system

Abstract

We provide the system. [Solution] Multiple sensors for collecting agricultural data, A data processing device equipped with a machine learning algorithm for predicting optimal growing conditions for crops based on collected data, A control device for automatically adjusting the cultivation environment of crops based on the aforementioned growth conditions, Based on the environmental adjustments made by the control device, a notification means predicts the harvest time of crops and notifies the user; 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in agriculture, due to climate change, aging of agricultural workers, lack of experience, etc., it has become difficult to stably produce agricultural crops. Therefore, a technical solution for realizing efficient and stable cultivation and supply of agricultural crops is required. In particular, the development of a system that automates environmental control such as water adjustment and sunlight management and predicts the optimal harvest time has become an issue.

Means for Solving the Problems

[0005] This invention provides a data processing device that acquires real-time data using multiple sensors for collecting agricultural data, and uses a machine learning algorithm based on that data to predict the optimal growing conditions for crops. Furthermore, it includes a control device that automatically adjusts the cultivation environment based on these growing conditions, and a system that notifies the user of the optimal harvest time. This makes it possible to achieve stable production of crops and reduce costs.

[0006] "Agricultural data" refers to information related to the growth of crops, and includes environmental information such as soil moisture content, sunlight, temperature, and humidity, as well as data based on the experience of agricultural workers.

[0007] A "sensor" is a device that measures physical environmental parameters and outputs that data as an electronic signal. It is used to measure soil moisture content and sunlight levels.

[0008] A "machine learning algorithm" is a computational method that uses past data to build a model and predict future data, and is used to optimize growth conditions.

[0009] A "data processing device" is a computer system that analyzes collected data and issues instructions based on the results.

[0010] A "control device" is a device that operates actual machinery or systems based on instructed actions and functions to achieve desired environmental conditions.

[0011] A "notification method" is an interface for communicating information to users, and is a function used to inform them of harvest time via email or mobile app. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (Fifth Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, 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.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to a crop cultivation management system designed to improve production efficiency and stabilize market supply in agriculture. This system collects agricultural data using multiple sensors and utilizes that data to predict and manage optimal growing conditions for crops.

[0034] Specifically, the server collects experience data and historical weather data from farmers, and obtains the latest weather information via an API. Based on this data, the server uses machine learning algorithms to learn and model the optimal growing conditions.

[0035] The terminals are installed in each farmland and acquire data in real time from moisture sensors and sunlight sensors. This data is sent to a server, which analyzes the collected information. Based on the results, the terminals automatically adjust the irrigation system and light control devices to maintain an optimal environment.

[0036] Users can receive notifications via smartphone or computer regarding crop growth status and optimal harvest times. They can also receive immediate alerts in case of abnormalities, enabling quick responses. This allows users to efficiently cultivate crops and harvest them at the right time.

[0037] For example, if a tomato farmer implements the system, real-time soil and sunlight data is collected from their terminal and sent to a server. The server then uses machine learning to provide an optimal growing schedule. By performing tasks based on notifications from the system, the user can ensure stable production of high-quality tomatoes. In this way, the system of the present invention can significantly improve the efficiency of agricultural work.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server collects necessary data from farmers' experience data and historical weather databases. It also obtains current weather forecast data via an API. This data is then used to create prediction models and for environmental control.

[0041] Step 2:

[0042] The terminal uses various sensors to acquire environmental data such as soil moisture content and sunlight intensity in real time. The acquired data is immediately transmitted to the server, which uses it as basic information for data processing.

[0043] Step 3:

[0044] The server uses machine learning algorithms based on received environmental data to predict optimal growing conditions for crops. The AI ​​model analyzes growth patterns by combining historical production data with real-time weather and environmental data.

[0045] Step 4:

[0046] The server generates instructions to adjust the cultivation environment based on predicted growth conditions. Specifically, it determines environmental settings such as the required amount of water and appropriate shading / increasing of sunlight.

[0047] Step 5:

[0048] The terminal receives instructions from the server and adjusts the irrigation system and sunlight control equipment to create the optimal growing environment. This allows crops to grow healthily and efficiently.

[0049] Step 6:

[0050] Users receive notifications from the server and can check the growth status and predicted harvest time. They can monitor the farm's status in real time through smartphone and computer applications.

[0051] Step 7:

[0052] The server sends an alert to the user if it detects specific abnormal patterns or conditions. By receiving this alert and taking prompt action, the user can resolve the problem quickly.

[0053] (Example 1)

[0054] 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."

[0055] Efficient growth management is required in crop production, but conventional systems have made it difficult to make real-time adjustments or predict harvest times with high accuracy. Furthermore, the design of optimal growing conditions using environmental data and empirical knowledge has not been sufficient, posing challenges to improving the efficiency and productivity of agricultural work.

[0056] 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.

[0057] In this invention, the server includes multiple observation devices for collecting agriculture-related information, a learning processing device for predicting optimal plant growth conditions based on the collected information, and a control device for automatically adjusting the plant's growing environment based on the growth conditions. This enables highly accurate growth adjustment based on environmental and empirical data, as well as real-time monitoring of growth status and prediction of the optimal harvest time.

[0058] An "observation device" refers to a sensor or device used to acquire information related to agriculture, and is a device that has the function of measuring environmental data such as moisture content and solar radiation.

[0059] A "learning processing device" is a processor or computing device used to analyze and predict the optimal growth conditions for plants based on collected data. It has the function of executing algorithms and creating models using data.

[0060] A "control device" is a device designed to automatically adjust the plant's growth environment, performing operations to change water content and sunlight based on collected data and prediction results.

[0061] "Notification means" refers to methods and technologies for informing users of the calculated harvest time and growth status of plants, and includes sending electronic messages via smartphones and computers.

[0062] This crop cultivation management system aims to improve production efficiency and stabilize market supply in agriculture. Specifically, it includes three main components: servers, terminals, and users.

[0063] The server is responsible for collecting data from agricultural observation devices. It acquires daily information from sensors measuring moisture content and solar radiation, and stores it in a database. The server also obtains external weather information via APIs and integrates it with this data. Functioning as a learning processor, the server uses programming languages ​​like Python and libraries like Scikit-learn to analyze the collected data and build models to predict optimal crop growth conditions. The built models are updated in real time, ensuring they always reflect the latest environmental information.

[0064] The terminals are installed in each farmland and acquire data in real time using moisture and sunlight sensors. The data is sent to a server using communication technologies such as Wi-Fi and LoRa, and because of its energy-efficient design, it can be operated remotely for extended periods. For environmental control, the terminals work in conjunction with agricultural equipment such as irrigation systems and lighting control systems, automatically adjusting the farmland environment based on instructions from the server.

[0065] Users receive notifications from the server via their smartphones or computers. They receive feedback on harvest times and growing conditions, as well as alerts to prompt immediate action in case of anomalies. This allows users to improve work efficiency and achieve stable production of high-quality agricultural products.

[0066] As a concrete example, let's consider a case where a tomato farmer adopts this system. The terminal periodically measures the soil moisture in the tomato field and sends the results to the server. The server processes the data using Scikit-learn, predicts the irrigation schedule to be carried out the next day, and sends it to the user as a notification. This notification includes instructions such as, "Please irrigate tomorrow morning."

[0067] An example of a prompt message is: "Please tell me the optimal time to harvest tomatoes. Please suggest the best schedule, taking into account available sensor data and weather data."

[0068] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0069] Step 1:

[0070] The server collects data from agricultural observation equipment. This data includes environmental factors such as moisture content and solar radiation. Inputs include sensor data and weather information obtained from APIs, and the output is an integrated dataset. Data acquisition occurs daily or in real time during this process.

[0071] Step 2:

[0072] The server performs learning processing using the collected data. Here, data analysis is carried out using machine learning algorithms. The input is the integrated dataset obtained in the previous step, and the output is the construction of a predictive model for the optimal growth conditions of plants. Specifically, regression models and classification models are generated using the Scikit-learn library.

[0073] Step 3:

[0074] The terminals are placed in farmland and collect data from environmental sensors in real time. For example, they acquire data from moisture sensors and sunlight sensors, preprocess it, and then periodically send it to a server. The input is real-time sensor data, and the output is formatted data that is sent to the server. Wi-Fi and LoRa are used as communication technologies in this step.

[0075] Step 4:

[0076] The server receives the data transmitted in the previous step and performs analysis. The input is measurement data sent from the terminal, and the output generates instructions for adjusting the environment. Here, based on the analysis results, commands are generated for the irrigation system and light control devices, and a list of executable instructions is created.

[0077] Step 5:

[0078] The terminal receives instructions from the server and performs environmental adjustments to the farmland. Specifically, it operates irrigation equipment and lighting control systems. The input is a list of instructions from the server, and the output is the adjusted farmland environment. This operation is performed automatically, so no human intervention is required.

[0079] Step 6:

[0080] Users can check the growth status and the results of environmental adjustments via smartphones or computers. The server notifies users of the current state of crops and future harvest predictions. Input is notification data from the server, and output is information provided to the user. Users can use this information to create work plans and utilize them to produce high-quality agricultural products.

[0081] (Application Example 1)

[0082] 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."

[0083] In modern industrial production sites, maintaining optimal machine operation is crucial. However, with the increasing complexity of equipment and the massive volume of data, real-time data analysis and machine control adjustments have become difficult. As a result, it is challenging to prevent decreased operational efficiency and unexpected breakdowns. This invention aims to solve these problems and improve the efficiency and stability of machine operation in industrial sites.

[0084] 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.

[0085] In this invention, the server includes a plurality of detection devices for collecting industrial data, information processing means equipped with a machine learning algorithm for predicting optimal operating conditions based on the collected data, and control means for automatically adjusting the operating environment of the machine based on the operating conditions. This makes it possible to optimize the operating state of the machine in real time, prevent unexpected failures, and improve operating efficiency.

[0086] "Industrial data" refers to information about operating conditions and environmental conditions collected from machinery and equipment within a factory.

[0087] A "detection device" refers to various sensors and devices used to collect data related to the operating status of machinery.

[0088] "Information processing means" refers to a system or algorithm used to analyze collected data and derive the optimal operating conditions for a machine.

[0089] A "machine learning algorithm" is a mathematical method used to predict the optimal current and future machine operating conditions based on past data.

[0090] "Control means" refers to a device or system for automatically adjusting the operation of machinery or equipment based on optimal operating conditions.

[0091] A "notification means" is a device or program that has a notification function to inform human users of analysis results or predicted information.

[0092] This invention provides a system in which servers, terminals, and users each have their own roles in order to optimize the operation of machinery in an industrial environment.

[0093] The server receives real-time operating status data from multiple detection devices installed within the factory. This data includes information such as temperature, vibration, and power consumption. Hardware such as Raspberry Pi and Arduino are used to collect the detected data and send it to the cloud server via the MQTT protocol. On the cloud server, a machine learning algorithm written in Python analyzes the data using the Scikit-learn library and predicts the optimal operating parameters.

[0094] The analysis results are sent to the user's smartphone or computer via a notification system using Flask. This allows the user to instantly understand optimal driving conditions and any abnormality alerts.

[0095] As a concrete example, vibration data from machine tools used in a factory can be collected, and if abnormal vibrations are detected, the machine can be automatically stopped, and the user can be notified of the vibration level details and the cause of the stoppage. In this way, the efficiency of factory operations and the protection of equipment can be achieved.

[0096] An example of a prompt message to be fed into a generative AI model is: "Based on real-time data collected from machinery within the factory, predict the optimal conditions for improving the operating efficiency of the machinery."

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The terminal collects machine operating status data using various detection devices within the factory. This includes real-time data from temperature and vibration sensors. This data is aggregated by a Raspberry Pi and filtered using pre-configured thresholds. As a result, the filtered operating data is sent to the server via the MQTT protocol.

[0100] Step 2:

[0101] The server inputs data received via the MQTT protocol into a machine learning algorithm written in Python. The server uses the Scikit-learn library to perform data analysis in real time to predict optimal operating parameters. The input is filtered operating data, and the output is optimized operating conditions.

[0102] Step 3:

[0103] The server sends notifications to the user's smartphone or computer using Flask, containing the results of its analysis. These notifications include optimal operating parameters and necessary actions. The user can then make manual adjustments as needed based on the received notifications. If an anomaly alert is included, detailed information will be provided to prompt a quick response.

[0104] Step 4:

[0105] The generating AI model uses previously recorded operating data and current operating conditions to generate further suggestions that contribute to optimizing operations, using prompt messages. Specifically, the user can input a prompt into the system such as, "Based on real-time data of machinery collected within the factory, predict the optimal conditions for improving the operating efficiency of the machinery," and then use the results in daily operations.

[0106] 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.

[0107] This invention aims to optimize the cultivation environment and improve the user experience by combining a crop cultivation management system with an emotion engine that recognizes user emotions. The system comprises multiple sensors for collecting agricultural data, a machine learning algorithm based on the data, a control device that automatically adjusts the cultivation environment, and an emotion engine.

[0108] The server collects agricultural data and uses machine learning algorithms based on the collected data to predict optimal growing conditions for crops. In addition, the server has the ability to analyze user emotional data using an emotion engine. The emotion engine identifies the user's emotions using data obtained from the user's words and actions and biosensors, and sends the results to the server.

[0109] The device adjusts soil moisture and sunlight levels in real time according to instructions from the server. Furthermore, it makes adjustments based on the user's emotions, analyzed by the emotion engine, to provide the most relaxing environment for that specific emotional state.

[0110] Users can not only receive notifications about growing conditions and harvest times via their smartphones or tablets, but also check emotion-based feedback provided by the system. For example, if a user is feeling stressed, the emotion engine can sense this state, and the server can adjust the instructions for the growing environment accordingly to provide a more comfortable working environment.

[0111] For example, if a user cultivating tomatoes is using this system, the device can efficiently support tomato growth while also providing interaction such as playing relaxing background music or adjusting lighting based on the user's emotions. In this way, crop cultivation that also takes the user's mental health into consideration can be realized.

[0112] The following describes the processing flow.

[0113] Step 1:

[0114] The server collects experience data from agricultural workers and historical weather data from a database, and retrieves the latest weather forecasts via an API. This information is then used for subsequent data analysis and the creation of prediction models.

[0115] Step 2:

[0116] The device acquires real-time data from multiple sensors to measure soil moisture and sunlight levels. This data is sent to a server and immediately used for environmental analysis.

[0117] Step 3:

[0118] The server uses machine learning algorithms to analyze received environmental data and predict the optimal growing conditions for crops. Based on this model, it generates instructions for fine-tuning the growing environment.

[0119] Step 4:

[0120] The terminal automatically operates the irrigation system and light control equipment according to instructions from the server, maintaining optimal environmental conditions. This allows crops to be grown efficiently.

[0121] Step 5:

[0122] The server utilizes an emotion engine to analyze the user's emotional data. This emotional data is collected through biosensors and user interactions and sent to the server.

[0123] Step 6:

[0124] Based on the user's emotional data, the server adjusts the cultivation environment according to the user's emotional state. For example, if the user is feeling stressed, the server may instruct the server to play music or change the lighting to improve their mood.

[0125] Step 7:

[0126] Users can receive notifications from the server and check the results of environmental adjustments based on crop growth status and emotional state. This allows users to perform farming in a more comfortable environment, leading to more efficient harvests.

[0127] (Example 2)

[0128] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0129] Conventional crop cultivation management systems simply optimized crop growth conditions without considering user emotions or working environment. This could potentially compromise user comfort, and even if farming efficiency improved, reducing user stress and increasing satisfaction remained challenges.

[0130] 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.

[0131] In this invention, the server includes a plurality of sensor means for collecting agricultural data, a data processing means equipped with a machine learning algorithm for predicting optimal crop growth conditions based on the collected data, a control means for automatically adjusting the crop cultivation environment based on the growth conditions, an emotion analysis means for analyzing the user's emotional data and influencing the crop cultivation environment, and an output means that considers the user's emotional state based on the emotion analysis and provides emotion-based feedback. This makes it possible to optimize crop growth while simultaneously providing a comfortable working environment that takes the user's emotions into consideration.

[0132] "Agricultural data" refers to information related to the growing environment of crops, such as soil moisture content, temperature, humidity, and amount of sunlight, and is acquired using sensors.

[0133] "Sensing means" refers to devices and equipment used to collect environmental information, which enables data processing.

[0134] "Data processing means" refers to a device consisting of hardware and software used to analyze collected data and convert it into meaningful information.

[0135] A "machine learning algorithm" is a mathematical method used to learn patterns and trends from data and perform predictions and classifications.

[0136] "Control means" refers to hardware and software used to adjust the state of a specific work environment based on information collected and analyzed by the system.

[0137] "Emotion analysis tools" are used to identify users' emotions and analyze how they affect the cultivation environment of agricultural products.

[0138] "Output means" refers to a device or interface for transmitting processed information or analysis results to the user.

[0139] This invention describes a form for realizing a crop cultivation management system that takes user emotions into consideration.

[0140] The server collects agricultural data using multiple sensors. These include temperature and humidity sensors and soil moisture sensors, which acquire environmental data such as soil moisture content, temperature, humidity, and sunlight levels. This data is transmitted to the server in real time and stored in a database. Based on the collected data, the server uses a machine learning framework that utilizes machine learning algorithms to predict the optimal growing conditions for crops.

[0141] Furthermore, the server runs "emotion analysis software" to analyze the user's emotional data. It utilizes data from "biosensors" and "camera devices" that can acquire the user's facial expressions and biometric indicators, and analyzes this data to understand the user's emotional state.

[0142] The terminal automatically controls the cultivation environment based on commands from the server. Using a "control program," it operates water pumps and lighting devices to adjust soil moisture levels and lighting conditions. It also plays background music through sound equipment and adjusts lighting to help the user relax.

[0143] Users receive information from the system via their smartphones or tablets. The user interface not only monitors the growth status and notifies users of harvest time, but also provides emotion-based feedback. For example, by inputting prompts such as "Please suggest ways to adjust the growing environment so that the user can relax" into the generating AI model, it is possible to obtain suggestions based on the user's emotions.

[0144] A concrete example is a user growing tomatoes in a cool climate. The server issues commands to maintain the appropriate temperature and humidity for the tomatoes, and the terminal uses warm lighting as supplementary support to adjust the climate. At the same time, if the system detects that the user is experiencing stress, it plays relaxing music to provide a comfortable working environment.

[0145] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0146] Step 1:

[0147] The server collects agricultural data from multiple sensors. Specifically, it uses temperature, humidity, and soil moisture sensors to acquire environmental information. It receives real-time data from the sensors as input and stores the acquired data in a database as output. Data processing includes noise reduction and correction to generate highly reliable data.

[0148] Step 2:

[0149] The server analyzes agricultural data stored in a database using machine learning algorithms. Specifically, it runs growth models tailored to specific conditions using a "machine learning framework" to predict optimal growth conditions. It uses historical data and current environmental data as input and generates recommended growth conditions as output. Regression analysis and pattern recognition are used for data calculations.

[0150] Step 3:

[0151] The server analyzes user emotional data using emotion analysis software. Specifically, it acquires user facial expression data and biometric indicators from camera devices and biosensors. It receives this data as input and identifies the user's emotional state as output. In data processing, image processing and statistical methods are used to assign emotion labels.

[0152] Step 4:

[0153] The terminal automatically controls the cultivation environment based on commands from the server. Specifically, it operates water pumps and lighting devices using a control program. It receives recommended growth conditions and sentiment analysis results generated by the server as input, and provides optimized environmental conditions as output. Its operations include operating pumps and adjusting lighting.

[0154] Step 5:

[0155] Users receive information from the system via their smartphone or tablet. The system uses growth status and emotional feedback received from the device as input, and displays this information on the user interface as output. Based on this information, users can select specific actions to perform, such as cultivation or environmental adjustments. These actions include notification sounds and screen displays.

[0156] Step 6:

[0157] The server utilizes a generative AI model to generate suggestions for the user. For example, it takes a prompt such as, "Please suggest ways to adjust the cultivation environment so that the user can relax," as input and generates customized suggestions for the user as output. Data processing includes natural language processing and content generation.

[0158] (Application Example 2)

[0159] 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 device 14 will be referred to as the "terminal."

[0160] Conventional crop cultivation management systems primarily focused on optimizing the growing environment for crops, but lacked the functionality to adjust the working environment based on the emotional state of the workers. This made it difficult to improve work efficiency and the mental health of workers. This invention solves this problem by providing a system for crop cultivation management that also takes into account the emotional state of the workers.

[0161] 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.

[0162] In this invention, the server includes multiple detection device means for collecting agricultural data, machine learning algorithm means for predicting optimal crop growth conditions based on the collected data, and emotion analysis means for analyzing the emotional state of workers and adjusting the work environment. This makes it possible to simultaneously optimize the crop growth environment and the work environment.

[0163] "Agricultural data" refers to information about crop growth and environmental conditions, including soil moisture content, sunlight, temperature, and humidity.

[0164] A "detection device" is a sensor or measuring instrument used to collect agricultural data, thereby acquiring environmental information and crop conditions.

[0165] A "machine learning algorithm" is a mathematical or statistical method used to predict crop growth conditions using historical data and empirical information.

[0166] A "data processing device" is a device that uses machine learning algorithms to analyze and process collected agricultural data.

[0167] A "control device" is a device that automatically adjusts the cultivation environment for crops based on predicted growth conditions.

[0168] "Information transmission means" refers to devices or methods for notifying users of predictive information such as the harvest time of agricultural products.

[0169] "Emotional analysis means" refers to a method for identifying a user's emotional state by analyzing their biometric information and behavior, and then adjusting the work environment based on that.

[0170] To implement this invention, it is first necessary for the server to collect information on the growing environment using multiple detection devices for agricultural data collection. The collected data is analyzed by a data processing device, and optimal growing conditions are predicted using a machine learning algorithm. In addition to this agricultural data, the data processing device analyzes the user's emotional state using an emotion analysis means. Biometric sensors and user behavior data are utilized for emotion analysis.

[0171] The control system adjusts the cultivation environment based on predicted growth conditions and the user's emotional state. For example, it rationally adjusts temperature and lighting to provide an optimal working space. It also notifies the user of crop harvest times and provides feedback on environmental adjustments based on their emotions through information transmission means.

[0172] For example, if a user cultivating tomatoes is determined to be stressed by an emotion analysis tool, the server can lower the temperature and play relaxing music to provide a more comfortable working environment.

[0173] An example of a prompt is, "Considering the emotional state of the workers, how can we provide a relaxing environment within the factory?" This prompt serves as a guide for the generating AI model when creating environmental improvement measures that respond to the user's emotional state.

[0174] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0175] Step 1:

[0176] The server acquires environmental data such as soil moisture content, sunlight intensity, temperature, and humidity from multiple detection devices used to collect agricultural data. The server stores this input data for later analysis.

[0177] Step 2:

[0178] The server transfers the collected environmental data to a data processing unit and uses machine learning algorithms to predict the optimal growing conditions for crops. It takes historical weather data and agricultural workers' experience as input and calculates optimal growing conditions such as temperature and moisture content as output.

[0179] Step 3:

[0180] The server acquires biometric and behavioral data from the user through emotion analysis. This identifies the user's emotional state (e.g., stress level). This analysis is performed using a generative AI model, which takes the input data and outputs the emotional state.

[0181] Step 4:

[0182] Based on the user's emotional state and predicted growing conditions, the server automatically adjusts the cultivation environment via a control device. Specifically, it optimizes environmental factors such as adjusting temperature and lighting.

[0183] Step 5:

[0184] The server uses information transmission methods to notify users of crop harvest times and feedback on environmental adjustments based on emotions. For example, it suggests appropriate actions to users through email or app notifications.

[0185] Step 6:

[0186] The user checks the notifications from the server and makes changes to the work environment as needed. Following these prompts, the generating AI model plays a role in providing new data to generate further improvements to the environment.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Second Embodiment]

[0191] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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".

[0203] This invention relates to a crop cultivation management system designed to improve production efficiency and stabilize market supply in agriculture. This system collects agricultural data using multiple sensors and utilizes that data to predict and manage optimal growing conditions for crops.

[0204] Specifically, the server collects experience data and historical weather data from farmers, and obtains the latest weather information via an API. Based on this data, the server uses machine learning algorithms to learn and model the optimal growing conditions.

[0205] The terminals are installed in each farmland and acquire data in real time from moisture sensors and sunlight sensors. This data is sent to a server, which analyzes the collected information. Based on the results, the terminals automatically adjust the irrigation system and light control devices to maintain an optimal environment.

[0206] Users can receive notifications via smartphone or computer regarding crop growth status and optimal harvest times. They can also receive immediate alerts in case of abnormalities, enabling quick responses. This allows users to efficiently cultivate crops and harvest them at the right time.

[0207] For example, if a tomato farmer implements the system, real-time soil and sunlight data is collected from their terminal and sent to a server. The server then uses machine learning to provide an optimal growing schedule. By performing tasks based on notifications from the system, the user can ensure stable production of high-quality tomatoes. In this way, the system of the present invention can significantly improve the efficiency of agricultural work.

[0208] The following describes the processing flow.

[0209] Step 1:

[0210] The server collects necessary data from farmers' experience data and historical weather databases. It also obtains current weather forecast data via an API. This data is then used to create prediction models and for environmental control.

[0211] Step 2:

[0212] The terminal uses various sensors to acquire environmental data such as soil moisture content and sunlight intensity in real time. The acquired data is immediately transmitted to the server, which uses it as basic information for data processing.

[0213] Step 3:

[0214] The server uses machine learning algorithms based on received environmental data to predict optimal growing conditions for crops. The AI ​​model analyzes growth patterns by combining historical production data with real-time weather and environmental data.

[0215] Step 4:

[0216] The server generates instructions to adjust the cultivation environment based on predicted growth conditions. Specifically, it determines environmental settings such as the required amount of water and appropriate shading / increasing of sunlight.

[0217] Step 5:

[0218] The terminal receives instructions from the server and adjusts the irrigation system and sunlight control equipment to create the optimal growing environment. This allows crops to grow healthily and efficiently.

[0219] Step 6:

[0220] Users receive notifications from the server and can check the growth status and predicted harvest time. They can monitor the farm's status in real time through smartphone and computer applications.

[0221] Step 7:

[0222] The server sends an alert to the user if it detects specific abnormal patterns or conditions. By receiving this alert and taking prompt action, the user can resolve the problem quickly.

[0223] (Example 1)

[0224] 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."

[0225] Efficient growth management is required in crop production, but conventional systems have made it difficult to make real-time adjustments or predict harvest times with high accuracy. Furthermore, the design of optimal growing conditions using environmental data and empirical knowledge has not been sufficient, posing challenges to improving the efficiency and productivity of agricultural work.

[0226] 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.

[0227] In this invention, the server includes multiple observation devices for collecting agriculture-related information, a learning processing device for predicting optimal plant growth conditions based on the collected information, and a control device for automatically adjusting the plant's growing environment based on the growth conditions. This enables highly accurate growth adjustment based on environmental and empirical data, as well as real-time monitoring of growth status and prediction of the optimal harvest time.

[0228] An "observation device" refers to a sensor or device used to acquire information related to agriculture, and is a device that has the function of measuring environmental data such as moisture content and solar radiation.

[0229] A "learning processing device" is a processor or computing device used to analyze and predict the optimal growth conditions for plants based on collected data. It has the function of executing algorithms and creating models using data.

[0230] A "control device" is a device designed to automatically adjust the plant's growth environment, performing operations to change water content and sunlight based on collected data and prediction results.

[0231] "Notification means" refers to methods and technologies for informing users of the calculated harvest time and growth status of plants, and includes sending electronic messages via smartphones and computers.

[0232] This crop cultivation management system aims to improve production efficiency and stabilize market supply in agriculture. Specifically, it includes three main components: servers, terminals, and users.

[0233] The server is responsible for collecting data from agricultural observation devices. It acquires daily information from sensors measuring moisture content and solar radiation, and stores it in a database. The server also obtains external weather information via APIs and integrates it with this data. Functioning as a learning processor, the server uses programming languages ​​like Python and libraries like Scikit-learn to analyze the collected data and build models to predict optimal crop growth conditions. The built models are updated in real time, ensuring they always reflect the latest environmental information.

[0234] The terminals are installed in each farmland and acquire data in real time using moisture and sunlight sensors. The data is sent to a server using communication technologies such as Wi-Fi and LoRa, and because of its energy-efficient design, it can be operated remotely for extended periods. For environmental control, the terminals work in conjunction with agricultural equipment such as irrigation systems and lighting control systems, automatically adjusting the farmland environment based on instructions from the server.

[0235] Users receive notifications from the server via their smartphones or computers. They receive feedback on harvest times and growing conditions, as well as alerts to prompt immediate action in case of anomalies. This allows users to improve work efficiency and achieve stable production of high-quality agricultural products.

[0236] As a concrete example, let's consider a case where a tomato farmer adopts this system. The terminal periodically measures the soil moisture in the tomato field and sends the results to the server. The server processes the data using Scikit-learn, predicts the irrigation schedule to be carried out the next day, and sends it to the user as a notification. This notification includes instructions such as, "Please irrigate tomorrow morning."

[0237] An example of a prompt message is: "Please tell me the optimal time to harvest tomatoes. Please suggest the best schedule, taking into account available sensor data and weather data."

[0238] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0239] Step 1:

[0240] The server collects data from agricultural observation equipment. This data includes environmental factors such as moisture content and solar radiation. Inputs include sensor data and weather information obtained from APIs, and the output is an integrated dataset. Data acquisition occurs daily or in real time during this process.

[0241] Step 2:

[0242] The server performs learning processing using the collected data. Here, data analysis is carried out using machine learning algorithms. The input is the integrated dataset obtained in the previous step, and the output is the construction of a predictive model for the optimal growth conditions of plants. Specifically, regression models and classification models are generated using the Scikit-learn library.

[0243] Step 3:

[0244] The terminals are placed in farmland and collect data from environmental sensors in real time. For example, they acquire data from moisture sensors and sunlight sensors, preprocess it, and then periodically send it to a server. The input is real-time sensor data, and the output is formatted data that is sent to the server. Wi-Fi and LoRa are used as communication technologies in this step.

[0245] Step 4:

[0246] The server receives the data transmitted in the previous step and performs analysis. The input is measurement data sent from the terminal, and the output generates instructions for adjusting the environment. Here, based on the analysis results, commands are generated for the irrigation system and light control devices, and a list of executable instructions is created.

[0247] Step 5:

[0248] The terminal receives instructions from the server and performs environmental adjustments to the farmland. Specifically, it operates irrigation equipment and lighting control systems. The input is a list of instructions from the server, and the output is the adjusted farmland environment. This operation is performed automatically, so no human intervention is required.

[0249] Step 6:

[0250] Users can check the growth status and the results of environmental adjustments via smartphones or computers. The server notifies users of the current state of crops and future harvest predictions. Input is notification data from the server, and output is information provided to the user. Users can use this information to create work plans and utilize them to produce high-quality agricultural products.

[0251] (Application Example 1)

[0252] 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."

[0253] In modern industrial production sites, maintaining optimal machine operation is crucial. However, with the increasing complexity of equipment and the massive volume of data, real-time data analysis and machine control adjustments have become difficult. As a result, it is challenging to prevent decreased operational efficiency and unexpected breakdowns. This invention aims to solve these problems and improve the efficiency and stability of machine operation in industrial sites.

[0254] 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.

[0255] In this invention, the server includes a plurality of detection devices for collecting industrial data, information processing means equipped with a machine learning algorithm for predicting optimal operating conditions based on the collected data, and control means for automatically adjusting the operating environment of the machine based on the operating conditions. This makes it possible to optimize the operating state of the machine in real time, prevent unexpected failures, and improve operating efficiency.

[0256] "Industrial data" refers to information about operating conditions and environmental conditions collected from machinery and equipment within a factory.

[0257] A "detection device" refers to various sensors and devices used to collect data related to the operating status of machinery.

[0258] "Information processing means" refers to a system or algorithm used to analyze collected data and derive the optimal operating conditions for a machine.

[0259] A "machine learning algorithm" is a mathematical method used to predict the optimal current and future machine operating conditions based on past data.

[0260] "Control means" refers to a device or system for automatically adjusting the operation of machinery or equipment based on optimal operating conditions.

[0261] A "notification means" is a device or program that has a notification function to inform human users of analysis results or predicted information.

[0262] This invention provides a system in which servers, terminals, and users each have their own roles in order to optimize the operation of machinery in an industrial environment.

[0263] The server receives real-time operating status data from multiple detection devices installed within the factory. This data includes information such as temperature, vibration, and power consumption. Hardware such as Raspberry Pi and Arduino are used to collect the detected data and send it to the cloud server via the MQTT protocol. On the cloud server, a machine learning algorithm written in Python analyzes the data using the Scikit-learn library and predicts the optimal operating parameters.

[0264] The analysis results are sent to the user's smartphone or computer via a notification system using Flask. This allows the user to instantly understand optimal driving conditions and any abnormality alerts.

[0265] As a concrete example, vibration data from machine tools used in a factory can be collected, and if abnormal vibrations are detected, the machine can be automatically stopped, and the user can be notified of the vibration level details and the cause of the stoppage. In this way, the efficiency of factory operations and the protection of equipment can be achieved.

[0266] An example of a prompt message to be fed into a generative AI model is: "Based on real-time data collected from machinery within the factory, predict the optimal conditions for improving the operating efficiency of the machinery."

[0267] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0268] Step 1:

[0269] The terminal collects machine operating status data using various detection devices within the factory. This includes real-time data from temperature and vibration sensors. This data is aggregated by a Raspberry Pi and filtered using pre-configured thresholds. As a result, the filtered operating data is sent to the server via the MQTT protocol.

[0270] Step 2:

[0271] The server inputs data received via the MQTT protocol into a machine learning algorithm written in Python. The server uses the Scikit-learn library to perform data analysis in real time to predict optimal operating parameters. The input is filtered operating data, and the output is optimized operating conditions.

[0272] Step 3:

[0273] The server sends notifications to the user's smartphone or computer using Flask, containing the results of its analysis. These notifications include optimal operating parameters and necessary actions. The user can then make manual adjustments as needed based on the received notifications. If an anomaly alert is included, detailed information will be provided to prompt a quick response.

[0274] Step 4:

[0275] The generating AI model uses previously recorded operating data and current operating conditions to generate further suggestions that contribute to optimizing operations, using prompt messages. Specifically, the user can input a prompt into the system such as, "Based on real-time data of machinery collected within the factory, predict the optimal conditions for improving the operating efficiency of the machinery," and then use the results in daily operations.

[0276] 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.

[0277] This invention aims to optimize the cultivation environment and improve the user experience by combining a crop cultivation management system with an emotion engine that recognizes user emotions. The system comprises multiple sensors for collecting agricultural data, a machine learning algorithm based on the data, a control device that automatically adjusts the cultivation environment, and an emotion engine.

[0278] The server collects agricultural data and uses machine learning algorithms based on the collected data to predict optimal growing conditions for crops. In addition, the server has the ability to analyze user emotional data using an emotion engine. The emotion engine identifies the user's emotions using data obtained from the user's words and actions and biosensors, and sends the results to the server.

[0279] The device adjusts soil moisture and sunlight levels in real time according to instructions from the server. Furthermore, it makes adjustments based on the user's emotions, analyzed by the emotion engine, to provide the most relaxing environment for that specific emotional state.

[0280] Users can not only receive notifications about growing conditions and harvest times via their smartphones or tablets, but also check emotion-based feedback provided by the system. For example, if a user is feeling stressed, the emotion engine can sense this state, and the server can adjust the instructions for the growing environment accordingly to provide a more comfortable working environment.

[0281] For example, if a user cultivating tomatoes is using this system, the device can efficiently support tomato growth while also providing interaction such as playing relaxing background music or adjusting lighting based on the user's emotions. In this way, crop cultivation that also takes the user's mental health into consideration can be realized.

[0282] The following describes the processing flow.

[0283] Step 1:

[0284] The server collects the experience data of farmers and past weather data from the database and obtains the latest weather forecast through the API. This information is used for subsequent data analysis and prediction model creation.

[0285] Step 2:

[0286] The terminal obtains data in real time from a plurality of sensors for measuring the soil moisture content and sunlight amount. These data are sent to the server and immediately used for environmental analysis.

[0287] Step 3:

[0288] The server utilizes machine learning algorithms to analyze the received environmental data and predict the optimal growth conditions for crops. Based on this model, instructions for fine-tuning the growth environment are generated.

[0289] Step 4:

[0290] The terminal automatically operates the irrigation system and light control equipment according to the instructions from the server to maintain optimal environmental conditions. As a result, the crops are efficiently grown.

[0291] Step 5:

[0292] The server utilizes an emotion engine to analyze the user's emotion data. The emotion data is collected through biosensors and interactions with the user and sent to the server.

[0293] Step 6:

[0294] Based on the obtained user emotion data, the server adjusts the cultivation environment according to the user's emotion state. For example, when the user is feeling stressed, the server issues instructions to play music and change the lighting to improve the mood.

[0295] Step 7:

[0296] Users can receive notifications from the server and check the results of environmental adjustments based on crop growth status and emotional state. This allows users to perform farming in a more comfortable environment, leading to more efficient harvests.

[0297] (Example 2)

[0298] 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".

[0299] Conventional crop cultivation management systems simply optimized crop growth conditions without considering user emotions or working environment. This could potentially compromise user comfort, and even if farming efficiency improved, reducing user stress and increasing satisfaction remained challenges.

[0300] 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.

[0301] In this invention, the server includes a plurality of sensor means for collecting agricultural data, a data processing means equipped with a machine learning algorithm for predicting optimal crop growth conditions based on the collected data, a control means for automatically adjusting the crop cultivation environment based on the growth conditions, an emotion analysis means for analyzing the user's emotional data and influencing the crop cultivation environment, and an output means that considers the user's emotional state based on the emotion analysis and provides emotion-based feedback. This makes it possible to optimize crop growth while simultaneously providing a comfortable working environment that takes the user's emotions into consideration.

[0302] "Agricultural data" refers to information related to the growing environment of crops, such as soil moisture content, temperature, humidity, and amount of sunlight, and is acquired using sensors.

[0303] "Sensor means" refers to devices and equipment used to collect environmental information, enabling data processing.

[0304] "Data processing means" is a device composed of hardware and software used to analyze the collected data and convert it into meaningful information.

[0305] "Machine learning algorithm" is a mathematical method for learning patterns and trends from data for prediction and classification.

[0306] "Control means" is hardware and software for adjusting the state of a specific working environment based on the information collected and analyzed by the system.

[0307] "Emotion analysis means" is for identifying the user's emotions and analyzing how they affect the crop cultivation environment.

[0308] "Output means" is a device or interface for transmitting the processed information and analysis results to the user.

[0309] This invention will describe a form for realizing a crop cultivation management system that takes into account the user's emotions.

[0310] The server collects agricultural data using multiple sensors. This includes "temperature and humidity sensors", "soil moisture sensors", etc., and acquires environmental data such as the soil moisture content, temperature, humidity, and amount of sunlight. This data is transmitted to the server in real time and stored in the database. The server uses a "machine learning framework" based on the collected data to utilize machine learning algorithms to predict the optimal growth conditions for crops.

[0311] Furthermore, the server runs "emotion analysis software" to analyze the user's emotional data. It utilizes data from "biosensors" and "camera devices" that can acquire the user's facial expressions and biometric indicators, and analyzes this data to understand the user's emotional state.

[0312] The terminal automatically controls the cultivation environment based on commands from the server. Using a "control program," it operates water pumps and lighting devices to adjust soil moisture levels and lighting conditions. It also plays background music through sound equipment and adjusts lighting to help the user relax.

[0313] Users receive information from the system via their smartphones or tablets. The user interface not only monitors the growth status and notifies users of harvest time, but also provides emotion-based feedback. For example, by inputting prompts such as "Please suggest ways to adjust the growing environment so that the user can relax" into the generating AI model, it is possible to obtain suggestions based on the user's emotions.

[0314] A concrete example is a user growing tomatoes in a cool climate. The server issues commands to maintain the appropriate temperature and humidity for the tomatoes, and the terminal uses warm lighting as supplementary support to adjust the climate. At the same time, if the system detects that the user is experiencing stress, it plays relaxing music to provide a comfortable working environment.

[0315] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0316] Step 1:

[0317] The server collects agricultural data from multiple sensors. Specifically, it uses temperature, humidity, and soil moisture sensors to acquire environmental information. It receives real-time data from the sensors as input and stores the acquired data in a database as output. Data processing includes noise reduction and correction to generate highly reliable data.

[0318] Step 2:

[0319] The server analyzes agricultural data stored in a database using machine learning algorithms. Specifically, it runs growth models tailored to specific conditions using a "machine learning framework" to predict optimal growth conditions. It uses historical data and current environmental data as input and generates recommended growth conditions as output. Regression analysis and pattern recognition are used for data calculations.

[0320] Step 3:

[0321] The server analyzes user emotional data using emotion analysis software. Specifically, it acquires user facial expression data and biometric indicators from camera devices and biosensors. It receives this data as input and identifies the user's emotional state as output. In data processing, image processing and statistical methods are used to assign emotion labels.

[0322] Step 4:

[0323] The terminal automatically controls the cultivation environment based on commands from the server. Specifically, it operates water pumps and lighting devices using a control program. It receives recommended growth conditions and sentiment analysis results generated by the server as input, and provides optimized environmental conditions as output. Its operations include operating pumps and adjusting lighting.

[0324] Step 5:

[0325] Users receive information from the system via their smartphone or tablet. The system uses growth status and emotional feedback received from the device as input, and displays this information on the user interface as output. Based on this information, users can select specific actions to perform, such as cultivation or environmental adjustments. These actions include notification sounds and screen displays.

[0326] Step 6:

[0327] The server utilizes a generative AI model to generate suggestions for the user. For example, it takes a prompt such as, "Please suggest ways to adjust the cultivation environment so that the user can relax," as input and generates customized suggestions for the user as output. Data processing includes natural language processing and content generation.

[0328] (Application Example 2)

[0329] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0330] Conventional crop cultivation management systems primarily focused on optimizing the growing environment for crops, but lacked the functionality to adjust the working environment based on the emotional state of the workers. This made it difficult to improve work efficiency and the mental health of workers. This invention solves this problem by providing a system for crop cultivation management that also takes into account the emotional state of the workers.

[0331] 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.

[0332] In this invention, the server includes multiple detection device means for collecting agricultural data, machine learning algorithm means for predicting optimal crop growth conditions based on the collected data, and emotion analysis means for analyzing the emotional state of workers and adjusting the work environment. This makes it possible to simultaneously optimize the crop growth environment and the work environment.

[0333] "Agricultural data" refers to information about crop growth and environmental conditions, including soil moisture content, sunlight, temperature, and humidity.

[0334] A "detection device" is a sensor or measuring instrument used to collect agricultural data, thereby acquiring environmental information and crop conditions.

[0335] A "machine learning algorithm" is a mathematical or statistical method used to predict crop growth conditions using historical data and empirical information.

[0336] A "data processing device" is a device that uses machine learning algorithms to analyze and process collected agricultural data.

[0337] A "control device" is a device that automatically adjusts the cultivation environment for crops based on predicted growth conditions.

[0338] "Information transmission means" refers to devices or methods for notifying users of predictive information such as the harvest time of agricultural products.

[0339] "Emotional analysis means" refers to a method for identifying a user's emotional state by analyzing their biometric information and behavior, and then adjusting the work environment based on that.

[0340] To implement this invention, it is first necessary for the server to collect information on the growing environment using multiple detection devices for agricultural data collection. The collected data is analyzed by a data processing device, and optimal growing conditions are predicted using a machine learning algorithm. In addition to this agricultural data, the data processing device analyzes the user's emotional state using an emotion analysis means. Biometric sensors and user behavior data are utilized for emotion analysis.

[0341] The control system adjusts the cultivation environment based on predicted growth conditions and the user's emotional state. For example, it rationally adjusts temperature and lighting to provide an optimal working space. It also notifies the user of crop harvest times and provides feedback on environmental adjustments based on their emotions through information transmission means.

[0342] For example, if a user cultivating tomatoes is determined to be stressed by an emotion analysis tool, the server can lower the temperature and play relaxing music to provide a more comfortable working environment.

[0343] An example of a prompt is, "Considering the emotional state of the workers, how can we provide a relaxing environment within the factory?" This prompt serves as a guide for the generating AI model when creating environmental improvement measures that respond to the user's emotional state.

[0344] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0345] Step 1:

[0346] The server acquires environmental data such as soil moisture content, sunlight intensity, temperature, and humidity from multiple detection devices used to collect agricultural data. The server stores this input data for later analysis.

[0347] Step 2:

[0348] The server transfers the collected environmental data to a data processing unit and uses machine learning algorithms to predict the optimal growing conditions for crops. It takes historical weather data and agricultural workers' experience as input and calculates optimal growing conditions such as temperature and moisture content as output.

[0349] Step 3:

[0350] The server acquires biometric and behavioral data from the user through emotion analysis. This identifies the user's emotional state (e.g., stress level). This analysis is performed using a generative AI model, which takes the input data and outputs the emotional state.

[0351] Step 4:

[0352] Based on the user's emotional state and predicted growing conditions, the server automatically adjusts the cultivation environment via a control device. Specifically, it optimizes environmental factors such as adjusting temperature and lighting.

[0353] Step 5:

[0354] The server uses information transmission methods to notify users of crop harvest times and feedback on environmental adjustments based on emotions. For example, it suggests appropriate actions to users through email or app notifications.

[0355] Step 6:

[0356] The user checks the notifications from the server and makes changes to the work environment as needed. Following these prompts, the generating AI model plays a role in providing new data to generate further improvements to the environment.

[0357] 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.

[0358] 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.

[0359] 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.

[0360] [Third Embodiment]

[0361] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0362] 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.

[0363] 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).

[0364] 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.

[0365] 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.

[0366] 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).

[0367] 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.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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".

[0373] This invention relates to a crop cultivation management system designed to improve production efficiency and stabilize market supply in agriculture. This system collects agricultural data using multiple sensors and utilizes that data to predict and manage optimal growing conditions for crops.

[0374] Specifically, the server collects experience data and historical weather data from farmers, and obtains the latest weather information via an API. Based on this data, the server uses machine learning algorithms to learn and model the optimal growing conditions.

[0375] The terminals are installed in each farmland and acquire data in real time from moisture sensors and sunlight sensors. This data is sent to a server, which analyzes the collected information. Based on the results, the terminals automatically adjust the irrigation system and light control devices to maintain an optimal environment.

[0376] Users can receive notifications via smartphone or computer regarding crop growth status and optimal harvest times. They can also receive immediate alerts in case of abnormalities, enabling quick responses. This allows users to efficiently cultivate crops and harvest them at the right time.

[0377] For example, if a tomato farmer implements the system, real-time soil and sunlight data is collected from their terminal and sent to a server. The server then uses machine learning to provide an optimal growing schedule. By performing tasks based on notifications from the system, the user can ensure stable production of high-quality tomatoes. In this way, the system of the present invention can significantly improve the efficiency of agricultural work.

[0378] The following describes the processing flow.

[0379] Step 1:

[0380] The server collects necessary data from farmers' experience data and historical weather databases. It also obtains current weather forecast data via an API. This data is then used to create prediction models and for environmental control.

[0381] Step 2:

[0382] The terminal uses various sensors to acquire environmental data such as soil moisture content and sunlight intensity in real time. The acquired data is immediately transmitted to the server, which uses it as basic information for data processing.

[0383] Step 3:

[0384] The server uses machine learning algorithms based on received environmental data to predict optimal growing conditions for crops. The AI ​​model analyzes growth patterns by combining historical production data with real-time weather and environmental data.

[0385] Step 4:

[0386] The server generates instructions to adjust the cultivation environment based on predicted growth conditions. Specifically, it determines environmental settings such as the required amount of water and appropriate shading / increasing of sunlight.

[0387] Step 5:

[0388] The terminal receives instructions from the server and adjusts the irrigation system and sunlight control equipment to create the optimal growing environment. This allows crops to grow healthily and efficiently.

[0389] Step 6:

[0390] Users receive notifications from the server and can check the growth status and predicted harvest time. They can monitor the farm's status in real time through smartphone and computer applications.

[0391] Step 7:

[0392] The server sends an alert to the user if it detects specific abnormal patterns or conditions. By receiving this alert and taking prompt action, the user can resolve the problem quickly.

[0393] (Example 1)

[0394] 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."

[0395] Efficient growth management is required in crop production, but conventional systems have made it difficult to make real-time adjustments or predict harvest times with high accuracy. Furthermore, the design of optimal growing conditions using environmental data and empirical knowledge has not been sufficient, posing challenges to improving the efficiency and productivity of agricultural work.

[0396] 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.

[0397] In this invention, the server includes multiple observation devices for collecting agriculture-related information, a learning processing device for predicting optimal plant growth conditions based on the collected information, and a control device for automatically adjusting the plant's growing environment based on the growth conditions. This enables highly accurate growth adjustment based on environmental and empirical data, as well as real-time monitoring of growth status and prediction of the optimal harvest time.

[0398] An "observation device" refers to a sensor or device used to acquire information related to agriculture, and is a device that has the function of measuring environmental data such as moisture content and solar radiation.

[0399] A "learning processing device" is a processor or computing device used to analyze and predict the optimal growth conditions for plants based on collected data. It has the function of executing algorithms and creating models using data.

[0400] A "control device" is a device designed to automatically adjust the plant's growth environment, performing operations to change water content and sunlight based on collected data and prediction results.

[0401] "Notification means" refers to methods and technologies for informing users of the calculated harvest time and growth status of plants, and includes sending electronic messages via smartphones and computers.

[0402] This crop cultivation management system aims to improve production efficiency and stabilize market supply in agriculture. Specifically, it includes three main components: servers, terminals, and users.

[0403] The server is responsible for collecting data from agricultural observation devices. It acquires daily information from sensors measuring moisture content and solar radiation, and stores it in a database. The server also obtains external weather information via APIs and integrates it with this data. Functioning as a learning processor, the server uses programming languages ​​like Python and libraries like Scikit-learn to analyze the collected data and build models to predict optimal crop growth conditions. The built models are updated in real time, ensuring they always reflect the latest environmental information.

[0404] The terminals are installed in each farmland and acquire data in real time using moisture and sunlight sensors. The data is sent to a server using communication technologies such as Wi-Fi and LoRa, and because of its energy-efficient design, it can be operated remotely for extended periods. For environmental control, the terminals work in conjunction with agricultural equipment such as irrigation systems and lighting control systems, automatically adjusting the farmland environment based on instructions from the server.

[0405] Users receive notifications from the server via their smartphones or computers. They receive feedback on harvest times and growing conditions, as well as alerts to prompt immediate action in case of anomalies. This allows users to improve work efficiency and achieve stable production of high-quality agricultural products.

[0406] As a concrete example, let's consider a case where a tomato farmer adopts this system. The terminal periodically measures the soil moisture in the tomato field and sends the results to the server. The server processes the data using Scikit-learn, predicts the irrigation schedule to be carried out the next day, and sends it to the user as a notification. This notification includes instructions such as, "Please irrigate tomorrow morning."

[0407] An example of a prompt message is: "Please tell me the optimal time to harvest tomatoes. Please suggest the best schedule, taking into account available sensor data and weather data."

[0408] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0409] Step 1:

[0410] The server collects data from agricultural observation equipment. This data includes environmental factors such as moisture content and solar radiation. Inputs include sensor data and weather information obtained from APIs, and the output is an integrated dataset. Data acquisition occurs daily or in real time during this process.

[0411] Step 2:

[0412] The server performs learning processing using the collected data. Here, data analysis is carried out using machine learning algorithms. The input is the integrated dataset obtained in the previous step, and the output is the construction of a predictive model for the optimal growth conditions of plants. Specifically, regression models and classification models are generated using the Scikit-learn library.

[0413] Step 3:

[0414] The terminals are placed in farmland and collect data from environmental sensors in real time. For example, they acquire data from moisture sensors and sunlight sensors, preprocess it, and then periodically send it to a server. The input is real-time sensor data, and the output is formatted data that is sent to the server. Wi-Fi and LoRa are used as communication technologies in this step.

[0415] Step 4:

[0416] The server receives the data transmitted in the previous step and performs analysis. The input is measurement data sent from the terminal, and the output generates instructions for adjusting the environment. Here, based on the analysis results, commands are generated for the irrigation system and light control devices, and a list of executable instructions is created.

[0417] Step 5:

[0418] The terminal receives instructions from the server and performs environmental adjustments to the farmland. Specifically, it operates irrigation equipment and lighting control systems. The input is a list of instructions from the server, and the output is the adjusted farmland environment. This operation is performed automatically, so no human intervention is required.

[0419] Step 6:

[0420] Users can check the growth status and the results of environmental adjustments via smartphones or computers. The server notifies users of the current state of crops and future harvest predictions. Input is notification data from the server, and output is information provided to the user. Users can use this information to create work plans and utilize them to produce high-quality agricultural products.

[0421] (Application Example 1)

[0422] 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."

[0423] In modern industrial production sites, maintaining optimal machine operation is crucial. However, with the increasing complexity of equipment and the massive volume of data, real-time data analysis and machine control adjustments have become difficult. As a result, it is challenging to prevent decreased operational efficiency and unexpected breakdowns. This invention aims to solve these problems and improve the efficiency and stability of machine operation in industrial sites.

[0424] 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.

[0425] In this invention, the server includes a plurality of detection devices for collecting industrial data, information processing means equipped with a machine learning algorithm for predicting optimal operating conditions based on the collected data, and control means for automatically adjusting the operating environment of the machine based on the operating conditions. This makes it possible to optimize the operating state of the machine in real time, prevent unexpected failures, and improve operating efficiency.

[0426] "Industrial data" refers to information about operating conditions and environmental conditions collected from machinery and equipment within a factory.

[0427] A "detection device" refers to various sensors and devices used to collect data related to the operating status of machinery.

[0428] "Information processing means" refers to a system or algorithm used to analyze collected data and derive the optimal operating conditions for a machine.

[0429] A "machine learning algorithm" is a mathematical method used to predict the optimal current and future machine operating conditions based on past data.

[0430] "Control means" refers to a device or system for automatically adjusting the operation of machinery or equipment based on optimal operating conditions.

[0431] A "notification means" is a device or program that has a notification function to inform human users of analysis results or predicted information.

[0432] This invention provides a system in which servers, terminals, and users each have their own roles in order to optimize the operation of machinery in an industrial environment.

[0433] The server receives real-time operating status data from multiple detection devices installed within the factory. This data includes information such as temperature, vibration, and power consumption. Hardware such as Raspberry Pi and Arduino are used to collect the detected data and send it to the cloud server via the MQTT protocol. On the cloud server, a machine learning algorithm written in Python analyzes the data using the Scikit-learn library and predicts the optimal operating parameters.

[0434] The analysis results are sent to the user's smartphone or computer via a notification system using Flask. This allows the user to instantly understand optimal driving conditions and any abnormality alerts.

[0435] As a concrete example, vibration data from machine tools used in a factory can be collected, and if abnormal vibrations are detected, the machine can be automatically stopped, and the user can be notified of the vibration level details and the cause of the stoppage. In this way, the efficiency of factory operations and the protection of equipment can be achieved.

[0436] An example of a prompt message to be fed into a generative AI model is: "Based on real-time data collected from machinery within the factory, predict the optimal conditions for improving the operating efficiency of the machinery."

[0437] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0438] Step 1:

[0439] The terminal collects machine operating status data using various detection devices within the factory. This includes real-time data from temperature and vibration sensors. This data is aggregated by a Raspberry Pi and filtered using pre-configured thresholds. As a result, the filtered operating data is sent to the server via the MQTT protocol.

[0440] Step 2:

[0441] The server inputs data received via the MQTT protocol into a machine learning algorithm written in Python. The server uses the Scikit-learn library to perform data analysis in real time to predict optimal operating parameters. The input is filtered operating data, and the output is optimized operating conditions.

[0442] Step 3:

[0443] The server sends notifications to the user's smartphone or computer using Flask, containing the results of its analysis. These notifications include optimal operating parameters and necessary actions. The user can then make manual adjustments as needed based on the received notifications. If an anomaly alert is included, detailed information will be provided to prompt a quick response.

[0444] Step 4:

[0445] The generating AI model uses previously recorded operating data and current operating conditions to generate further suggestions that contribute to optimizing operations, using prompt messages. Specifically, the user can input a prompt into the system such as, "Based on real-time data of machinery collected within the factory, predict the optimal conditions for improving the operating efficiency of the machinery," and then use the results in daily operations.

[0446] 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.

[0447] This invention aims to optimize the cultivation environment and improve the user experience by combining a crop cultivation management system with an emotion engine that recognizes user emotions. The system comprises multiple sensors for collecting agricultural data, a machine learning algorithm based on the data, a control device that automatically adjusts the cultivation environment, and an emotion engine.

[0448] The server collects agricultural data and uses machine learning algorithms based on the collected data to predict optimal growing conditions for crops. In addition, the server has the ability to analyze user emotional data using an emotion engine. The emotion engine identifies the user's emotions using data obtained from the user's words and actions and biosensors, and sends the results to the server.

[0449] The device adjusts soil moisture and sunlight levels in real time according to instructions from the server. Furthermore, it makes adjustments based on the user's emotions, analyzed by the emotion engine, to provide the most relaxing environment for that specific emotional state.

[0450] Users can not only receive notifications about growing conditions and harvest times via their smartphones or tablets, but also check emotion-based feedback provided by the system. For example, if a user is feeling stressed, the emotion engine can sense this state, and the server can adjust the instructions for the growing environment accordingly to provide a more comfortable working environment.

[0451] For example, if a user cultivating tomatoes is using this system, the device can efficiently support tomato growth while also providing interaction such as playing relaxing background music or adjusting lighting based on the user's emotions. In this way, crop cultivation that also takes the user's mental health into consideration can be realized.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The server collects experience data from agricultural workers and historical weather data from a database, and retrieves the latest weather forecasts via an API. This information is then used for subsequent data analysis and the creation of prediction models.

[0455] Step 2:

[0456] The device acquires real-time data from multiple sensors to measure soil moisture and sunlight levels. This data is sent to a server and immediately used for environmental analysis.

[0457] Step 3:

[0458] The server uses machine learning algorithms to analyze received environmental data and predict the optimal growing conditions for crops. Based on this model, it generates instructions for fine-tuning the growing environment.

[0459] Step 4:

[0460] The terminal automatically operates the irrigation system and light control equipment according to instructions from the server, maintaining optimal environmental conditions. This allows crops to be grown efficiently.

[0461] Step 5:

[0462] The server utilizes an emotion engine to analyze the user's emotional data. This emotional data is collected through biosensors and user interactions and sent to the server.

[0463] Step 6:

[0464] Based on the user's emotional data, the server adjusts the cultivation environment according to the user's emotional state. For example, if the user is feeling stressed, the server may instruct the server to play music or change the lighting to improve their mood.

[0465] Step 7:

[0466] Users can receive notifications from the server and check the results of environmental adjustments based on crop growth status and emotional state. This allows users to perform farming in a more comfortable environment, leading to more efficient harvests.

[0467] (Example 2)

[0468] 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."

[0469] Conventional crop cultivation management systems simply optimized crop growth conditions without considering user emotions or working environment. This could potentially compromise user comfort, and even if farming efficiency improved, reducing user stress and increasing satisfaction remained challenges.

[0470] 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.

[0471] In this invention, the server includes a plurality of sensor means for collecting agricultural data, a data processing means equipped with a machine learning algorithm for predicting optimal crop growth conditions based on the collected data, a control means for automatically adjusting the crop cultivation environment based on the growth conditions, an emotion analysis means for analyzing the user's emotional data and influencing the crop cultivation environment, and an output means that considers the user's emotional state based on the emotion analysis and provides emotion-based feedback. This makes it possible to optimize crop growth while simultaneously providing a comfortable working environment that takes the user's emotions into consideration.

[0472] "Agricultural data" refers to information related to the growing environment of crops, such as soil moisture content, temperature, humidity, and amount of sunlight, and is acquired using sensors.

[0473] "Sensing means" refers to devices and equipment used to collect environmental information, which enables data processing.

[0474] "Data processing means" refers to a device consisting of hardware and software used to analyze collected data and convert it into meaningful information.

[0475] A "machine learning algorithm" is a mathematical method used to learn patterns and trends from data and perform predictions and classifications.

[0476] "Control means" refers to hardware and software used to adjust the state of a specific work environment based on information collected and analyzed by the system.

[0477] "Emotion analysis tools" are used to identify users' emotions and analyze how they affect the cultivation environment of agricultural products.

[0478] "Output means" refers to a device or interface for transmitting processed information or analysis results to the user.

[0479] This invention describes a form for realizing a crop cultivation management system that takes user emotions into consideration.

[0480] The server collects agricultural data using multiple sensors. These include temperature and humidity sensors and soil moisture sensors, which acquire environmental data such as soil moisture content, temperature, humidity, and sunlight levels. This data is transmitted to the server in real time and stored in a database. Based on the collected data, the server uses a machine learning framework that utilizes machine learning algorithms to predict the optimal growing conditions for crops.

[0481] Furthermore, the server runs "emotion analysis software" to analyze the user's emotional data. It utilizes data from "biosensors" and "camera devices" that can acquire the user's facial expressions and biometric indicators, and analyzes this data to understand the user's emotional state.

[0482] The terminal automatically controls the cultivation environment based on commands from the server. Using a "control program," it operates water pumps and lighting devices to adjust soil moisture levels and lighting conditions. It also plays background music through sound equipment and adjusts lighting to help the user relax.

[0483] Users receive information from the system via their smartphones or tablets. The user interface not only monitors the growth status and notifies users of harvest time, but also provides emotion-based feedback. For example, by inputting prompts such as "Please suggest ways to adjust the growing environment so that the user can relax" into the generating AI model, it is possible to obtain suggestions based on the user's emotions.

[0484] A concrete example is a user growing tomatoes in a cool climate. The server issues commands to maintain the appropriate temperature and humidity for the tomatoes, and the terminal uses warm lighting as supplementary support to adjust the climate. At the same time, if the system detects that the user is experiencing stress, it plays relaxing music to provide a comfortable working environment.

[0485] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0486] Step 1:

[0487] The server collects agricultural data from multiple sensors. Specifically, it uses temperature, humidity, and soil moisture sensors to acquire environmental information. It receives real-time data from the sensors as input and stores the acquired data in a database as output. Data processing includes noise reduction and correction to generate highly reliable data.

[0488] Step 2:

[0489] The server analyzes agricultural data stored in a database using machine learning algorithms. Specifically, it runs growth models tailored to specific conditions using a "machine learning framework" to predict optimal growth conditions. It uses historical data and current environmental data as input and generates recommended growth conditions as output. Regression analysis and pattern recognition are used for data calculations.

[0490] Step 3:

[0491] The server analyzes user emotional data using emotion analysis software. Specifically, it acquires user facial expression data and biometric indicators from camera devices and biosensors. It receives this data as input and identifies the user's emotional state as output. In data processing, image processing and statistical methods are used to assign emotion labels.

[0492] Step 4:

[0493] The terminal automatically controls the cultivation environment based on commands from the server. Specifically, it operates water pumps and lighting devices using a control program. It receives recommended growth conditions and sentiment analysis results generated by the server as input, and provides optimized environmental conditions as output. Its operations include operating pumps and adjusting lighting.

[0494] Step 5:

[0495] Users receive information from the system via their smartphone or tablet. The system uses growth status and emotional feedback received from the device as input, and displays this information on the user interface as output. Based on this information, users can select specific actions to perform, such as cultivation or environmental adjustments. These actions include notification sounds and screen displays.

[0496] Step 6:

[0497] The server utilizes a generative AI model to generate suggestions for the user. For example, it takes a prompt such as, "Please suggest ways to adjust the cultivation environment so that the user can relax," as input and generates customized suggestions for the user as output. Data processing includes natural language processing and content generation.

[0498] (Application Example 2)

[0499] 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."

[0500] Conventional crop cultivation management systems primarily focused on optimizing the growing environment for crops, but lacked the functionality to adjust the working environment based on the emotional state of the workers. This made it difficult to improve work efficiency and the mental health of workers. This invention solves this problem by providing a system for crop cultivation management that also takes into account the emotional state of the workers.

[0501] 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.

[0502] In this invention, the server includes multiple detection device means for collecting agricultural data, machine learning algorithm means for predicting optimal crop growth conditions based on the collected data, and emotion analysis means for analyzing the emotional state of workers and adjusting the work environment. This makes it possible to simultaneously optimize the crop growth environment and the work environment.

[0503] "Agricultural data" refers to information about crop growth and environmental conditions, including soil moisture content, sunlight, temperature, and humidity.

[0504] A "detection device" is a sensor or measuring instrument used to collect agricultural data, thereby acquiring environmental information and crop conditions.

[0505] A "machine learning algorithm" is a mathematical or statistical method used to predict crop growth conditions using historical data and empirical information.

[0506] A "data processing device" is a device that uses machine learning algorithms to analyze and process collected agricultural data.

[0507] A "control device" is a device that automatically adjusts the cultivation environment for crops based on predicted growth conditions.

[0508] "Information transmission means" refers to devices or methods for notifying users of predictive information such as the harvest time of agricultural products.

[0509] "Emotional analysis means" refers to a method for identifying a user's emotional state by analyzing their biometric information and behavior, and then adjusting the work environment based on that.

[0510] To implement this invention, it is first necessary for the server to collect information on the growing environment using multiple detection devices for agricultural data collection. The collected data is analyzed by a data processing device, and optimal growing conditions are predicted using a machine learning algorithm. In addition to this agricultural data, the data processing device analyzes the user's emotional state using an emotion analysis means. Biometric sensors and user behavior data are utilized for emotion analysis.

[0511] The control system adjusts the cultivation environment based on predicted growth conditions and the user's emotional state. For example, it rationally adjusts temperature and lighting to provide an optimal working space. It also notifies the user of crop harvest times and provides feedback on environmental adjustments based on their emotions through information transmission means.

[0512] For example, if a user cultivating tomatoes is determined to be stressed by an emotion analysis tool, the server can lower the temperature and play relaxing music to provide a more comfortable working environment.

[0513] An example of a prompt is, "Considering the emotional state of the workers, how can we provide a relaxing environment within the factory?" This prompt serves as a guide for the generating AI model when creating environmental improvement measures that respond to the user's emotional state.

[0514] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0515] Step 1:

[0516] The server acquires environmental data such as soil moisture content, sunlight intensity, temperature, and humidity from multiple detection devices used to collect agricultural data. The server stores this input data for later analysis.

[0517] Step 2:

[0518] The server transfers the collected environmental data to a data processing unit and uses machine learning algorithms to predict the optimal growing conditions for crops. It takes historical weather data and agricultural workers' experience as input and calculates optimal growing conditions such as temperature and moisture content as output.

[0519] Step 3:

[0520] The server acquires biometric and behavioral data from the user through emotion analysis. This identifies the user's emotional state (e.g., stress level). This analysis is performed using a generative AI model, which takes the input data and outputs the emotional state.

[0521] Step 4:

[0522] Based on the user's emotional state and predicted growing conditions, the server automatically adjusts the cultivation environment via a control device. Specifically, it optimizes environmental factors such as adjusting temperature and lighting.

[0523] Step 5:

[0524] The server uses information transmission methods to notify users of crop harvest times and feedback on environmental adjustments based on emotions. For example, it suggests appropriate actions to users through email or app notifications.

[0525] Step 6:

[0526] The user checks the notifications from the server and makes changes to the work environment as needed. Following these prompts, the generating AI model plays a role in providing new data to generate further improvements to the environment.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] [Fourth Embodiment]

[0531] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0532] 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.

[0533] 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).

[0534] 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.

[0535] 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.

[0536] 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).

[0537] 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.

[0538] 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.

[0539] 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.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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".

[0544] This invention relates to a crop cultivation management system designed to improve production efficiency and stabilize market supply in agriculture. This system collects agricultural data using multiple sensors and utilizes that data to predict and manage optimal growing conditions for crops.

[0545] Specifically, the server collects experience data and historical weather data from farmers, and obtains the latest weather information via an API. Based on this data, the server uses machine learning algorithms to learn and model the optimal growing conditions.

[0546] The terminals are installed in each farmland and acquire data in real time from moisture sensors and sunlight sensors. This data is sent to a server, which analyzes the collected information. Based on the results, the terminals automatically adjust the irrigation system and light control devices to maintain an optimal environment.

[0547] Users can receive notifications via smartphone or computer regarding crop growth status and optimal harvest times. They can also receive immediate alerts in case of abnormalities, enabling quick responses. This allows users to efficiently cultivate crops and harvest them at the right time.

[0548] For example, if a tomato farmer implements the system, real-time soil and sunlight data is collected from their terminal and sent to a server. The server then uses machine learning to provide an optimal growing schedule. By performing tasks based on notifications from the system, the user can ensure stable production of high-quality tomatoes. In this way, the system of the present invention can significantly improve the efficiency of agricultural work.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The server collects necessary data from farmers' experience data and historical weather databases. It also obtains current weather forecast data via an API. This data is then used to create prediction models and for environmental control.

[0552] Step 2:

[0553] The terminal uses various sensors to acquire environmental data such as soil moisture content and sunlight intensity in real time. The acquired data is immediately transmitted to the server, which uses it as basic information for data processing.

[0554] Step 3:

[0555] The server uses machine learning algorithms based on received environmental data to predict optimal growing conditions for crops. The AI ​​model analyzes growth patterns by combining historical production data with real-time weather and environmental data.

[0556] Step 4:

[0557] The server generates instructions to adjust the cultivation environment based on predicted growth conditions. Specifically, it determines environmental settings such as the required amount of water and appropriate shading / increasing of sunlight.

[0558] Step 5:

[0559] The terminal receives instructions from the server and adjusts the irrigation system and sunlight control equipment to create the optimal growing environment. This allows crops to grow healthily and efficiently.

[0560] Step 6:

[0561] Users receive notifications from the server and can check the growth status and predicted harvest time. They can monitor the farm's status in real time through smartphone and computer applications.

[0562] Step 7:

[0563] The server sends an alert to the user if it detects specific abnormal patterns or conditions. By receiving this alert and taking prompt action, the user can resolve the problem quickly.

[0564] (Example 1)

[0565] 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".

[0566] Efficient growth management is required in crop production, but conventional systems have made it difficult to make real-time adjustments or predict harvest times with high accuracy. Furthermore, the design of optimal growing conditions using environmental data and empirical knowledge has not been sufficient, posing challenges to improving the efficiency and productivity of agricultural work.

[0567] 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.

[0568] In this invention, the server includes multiple observation devices for collecting agriculture-related information, a learning processing device for predicting optimal plant growth conditions based on the collected information, and a control device for automatically adjusting the plant's growing environment based on the growth conditions. This enables highly accurate growth adjustment based on environmental and empirical data, as well as real-time monitoring of growth status and prediction of the optimal harvest time.

[0569] An "observation device" refers to a sensor or device used to acquire information related to agriculture, and is a device that has the function of measuring environmental data such as moisture content and solar radiation.

[0570] A "learning processing device" is a processor or computing device used to analyze and predict the optimal growth conditions for plants based on collected data. It has the function of executing algorithms and creating models using data.

[0571] A "control device" is a device designed to automatically adjust the plant's growth environment, performing operations to change water content and sunlight based on collected data and prediction results.

[0572] "Notification means" refers to methods and technologies for informing users of the calculated harvest time and growth status of plants, and includes sending electronic messages via smartphones and computers.

[0573] This crop cultivation management system aims to improve production efficiency and stabilize market supply in agriculture. Specifically, it includes three main components: servers, terminals, and users.

[0574] The server is responsible for collecting data from agricultural observation devices. It acquires daily information from sensors measuring moisture content and solar radiation, and stores it in a database. The server also obtains external weather information via APIs and integrates it with this data. Functioning as a learning processor, the server uses programming languages ​​like Python and libraries like Scikit-learn to analyze the collected data and build models to predict optimal crop growth conditions. The built models are updated in real time, ensuring they always reflect the latest environmental information.

[0575] The terminals are installed in each farmland and acquire data in real time using moisture and sunlight sensors. The data is sent to a server using communication technologies such as Wi-Fi and LoRa, and because of its energy-efficient design, it can be operated remotely for extended periods. For environmental control, the terminals work in conjunction with agricultural equipment such as irrigation systems and lighting control systems, automatically adjusting the farmland environment based on instructions from the server.

[0576] Users receive notifications from the server via their smartphones or computers. They receive feedback on harvest times and growing conditions, as well as alerts to prompt immediate action in case of anomalies. This allows users to improve work efficiency and achieve stable production of high-quality agricultural products.

[0577] As a concrete example, let's consider a case where a tomato farmer adopts this system. The terminal periodically measures the soil moisture in the tomato field and sends the results to the server. The server processes the data using Scikit-learn, predicts the irrigation schedule to be carried out the next day, and sends it to the user as a notification. This notification includes instructions such as, "Please irrigate tomorrow morning."

[0578] An example of a prompt message is: "Please tell me the optimal time to harvest tomatoes. Please suggest the best schedule, taking into account available sensor data and weather data."

[0579] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0580] Step 1:

[0581] The server collects data from agricultural observation equipment. This data includes environmental factors such as moisture content and solar radiation. Inputs include sensor data and weather information obtained from APIs, and the output is an integrated dataset. Data acquisition occurs daily or in real time during this process.

[0582] Step 2:

[0583] The server performs learning processing using the collected data. Here, data analysis is carried out using machine learning algorithms. The input is the integrated dataset obtained in the previous step, and the output is the construction of a predictive model for the optimal growth conditions of plants. Specifically, regression models and classification models are generated using the Scikit-learn library.

[0584] Step 3:

[0585] The terminals are placed in farmland and collect data from environmental sensors in real time. For example, they acquire data from moisture sensors and sunlight sensors, preprocess it, and then periodically send it to a server. The input is real-time sensor data, and the output is formatted data that is sent to the server. Wi-Fi and LoRa are used as communication technologies in this step.

[0586] Step 4:

[0587] The server receives the data transmitted in the previous step and performs analysis. The input is measurement data sent from the terminal, and the output generates instructions for adjusting the environment. Here, based on the analysis results, commands are generated for the irrigation system and light control devices, and a list of executable instructions is created.

[0588] Step 5:

[0589] The terminal receives instructions from the server and performs environmental adjustments to the farmland. Specifically, it operates irrigation equipment and lighting control systems. The input is a list of instructions from the server, and the output is the adjusted farmland environment. This operation is performed automatically, so no human intervention is required.

[0590] Step 6:

[0591] Users can check the growth status and the results of environmental adjustments via smartphones or computers. The server notifies users of the current state of crops and future harvest predictions. Input is notification data from the server, and output is information provided to the user. Users can use this information to create work plans and utilize them to produce high-quality agricultural products.

[0592] (Application Example 1)

[0593] 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".

[0594] In modern industrial production sites, maintaining optimal machine operation is crucial. However, with the increasing complexity of equipment and the massive volume of data, real-time data analysis and machine control adjustments have become difficult. As a result, it is challenging to prevent decreased operational efficiency and unexpected breakdowns. This invention aims to solve these problems and improve the efficiency and stability of machine operation in industrial sites.

[0595] 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.

[0596] In this invention, the server includes a plurality of detection devices for collecting industrial data, information processing means equipped with a machine learning algorithm for predicting optimal operating conditions based on the collected data, and control means for automatically adjusting the operating environment of the machine based on the operating conditions. This makes it possible to optimize the operating state of the machine in real time, prevent unexpected failures, and improve operating efficiency.

[0597] "Industrial data" refers to information about operating conditions and environmental conditions collected from machinery and equipment within a factory.

[0598] A "detection device" refers to various sensors and devices used to collect data related to the operating status of machinery.

[0599] "Information processing means" refers to a system or algorithm used to analyze collected data and derive the optimal operating conditions for a machine.

[0600] A "machine learning algorithm" is a mathematical method used to predict the optimal current and future machine operating conditions based on past data.

[0601] "Control means" refers to a device or system for automatically adjusting the operation of machinery or equipment based on optimal operating conditions.

[0602] A "notification means" is a device or program that has a notification function to inform human users of analysis results or predicted information.

[0603] This invention provides a system in which servers, terminals, and users each have their own roles in order to optimize the operation of machinery in an industrial environment.

[0604] The server receives real-time operating status data from multiple detection devices installed within the factory. This data includes information such as temperature, vibration, and power consumption. Hardware such as Raspberry Pi and Arduino are used to collect the detected data and send it to the cloud server via the MQTT protocol. On the cloud server, a machine learning algorithm written in Python analyzes the data using the Scikit-learn library and predicts the optimal operating parameters.

[0605] The analysis results are sent to the user's smartphone or computer via a notification system using Flask. This allows the user to instantly understand optimal driving conditions and any abnormality alerts.

[0606] As a concrete example, vibration data from machine tools used in a factory can be collected, and if abnormal vibrations are detected, the machine can be automatically stopped, and the user can be notified of the vibration level details and the cause of the stoppage. In this way, the efficiency of factory operations and the protection of equipment can be achieved.

[0607] An example of a prompt message to be fed into a generative AI model is: "Based on real-time data collected from machinery within the factory, predict the optimal conditions for improving the operating efficiency of the machinery."

[0608] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0609] Step 1:

[0610] The terminal collects machine operating status data using various detection devices within the factory. This includes real-time data from temperature and vibration sensors. This data is aggregated by a Raspberry Pi and filtered using pre-configured thresholds. As a result, the filtered operating data is sent to the server via the MQTT protocol.

[0611] Step 2:

[0612] The server inputs data received via the MQTT protocol into a machine learning algorithm written in Python. The server uses the Scikit-learn library to perform data analysis in real time to predict optimal operating parameters. The input is filtered operating data, and the output is optimized operating conditions.

[0613] Step 3:

[0614] The server sends notifications to the user's smartphone or computer using Flask, containing the results of its analysis. These notifications include optimal operating parameters and necessary actions. The user can then make manual adjustments as needed based on the received notifications. If an anomaly alert is included, detailed information will be provided to prompt a quick response.

[0615] Step 4:

[0616] The generating AI model uses previously recorded operating data and current operating conditions to generate further suggestions that contribute to optimizing operations, using prompt messages. Specifically, the user can input a prompt into the system such as, "Based on real-time data of machinery collected within the factory, predict the optimal conditions for improving the operating efficiency of the machinery," and then use the results in daily operations.

[0617] 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.

[0618] This invention aims to optimize the cultivation environment and improve the user experience by combining a crop cultivation management system with an emotion engine that recognizes user emotions. The system comprises multiple sensors for collecting agricultural data, a machine learning algorithm based on the data, a control device that automatically adjusts the cultivation environment, and an emotion engine.

[0619] The server collects agricultural data and uses machine learning algorithms based on the collected data to predict optimal growing conditions for crops. In addition, the server has the ability to analyze user emotional data using an emotion engine. The emotion engine identifies the user's emotions using data obtained from the user's words and actions and biosensors, and sends the results to the server.

[0620] The device adjusts soil moisture and sunlight levels in real time according to instructions from the server. Furthermore, it makes adjustments based on the user's emotions, analyzed by the emotion engine, to provide the most relaxing environment for that specific emotional state.

[0621] Users can not only receive notifications about growing conditions and harvest times via their smartphones or tablets, but also check emotion-based feedback provided by the system. For example, if a user is feeling stressed, the emotion engine can sense this state, and the server can adjust the instructions for the growing environment accordingly to provide a more comfortable working environment.

[0622] For example, if a user cultivating tomatoes is using this system, the device can efficiently support tomato growth while also providing interaction such as playing relaxing background music or adjusting lighting based on the user's emotions. In this way, crop cultivation that also takes the user's mental health into consideration can be realized.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The server collects experience data from agricultural workers and historical weather data from a database, and retrieves the latest weather forecasts via an API. This information is then used for subsequent data analysis and the creation of prediction models.

[0626] Step 2:

[0627] The device acquires real-time data from multiple sensors to measure soil moisture and sunlight levels. This data is sent to a server and immediately used for environmental analysis.

[0628] Step 3:

[0629] The server uses machine learning algorithms to analyze received environmental data and predict the optimal growing conditions for crops. Based on this model, it generates instructions for fine-tuning the growing environment.

[0630] Step 4:

[0631] The terminal automatically operates the irrigation system and light control equipment according to instructions from the server, maintaining optimal environmental conditions. This allows crops to be grown efficiently.

[0632] Step 5:

[0633] The server utilizes an emotion engine to analyze the user's emotional data. This emotional data is collected through biosensors and user interactions and sent to the server.

[0634] Step 6:

[0635] Based on the user's emotional data, the server adjusts the cultivation environment according to the user's emotional state. For example, if the user is feeling stressed, the server may instruct the server to play music or change the lighting to improve their mood.

[0636] Step 7:

[0637] Users can receive notifications from the server and check the results of environmental adjustments based on crop growth status and emotional state. This allows users to perform farming in a more comfortable environment, leading to more efficient harvests.

[0638] (Example 2)

[0639] 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".

[0640] Conventional crop cultivation management systems simply optimized crop growth conditions without considering user emotions or working environment. This could potentially compromise user comfort, and even if farming efficiency improved, reducing user stress and increasing satisfaction remained challenges.

[0641] 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.

[0642] In this invention, the server includes a plurality of sensor means for collecting agricultural data, a data processing means equipped with a machine learning algorithm for predicting optimal crop growth conditions based on the collected data, a control means for automatically adjusting the crop cultivation environment based on the growth conditions, an emotion analysis means for analyzing the user's emotional data and influencing the crop cultivation environment, and an output means that considers the user's emotional state based on the emotion analysis and provides emotion-based feedback. This makes it possible to optimize crop growth while simultaneously providing a comfortable working environment that takes the user's emotions into consideration.

[0643] "Agricultural data" refers to information related to the growing environment of crops, such as soil moisture content, temperature, humidity, and amount of sunlight, and is acquired using sensors.

[0644] "Sensing means" refers to devices and equipment used to collect environmental information, which enables data processing.

[0645] "Data processing means" refers to a device consisting of hardware and software used to analyze collected data and convert it into meaningful information.

[0646] A "machine learning algorithm" is a mathematical method used to learn patterns and trends from data and perform predictions and classifications.

[0647] "Control means" refers to hardware and software used to adjust the state of a specific work environment based on information collected and analyzed by the system.

[0648] "Emotion analysis tools" are used to identify users' emotions and analyze how they affect the cultivation environment of agricultural products.

[0649] "Output means" refers to a device or interface for transmitting processed information or analysis results to the user.

[0650] This invention describes a form for realizing a crop cultivation management system that takes user emotions into consideration.

[0651] The server collects agricultural data using multiple sensors. These include temperature and humidity sensors and soil moisture sensors, which acquire environmental data such as soil moisture content, temperature, humidity, and sunlight levels. This data is transmitted to the server in real time and stored in a database. Based on the collected data, the server uses a machine learning framework that utilizes machine learning algorithms to predict the optimal growing conditions for crops.

[0652] Furthermore, the server runs "emotion analysis software" to analyze the user's emotional data. It utilizes data from "biosensors" and "camera devices" that can acquire the user's facial expressions and biometric indicators, and analyzes this data to understand the user's emotional state.

[0653] The terminal automatically controls the cultivation environment based on commands from the server. Using a "control program," it operates water pumps and lighting devices to adjust soil moisture levels and lighting conditions. It also plays background music through sound equipment and adjusts lighting to help the user relax.

[0654] Users receive information from the system via their smartphones or tablets. The user interface not only monitors the growth status and notifies users of harvest time, but also provides emotion-based feedback. For example, by inputting prompts such as "Please suggest ways to adjust the growing environment so that the user can relax" into the generating AI model, it is possible to obtain suggestions based on the user's emotions.

[0655] A concrete example is a user growing tomatoes in a cool climate. The server issues commands to maintain the appropriate temperature and humidity for the tomatoes, and the terminal uses warm lighting as supplementary support to adjust the climate. At the same time, if the system detects that the user is experiencing stress, it plays relaxing music to provide a comfortable working environment.

[0656] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0657] Step 1:

[0658] The server collects agricultural data from multiple sensors. Specifically, it uses temperature, humidity, and soil moisture sensors to acquire environmental information. It receives real-time data from the sensors as input and stores the acquired data in a database as output. Data processing includes noise reduction and correction to generate highly reliable data.

[0659] Step 2:

[0660] The server analyzes agricultural data stored in a database using machine learning algorithms. Specifically, it runs growth models tailored to specific conditions using a "machine learning framework" to predict optimal growth conditions. It uses historical data and current environmental data as input and generates recommended growth conditions as output. Regression analysis and pattern recognition are used for data calculations.

[0661] Step 3:

[0662] The server analyzes user emotional data using emotion analysis software. Specifically, it acquires user facial expression data and biometric indicators from camera devices and biosensors. It receives this data as input and identifies the user's emotional state as output. In data processing, image processing and statistical methods are used to assign emotion labels.

[0663] Step 4:

[0664] The terminal automatically controls the cultivation environment based on commands from the server. Specifically, it operates water pumps and lighting devices using a control program. It receives recommended growth conditions and sentiment analysis results generated by the server as input, and provides optimized environmental conditions as output. Its operations include operating pumps and adjusting lighting.

[0665] Step 5:

[0666] Users receive information from the system via their smartphone or tablet. The system uses growth status and emotional feedback received from the device as input, and displays this information on the user interface as output. Based on this information, users can select specific actions to perform, such as cultivation or environmental adjustments. These actions include notification sounds and screen displays.

[0667] Step 6:

[0668] The server utilizes a generative AI model to generate suggestions for the user. For example, it takes a prompt such as, "Please suggest ways to adjust the cultivation environment so that the user can relax," as input and generates customized suggestions for the user as output. Data processing includes natural language processing and content generation.

[0669] (Application Example 2)

[0670] 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".

[0671] Conventional crop cultivation management systems primarily focused on optimizing the growing environment for crops, but lacked the functionality to adjust the working environment based on the emotional state of the workers. This made it difficult to improve work efficiency and the mental health of workers. This invention solves this problem by providing a system for crop cultivation management that also takes into account the emotional state of the workers.

[0672] 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.

[0673] In this invention, the server includes multiple detection device means for collecting agricultural data, machine learning algorithm means for predicting optimal crop growth conditions based on the collected data, and emotion analysis means for analyzing the emotional state of workers and adjusting the work environment. This makes it possible to simultaneously optimize the crop growth environment and the work environment.

[0674] "Agricultural data" refers to information about crop growth and environmental conditions, including soil moisture content, sunlight, temperature, and humidity.

[0675] A "detection device" is a sensor or measuring instrument used to collect agricultural data, thereby acquiring environmental information and crop conditions.

[0676] A "machine learning algorithm" is a mathematical or statistical method used to predict crop growth conditions using historical data and empirical information.

[0677] A "data processing device" is a device that uses machine learning algorithms to analyze and process collected agricultural data.

[0678] A "control device" is a device that automatically adjusts the cultivation environment for crops based on predicted growth conditions.

[0679] "Information transmission means" refers to devices or methods for notifying users of predictive information such as the harvest time of agricultural products.

[0680] "Emotional analysis means" refers to a method for identifying a user's emotional state by analyzing their biometric information and behavior, and then adjusting the work environment based on that.

[0681] To implement this invention, it is first necessary for the server to collect information on the growing environment using multiple detection devices for agricultural data collection. The collected data is analyzed by a data processing device, and optimal growing conditions are predicted using a machine learning algorithm. In addition to this agricultural data, the data processing device analyzes the user's emotional state using an emotion analysis means. Biometric sensors and user behavior data are utilized for emotion analysis.

[0682] The control system adjusts the cultivation environment based on predicted growth conditions and the user's emotional state. For example, it rationally adjusts temperature and lighting to provide an optimal working space. It also notifies the user of crop harvest times and provides feedback on environmental adjustments based on their emotions through information transmission means.

[0683] For example, if a user cultivating tomatoes is determined to be stressed by an emotion analysis tool, the server can lower the temperature and play relaxing music to provide a more comfortable working environment.

[0684] An example of a prompt is, "Considering the emotional state of the workers, how can we provide a relaxing environment within the factory?" This prompt serves as a guide for the generating AI model when creating environmental improvement measures that respond to the user's emotional state.

[0685] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0686] Step 1:

[0687] The server acquires environmental data such as soil moisture content, sunlight intensity, temperature, and humidity from multiple detection devices used to collect agricultural data. The server stores this input data for later analysis.

[0688] Step 2:

[0689] The server transfers the collected environmental data to a data processing unit and uses machine learning algorithms to predict the optimal growing conditions for crops. It takes historical weather data and agricultural workers' experience as input and calculates optimal growing conditions such as temperature and moisture content as output.

[0690] Step 3:

[0691] The server acquires biometric and behavioral data from the user through emotion analysis. This identifies the user's emotional state (e.g., stress level). This analysis is performed using a generative AI model, which takes the input data and outputs the emotional state.

[0692] Step 4:

[0693] Based on the user's emotional state and predicted growing conditions, the server automatically adjusts the cultivation environment via a control device. Specifically, it optimizes environmental factors such as adjusting temperature and lighting.

[0694] Step 5:

[0695] The server uses information transmission methods to notify users of crop harvest times and feedback on environmental adjustments based on emotions. For example, it suggests appropriate actions to users through email or app notifications.

[0696] Step 6:

[0697] The user checks the notifications from the server and makes changes to the work environment as needed. Following these prompts, the generating AI model plays a role in providing new data to generate further improvements to the environment.

[0698] 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.

[0699] 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.

[0700] 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 robot 414.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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."

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] The following is further disclosed regarding the embodiments described above.

[0720] (Claim 1)

[0721] Multiple sensors for collecting agricultural data,

[0722] A data processing device equipped with a machine learning algorithm for predicting optimal growing conditions for crops based on collected data,

[0723] A control device for automatically adjusting the cultivation environment of crops based on the aforementioned growth conditions,

[0724] Based on the environmental adjustments made by the control device, a notification means predicts the harvest time of crops and notifies the user;

[0725] A system that includes this.

[0726] (Claim 2)

[0727] The system according to claim 1, wherein the machine learning algorithm predicts growth conditions using the experience data of agricultural workers and past weather data.

[0728] (Claim 3)

[0729] The system according to claim 1, wherein the control device is designed to adjust the soil moisture content and sunlight content in real time.

[0730] "Example 1"

[0731] (Claim 1)

[0732] Multiple observation devices for collecting information related to agriculture,

[0733] A learning processing device for predicting the optimal growth conditions for plants based on collected information,

[0734] A control device for automatically adjusting the plant growth environment based on the aforementioned growth conditions,

[0735] Based on the adjustment of the growing environment by the control device, a notification means calculates the plant harvest time and informs the user;

[0736] A system that includes this.

[0737] (Claim 2)

[0738] The system according to claim 1, wherein the learning processing device predicts growth conditions using knowledge data of agricultural workers and past weather information.

[0739] (Claim 3)

[0740] The system according to claim 1, wherein the control device is designed to adjust the amount of ground moisture and solar radiation in real time.

[0741] "Application Example 1"

[0742] (Claim 1)

[0743] Multiple detection devices for collecting industrial data,

[0744] An information processing means equipped with a machine learning algorithm for predicting optimal operating conditions based on collected data,

[0745] A control means for automatically adjusting the operating environment of the machine based on the aforementioned operating conditions,

[0746] Based on the environmental adjustments made by the control means, a notification means predicts the operating status of the machine and notifies the user;

[0747] A system that includes this.

[0748] (Claim 2)

[0749] The system according to claim 1, wherein the machine learning algorithm predicts operating conditions using the experience data of the work personnel and past operational data.

[0750] (Claim 3)

[0751] The system according to claim 1, wherein the control means is designed to adjust the temperature and vibration of the machine in real time.

[0752] "Example 2 of combining an emotion engine"

[0753] (Claim 1)

[0754] Multiple sensor means for collecting agricultural data,

[0755] A data processing means equipped with a machine learning algorithm for predicting optimal growing conditions for crops based on collected data,

[0756] A control means for automatically adjusting the cultivation environment of crops based on the aforementioned growth conditions,

[0757] A means of analyzing user sentiment data and influencing the cultivation environment of agricultural products,

[0758] Based on the aforementioned emotion analysis, an output means that considers the user's emotional state and provides emotion-based feedback,

[0759] A system that includes this.

[0760] (Claim 2)

[0761] The system according to claim 1, wherein the machine learning algorithm predicts growth conditions using the experience data of agricultural workers and past weather data.

[0762] (Claim 3)

[0763] The system according to claim 1, wherein the control means is designed to adjust the soil moisture content and sunlight content in real time.

[0764] "Application example 2 of combining emotional engines"

[0765] (Claim 1)

[0766] Multiple detection devices for collecting agricultural data,

[0767] A data processing device equipped with a machine learning algorithm for predicting optimal growing conditions for crops based on collected data,

[0768] A control device for automatically adjusting the cultivation environment of crops based on the aforementioned growth conditions,

[0769] Based on the environmental adjustments made by the control device, an information transmission means predicts the harvest time of crops and notifies the user;

[0770] An emotion analysis means that analyzes the user's emotional state and adjusts the work environment accordingly,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, wherein the machine learning algorithm predicts growth conditions using the experience information of agricultural workers and past weather information.

[0774] (Claim 3)

[0775] The system according to claim 1, wherein the control device is designed to adjust soil moisture content and sunlight content in real time, and to adjust environmental factors based on the user's emotions. [Explanation of Symbols]

[0776] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Multiple sensors for collecting agricultural data, A data processing device equipped with a machine learning algorithm for predicting optimal growing conditions for crops based on collected data, A control device for automatically adjusting the cultivation environment of crops based on the aforementioned growth conditions, Based on the environmental adjustments made by the control device, a notification means predicts the harvest time of crops and notifies the user; A system that includes this.

2. The system according to claim 1, wherein the machine learning algorithm predicts growth conditions using the experience data of agricultural workers and past weather data.

3. The system according to claim 1, wherein the control device is designed to adjust the soil moisture content and sunlight content in real time.

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