【0297】 Users can receive notifications and view plan details on their devices. Because an emotion engine is built in, the content of the notifications is optimized for each individual user. For example, an excited user can be provided with additional information about a new fertilization method to facilitate a deeper understanding. 【0298】 This invention enables efficient and user-friendly cultivation management by realizing advanced analysis based on environmental data and appropriate responses based on the user's emotions. Specific application scenarios include appropriate water management during the rapid growth period of crops and optimization of the harvest time. This can simultaneously achieve an improvement in the growth state of agricultural crops and an enhancement in the work efficiency of agricultural workers. 【0299】 The processing flow is described below. 【0300】 Step 1: 【0301】 The terminal periodically collects environmental data using temperature sensors, humidity sensors, and soil condition sensors arranged throughout the farm. These data are integrated from the sensors in real time and temporarily stored in the terminal. 【0302】 Step 2: 【0303】 The terminal transmits the collected environmental data to the server at regular intervals. The data to be transmitted also includes the identification information and timestamp of each sensor. 【0304】 Step 3: 【0305】 The server receives the data transmitted from the terminal and checks the integrity and completeness of the data. If an abnormality is detected, the data is recorded in a log and excluded from the analysis. 【0306】 Step 4: 【0307】 The server preprocesses the received data. Specifically, it performs tasks such as complementing missing values, filtering outliers, and removing noise to prepare the data in a form suitable for analysis. 【0308】 Step 5: 【0309】 The server inputs pre-processed data into machine learning algorithms based on historical data and regional climate models to predict crop growth. Based on the analysis results, it develops an optimal cultivation plan. 【0310】 Step 6: 【0311】 The server sends the generated cultivation plan to the emotion engine and adjusts the notification content based on the user's current emotional state. It also refers to the user's past response history to customize how the plan is presented and what it contains. 【0312】 Step 7: 【0313】 The server notifies the user's device of the customized cultivation plan. At this stage, depending on the user's emotional state, it may include encouraging messages or additional explanatory information. 【0314】 Step 8: 【0315】 Users check notifications received on their devices and perform necessary cultivation tasks manually or through automated tools. After completing the tasks, users send feedback to the server via their devices, contributing to improving the accuracy of the emotion engine. 【0316】 This series of processes enables efficient cultivation management tailored to the condition of the crops, improving the user experience. 【0317】 (Example 2) 【0318】 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". 【0319】 Efficient crop cultivation requires proper environmental management and rapid response, but conventional technologies are not sufficiently efficient in collecting and analyzing environmental data, making it time-consuming to generate optimal cultivation plans. Furthermore, information is not provided in a way that takes into account the user's emotional state, indicating room for improvement in the user experience. 【0320】 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. 【0321】 In this invention, the server includes information gathering means for collecting environmental information, analysis means for analyzing the collected environmental information, plan generation means for generating an optimal cultivation plan based on the analysis results, and emotion recognition means. This enables efficient processing of environmental data and the provision of an optimal cultivation plan that responds to the user's emotions. 【0322】 "Environmental information" refers to information that indicates external factors related to crop cultivation, including temperature, humidity, and soil conditions. 【0323】 "Information gathering means" is a general term for devices and technologies used to acquire environmental information, and includes data acquisition devices such as sensors. 【0324】 "Analysis methods" is a general term for technologies and devices used to analyze collected environmental information and derive useful insights and predictions. 【0325】 "Plan generation means" is a general term for technologies and systems that create an execution plan for optimal crop cultivation based on analysis results. 【0326】 "Notification means" is a general term for methods and devices used to communicate generated plans and information to users. 【0327】 "Emotion recognition means" is a general term for technologies and devices that evaluate a user's emotional state and enable the provision of adapted information based on that feedback. 【0328】 This invention is a system that supports the efficient cultivation of crops by collecting and analyzing environmental information and providing the user with an optimal cultivation plan. The main components of the system include information collection means, analysis means, plan generation means, notification means, and emotion recognition means. 【0329】 The server uses devices such as temperature sensors, humidity sensors, and soil condition sensors to collect environmental information. These devices are placed on the farm and acquire information in real time. The terminal then transmits this environmental information to the server. 【0330】 The server uses machine learning models based on historical data and regional climate to analyze the collected data. These models are built using machine learning libraries such as TensorFlow and PyTorch, and are capable of predicting changes in environmental conditions. Based on the analysis results obtained, the server generates an optimal cultivation plan. 【0331】 The server's plan generation mechanism uses this analysis result to plan suitable growth conditions for the crop, proposing things like irrigation schedules and fertilization timings. The generated plan is sent to the user's terminal via a notification mechanism. 【0332】 Emotion recognition tools evaluate user feedback and reactions and customize notifications accordingly. If a user is feeling stressed, suggestions can be made more concise, or encouraging messages can be added. For example, if a user is agitated, additional information on a new fertilization method can be provided to deepen their understanding. 【0333】 As an example of a prompt, the generating AI model could be asked a question such as, "If the user is feeling stressed, what encouraging message should be added to the cultivation plan notification?" This improves the system's user experience and enables more efficient and effective crop cultivation. 【0334】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0335】 Step 1: 【0336】 The server collects environmental information from the farm using temperature, humidity, and soil condition sensors. Terminals periodically transmit data from these sensors to the server. Inputs are real-time data from each sensor, while outputs are time-series data stored in a database. This data is accumulated for future analysis. 【0337】 Step 2: 【0338】 The server analyzes the collected environmental information. The input is accumulated historical environmental data, and the output is the analysis results indicating the optimal cultivation conditions. Specifically, the server processes the data using a machine learning model. The model predicts future environmental conditions based on past climate patterns and plant growth history. 【0339】 Step 3: 【0340】 The server generates an optimal cultivation plan based on the analysis results. The input is the analysis results obtained in step 2, and the output is a detailed cultivation plan. For example, it generates irrigation timing and fertilization schedules. This plan is optimized to maximize plant growth. 【0341】 Step 4: 【0342】 The server sends the generated cultivation plan to the user via a notification system. The input is the cultivation plan generated in step 3, and the output is a notification of the plan displayed on the user's terminal. The user can receive the notification using their terminal and check the details. 【0343】 Step 5: 【0344】 Emotion recognition systems collect user feedback and evaluate the user's emotional state. Input is the user's feedback and responses, while output is customized advice and supplementary information tailored to the user's emotions. For example, if a user is experiencing stress, the system can adjust the notification content and add encouraging messages. 【0345】 (Application Example 2) 【0346】 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." 【0347】 In today's increasingly urbanized world, many individuals and communities are showing interest in urban gardening. However, novice gardeners often face difficulties in proper environmental management and cultivation planning, as well as a lack of flexible advice tailored to their individual needs. In this context, there is a need for a system that enables efficient and optimal growth management tailored to individual circumstances. 【0348】 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. 【0349】 In this invention, the server includes information gathering means for collecting environmental information, analysis means for analyzing the environmental information collected by the information gathering means, and plan generation means for generating an optimal growth plan based on the analysis results of the analysis means. This makes it possible to provide each user with an optimal growth plan based on the information, and further adjust the content of notifications according to the user's emotions. 【0350】 "Environmental information" refers to data about the surrounding conditions that affect the growth of crops, and includes temperature, humidity, soil conditions, and so on. 【0351】 "Information gathering means" is a general term for equipment and sensors used to acquire environmental information, and includes temperature sensors, humidity sensors, soil condition sensors, etc. 【0352】 "Analytical means" refers to the processes and methods used to analyze collected environmental information and derive meaningful conclusions from the results. 【0353】 A "growth plan" is a specific guideline, generated through analytical methods, aimed at the effective cultivation of crops. 【0354】 "Plan generation means" refers to devices or software used to create growth plans based on the results of analysis means. 【0355】 "Notification means" refers to methods and means for informing users of the generated growth plan, and includes information terminals. 【0356】 "Emotional state" refers to the user's current psychological condition, and includes stress, anxiety, excitement, etc. 【0357】 "Emotional evaluation means" refers to technologies and devices used to measure and evaluate a user's emotional state. 【0358】 "Adjustment means" refers to a device or process for changing the content or method of notifications based on the user's emotional state. 【0359】 This system is designed to optimize crop growth and includes sensors for collecting environmental information, a server for analyzing the information, and terminals for notifying users. 【0360】 The server first receives environmental information from temperature sensors, humidity sensors, and soil condition sensors connected to the information gathering system. These sensors are installed at different points on the farmland and acquire data in real time. Next, the server processes the collected information using an analysis system. The analysis system incorporates a machine learning algorithm that has learned from historical data and local climate conditions, and uses a Python framework (e.g., TensorFlow). Based on this processing, the server generates a growth plan. 【0361】 Once a growth plan is generated, the server notifies the user's device. The notification is made up of digital devices such as smartphones and tablets, and the notification content is adjusted based on the user's current emotional state using an emotion evaluation tool. The emotion evaluation tool uses an API (e.g., Azure Emotion API) that analyzes the user's voice and input data, and the optimal notification content is determined based on the results. 【0362】 For example, suppose a user is managing a vegetable garden in a smart city, and based on data from sensors, it is determined that the garden is not being watered sufficiently. In this case, the server generates a notification recommending watering. At the same time, if the notification receives an emotional assessment indicating that the user is feeling anxious, it will also include an encouraging message such as, "It's dry, but don't panic, let's deal with it." Other examples of prompts include, "It looks like it's going to rain, do I need to water the plants?" or "How can I grow healthier tomato seedlings?" 【0363】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0364】 Step 1: 【0365】 The server receives environmental information from temperature sensors, humidity sensors, and soil condition sensors. The sensors transmit data in real time from their respective points, and the server integrates and preprocesses this data. For example, it imputes missing values ​​and detects and removes outliers. 【0366】 Step 2: 【0367】 The server feeds pre-processed environmental information into a machine learning algorithm. Using the Python TensorFlow framework, it applies a model trained on historical data and regional climate conditions to perform the analysis. Based on the input information, it calculates plant growth predictions and generates an optimal growth plan. 【0368】 Step 3: 【0369】 After the server generates a growth plan, it sends a summary of the plan to the terminal. This growth plan includes specific measures (e.g., watering frequency and fertilizer amount). The plan data is encoded and compressed before transmission, minimizing communication load. 【0370】 Step 4: 【0371】 The terminal presents the user with a growth plan received from the server. The user can review recommended measures on the screen and understand the specific steps for implementation. In this process, an intuitive user interface is crucial, including visual guidelines. 【0372】 Step 5: 【0373】 The device analyzes the user's voice data and touch input to estimate their emotional state for emotion assessment. It uses the Azure Emotion API to send the acquired emotional state data as feedback to the server. 【0374】 Step 6: 【0375】 The server adjusts the notification content based on the emotional state data it receives. For example, if the user is feeling anxious, it adds an encouraging message to the growth plan notification. The adjusted notification message is then sent to the device. 【0376】 Step 7: 【0377】 The device presents users with messages tailored to their final growth plan and emotions. This allows users to work with confidence and provide feedback to the system as needed. 【0378】 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. 【0379】 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. 【0380】 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. 【0381】 [Third Embodiment] 【0382】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0383】 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. 【0384】 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). 【0385】 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. 【0386】 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. 【0387】 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). 【0388】 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. 【0389】 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. 【0390】 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. 【0391】 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. 【0392】 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. 【0393】 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". 【0394】 This invention provides an information processing system for streamlining the crop cultivation process, and in particular aims to automate a series of processes such as collecting and analyzing environmental data, generating cultivation plans, and notifying users. 【0395】 The system uses multiple sensor devices to collect environmental data such as temperature, humidity, and soil conditions within the farm. These sensor devices are placed around the crops and have the function of periodically measuring data. The obtained data is transmitted to a central server via a wireless network. 【0396】 The server is responsible for analyzing the received environmental data. This analysis process includes filtering the data and imputing missing values. The appropriately pre-processed data is then fed into a machine learning model to generate an optimal crop cultivation plan. This model has learned from existing data and can make advanced predictions based on climate and growth patterns. 【0397】 The generated cultivation plan includes specific details of agricultural tasks such as watering, fertilizing, and harvesting timing. Once this plan is complete, the server notifies the user's device of the information. 【0398】 The user's device, such as a smartphone or tablet, receives the cultivation plan sent from the server. This allows the user to see the next steps to be taken on the farm. For example, they might be notified that they need to water the plants early in the morning based on a forecast of high temperatures the following day. The user can then manually perform the necessary tasks according to the instructions on their device. 【0399】 Furthermore, the system can be integrated with IoT-enabled automation tools, allowing for the automation of watering and fertilizing operations as needed. This feature further reduces the user's workload by accurately and efficiently executing tasks as instructed. 【0400】 The following describes the processing flow. 【0401】 Step 1: 【0402】 The terminal uses temperature, humidity, and soil condition sensors placed on the farm to acquire environmental data at regular intervals. The acquired data is temporarily stored in the terminal and waits until the next communication timing. 【0403】 Step 2: 【0404】 The terminal transmits collected environmental data to the server via a wireless communication network. Transmission is performed in batches, with data sent according to availability and communication conditions. 【0405】 Step 3: 【0406】 The server receives data from the terminal and performs a format check. If any data abnormalities or missing data are detected, the server logs the problem and performs corrective processing to proceed to the next processing step. 【0407】 Step 4: 【0408】 The server preprocesses the received dataset. Specifically, it performs missing value imputation, filters out large outliers, and normalizes the data as needed. This prepares the data for optimal input to machine learning models. 【0409】 Step 5: 【0410】 The server feeds pre-processed data to a machine learning model and begins the analysis. Based on historical and current environmental data, the model predicts crop growth and nutrient demand and generates an optimal cultivation plan. 【0411】 Step 6: 【0412】 The server begins preparing to notify the user of the generated cultivation plan. It converts the plan, including specific farming details, into a push notification format and sends it to the user's device. 【0413】 Step 7: 【0414】 Users check their cultivation plan on their devices. They review the planned watering and fertilizing schedules and perform farming tasks accordingly if manual work is required. 【0415】 Step 8: 【0416】 If a user has automated IoT devices, they can send plan-based instructions via a server to control the devices. This allows for automated watering and fertilizing. 【0417】 (Example 1) 【0418】 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." 【0419】 In the crop cultivation process, there is a need for the automated generation and implementation of efficient cultivation plans based on environmental information. However, the conventional manual collection and analysis of environmental information is time-consuming and labor-intensive, and it is difficult to quickly formulate appropriate cultivation plans. Therefore, a system is needed that reduces the burden on farmers while providing optimal cultivation conditions. 【0420】 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. 【0421】 In this invention, the server includes data collection means for collecting environmental information, data processing means for preprocessing the environmental information collected by the data collection means, and plan generation means for generating an optimal cultivation plan using a machine learning algorithm based on the results of the data processing means. This makes it possible to quickly and automatically create an optimal cultivation plan based on the environmental conditions of the farm and notify the user. 【0422】 "Environmental information" is a general term for information related to weather and ground conditions on a farm, such as temperature, humidity, and soil conditions. 【0423】 "Data collection methods" refer to devices and technologies for acquiring environmental information, and mainly include temperature measuring devices, humidity measuring devices, and soil condition measuring devices. 【0424】 "Data processing means" refers to technologies and processes for organizing collected environmental information, removing unnecessary data, and imputing missing values. 【0425】 "Plan generation means" refers to a function that automatically creates an optimal cultivation plan using a machine learning algorithm based on pre-processed data. 【0426】 "Notification means" refers to a mechanism for informing users of the generated cultivation plan, and includes the process of sending information using email or a dedicated application. 【0427】 A "machine learning algorithm" is a series of computational methods that automatically learn from past data and perform pattern recognition and prediction. 【0428】 This invention provides an information processing system for streamlining the crop cultivation process. Specifically, it is a system that integrates the collection of environmental information, automatic analysis, generation of cultivation plans, and notification into a single workflow. 【0429】 First, the server collects environmental information about the farm using data collection devices such as temperature measuring devices, humidity measuring devices, and soil condition measuring devices installed on the farm. These sensor devices are placed in appropriate locations and are configured to measure collected data at a specified frequency and transmit it to the server via a wireless network. 【0430】 Next, the server preprocesses the received data using data processing techniques such as noise filtering and missing value imputation. The preprocessed data is then analyzed by a planning generation method using a generative AI model, and an optimal cultivation plan is automatically generated. Here, machine learning algorithms based on past weather patterns and crop growth data are used, and for example, a fertilization schedule is created that takes into account weather forecasts such as "continuous rain in the middle of next week." 【0431】 The generated cultivation plan is transmitted from the server to the user's terminal via a notification system. The user's terminal is assumed to be a smartphone or tablet-type information processing terminal. The user can check the specific steps of the next farming task on the terminal screen. For example, instructions such as "The temperature will drop tomorrow morning, so please take measures to protect against the cold" will be notified. 【0432】 Furthermore, this system can integrate with IoT technology to enable automated watering and fertilization, thereby increasing work efficiency. An example of a prompt message input to the AI ​​model would be, "Based on the temperature, humidity, and soil condition data for the past three days, please suggest the optimal next steps for tomato cultivation." Based on this prompt, the AI ​​model would recommend the next steps to be taken, which the user can then view on their device. 【0433】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0434】 Step 1: 【0435】 The server collects environmental information through various measuring devices placed on the farm. The input consists of raw data from temperature, humidity, and soil condition sensors. This data is collected in real time or at set time intervals and transmitted to the server via a wireless network. Specifically, each sensor records a specific measurement and sends it to the server as digital data. 【0436】 Step 2: 【0437】 The server preprocesses the collected environmental information using data processing tools. The input is raw data sent from the sensors. Data processing involves removing outliers through noise filtering and ensuring data integrity through missing value imputation. The output becomes clean data suitable for analysis and input to machine learning models. In this process, for example, missing data is imputed using the most recent normal value. 【0438】 Step 3: 【0439】 The server inputs pre-processed data into a generating AI model to create an optimal cultivation plan. The input for this step is clean environmental data, which is then supplied to the AI ​​model. The AI ​​model uses historical data and regional climate data to predict weather patterns, appropriate fertilization schedules, harvest times, and more. The output is set as a specific cultivation plan. For example, it calculates the appropriate timing for fertilization and watering based on the weather conditions expected for the following week. 【0440】 Step 4: 【0441】 The server notifies the user's terminal of the generated cultivation plan. The input is the cultivation plan generated by the AI ​​model. This plan is sent to the user's terminal via a notification system and made available for display. The output is the farming procedures and precautions displayed on the terminal. For example, an instruction such as "A drop in temperature is expected the following day, so take measures to protect against the cold" is sent. 【0442】 Step 5: 【0443】 The user performs the necessary tasks on the farm based on the cultivation plan instructions received on the terminal. The input is the content of the cultivation plan displayed on the terminal. The user can refer to this and perform tasks such as watering and fertilizing manually, or have them performed automatically using IoT devices. The output is the farm work that has been performed. This process allows the user to perform farm work efficiently. 【0444】 (Application Example 1) 【0445】 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." 【0446】 In urban areas and community farms, crop cultivation management is generally done manually, which presents challenges in terms of efficiency and accuracy. In particular, timely watering and fertilization are difficult due to fluctuations in climate and soil conditions, increasing the burden on workers. There is a need for a system that can solve these problems and realize efficient and accurate crop cultivation. 【0447】 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. 【0448】 In this invention, the server includes an information gathering means for collecting environmental information, an analysis means for analyzing the environmental information collected by the information gathering means, and a plan generation means for generating an optimal cultivation plan based on the analysis results of the analysis means. This enables the automation and efficiency of agricultural management in urban areas. 【0449】 "Environmental information" refers to natural conditions and weather data that affect crop cultivation, such as temperature, humidity, and soil conditions. 【0450】 "Information gathering means" refers to devices or systems that have the function of acquiring and collecting environmental information using sensor devices. 【0451】 "Analysis means" refers to devices or programs that have the function of processing and analyzing collected environmental information to obtain useful insights regarding cultivation. 【0452】 A "plan generation means" refers to a device or system that has the function of creating the most suitable schedule and method for cultivating crops based on information obtained by an analysis means. 【0453】 "Notification means" refers to devices or applications that have the function of communicating the generated cultivation plan to the user. 【0454】 "Automation methods" refer to devices and systems that have the function of performing agricultural work by machine or system rather than manually. 【0455】 This invention provides a system for efficiently managing crop cultivation in urban areas. The system includes information gathering means, analysis means, plan generation means, notification means, and automation means. 【0456】 The server uses sensor devices to collect environmental information. These devices are installed in agricultural fields and periodically measure data such as temperature, humidity, and soil conditions. The collected data is transmitted to the server via a wireless network. 【0457】 The server analyzes the received environmental information. The analysis process includes data filtering and imputation of missing values. Then, based on the data preprocessed by the analysis, a learning model is used to generate an optimal cultivation plan. This learning model can learn from existing information and make advanced predictions that take environmental fluctuations into account. 【0458】 The generated plan is sent to the user's device via a notification system. The user can review the plan through their device and understand the next steps in the farming process. If necessary, IoT-enabled automated equipment will execute the tasks. 【0459】 For example, in a community garden within an urban area, if the weekend forecast indicates rising temperatures, users will be notified that additional watering is necessary. Users can then use the app to instruct automated watering systems to do so. 【0460】 An example of a prompt given to a generating AI model would be, "Based on historical temperature and humidity data, please generate the optimal fertilization schedule for next week." 【0461】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0462】 Step 1: 【0463】 The server collects environmental information from sensor devices. This information includes temperature, humidity, and soil conditions, and is transmitted to the server periodically via a wireless network. The input is measurement data from the sensor devices, and the output is recorded in the server. 【0464】 Step 2: 【0465】 The server analyzes the collected environmental information. It removes noise through filtering, imputes missing data values, and cleans the data using statistical methods. The input is the data recorded in step 1, and the output is the clean data after analysis. 【0466】 Step 3: 【0467】 The server generates an optimal cultivation plan using a generative AI model based on the analyzed environmental information. It makes predictions about cultivation, taking into account historical data and regional climate patterns, and formulates watering and fertilization schedules. The input is the clean data from step 2, and the output is a specific cultivation plan. 【0468】 Step 4: 【0469】 The server notifies the terminal of the generated cultivation plan. The notified plan is displayed to the user through the app, clearly indicating the next steps of the farming work to be done. The input is the cultivation plan from step 3, and the output is the user-oriented instruction information displayed on the terminal. 【0470】 Step 5: 【0471】 Users can view information received through their devices and operate IoT-enabled automation devices. The automation devices automatically perform tasks such as watering and fertilizing based on specific instructions. The input is the user's instructed operation, and the output is the performed agricultural work. 【0472】 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. 【0473】 This invention provides a system that collects and analyzes environmental data to improve the efficiency of crop cultivation processes, generates an optimal cultivation plan, and notifies the user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the user experience can be enhanced. 【0474】 The system acquires environmental data from the farm using temperature, humidity, and soil condition sensors. This data is transmitted to a server via a terminal, where it is analyzed. The analysis uses machine learning models based on historical data and regional climate. This model leverages the environmental data to predict optimal growing conditions. 【0475】 Next, the server uses this analysis to generate a cultivation plan to promote optimal crop growth and notifies the user. This is where the emotion engine plays a crucial role. The emotion engine evaluates how the user interacts with the system and customizes the content of notifications and suggestions according to the user's emotions. For example, if the user is feeling stressed, the suggestions can be made more concise or encouraging messages can be added. 【0476】 Users can receive notifications and view plan details on their devices. Because an emotion engine is built in, the content of the notifications is optimized for each individual user. For example, an excited user can be provided with additional information about a new fertilization method to facilitate a deeper understanding. 【0477】 This invention enables efficient and user-friendly cultivation management by combining advanced analysis based on environmental data with appropriate responses based on user emotions. Specific application scenarios include appropriate water management during the rapid growth phase of crops and optimization of harvest timing. This simultaneously improves crop growth and enhances the work efficiency of agricultural workers. 【0478】 The following describes the processing flow. 【0479】 Step 1: 【0480】 The terminal periodically collects environmental data using temperature, humidity, and soil condition sensors placed throughout the farm. This data is collected in real time from the sensors and temporarily stored on the terminal. 【0481】 Step 2: 【0482】 The terminal transmits the collected environmental data to the server at regular intervals. The transmitted data includes identification information and timestamps for each sensor. 【0483】 Step 3: 【0484】 The server receives data sent from the terminal and verifies its integrity and completeness. If an anomaly is detected, the data is logged and excluded from analysis. 【0485】 Step 4: 【0486】 The server preprocesses the received data. Specifically, it performs tasks such as imputing missing values, filtering outliers, and removing noise to prepare the data for analysis. 【0487】 Step 5: 【0488】 The server inputs pre-processed data into machine learning algorithms based on historical data and regional climate models to predict crop growth. Based on the analysis results, it develops an optimal cultivation plan. 【0489】 Step 6: 【0490】 The server sends the generated cultivation plan to the emotion engine and adjusts the notification content based on the user's current emotional state. It also refers to the user's past response history to customize how the plan is presented and what it contains. 【0491】 Step 7: 【0492】 The server notifies the user's device of the customized cultivation plan. At this stage, depending on the user's emotional state, it may include encouraging messages or additional explanatory information. 【0493】 Step 8: 【0494】 Users check notifications received on their devices and perform necessary cultivation tasks manually or through automated tools. After completing the tasks, users send feedback to the server via their devices, contributing to improving the accuracy of the emotion engine. 【0495】 This series of processes enables efficient cultivation management tailored to the condition of the crops, improving the user experience. 【0496】 (Example 2) 【0497】 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." 【0498】 Efficient crop cultivation requires proper environmental management and rapid response, but conventional technologies are not sufficiently efficient in collecting and analyzing environmental data, making it time-consuming to generate optimal cultivation plans. Furthermore, information is not provided in a way that takes into account the user's emotional state, indicating room for improvement in the user experience. 【0499】 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. 【0500】 In this invention, the server includes information gathering means for collecting environmental information, analysis means for analyzing the collected environmental information, plan generation means for generating an optimal cultivation plan based on the analysis results, and emotion recognition means. This enables efficient processing of environmental data and the provision of an optimal cultivation plan that responds to the user's emotions. 【0501】 "Environmental information" refers to information that indicates external factors related to crop cultivation, including temperature, humidity, and soil conditions. 【0502】 "Information gathering means" is a general term for devices and technologies used to acquire environmental information, and includes data acquisition devices such as sensors. 【0503】 "Analysis methods" is a general term for technologies and devices used to analyze collected environmental information and derive useful insights and predictions. 【0504】 "Plan generation means" is a general term for technologies and systems that create an execution plan for optimal crop cultivation based on analysis results. 【0505】 "Notification means" is a general term for methods and devices used to communicate generated plans and information to users. 【0506】 "Emotion recognition means" is a general term for technologies and devices that evaluate a user's emotional state and enable the provision of adapted information based on that feedback. 【0507】 This invention is a system that supports the efficient cultivation of crops by collecting and analyzing environmental information and providing the user with an optimal cultivation plan. The main components of the system include information collection means, analysis means, plan generation means, notification means, and emotion recognition means. 【0508】 The server uses devices such as temperature sensors, humidity sensors, and soil condition sensors to collect environmental information. These devices are placed on the farm and acquire information in real time. The terminal then transmits this environmental information to the server. 【0509】 The server uses machine learning models based on historical data and regional climate to analyze the collected data. These models are built using machine learning libraries such as TensorFlow and PyTorch, and are capable of predicting changes in environmental conditions. Based on the analysis results obtained, the server generates an optimal cultivation plan. 【0510】 The server's plan generation mechanism uses this analysis result to plan suitable growth conditions for the crop, proposing things like irrigation schedules and fertilization timings. The generated plan is sent to the user's terminal via a notification mechanism. 【0511】 Emotion recognition tools evaluate user feedback and reactions and customize notifications accordingly. If a user is feeling stressed, suggestions can be made more concise, or encouraging messages can be added. For example, if a user is agitated, additional information on a new fertilization method can be provided to deepen their understanding. 【0512】 As an example of a prompt, the generating AI model could be asked a question such as, "If the user is feeling stressed, what encouraging message should be added to the cultivation plan notification?" This improves the system's user experience and enables more efficient and effective crop cultivation. 【0513】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0514】 Step 1: 【0515】 The server collects environmental information from the farm using temperature, humidity, and soil condition sensors. Terminals periodically transmit data from these sensors to the server. Inputs are real-time data from each sensor, while outputs are time-series data stored in a database. This data is accumulated for future analysis. 【0516】 Step 2: 【0517】 The server analyzes the collected environmental information. The input is accumulated historical environmental data, and the output is the analysis results indicating the optimal cultivation conditions. Specifically, the server processes the data using a machine learning model. The model predicts future environmental conditions based on past climate patterns and plant growth history. 【0518】 Step 3: 【0519】 The server generates an optimal cultivation plan based on the analysis results. The input is the analysis results obtained in step 2, and the output is a detailed cultivation plan. For example, it generates irrigation timing and fertilization schedules. This plan is optimized to maximize plant growth. 【0520】 Step 4: 【0521】 The server sends the generated cultivation plan to the user via a notification system. The input is the cultivation plan generated in step 3, and the output is a notification of the plan displayed on the user's terminal. The user can receive the notification using their terminal and check the details. 【0522】 Step 5: 【0523】 Emotion recognition systems collect user feedback and evaluate the user's emotional state. Input is the user's feedback and responses, while output is customized advice and supplementary information tailored to the user's emotions. For example, if a user is experiencing stress, the system can adjust the notification content and add encouraging messages. 【0524】 (Application Example 2) 【0525】 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." 【0526】 In today's increasingly urbanized world, many individuals and communities are showing interest in urban gardening. However, novice gardeners often face difficulties in proper environmental management and cultivation planning, as well as a lack of flexible advice tailored to their individual needs. In this context, there is a need for a system that enables efficient and optimal growth management tailored to individual circumstances. 【0527】 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. 【0528】 In this invention, the server includes information gathering means for collecting environmental information, analysis means for analyzing the environmental information collected by the information gathering means, and plan generation means for generating an optimal growth plan based on the analysis results of the analysis means. This makes it possible to provide each user with an optimal growth plan based on the information, and further adjust the content of notifications according to the user's emotions. 【0529】 "Environmental information" refers to data about the surrounding conditions that affect the growth of crops, and includes temperature, humidity, soil conditions, and so on. 【0530】 "Information gathering means" is a general term for equipment and sensors used to acquire environmental information, and includes temperature sensors, humidity sensors, soil condition sensors, etc. 【0531】 "Analytical means" refers to the processes and methods used to analyze collected environmental information and derive meaningful conclusions from the results. 【0532】 A "growth plan" is a specific guideline, generated through analytical methods, aimed at the effective cultivation of crops. 【0533】 "Plan generation means" refers to devices or software used to create growth plans based on the results of analysis means. 【0534】 "Notification means" refers to methods and means for informing users of the generated growth plan, and includes information terminals. 【0535】 "Emotional state" refers to the user's current psychological condition, and includes stress, anxiety, excitement, etc. 【0536】 "Emotional evaluation means" refers to technologies and devices used to measure and evaluate a user's emotional state. 【0537】 "Adjustment means" refers to a device or process for changing the content or method of notifications based on the user's emotional state. 【0538】 This system is designed to optimize crop growth and includes sensors for collecting environmental information, a server for analyzing the information, and terminals for notifying users. 【0539】 The server first receives environmental information from temperature sensors, humidity sensors, and soil condition sensors connected to the information gathering system. These sensors are installed at different points on the farmland and acquire data in real time. Next, the server processes the collected information using an analysis system. The analysis system incorporates a machine learning algorithm that has learned from historical data and local climate conditions, and uses a Python framework (e.g., TensorFlow). Based on this processing, the server generates a growth plan. 【0540】 Once a growth plan is generated, the server notifies the user's device. The notification is made up of digital devices such as smartphones and tablets, and the notification content is adjusted based on the user's current emotional state using an emotion evaluation tool. The emotion evaluation tool uses an API (e.g., Azure Emotion API) that analyzes the user's voice and input data, and the optimal notification content is determined based on the results. 【0541】 For example, suppose a user is managing a vegetable garden in a smart city, and based on data from sensors, it is determined that the garden is not being watered sufficiently. In this case, the server generates a notification recommending watering. At the same time, if the notification receives an emotional assessment indicating that the user is feeling anxious, it will also include an encouraging message such as, "It's dry, but don't panic, let's deal with it." Other examples of prompts include, "It looks like it's going to rain, do I need to water the plants?" or "How can I grow healthier tomato seedlings?" 【0542】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0543】 Step 1: 【0544】 The server receives environmental information from temperature sensors, humidity sensors, and soil condition sensors. The sensors transmit data in real time from their respective points, and the server integrates and preprocesses this data. For example, it imputes missing values ​​and detects and removes outliers. 【0545】 Step 2: 【0546】 The server feeds pre-processed environmental information into a machine learning algorithm. Using the Python TensorFlow framework, it applies a model trained on historical data and regional climate conditions to perform the analysis. Based on the input information, it calculates plant growth predictions and generates an optimal growth plan. 【0547】 Step 3: 【0548】 After the server generates a growth plan, it sends a summary of the plan to the terminal. This growth plan includes specific measures (e.g., watering frequency and fertilizer amount). The plan data is encoded and compressed before transmission, minimizing communication load. 【0549】 Step 4: 【0550】 The terminal presents the user with a growth plan received from the server. The user can review recommended measures on the screen and understand the specific steps for implementation. In this process, an intuitive user interface is crucial, including visual guidelines. 【0551】 Step 5: 【0552】 The device analyzes the user's voice data and touch input to estimate their emotional state for emotion assessment. It uses the Azure Emotion API to send the acquired emotional state data as feedback to the server. 【0553】 Step 6: 【0554】 The server adjusts the notification content based on the emotional state data it receives. For example, if the user is feeling anxious, it adds an encouraging message to the growth plan notification. The adjusted notification message is then sent to the device. 【0555】 Step 7: 【0556】 The device presents users with messages tailored to their final growth plan and emotions. This allows users to work with confidence and provide feedback to the system as needed. 【0557】 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. 【0558】 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. 【0559】 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. 【0560】 [Fourth Embodiment] 【0561】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0562】 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. 【0563】 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). 【0564】 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. 【0565】 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. 【0566】 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). 【0567】 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. 【0568】 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. 【0569】 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. 【0570】 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. 【0571】 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. 【0572】 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. 【0573】 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". 【0574】 This invention provides an information processing system for streamlining the crop cultivation process, and in particular aims to automate a series of processes such as collecting and analyzing environmental data, generating cultivation plans, and notifying users. 【0575】 The system uses multiple sensor devices to collect environmental data such as temperature, humidity, and soil conditions within the farm. These sensor devices are placed around the crops and have the function of periodically measuring data. The obtained data is transmitted to a central server via a wireless network. 【0576】 The server is responsible for analyzing the received environmental data. This analysis process includes filtering the data and imputing missing values. The appropriately pre-processed data is then fed into a machine learning model to generate an optimal crop cultivation plan. This model has learned from existing data and can make advanced predictions based on climate and growth patterns. 【0577】 The generated cultivation plan includes specific details of agricultural tasks such as watering, fertilizing, and harvesting timing. Once this plan is complete, the server notifies the user's device of the information. 【0578】 The user's device, such as a smartphone or tablet, receives the cultivation plan sent from the server. This allows the user to see the next steps to be taken on the farm. For example, they might be notified that they need to water the plants early in the morning based on a forecast of high temperatures the following day. The user can then manually perform the necessary tasks according to the instructions on their device. 【0579】 Furthermore, the system can be integrated with IoT-enabled automation tools, allowing for the automation of watering and fertilizing operations as needed. This feature further reduces the user's workload by accurately and efficiently executing tasks as instructed. 【0580】 The following describes the processing flow. 【0581】 Step 1: 【0582】 The terminal uses temperature, humidity, and soil condition sensors placed on the farm to acquire environmental data at regular intervals. The acquired data is temporarily stored in the terminal and waits until the next communication timing. 【0583】 Step 2: 【0584】 The terminal transmits collected environmental data to the server via a wireless communication network. Transmission is performed in batches, with data sent according to availability and communication conditions. 【0585】 Step 3: 【0586】 The server receives data from the terminal and performs a format check. If any data abnormalities or missing data are detected, the server logs the problem and performs corrective processing to proceed to the next processing step. 【0587】 Step 4: 【0588】 The server preprocesses the received dataset. Specifically, it performs missing value imputation, filters out large outliers, and normalizes the data as needed. This prepares the data for optimal input to machine learning models. 【0589】 Step 5: 【0590】 The server feeds pre-processed data to a machine learning model and begins the analysis. Based on historical and current environmental data, the model predicts crop growth and nutrient demand and generates an optimal cultivation plan. 【0591】 Step 6: 【0592】 The server begins preparing to notify the user of the generated cultivation plan. It converts the plan, including specific farming details, into a push notification format and sends it to the user's device. 【0593】 Step 7: 【0594】 Users check their cultivation plan on their devices. They review the planned watering and fertilizing schedules and perform farming tasks accordingly if manual work is required. 【0595】 Step 8: 【0596】 If a user has automated IoT devices, they can send plan-based instructions via a server to control the devices. This allows for automated watering and fertilizing. 【0597】 (Example 1) 【0598】 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". 【0599】 In the crop cultivation process, there is a need for the automated generation and implementation of efficient cultivation plans based on environmental information. However, the conventional manual collection and analysis of environmental information is time-consuming and labor-intensive, and it is difficult to quickly formulate appropriate cultivation plans. Therefore, a system is needed that reduces the burden on farmers while providing optimal cultivation conditions. 【0600】 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. 【0601】 In this invention, the server includes data collection means for collecting environmental information, data processing means for preprocessing the environmental information collected by the data collection means, and plan generation means for generating an optimal cultivation plan using a machine learning algorithm based on the results of the data processing means. This makes it possible to quickly and automatically create an optimal cultivation plan based on the environmental conditions of the farm and notify the user. 【0602】 "Environmental information" is a general term for information related to weather and ground conditions on a farm, such as temperature, humidity, and soil conditions. 【0603】 "Data collection methods" refer to devices and technologies for acquiring environmental information, and mainly include temperature measuring devices, humidity measuring devices, and soil condition measuring devices. 【0604】 "Data processing means" refers to technologies and processes for organizing collected environmental information, removing unnecessary data, and imputing missing values. 【0605】 "Plan generation means" refers to a function that automatically creates an optimal cultivation plan using a machine learning algorithm based on pre-processed data. 【0606】 "Notification means" refers to a mechanism for informing users of the generated cultivation plan, and includes the process of sending information using email or a dedicated application. 【0607】 A "machine learning algorithm" is a series of computational methods that automatically learn from past data and perform pattern recognition and prediction. 【0608】 This invention provides an information processing system for streamlining the crop cultivation process. Specifically, it is a system that integrates the collection of environmental information, automatic analysis, generation of cultivation plans, and notification into a single workflow. 【0609】 First, the server collects environmental information about the farm using data collection devices such as temperature measuring devices, humidity measuring devices, and soil condition measuring devices installed on the farm. These sensor devices are placed in appropriate locations and are configured to measure collected data at a specified frequency and transmit it to the server via a wireless network. 【0610】 Next, the server preprocesses the received data using data processing techniques such as noise filtering and missing value imputation. The preprocessed data is then analyzed by a planning generation method using a generative AI model, and an optimal cultivation plan is automatically generated. Here, machine learning algorithms based on past weather patterns and crop growth data are used, and for example, a fertilization schedule is created that takes into account weather forecasts such as "continuous rain in the middle of next week." 【0611】 The generated cultivation plan is transmitted from the server to the user's terminal via a notification system. The user's terminal is assumed to be a smartphone or tablet-type information processing terminal. The user can check the specific steps of the next farming task on the terminal screen. For example, instructions such as "The temperature will drop tomorrow morning, so please take measures to protect against the cold" will be notified. 【0612】 Furthermore, this system can integrate with IoT technology to enable automated watering and fertilization, thereby increasing work efficiency. An example of a prompt message input to the AI ​​model would be, "Based on the temperature, humidity, and soil condition data for the past three days, please suggest the optimal next steps for tomato cultivation." Based on this prompt, the AI ​​model would recommend the next steps to be taken, which the user can then view on their device. 【0613】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0614】 Step 1: 【0615】 The server collects environmental information through various measuring devices placed on the farm. The input consists of raw data from temperature, humidity, and soil condition sensors. This data is collected in real time or at set time intervals and transmitted to the server via a wireless network. Specifically, each sensor records a specific measurement and sends it to the server as digital data. 【0616】 Step 2: 【0617】 The server preprocesses the collected environmental information using data processing tools. The input is raw data sent from the sensors. Data processing involves removing outliers through noise filtering and ensuring data integrity through missing value imputation. The output becomes clean data suitable for analysis and input to machine learning models. In this process, for example, missing data is imputed using the most recent normal value. 【0618】 Step 3: 【0619】 The server inputs pre-processed data into a generating AI model to create an optimal cultivation plan. The input for this step is clean environmental data, which is then supplied to the AI ​​model. The AI ​​model uses historical data and regional climate data to predict weather patterns, appropriate fertilization schedules, harvest times, and more. The output is set as a specific cultivation plan. For example, it calculates the appropriate timing for fertilization and watering based on the weather conditions expected for the following week. 【0620】 Step 4: 【0621】 The server notifies the user's terminal of the generated cultivation plan. The input is the cultivation plan generated by the AI ​​model. This plan is sent to the user's terminal via a notification system and made available for display. The output is the farming procedures and precautions displayed on the terminal. For example, an instruction such as "A drop in temperature is expected the following day, so take measures to protect against the cold" is sent. 【0622】 Step 5: 【0623】 The user performs the necessary tasks on the farm based on the cultivation plan instructions received on the terminal. The input is the content of the cultivation plan displayed on the terminal. The user can refer to this and perform tasks such as watering and fertilizing manually, or have them performed automatically using IoT devices. The output is the farm work that has been performed. This process allows the user to perform farm work efficiently. 【0624】 (Application Example 1) 【0625】 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". 【0626】 In urban areas and community farms, crop cultivation management is generally done manually, which presents challenges in terms of efficiency and accuracy. In particular, timely watering and fertilization are difficult due to fluctuations in climate and soil conditions, increasing the burden on workers. There is a need for a system that can solve these problems and realize efficient and accurate crop cultivation. 【0627】 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. 【0628】 In this invention, the server includes an information gathering means for collecting environmental information, an analysis means for analyzing the environmental information collected by the information gathering means, and a plan generation means for generating an optimal cultivation plan based on the analysis results of the analysis means. This enables the automation and efficiency of agricultural management in urban areas. 【0629】 "Environmental information" refers to natural conditions and weather data that affect crop cultivation, such as temperature, humidity, and soil conditions. 【0630】 "Information gathering means" refers to devices or systems that have the function of acquiring and collecting environmental information using sensor devices. 【0631】 "Analysis means" refers to devices or programs that have the function of processing and analyzing collected environmental information to obtain useful insights regarding cultivation. 【0632】 A "plan generation means" refers to a device or system that has the function of creating the most suitable schedule and method for cultivating crops based on information obtained by an analysis means. 【0633】 "Notification means" refers to devices or applications that have the function of communicating the generated cultivation plan to the user. 【0634】 "Automation methods" refer to devices and systems that have the function of performing agricultural work by machine or system rather than manually. 【0635】 This invention provides a system for efficiently managing crop cultivation in urban areas. The system includes information gathering means, analysis means, plan generation means, notification means, and automation means. 【0636】 The server uses sensor devices to collect environmental information. These devices are installed in agricultural fields and periodically measure data such as temperature, humidity, and soil conditions. The collected data is transmitted to the server via a wireless network. 【0637】 The server analyzes the received environmental information. The analysis process includes data filtering and imputation of missing values. Then, based on the data preprocessed by the analysis, a learning model is used to generate an optimal cultivation plan. This learning model can learn from existing information and make advanced predictions that take environmental fluctuations into account. 【0638】 The generated plan is sent to the user's device via a notification system. The user can review the plan through their device and understand the next steps in the farming process. If necessary, IoT-enabled automated equipment will execute the tasks. 【0639】 For example, in a community garden within an urban area, if the weekend forecast indicates rising temperatures, users will be notified that additional watering is necessary. Users can then use the app to instruct automated watering systems to do so. 【0640】 An example of a prompt given to a generating AI model would be, "Based on historical temperature and humidity data, please generate the optimal fertilization schedule for next week." 【0641】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0642】 Step 1: 【0643】 The server collects environmental information from sensor devices. This information includes temperature, humidity, and soil conditions, and is transmitted to the server periodically via a wireless network. The input is measurement data from the sensor devices, and the output is recorded in the server. 【0644】 Step 2: 【0645】 The server analyzes the collected environmental information. It removes noise through filtering, imputes missing data values, and cleans the data using statistical methods. The input is the data recorded in step 1, and the output is the clean data after analysis. 【0646】 Step 3: 【0647】 The server generates an optimal cultivation plan using a generative AI model based on the analyzed environmental information. It makes predictions about cultivation, taking into account historical data and regional climate patterns, and formulates watering and fertilization schedules. The input is the clean data from step 2, and the output is a specific cultivation plan. 【0648】 Step 4: 【0649】 The server notifies the terminal of the generated cultivation plan. The notified plan is displayed to the user through the app, clearly indicating the next steps of the farming work to be done. The input is the cultivation plan from step 3, and the output is the user-oriented instruction information displayed on the terminal. 【0650】 Step 5: 【0651】 Users can view information received through their devices and operate IoT-enabled automation devices. The automation devices automatically perform tasks such as watering and fertilizing based on specific instructions. The input is the user's instructed operation, and the output is the performed agricultural work. 【0652】 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. 【0653】 This invention provides a system that collects and analyzes environmental data to improve the efficiency of crop cultivation processes, generates an optimal cultivation plan, and notifies the user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the user experience can be enhanced. 【0654】 The system acquires environmental data from the farm using temperature, humidity, and soil condition sensors. This data is transmitted to a server via a terminal, where it is analyzed. The analysis uses machine learning models based on historical data and regional climate. This model leverages the environmental data to predict optimal growing conditions. 【0655】 Next, the server uses this analysis to generate a cultivation plan to promote optimal crop growth and notifies the user. This is where the emotion engine plays a crucial role. The emotion engine evaluates how the user interacts with the system and customizes the content of notifications and suggestions according to the user's emotions. For example, if the user is feeling stressed, the suggestions can be made more concise or encouraging messages can be added. 【0656】 Users can receive notifications and view plan details on their devices. Because an emotion engine is built in, the content of the notifications is optimized for each individual user. For example, an excited user can be provided with additional information about a new fertilization method to facilitate a deeper understanding. 【0657】 This invention enables efficient and user-friendly cultivation management by combining advanced analysis based on environmental data with appropriate responses based on user emotions. Specific application scenarios include appropriate water management during the rapid growth phase of crops and optimization of harvest timing. This simultaneously improves crop growth and enhances the work efficiency of agricultural workers. 【0658】 The following describes the processing flow. 【0659】 Step 1: 【0660】 The terminal periodically collects environmental data using temperature, humidity, and soil condition sensors placed throughout the farm. This data is collected in real time from the sensors and temporarily stored on the terminal. 【0661】 Step 2: 【0662】 The terminal transmits the collected environmental data to the server at regular intervals. The transmitted data includes identification information and timestamps for each sensor. 【0663】 Step 3: 【0664】 The server receives data sent from the terminal and verifies its integrity and completeness. If an anomaly is detected, the data is logged and excluded from analysis. 【0665】 Step 4: 【0666】 The server preprocesses the received data. Specifically, it performs tasks such as imputing missing values, filtering outliers, and removing noise to prepare the data for analysis. 【0667】 Step 5: 【0668】 The server inputs pre-processed data into machine learning algorithms based on historical data and regional climate models to predict crop growth. Based on the analysis results, it develops an optimal cultivation plan. 【0669】 Step 6: 【0670】 The server sends the generated cultivation plan to the emotion engine and adjusts the notification content based on the user's current emotional state. It also refers to the user's past response history to customize how the plan is presented and what it contains. 【0671】 Step 7: 【0672】 The server notifies the user's device of the customized cultivation plan. At this stage, depending on the user's emotional state, it may include encouraging messages or additional explanatory information. 【0673】 Step 8: 【0674】 Users check notifications received on their devices and perform necessary cultivation tasks manually or through automated tools. After completing the tasks, users send feedback to the server via their devices, contributing to improving the accuracy of the emotion engine. 【0675】 This series of processes enables efficient cultivation management tailored to the condition of the crops, improving the user experience. 【0676】 (Example 2) 【0677】 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". 【0678】 Efficient crop cultivation requires proper environmental management and rapid response, but conventional technologies are not sufficiently efficient in collecting and analyzing environmental data, making it time-consuming to generate optimal cultivation plans. Furthermore, information is not provided in a way that takes into account the user's emotional state, indicating room for improvement in the user experience. 【0679】 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. 【0680】 In this invention, the server includes information gathering means for collecting environmental information, analysis means for analyzing the collected environmental information, plan generation means for generating an optimal cultivation plan based on the analysis results, and emotion recognition means. This enables efficient processing of environmental data and the provision of an optimal cultivation plan that responds to the user's emotions. 【0681】 "Environmental information" refers to information that indicates external factors related to crop cultivation, including temperature, humidity, and soil conditions. 【0682】 "Information gathering means" is a general term for devices and technologies used to acquire environmental information, and includes data acquisition devices such as sensors. 【0683】 "Analysis methods" is a general term for technologies and devices used to analyze collected environmental information and derive useful insights and predictions. 【0684】 "Plan generation means" is a general term for technologies and systems that create an execution plan for optimal crop cultivation based on analysis results. 【0685】 "Notification means" is a general term for methods and devices used to communicate generated plans and information to users. 【0686】 "Emotion recognition means" is a general term for technologies and devices that evaluate a user's emotional state and enable the provision of adapted information based on that feedback. 【0687】 This invention is a system that supports the efficient cultivation of crops by collecting and analyzing environmental information and providing the user with an optimal cultivation plan. The main components of the system include information collection means, analysis means, plan generation means, notification means, and emotion recognition means. 【0688】 The server uses devices such as temperature sensors, humidity sensors, and soil condition sensors to collect environmental information. These devices are placed on the farm and acquire information in real time. The terminal then transmits this environmental information to the server. 【0689】 The server uses machine learning models based on historical data and regional climate to analyze the collected data. These models are built using machine learning libraries such as TensorFlow and PyTorch, and are capable of predicting changes in environmental conditions. Based on the analysis results obtained, the server generates an optimal cultivation plan. 【0690】 The server's plan generation mechanism uses this analysis result to plan suitable growth conditions for the crop, proposing things like irrigation schedules and fertilization timings. The generated plan is sent to the user's terminal via a notification mechanism. 【0691】 Emotion recognition tools evaluate user feedback and reactions and customize notifications accordingly. If a user is feeling stressed, suggestions can be made more concise, or encouraging messages can be added. For example, if a user is agitated, additional information on a new fertilization method can be provided to deepen their understanding. 【0692】 As an example of a prompt, the generating AI model could be asked a question such as, "If the user is feeling stressed, what encouraging message should be added to the cultivation plan notification?" This improves the system's user experience and enables more efficient and effective crop cultivation. 【0693】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0694】 Step 1: 【0695】 The server collects environmental information from the farm using temperature, humidity, and soil condition sensors. Terminals periodically transmit data from these sensors to the server. Inputs are real-time data from each sensor, while outputs are time-series data stored in a database. This data is accumulated for future analysis. 【0696】 Step 2: 【0697】 The server analyzes the collected environmental information. The input is accumulated historical environmental data, and the output is the analysis results indicating the optimal cultivation conditions. Specifically, the server processes the data using a machine learning model. The model predicts future environmental conditions based on past climate patterns and plant growth history. 【0698】 Step 3: 【0699】 The server generates an optimal cultivation plan based on the analysis results. The input is the analysis results obtained in step 2, and the output is a detailed cultivation plan. For example, it generates irrigation timing and fertilization schedules. This plan is optimized to maximize plant growth. 【0700】 Step 4: 【0701】 The server sends the generated cultivation plan to the user via a notification system. The input is the cultivation plan generated in step 3, and the output is a notification of the plan displayed on the user's terminal. The user can receive the notification using their terminal and check the details. 【0702】 Step 5: 【0703】 Emotion recognition systems collect user feedback and evaluate the user's emotional state. Input is the user's feedback and responses, while output is customized advice and supplementary information tailored to the user's emotions. For example, if a user is experiencing stress, the system can adjust the notification content and add encouraging messages. 【0704】 (Application Example 2) 【0705】 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". 【0706】 In today's increasingly urbanized world, many individuals and communities are showing interest in urban gardening. However, novice gardeners often face difficulties in proper environmental management and cultivation planning, as well as a lack of flexible advice tailored to their individual needs. In this context, there is a need for a system that enables efficient and optimal growth management tailored to individual circumstances. 【0707】 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. 【0708】 In this invention, the server includes information gathering means for collecting environmental information, analysis means for analyzing the environmental information collected by the information gathering means, and plan generation means for generating an optimal growth plan based on the analysis results of the analysis means. This makes it possible to provide each user with an optimal growth plan based on the information, and further adjust the content of notifications according to the user's emotions. 【0709】 "Environmental information" refers to data about the surrounding conditions that affect the growth of crops, and includes temperature, humidity, soil conditions, and so on. 【0710】 "Information gathering means" is a general term for equipment and sensors used to acquire environmental information, and includes temperature sensors, humidity sensors, soil condition sensors, etc. 【0711】 "Analytical means" refers to the processes and methods used to analyze collected environmental information and derive meaningful conclusions from the results. 【0712】 A "growth plan" is a specific guideline, generated through analytical methods, aimed at the effective cultivation of crops. 【0713】 "Plan generation means" refers to devices or software used to create growth plans based on the results of analysis means. 【0714】 "Notification means" refers to methods and means for informing users of the generated growth plan, and includes information terminals. 【0715】 "Emotional state" refers to the user's current psychological condition, and includes stress, anxiety, excitement, etc. 【0716】 "Emotional evaluation means" refers to technologies and devices used to measure and evaluate a user's emotional state. 【0717】 "Adjustment means" refers to a device or process for changing the content or method of notifications based on the user's emotional state. 【0718】 This system is designed to optimize crop growth and includes sensors for collecting environmental information, a server for analyzing the information, and terminals for notifying users. 【0719】 The server first receives environmental information from temperature sensors, humidity sensors, and soil condition sensors connected to the information gathering system. These sensors are installed at different points on the farmland and acquire data in real time. Next, the server processes the collected information using an analysis system. The analysis system incorporates a machine learning algorithm that has learned from historical data and local climate conditions, and uses a Python framework (e.g., TensorFlow). Based on this processing, the server generates a growth plan. 【0720】 Once a growth plan is generated, the server notifies the user's device. The notification is made up of digital devices such as smartphones and tablets, and the notification content is adjusted based on the user's current emotional state using an emotion evaluation tool. The emotion evaluation tool uses an API (e.g., Azure Emotion API) that analyzes the user's voice and input data, and the optimal notification content is determined based on the results. 【0721】 For example, suppose a user is managing a vegetable garden in a smart city, and based on data from sensors, it is determined that the garden is not being watered sufficiently. In this case, the server generates a notification recommending watering. At the same time, if the notification receives an emotional assessment indicating that the user is feeling anxious, it will also include an encouraging message such as, "It's dry, but don't panic, let's deal with it." Other examples of prompts include, "It looks like it's going to rain, do I need to water the plants?" or "How can I grow healthier tomato seedlings?" 【0722】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0723】 Step 1: 【0724】 The server receives environmental information from temperature sensors, humidity sensors, and soil condition sensors. The sensors transmit data in real time from their respective points, and the server integrates and preprocesses this data. For example, it imputes missing values ​​and detects and removes outliers. 【0725】 Step 2: 【0726】 The server feeds pre-processed environmental information into a machine learning algorithm. Using the Python TensorFlow framework, it applies a model trained on historical data and regional climate conditions to perform the analysis. Based on the input information, it calculates plant growth predictions and generates an optimal growth plan. 【0727】 Step 3: 【0728】 After the server generates a growth plan, it sends a summary of the plan to the terminal. This growth plan includes specific measures (e.g., watering frequency and fertilizer amount). The plan data is encoded and compressed before transmission, minimizing communication load. 【0729】 Step 4: 【0730】 The terminal presents the user with a growth plan received from the server. The user can review recommended actions on the screen and understand the specific steps for implementation. In this process, an intuitive user interface is crucial, including visual guidelines. 【0731】 Step 5: 【0732】 The device analyzes the user's voice data and touch input to estimate their emotional state for emotion assessment. It uses the Azure Emotion API to send the acquired emotional state data as feedback to the server. 【0733】 Step 6: 【0734】 The server adjusts the notification content based on the emotional state data it receives. For example, if the user is feeling anxious, it adds an encouraging message to the growth plan notification. The adjusted notification message is then sent to the device. 【0735】 Step 7: 【0736】 The device presents users with messages tailored to their final growth plan and emotions. This allows users to work with confidence and provide feedback to the system as needed. 【0737】 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. 【0738】 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. 【0739】 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. 【0740】 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. 【0741】 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. 【0742】 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. 【0743】 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. 【0744】 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. 【0745】 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." 【0746】 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. 【0747】 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. 【0748】 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. 【0749】 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. 【0750】 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. 【0751】 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. 【0752】 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. 【0753】 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. 【0754】 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. 【0755】 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. 【0756】 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. 【0757】 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. 【0758】 The following is further disclosed regarding the embodiments described above. 【0759】 (Claim 1) 【0760】 Data collection means for collecting environmental data, 【0761】 Analysis means for analyzing environmental data collected by the aforementioned data collection means, 【0762】 A cultivation plan generation means for generating an optimal cultivation plan based on the analysis results of the aforementioned analysis means, 【0763】 A notification means for informing the user of the generated cultivation plan, 【0764】 A system that includes this. 【0765】 (Claim 2) 【0766】 The system according to claim 1, wherein the data acquisition means includes a temperature sensor, a humidity sensor, and a soil condition sensor. 【0767】 (Claim 3) 【0768】 The system according to claim 1, wherein the analysis means uses a machine learning model based on historical data and regional climate. 【0769】 "Example 1" 【0770】 (Claim 1) 【0771】 Data collection methods for collecting environmental information, 【0772】 A data processing means for preprocessing environmental information collected by the data collection means, 【0773】 Using the results of the data processing means, a plan generation means for generating an optimal cultivation plan using a machine learning algorithm, 【0774】 A notification means for notifying the user's terminal of the generated cultivation plan, 【0775】 A system that includes this. 【0776】 (Claim 2) 【0777】 The system according to claim 1, wherein the data acquisition means includes a temperature measuring device, a humidity measuring device, and a soil condition measuring device. 【0778】 (Claim 3) 【0779】 The system according to claim 1, wherein the plan generation means utilizes a learning model based on past information and local weather conditions. 【0780】 "Application Example 1" 【0781】 (Claim 1) 【0782】 Information gathering methods for collecting environmental information, 【0783】 Analysis means for analyzing environmental information collected by the aforementioned information collection means, 【0784】 A plan generation means for generating an optimal cultivation plan based on the analysis results of the aforementioned analysis means, 【0785】 A notification means for informing users of the generated plan, 【0786】 Automation methods for realizing agricultural management in urban areas, 【0787】 A system that includes this. 【0788】 (Claim 2) 【0789】 The system according to claim 1, wherein the information gathering means includes a temperature sensing device, a humidity sensing device, and a soil condition sensing device. 【0790】 (Claim 3) 【0791】 The system according to claim 1, wherein the analysis means uses a learning model based on historical information and regional climate to support the automated management of urban agriculture. 【0792】 "Example 2 of combining an emotion engine" 【0793】 (Claim 1) 【0794】 Information gathering methods for collecting environmental information, 【0795】 Analysis means for analyzing environmental information collected by the aforementioned information collection means, 【0796】 A plan generation means for generating an optimal cultivation plan based on the analysis results of the aforementioned analysis means, 【0797】 A notification means for informing users of the generated cultivation plan, 【0798】 A means of recognizing emotions to evaluate the user's emotional state and customize notification content, 【0799】 A system that includes this. 【0800】 (Claim 2) 【0801】 The system according to claim 1, wherein the information gathering means includes a temperature sensor, a humidity sensor, and a soil condition sensor. 【0802】 (Claim 3) 【0803】 The system according to claim 1, wherein the analysis means uses a machine learning model based on historical information and regional climate. 【0804】 "Application example 2 when combining with an emotional engine" 【0805】 (Claim 1) 【0806】 Information gathering methods for collecting environmental information, 【0807】 Analysis means for analyzing environmental information collected by the aforementioned information collection means, 【0808】 A plan generation means for generating an optimal growth plan based on the analysis results of the aforementioned analysis means, 【0809】 A notification means for informing users of the generated growth plan, 【0810】 A means of evaluating the emotional state of a user, 【0811】 An adjustment means for adjusting notification content based on the emotional state evaluated by the emotion evaluation means, 【0812】 A system that includes this. 【0813】 (Claim 2) 【0814】 The system according to claim 1, wherein the information gathering means includes a temperature sensor, a humidity sensor, and a soil condition sensor. 【0815】 (Claim 3) 【0816】 The system according to claim 1, wherein the analysis means uses a machine learning algorithm based on historical information and regional climate. [Explanation of Symbols] 【0817】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Data collection means for collecting environmental data, Analysis means for analyzing environmental data collected by the aforementioned data collection means, A cultivation plan generation means for generating an optimal cultivation plan based on the analysis results of the aforementioned analysis means, A notification means for informing the user of the generated cultivation plan, A system that includes this. [Claim 2] The system according to claim 1, wherein the data acquisition means includes a temperature sensor, a humidity sensor, and a soil condition sensor. [Claim 3] The system according to claim 1, wherein the analysis means uses a machine learning model based on historical data and regional climate.

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