System

Abstract

An object of a system according to an embodiment is to grasp a situation of a farm in real time and provide optimal crop management advice.SOLUTION: A system according to an embodiment includes an Internet of Things (IoT) device, a generation AI, and an advice providing unit. The IoT device may monitor a situation of a farm in real time. The data management unit centrally manages data collected from the IoT devices. The generation AI analyzes the information managed by the information management unit. The advice providing unit provides optimal crop management advice based on the AI analyzed by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately grasp the status of farms in real time and provide optimal crop management advice, so there is room for improvement.

[0005] The system according to the embodiment aims to grasp the status of a farm in real time and provide optimal advice on crop management. [Means for solving the problem]

[0006] The system according to the embodiment includes an IoT device, a data management unit, a generation AI, and an advice providing unit. The IoT device monitors the status of the farm in real time using the IoT device. The data management unit centrally manages data collected from the IoT device. The generation AI analyzes the data managed by the data management unit. The advice providing unit provides optimal crop management advice based on the data analyzed by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the status of the farm in real time and provide optimal crop management advice. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The agricultural support system according to an embodiment of the present invention uses IoT devices to monitor the status of farms in real time, and a generative AI analyzes the data and provides optimal crop management advice. This allows the agricultural support system to grasp the status of farms in real time and realize efficient crop management.

[0029] An agricultural support system according to an embodiment includes an IoT device, a data management unit, a generation AI, and an advice providing unit. The IoT device monitors the status of a farm in real time. For example, the IoT device uses soil sensors and temperature sensors to monitor soil moisture content, nutrient status, and temperature fluctuations. The IoT device can also monitor the status of the entire farm using cameras and drones. The data management unit centrally manages data collected from the IoT device. For example, the data management unit stores data using cloud storage and enables real-time monitoring of the farm status via a dashboard. The data management unit can also enable data viewing and manipulation using a smartphone or tablet. The generation AI analyzes the data managed by the data management unit. For example, the generation AI can analyze temperature, humidity, and soil conditions using machine learning and deep learning to suggest optimal irrigation and fertilization timing. The generation AI can also predict crop growth and warn of disease. The advice providing unit provides optimal crop management advice based on the data analyzed by the generation AI. For example, the advice providing unit provides advice on irrigation and fertilization timing, disease prevention, etc. The advice providing unit can also provide advice via a smartphone or tablet, which allows the agricultural support system according to the embodiment to grasp the status of the farm in real time and achieve efficient crop management.

[0030] The IoT device may include a soil sensor that measures the moisture content and nutrient status of the soil, and a temperature sensor that monitors temperature fluctuations. The IoT device may, for example, use the soil sensor to measure the moisture content and nutrient status of the soil. For example, the soil sensor may measure the soil condition using an electrical conductivity sensor or a pH sensor. The IoT device may also monitor temperature fluctuations using a temperature sensor. For example, the temperature sensor may measure the temperature using a thermistor or an infrared sensor. This allows for real-time monitoring of the moisture content and nutrient status of the soil, as well as temperature fluctuations, to provide a more detailed understanding of the farm situation.

[0031] The data management unit can provide a dashboard that allows the status of the farm to be checked in real time using a smartphone or tablet. The data management unit can provide a dashboard that allows the status of the farm to be checked in real time using a smartphone or tablet, for example. For example, the dashboard can have functions such as graph display, alert functions, and data filtering. The data management unit can also store data using cloud storage so that the status of the farm can be checked in real time through the dashboard. This allows the status of the farm to be checked in real time using a smartphone or tablet, thereby improving the efficiency of farming work.

[0032] Generative AI can analyze temperature, humidity, and soil conditions and suggest optimal timing for irrigation and fertilization. For example, generative AI can use machine learning and deep learning to analyze temperature, humidity, and soil conditions and suggest optimal timing for irrigation and fertilization. Generative AI can also predict crop growth and warn of disease. For example, it can predict the risk of disease from fluctuations in temperature and humidity and take early action. This allows crop growth to be optimized by analyzing temperature, humidity, and soil conditions and suggesting optimal timing for irrigation and fertilization.

[0033] Generative AI can predict the risk of disease from fluctuations in temperature and humidity, allowing for early countermeasures to be taken. For example, generative AI can use machine learning and deep learning to analyze fluctuations in temperature and humidity and predict the risk of disease. Generative AI can also predict disease occurrence patterns based on past disease data and issue early warnings. This makes it possible to minimize crop losses by predicting the risk of disease from fluctuations in temperature and humidity and taking early countermeasures.

[0034] Generative AI can analyze temperature, hours of sunlight, and soil conditions to predict crop growth rates and harvest times. Generative AI can, for example, analyze temperature, hours of sunlight, and soil conditions to predict crop growth rates and harvest times. For example, generative AI can use machine learning and deep learning to analyze temperature, hours of sunlight, and soil conditions to predict crop growth rates and harvest times. Generative AI can also predict growth and harvest times based on past growth data. This allows for optimized crop management by analyzing temperature, hours of sunlight, and soil conditions to predict crop growth rates and harvest times.

[0035] Generative AI can monitor fluctuations in temperature and precipitation in real time and take appropriate measures. For example, generative AI can use machine learning and deep learning to analyze fluctuations in temperature and precipitation and propose appropriate measures. Generative AI can also simulate climate change scenarios and propose optimal agricultural strategies. This makes it possible to minimize the impact of climate change by monitoring fluctuations in temperature and precipitation in real time and taking appropriate measures.

[0036] IoT devices are equipped with microbial sensors that monitor the activity of microorganisms in the soil in real time, and can evaluate the health of the soil based on this microbial activity. IoT devices, for example, are equipped with microbial sensors and monitor the activity of microorganisms in the soil in real time. For example, they measure the types and numbers of microorganisms and evaluate the health of the soil. Furthermore, systems are built that collect microbial activity data and analyze the health of the soil using generative AI. For example, they analyze microbial activity patterns to evaluate the nutritional status and disease risk of the soil. Furthermore, microbial activity can be monitored through IoT devices and advice on fertilization and irrigation based on the health of the soil. For example, if microbial activity is declining, appropriate fertilization can be suggested. This allows for real-time monitoring of microbial activity in the soil and evaluation of soil health, optimizing crop growth.

[0037] IoT devices can use drones to collect aerial photographs of the entire farm in real time, and integrate them with ground sensors to perform three-dimensional environmental monitoring. For example, IoT devices can use cameras mounted on drones to collect aerial photographs of the entire farm in real time, and integrate them with ground sensors to perform three-dimensional environmental monitoring. For example, the growth status of crops and the location of disease outbreaks can be confirmed using aerial photographs. In addition, a system can be built that collects aerial photographs of the farm using drones and integrates and analyzes them with ground sensor data using generative AI. For example, the health of crops can be evaluated by combining aerial photographs and soil sensor data. In addition, aerial photographs can be collected in real time by drones, and environmental monitoring can be performed in conjunction with ground sensors. For example, the aerial photographs can be used to propose optimization of irrigation and fertilization. This allows aerial photographs of the entire farm to be collected in real time using drones, and integrated with ground sensors to perform three-dimensional environmental monitoring, allowing for a more detailed understanding of the farm's conditions.

[0038] IoT devices can be equipped with wildlife sensors that monitor the movements of wild animals around farms in real time, collecting data from an environmental protection perspective. For example, IoT devices can be equipped with wildlife sensors and monitor the movements of wild animals around farms in real time. For example, cameras and motion detection sensors can be used to analyze wild animal behavior patterns. IoT devices can also be used to monitor the growth of plants around farms and collect data from an environmental protection perspective. For example, plant growth rates and health can be measured and measures for environmental protection can be proposed. Ecological data around farms can also be collected through IoT devices, and generative AI can be used to analyze the data for environmental protection. For example, wildlife habitats and plant growth patterns can be analyzed and advice for environmental protection can be provided. This allows for real-time monitoring of wild animal movements around farms and collecting data from an environmental protection perspective, thereby protecting the farm's ecosystem.

[0039] IoT devices can be equipped with water flow sensors that monitor water resource usage on farms in real time, preventing water waste. For example, IoT devices can be equipped with water flow sensors and monitor water resource usage on farms in real time. For example, they can measure the water flow rate of irrigation systems to prevent water waste. A system can also be built that collects water resource usage data and uses generative AI to propose optimal irrigation schedules. For example, it can analyze water usage and crop water demand to propose efficient irrigation methods. Furthermore, an alert system can be introduced to monitor water resource usage on farms through IoT devices and prevent water waste. For example, an alert can be issued if water usage is excessive. This enables efficient water management by monitoring water resource usage on farms in real time and preventing water waste.

[0040] The data management unit can introduce an anomaly detection algorithm to detect abnormal data patterns in real time. The data management unit, for example, introduces an anomaly detection algorithm to detect abnormal data patterns in real time. For example, an anomaly detection algorithm is introduced into a centralized data management system to detect abnormal values ​​in sensor data and issue an alert. In addition, an anomaly detection algorithm is used to build a system that detects abnormal data patterns in real time in the centralized data management system. For example, it detects abnormal temperature fluctuations and abnormal soil values. In addition, an anomaly detection algorithm is introduced into the centralized data management system to detect abnormal data patterns in real time. For example, it detects abnormal humidity fluctuations and abnormal moisture content values ​​and proposes appropriate countermeasures. In this way, detecting abnormal data patterns in real time enables rapid response.

[0041] The data management unit can be added with a voice input function, allowing data to be checked and operated by voice commands. The data management unit, for example, adds a voice input function, allowing data to be checked and operated by voice commands. For example, the voice input function is added to a centralized data management system, allowing sensor data to be checked and irrigation system to be operated by voice. Furthermore, the voice input function is used to build a system that allows operation by voice commands in the centralized data management system. For example, data can be searched and filtered by voice. Furthermore, the voice input function is added to the centralized data management system, allowing data to be checked and operated by voice commands. For example, alerts can be checked and settings can be changed by voice. This allows data to be checked and operated by voice commands, improving user convenience.

[0042] The data management unit works in conjunction with the sales management system and the logistics management system to centrally manage sales data and shipping status of agricultural products. The data management unit, for example, works in conjunction with the sales management system to centrally manage sales data of agricultural products. For example, it checks harvest yields and sales prices in real time. The data management unit also works in conjunction with the logistics management system to centrally manage shipping status of agricultural products. For example, it checks shipping schedules and delivery status in real time. The data management unit also works in conjunction with other agriculture-related systems to improve the efficiency of agricultural work. For example, it integrates sales data and logistics data to propose optimal work schedules. This centralized management of sales data and shipping status can improve the efficiency of agricultural work.

[0043] The data management unit can accumulate historical farm data and enable long-term trend analysis. For example, the data management unit accumulates historical farm data and performs long-term trend analysis. For example, it evaluates the impact of climate change based on past temperature and precipitation data. It also accumulates historical farm data and builds a system that performs long-term trend analysis using generative AI. For example, it analyzes past crop growth data and proposes optimal cultivation methods. The data management unit can also predict disease risk based on past disease occurrence data. This allows agricultural efficiency to be improved by accumulating historical farm data and performing long-term trend analysis.

[0044] Generative AI can analyze energy consumption data on farms and suggest ways to optimize energy efficiency. For example, generative AI can analyze energy consumption data on farms and suggest ways to optimize energy efficiency. For example, generative AI can use machine learning and deep learning to identify peak energy consumption times and suggest efficient energy usage methods. Generative AI can also collect energy consumption data on farms and analyze it using generative AI to suggest ways to optimize energy efficiency. For example, it can suggest specific measures to reduce energy waste. Generative AI can also make suggestions to promote the use of renewable energy. In this way, energy costs can be reduced by analyzing energy consumption data on farms and suggesting ways to optimize energy efficiency.

[0045] Generative AI can analyze farm labor data and propose optimal staffing allocation. Generative AI can, for example, analyze farm labor data and propose optimal staffing allocation. For example, generative AI can use machine learning and deep learning to propose staffing allocation according to busy work periods. In addition, by collecting farm labor data and analyzing it with generative AI, it can propose optimal staffing allocation. For example, it can propose staffing allocation to maximize work efficiency. Generative AI can also assign personnel with the appropriate skills for specific work. In this way, work efficiency can be improved by analyzing farm labor data and proposing optimal staffing allocation.

[0046] Generative AI can analyze farm economic data and propose strategies for reducing costs. For example, generative AI can analyze farm economic data and propose strategies for reducing costs. For example, generative AI can use machine learning and deep learning to optimize the timing and amount of material purchases. Generative AI can also collect farm economic data and analyze it using generative AI to propose strategies for reducing costs. For example, it can propose efficient methods of managing funds. Generative AI can also propose specific measures to reduce wasteful spending. In this way, economic efficiency can be improved by analyzing farm economic data and proposing strategies for reducing costs.

[0047] Generative AI can simulate the growth process of crops using a 3D model and visually predict growth. Generative AI can, for example, simulate the growth process of crops using a 3D model and visually predict growth. For example, generative AI can display the growth rate and harvest time of crops using a 3D model. In addition, by collecting crop growth data and using generative AI to simulate it using a 3D model, visual growth predictions can be made. For example, the growth process of crops can be displayed in 3D in real time. Generative AI can also make growth predictions by reflecting fluctuations in temperature and sunlight hours in the 3D model. This makes it possible to optimize crop management by simulating the growth process of crops using a 3D model and visually predicting growth.

[0048] Generative AI can analyze past disease data and predict disease occurrence patterns. Generative AI can, for example, analyze past disease data and predict disease occurrence patterns. For example, generative AI can predict disease risk based on past temperature and humidity data. Generative AI can also predict disease occurrence patterns by collecting past disease data and analyzing it using generative AI. For example, it can predict the risk of disease occurrence under specific weather conditions. Generative AI can also issue early warnings based on past disease occurrence data. This makes it possible to analyze past disease data and predict disease occurrence patterns, enabling early detection and countermeasures for diseases.

[0049] Generative AI can compare growth data between different crops and propose optimal crop combinations. For example, generative AI can compare growth data between different crops and propose optimal crop combinations. For example, generative AI can propose combinations of crops that complement each other. Generative AI can also propose optimal crop combinations by collecting growth data for different crops and analyzing it using generative AI. For example, it can propose crop combinations that reduce disease risk. Generative AI can also propose crop combinations that maximize yield. This makes it possible to improve crop yield and quality by comparing growth data between different crops and proposing optimal crop combinations.

[0050] Generative AI can analyze farm weather data and evaluate crop resistance to weather fluctuations. For example, generative AI analyzes farm weather data and evaluates crop resistance to weather fluctuations. For example, generative AI analyzes fluctuations in temperature and precipitation to evaluate crop resistance. In addition, by collecting farm weather data and analyzing it using generative AI, crop resistance to weather fluctuations can be evaluated. For example, crop resistance to extreme weather can be evaluated. Generative AI can also assist in the selection of crops that are resistant to climate change. This makes it possible to select and cultivate crops that are resistant to climate change by analyzing farm weather data and evaluating crop resistance to weather fluctuations.

[0051] Generative AI can predict soil changes due to climate change and propose appropriate soil improvement methods. Generative AI can, for example, predict soil changes due to climate change and propose appropriate soil improvement methods. For example, generative AI can predict the nutritional status of the soil based on fluctuations in temperature and precipitation, and propose appropriate fertilization methods. Generative AI can also collect data on soil changes due to climate change and analyze it using generative AI to propose appropriate soil improvement methods. For example, it can predict fluctuations in soil acidity and moisture content and propose appropriate improvement methods. Generative AI can also suggest the selection of soil improvement materials that are appropriate for climate change and the timing of their application. This makes it possible to optimize crop growth by predicting soil changes due to climate change and proposing appropriate soil improvement methods.

[0052] Generative AI can analyze climate data from different regions and propose the optimal agricultural strategy for each region. Generative AI can, for example, analyze climate data from different regions and propose the optimal agricultural strategy for each region. For example, generative AI can propose the optimal crop selection based on temperature and precipitation data for each region. In addition, by collecting climate data from different regions and analyzing it using generative AI, it can propose the optimal agricultural strategy for each region. For example, it can propose cultivation methods suitable for each region's climatic conditions. Generative AI can also evaluate climate change risks for each region and propose appropriate countermeasures. This makes it possible to analyze climate data from different regions and propose the optimal agricultural strategy for each region, thereby optimizing agriculture according to regional characteristics.

[0053] Generative AI can predict fluctuations in water resources due to climate change and propose appropriate water management methods. Generative AI can, for example, predict fluctuations in water resources due to climate change and propose appropriate water management methods. For example, generative AI can propose optimal irrigation schedules based on fluctuations in precipitation. Generative AI can also propose appropriate water management methods by collecting data on fluctuations in water resources due to climate change and analyzing it using generative AI. For example, it can propose irrigation methods that maximize the efficiency of water resource use. Generative AI can also propose methods for securing and using water resources in response to climate change. This makes it possible to predict fluctuations in water resources due to climate change and propose appropriate water management methods, thereby enabling the efficient use of water resources.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The agricultural support system can also be equipped with a health management unit that monitors the health of agricultural workers. For example, the health management unit measures the heart rate and body temperature of agricultural workers in real time and issues an alert if an abnormality is detected. The health management unit can also collect agricultural workers' health data and analyze it using generative AI to predict health risks and propose appropriate countermeasures. For example, if there is a high risk of overwork or heatstroke, it can recommend taking a break. The health management unit can also propose a work schedule based on the agricultural worker's health condition. In this way, by monitoring the agricultural worker's health condition and predicting health risks, it is possible to improve the health and work efficiency of agricultural workers.

[0056] The agricultural support system can further include a history management unit that records the work history of agricultural workers and improves work efficiency. For example, the history management unit records the work content and work time of agricultural workers and analyzes them using generative AI to make suggestions to improve work efficiency. For example, it can propose an optimal work schedule based on past work data. The history management unit can also visualize the work history of agricultural workers, allowing the progress of work to be checked in real time. This makes it possible to improve the efficiency of agricultural work by recording the work history of agricultural workers and making suggestions to improve work efficiency.

[0057] The agricultural support system can further include a performance evaluation unit that evaluates the work performance of agricultural workers. For example, the performance evaluation unit evaluates work performance by recording the work content and work time of agricultural workers and analyzing them using generative AI. For example, it can evaluate work efficiency based on past work data and suggest areas for improvement. The performance evaluation unit can also visualize the work performance of agricultural workers, allowing the progress of work to be confirmed in real time. This makes it possible to improve the efficiency of agricultural work by evaluating the work performance of agricultural workers and making suggestions to improve work efficiency.

[0058] The agricultural support system can further include an environmental monitoring unit that monitors the working environment of agricultural workers. For example, the environmental monitoring unit measures the temperature and humidity of the working environment of agricultural workers in real time and makes suggestions for maintaining an appropriate working environment. For example, if the temperature or humidity is not appropriate, it proposes appropriate measures. The environmental monitoring unit can also visualize the working environment of agricultural workers and propose improvements to the working environment. In this way, by monitoring the working environment of agricultural workers and making suggestions for maintaining an appropriate working environment, it is possible to improve the health and work efficiency of agricultural workers.

[0059] The agricultural support system can further include an environmental adjustment unit that adjusts the working environment of the farmworker. For example, the environmental adjustment unit measures the temperature and humidity of the farmworker's working environment in real time and makes suggestions for maintaining an appropriate working environment. For example, if the temperature or humidity is not appropriate, it proposes appropriate measures. The environmental adjustment unit can also visualize the farmworker's working environment and propose improvements to the working environment. This allows the system to adjust the farmworker's working environment and make suggestions for maintaining an appropriate working environment, thereby improving the farmworker's health and work efficiency.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: IoT devices monitor the status of the farm in real time. For example, IoT devices can use soil and temperature sensors to monitor soil moisture levels, nutrient status, and temperature fluctuations. IoT devices can also use cameras and drones to monitor the status of the entire farm. Step 2: The data management unit centrally manages the data collected from the IoT devices. For example, the data management unit can store the data using cloud storage and enable real-time monitoring of the farm's status via a dashboard. The data management unit can also enable data to be viewed and manipulated using a smartphone or tablet. Step 3: The generating AI analyzes the data managed by the data management unit. For example, the generating AI uses machine learning and deep learning to analyze temperature, humidity, and soil conditions, and suggests optimal irrigation and fertilization timing. The generating AI can also predict crop growth and warn of disease. Step 4: The advice provider provides optimal crop management advice based on the data analyzed by the generative AI. For example, the advice provider may provide advice on irrigation and fertilization timing, disease prevention measures, etc. The advice provider can also provide advice via smartphones or tablets.

[0062] (Example 2) The agricultural support system according to an embodiment of the present invention uses IoT devices to monitor the status of farms in real time, and a generative AI analyzes the data and provides optimal crop management advice. This allows the agricultural support system to grasp the status of farms in real time and realize efficient crop management.

[0063] An agricultural support system according to an embodiment includes an IoT device, a data management unit, a generation AI, and an advice providing unit. The IoT device monitors the status of a farm in real time. For example, the IoT device uses soil sensors and temperature sensors to monitor soil moisture content, nutrient status, and temperature fluctuations. The IoT device can also monitor the status of the entire farm using cameras and drones. The data management unit centrally manages data collected from the IoT device. For example, the data management unit stores data using cloud storage and enables real-time monitoring of the farm status via a dashboard. The data management unit can also enable data viewing and manipulation using a smartphone or tablet. The generation AI analyzes the data managed by the data management unit. For example, the generation AI can analyze temperature, humidity, and soil conditions using machine learning and deep learning to suggest optimal irrigation and fertilization timing. The generation AI can also predict crop growth and warn of disease. The advice providing unit provides optimal crop management advice based on the data analyzed by the generation AI. For example, the advice providing unit provides advice on irrigation and fertilization timing, disease prevention, etc. The advice providing unit can also provide advice via a smartphone or tablet, which allows the agricultural support system according to the embodiment to grasp the status of the farm in real time and achieve efficient crop management.

[0064] The IoT device may include a soil sensor that measures the moisture content and nutrient status of the soil, and a temperature sensor that monitors temperature fluctuations. The IoT device may, for example, use the soil sensor to measure the moisture content and nutrient status of the soil. For example, the soil sensor may measure the soil condition using an electrical conductivity sensor or a pH sensor. The IoT device may also monitor temperature fluctuations using a temperature sensor. For example, the temperature sensor may measure the temperature using a thermistor or an infrared sensor. This allows for real-time monitoring of the moisture content and nutrient status of the soil, as well as temperature fluctuations, to provide a more detailed understanding of the farm situation.

[0065] The data management unit can provide a dashboard that allows the status of the farm to be checked in real time using a smartphone or tablet. The data management unit can provide a dashboard that allows the status of the farm to be checked in real time using a smartphone or tablet, for example. For example, the dashboard can have functions such as graph display, alert functions, and data filtering. The data management unit can also store data using cloud storage so that the status of the farm can be checked in real time through the dashboard. This allows the status of the farm to be checked in real time using a smartphone or tablet, thereby improving the efficiency of farming work.

[0066] Generative AI can analyze temperature, humidity, and soil conditions and suggest optimal timing for irrigation and fertilization. For example, generative AI can use machine learning and deep learning to analyze temperature, humidity, and soil conditions and suggest optimal timing for irrigation and fertilization. Generative AI can also predict crop growth and warn of disease. For example, it can predict the risk of disease from fluctuations in temperature and humidity and take early action. This allows crop growth to be optimized by analyzing temperature, humidity, and soil conditions and suggesting optimal timing for irrigation and fertilization.

[0067] Generative AI can predict the risk of disease from fluctuations in temperature and humidity, allowing for early countermeasures to be taken. For example, generative AI can use machine learning and deep learning to analyze fluctuations in temperature and humidity and predict the risk of disease. Generative AI can also predict disease occurrence patterns based on past disease data and issue early warnings. This makes it possible to minimize crop losses by predicting the risk of disease from fluctuations in temperature and humidity and taking early countermeasures.

[0068] Generative AI can analyze temperature, hours of sunlight, and soil conditions to predict crop growth rates and harvest times. Generative AI can, for example, analyze temperature, hours of sunlight, and soil conditions to predict crop growth rates and harvest times. For example, generative AI can use machine learning and deep learning to analyze temperature, hours of sunlight, and soil conditions to predict crop growth rates and harvest times. Generative AI can also predict growth and harvest times based on past growth data. This allows for optimized crop management by analyzing temperature, hours of sunlight, and soil conditions to predict crop growth rates and harvest times.

[0069] Generative AI can monitor fluctuations in temperature and precipitation in real time and take appropriate measures. For example, generative AI can use machine learning and deep learning to analyze fluctuations in temperature and precipitation and propose appropriate measures. Generative AI can also simulate climate change scenarios and propose optimal agricultural strategies. This makes it possible to minimize the impact of climate change by monitoring fluctuations in temperature and precipitation in real time and taking appropriate measures.

[0070] The IoT device is equipped with an emotion estimation function that monitors the emotional state of farmers and can provide work advice based on the stress level estimated by the emotion estimation function. For example, the IoT device can analyze the facial expressions and voice of farmers to monitor their emotional state in real time. For example, it can measure stress levels using a camera or microphone and suggest appropriate break times. Furthermore, a system can be built using the emotion estimation function to provide work advice based on the farmer's stress level. For example, light work can be suggested when stress is high, and concentrated work can be recommended when stress is low. The IoT device can also monitor the farmer's emotional state and suggest relaxation methods based on the stress level. For example, music or relaxation exercises can be recommended. In this way, the emotional state of farmers can be monitored and work advice based on stress levels can be provided, improving the farmer's health and work efficiency.

[0071] IoT devices are equipped with microbial sensors that monitor the activity of microorganisms in the soil in real time, and can evaluate the health of the soil based on this microbial activity. IoT devices, for example, are equipped with microbial sensors and monitor the activity of microorganisms in the soil in real time. For example, they measure the types and numbers of microorganisms and evaluate the health of the soil. Furthermore, systems are built that collect microbial activity data and analyze the health of the soil using generative AI. For example, they analyze microbial activity patterns to evaluate the nutritional status and disease risk of the soil. Furthermore, microbial activity can be monitored through IoT devices and advice on fertilization and irrigation based on the health of the soil. For example, if microbial activity is declining, appropriate fertilization can be suggested. This allows for real-time monitoring of microbial activity in the soil and evaluation of soil health, optimizing crop growth.

[0072] IoT devices can use drones to collect aerial photographs of the entire farm in real time, and integrate them with ground sensors to perform three-dimensional environmental monitoring. For example, IoT devices can use cameras mounted on drones to collect aerial photographs of the entire farm in real time, and integrate them with ground sensors to perform three-dimensional environmental monitoring. For example, the growth status of crops and the location of disease outbreaks can be confirmed using aerial photographs. In addition, a system can be built that collects aerial photographs of the farm using drones and integrates and analyzes them with ground sensor data using generative AI. For example, the health of crops can be evaluated by combining aerial photographs and soil sensor data. In addition, aerial photographs can be collected in real time by drones, and environmental monitoring can be performed in conjunction with ground sensors. For example, the aerial photographs can be used to propose optimization of irrigation and fertilization. This allows aerial photographs of the entire farm to be collected in real time using drones, and integrated with ground sensors to perform three-dimensional environmental monitoring, allowing for a more detailed understanding of the farm's conditions.

[0073] IoT devices can be equipped with wildlife sensors that monitor the movements of wild animals around farms in real time, collecting data from an environmental protection perspective. For example, IoT devices can be equipped with wildlife sensors and monitor the movements of wild animals around farms in real time. For example, cameras and motion detection sensors can be used to analyze wild animal behavior patterns. IoT devices can also be used to monitor the growth of plants around farms and collect data from an environmental protection perspective. For example, plant growth rates and health can be measured and measures for environmental protection can be proposed. Ecological data around farms can also be collected through IoT devices, and generative AI can be used to analyze the data for environmental protection. For example, wildlife habitats and plant growth patterns can be analyzed and advice for environmental protection can be provided. This allows for real-time monitoring of wild animal movements around farms and collecting data from an environmental protection perspective, thereby protecting the farm's ecosystem.

[0074] IoT devices can be equipped with water flow sensors that monitor water resource usage on farms in real time, preventing water waste. For example, IoT devices can be equipped with water flow sensors and monitor water resource usage on farms in real time. For example, they can measure the water flow rate of irrigation systems to prevent water waste. A system can also be built that collects water resource usage data and uses generative AI to propose optimal irrigation schedules. For example, it can analyze water usage and crop water demand to propose efficient irrigation methods. Furthermore, an alert system can be introduced to monitor water resource usage on farms through IoT devices and prevent water waste. For example, an alert can be issued if water usage is excessive. This enables efficient water management by monitoring water resource usage on farms in real time and preventing water waste.

[0075] The IoT device is equipped with an emotion estimation function that monitors the emotional state of agricultural workers and can suggest break times to improve work efficiency. For example, the IoT device can analyze the facial expressions and voice of agricultural workers to monitor their emotional state in real time. For example, it can measure stress levels using cameras and microphones and suggest appropriate break times. Furthermore, a system can be built using the emotion estimation function to suggest break times based on the agricultural worker's stress level. For example, if stress is high, it can recommend a break, and if stress is low, it can continue working. The IoT device can also monitor the agricultural worker's emotional state and suggest relaxation methods to improve work efficiency. For example, it can recommend music or relaxation exercises. This improves the agricultural worker's health and work efficiency by monitoring their emotional state and suggesting break times to improve work efficiency.

[0076] The data management unit can integrate an emotion estimation function and customize the interface based on the user's emotions. The data management unit, for example, integrates the emotion estimation function and customizes the interface according to the user's emotional state. For example, if the user is feeling stressed, a simple interface is provided. Furthermore, a system is constructed that customizes the interface based on the user's emotions using the emotion estimation function. For example, if the user has a strong positive emotion, a colorful interface is provided. Furthermore, the data management unit can also provide a visually friendly interface if the user is tired. In this way, the user experience can be improved by customizing the interface based on the user's emotions.

[0077] The data management unit can introduce an anomaly detection algorithm to detect abnormal data patterns in real time. The data management unit, for example, introduces an anomaly detection algorithm to detect abnormal data patterns in real time. For example, an anomaly detection algorithm is introduced into a centralized data management system to detect abnormal values ​​in sensor data and issue an alert. In addition, an anomaly detection algorithm is used to build a system that detects abnormal data patterns in real time in the centralized data management system. For example, it detects abnormal temperature fluctuations and abnormal soil values. In addition, an anomaly detection algorithm is introduced into the centralized data management system to detect abnormal data patterns in real time. For example, it detects abnormal humidity fluctuations and abnormal moisture content values ​​and proposes appropriate countermeasures. In this way, detecting abnormal data patterns in real time enables rapid response.

[0078] The data management unit can be added with a voice input function, allowing data to be checked and operated by voice commands. The data management unit, for example, adds a voice input function, allowing data to be checked and operated by voice commands. For example, the voice input function is added to a centralized data management system, allowing sensor data to be checked and irrigation system to be operated by voice. Furthermore, the voice input function is used to build a system that allows operation by voice commands in the centralized data management system. For example, data can be searched and filtered by voice. Furthermore, the voice input function is added to the centralized data management system, allowing data to be checked and operated by voice commands. For example, alerts can be checked and settings can be changed by voice. This allows data to be checked and operated by voice commands, improving user convenience.

[0079] The data management unit works in conjunction with the sales management system and the logistics management system to centrally manage sales data and shipping status of agricultural products. The data management unit, for example, works in conjunction with the sales management system to centrally manage sales data of agricultural products. For example, it checks harvest yields and sales prices in real time. The data management unit also works in conjunction with the logistics management system to centrally manage shipping status of agricultural products. For example, it checks shipping schedules and delivery status in real time. The data management unit also works in conjunction with other agriculture-related systems to improve the efficiency of agricultural work. For example, it integrates sales data and logistics data to propose optimal work schedules. This centralized management of sales data and shipping status can improve the efficiency of agricultural work.

[0080] The data management unit can accumulate historical farm data and enable long-term trend analysis. For example, the data management unit accumulates historical farm data and performs long-term trend analysis. For example, it evaluates the impact of climate change based on past temperature and precipitation data. It also accumulates historical farm data and builds a system that performs long-term trend analysis using generative AI. For example, it analyzes past crop growth data and proposes optimal cultivation methods. The data management unit can also predict disease risk based on past disease occurrence data. This allows agricultural efficiency to be improved by accumulating historical farm data and performing long-term trend analysis.

[0081] The data management unit uses the emotion estimation function to suggest a data display method based on the user's emotion, thereby improving the user experience. The data management unit, for example, uses the emotion estimation function to suggest a data display method based on the user's emotional state. For example, if the user is feeling stressed, a simple data display is provided. The data management unit also analyzes the user's emotional state in real time and customizes the data display method based on the results. For example, if the user has strong positive emotions, a detailed data display is provided. The emotion estimation function also suggests a data display method based on the user's emotion, thereby improving the user experience. For example, if the user is tired, a visually friendly data display is provided. In this way, the user experience can be improved by suggesting a data display method based on the user's emotion.

[0082] The generative AI can integrate an emotion estimation function and customize the way in which analysis results are presented based on the user's emotions. For example, the generative AI can integrate an emotion estimation function and customize the way in which analysis results are presented according to the user's emotional state. For example, if the user is feeling stressed, it can provide concise analysis results. In addition, the emotion estimation function can be used to build a system that customizes the way in which analysis results are presented based on the user's emotions. For example, if the user has strong positive emotions, it can provide detailed analysis results. In addition, the generative AI can provide analysis results that are visually easier to understand if the user is tired. This makes it possible to improve the user experience by customizing the way in which analysis results are presented based on the user's emotions.

[0083] Generative AI can analyze energy consumption data on farms and suggest ways to optimize energy efficiency. For example, generative AI can analyze energy consumption data on farms and suggest ways to optimize energy efficiency. For example, generative AI can use machine learning and deep learning to identify peak energy consumption times and suggest efficient energy usage methods. Generative AI can also collect energy consumption data on farms and analyze it using generative AI to suggest ways to optimize energy efficiency. For example, it can suggest specific measures to reduce energy waste. Generative AI can also make suggestions to promote the use of renewable energy. In this way, energy costs can be reduced by analyzing energy consumption data on farms and suggesting ways to optimize energy efficiency.

[0084] Generative AI can analyze farm labor data and propose optimal staffing allocation. Generative AI can, for example, analyze farm labor data and propose optimal staffing allocation. For example, generative AI can use machine learning and deep learning to propose staffing allocation according to busy work periods. In addition, by collecting farm labor data and analyzing it with generative AI, it can propose optimal staffing allocation. For example, it can propose staffing allocation to maximize work efficiency. Generative AI can also assign personnel with the appropriate skills for specific work. In this way, work efficiency can be improved by analyzing farm labor data and proposing optimal staffing allocation.

[0085] Generative AI can analyze farm economic data and propose strategies for reducing costs. For example, generative AI can analyze farm economic data and propose strategies for reducing costs. For example, generative AI can use machine learning and deep learning to optimize the timing and amount of material purchases. Generative AI can also collect farm economic data and analyze it using generative AI to propose strategies for reducing costs. For example, it can propose efficient methods of managing funds. Generative AI can also propose specific measures to reduce wasteful spending. In this way, economic efficiency can be improved by analyzing farm economic data and proposing strategies for reducing costs.

[0086] The generative AI can use the emotion estimation function to collect feedback on analysis results based on the user's emotions and improve the accuracy of the analysis algorithm. For example, the generative AI uses the emotion estimation function to collect feedback on analysis results based on the user's emotions. For example, the generative AI prioritizes collecting analysis results in which the user shows positive emotions. The generative AI also analyzes the user's emotional reactions in real time and improves the accuracy of the analysis algorithm based on the results. For example, the generative AI adjusts the algorithm based on analysis results in which the user shows strong positive emotions. The generative AI can also improve analysis results in which the user shows negative emotions. In this way, by collecting feedback on analysis results based on the user's emotions, the accuracy of the analysis algorithm can be improved.

[0087] The generation AI can integrate emotion estimation functionality to customize the way in which growth forecasts and disease warnings are presented based on the farmer's emotional state. For example, the generation AI can integrate emotion estimation functionality to customize the way in which growth forecasts and disease warnings are presented based on the farmer's emotional state. For example, if the user is feeling stressed, the generation AI can provide a concise forecast and warning. The emotion estimation functionality can also be used to build a system that customizes the way in which growth forecasts and disease warnings are presented based on the farmer's emotions. For example, if the user has strong positive emotions, the generation AI can provide a detailed forecast and warning. The generation AI can also provide a more visually friendly forecast and warning if the user is tired. This improves the user experience by customizing the way in which growth forecasts and disease warnings are presented based on the farmer's emotional state.

[0088] Generative AI can simulate the growth process of crops using a 3D model and visually predict growth. Generative AI can, for example, simulate the growth process of crops using a 3D model and visually predict growth. For example, generative AI can display the growth rate and harvest time of crops using a 3D model. In addition, by collecting crop growth data and using generative AI to simulate it using a 3D model, visual growth predictions can be made. For example, the growth process of crops can be displayed in 3D in real time. Generative AI can also make growth predictions by reflecting fluctuations in temperature and sunlight hours in the 3D model. This makes it possible to optimize crop management by simulating the growth process of crops using a 3D model and visually predicting growth.

[0089] Generative AI can analyze past disease data and predict disease occurrence patterns. Generative AI can, for example, analyze past disease data and predict disease occurrence patterns. For example, generative AI can predict disease risk based on past temperature and humidity data. Generative AI can also predict disease occurrence patterns by collecting past disease data and analyzing it using generative AI. For example, it can predict the risk of disease occurrence under specific weather conditions. Generative AI can also issue early warnings based on past disease occurrence data. This makes it possible to analyze past disease data and predict disease occurrence patterns, enabling early detection and countermeasures for diseases.

[0090] Generative AI can compare growth data between different crops and propose optimal crop combinations. For example, generative AI can compare growth data between different crops and propose optimal crop combinations. For example, generative AI can propose combinations of crops that complement each other. Generative AI can also propose optimal crop combinations by collecting growth data for different crops and analyzing it using generative AI. For example, it can propose crop combinations that reduce disease risk. Generative AI can also propose crop combinations that maximize yield. This makes it possible to improve crop yield and quality by comparing growth data between different crops and proposing optimal crop combinations.

[0091] Generative AI can analyze farm weather data and evaluate crop resistance to weather fluctuations. For example, generative AI analyzes farm weather data and evaluates crop resistance to weather fluctuations. For example, generative AI analyzes fluctuations in temperature and precipitation to evaluate crop resistance. In addition, by collecting farm weather data and analyzing it using generative AI, crop resistance to weather fluctuations can be evaluated. For example, crop resistance to extreme weather can be evaluated. Generative AI can also assist in the selection of crops that are resistant to climate change. This makes it possible to select and cultivate crops that are resistant to climate change by analyzing farm weather data and evaluating crop resistance to weather fluctuations.

[0092] The generative AI can use its emotion estimation function to collect feedback on growth predictions and disease warnings based on the emotional state of the farmer, thereby improving prediction accuracy. For example, the generative AI uses its emotion estimation function to collect feedback on growth predictions and disease warnings based on the emotional state of the farmer. For example, the generative AI prioritizes collecting predictions and warnings for which the user expressed positive emotions. It also analyzes the farmer's emotional responses in real time and improves the accuracy of growth predictions and disease warnings based on the results. For example, it adjusts the algorithm based on predictions and warnings for which the user expressed strong positive emotions. The generative AI can also improve predictions and warnings for which the user expressed negative emotions. This allows for improved prediction accuracy by collecting feedback on growth predictions and disease warnings based on the farmer's emotional state.

[0093] The generative AI can integrate an emotion estimation function to propose climate change countermeasures based on the emotional state of the farmer. The generative AI can, for example, integrate an emotion estimation function to propose climate change countermeasures based on the emotional state of the farmer. For example, the generative AI can propose simple countermeasures if the user is feeling stressed. We also use the emotion estimation function to build a system that proposes climate change countermeasures based on the farmer's emotions. For example, if the user has strong positive emotions, detailed countermeasures can be proposed. The generative AI can also propose visually friendly countermeasures if the user is tired. This makes it possible to improve the user experience by proposing climate change countermeasures based on the farmer's emotional state.

[0094] Generative AI can predict soil changes due to climate change and propose appropriate soil improvement methods. Generative AI can, for example, predict soil changes due to climate change and propose appropriate soil improvement methods. For example, generative AI can predict the nutritional status of the soil based on fluctuations in temperature and precipitation, and propose appropriate fertilization methods. Generative AI can also collect data on soil changes due to climate change and analyze it using generative AI to propose appropriate soil improvement methods. For example, it can predict fluctuations in soil acidity and moisture content and propose appropriate improvement methods. Generative AI can also suggest the selection of soil improvement materials that are appropriate for climate change and the timing of their application. This makes it possible to optimize crop growth by predicting soil changes due to climate change and proposing appropriate soil improvement methods.

[0095] Generative AI can analyze climate data from different regions and propose the optimal agricultural strategy for each region. Generative AI can, for example, analyze climate data from different regions and propose the optimal agricultural strategy for each region. For example, generative AI can propose the optimal crop selection based on temperature and precipitation data for each region. In addition, by collecting climate data from different regions and analyzing it using generative AI, it can propose the optimal agricultural strategy for each region. For example, it can propose cultivation methods suitable for each region's climatic conditions. Generative AI can also evaluate climate change risks for each region and propose appropriate countermeasures. This makes it possible to analyze climate data from different regions and propose the optimal agricultural strategy for each region, thereby optimizing agriculture according to regional characteristics.

[0096] Generative AI can predict fluctuations in water resources due to climate change and propose appropriate water management methods. Generative AI can, for example, predict fluctuations in water resources due to climate change and propose appropriate water management methods. For example, generative AI can propose optimal irrigation schedules based on fluctuations in precipitation. Generative AI can also propose appropriate water management methods by collecting data on fluctuations in water resources due to climate change and analyzing it using generative AI. For example, it can propose irrigation methods that maximize the efficiency of water resource use. Generative AI can also propose methods for securing and using water resources in response to climate change. This makes it possible to predict fluctuations in water resources due to climate change and propose appropriate water management methods, thereby enabling the efficient use of water resources.

[0097] The generation AI uses the emotion estimation function to collect feedback on climate change countermeasures based on the emotional state of farmers, thereby improving the accuracy of the countermeasures. For example, the generation AI uses the emotion estimation function to collect feedback on climate change countermeasures based on the emotional state of farmers. For example, the generation AI prioritizes collecting countermeasures for which the user expressed positive emotions. The generation AI also analyzes the emotional responses of farmers in real time and improves the accuracy of climate change countermeasures based on the results. For example, the generation AI adjusts the algorithm based on countermeasures for which the user expressed strong positive emotions. The generation AI can also improve countermeasures for which the user expressed negative emotions. In this way, the accuracy of countermeasures can be improved by collecting feedback on climate change countermeasures based on the emotional state of farmers.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The agricultural support system can also be equipped with a health management unit that monitors the health of agricultural workers. For example, the health management unit measures the heart rate and body temperature of agricultural workers in real time and issues an alert if an abnormality is detected. The health management unit can also collect agricultural workers' health data and analyze it using generative AI to predict health risks and propose appropriate countermeasures. For example, if there is a high risk of overwork or heatstroke, it can recommend taking a break. The health management unit can also propose a work schedule based on the agricultural worker's health condition. In this way, by monitoring the agricultural worker's health condition and predicting health risks, it is possible to improve the health and work efficiency of agricultural workers.

[0100] The agricultural support system can also be equipped with an emotion estimation function that monitors the emotional state of farmers. For example, the emotion estimation function analyzes the facial expressions and voice of farmers to monitor their emotional state in real time. For example, a camera or microphone can be used to measure stress levels and suggest appropriate break times. The emotion estimation function can also be used to build a system that provides work advice based on the stress level of farmers. For example, light work can be suggested when stress is high, and concentrated work can be recommended when stress is low. The emotion estimation function can also monitor the emotional state of farmers and suggest relaxation methods based on their stress level. For example, music or relaxation exercises can be recommended. In this way, by monitoring the emotional state of farmers and providing work advice based on their stress level, it is possible to improve their health and work efficiency.

[0101] The agricultural support system can further include a history management unit that records the work history of agricultural workers and improves work efficiency. For example, the history management unit records the work content and work time of agricultural workers and analyzes them using generative AI to make suggestions to improve work efficiency. For example, it can propose an optimal work schedule based on past work data. The history management unit can also visualize the work history of agricultural workers, allowing the progress of work to be checked in real time. This makes it possible to improve the efficiency of agricultural work by recording the work history of agricultural workers and making suggestions to improve work efficiency.

[0102] The agricultural support system can further include an environment adjustment unit that adjusts the work environment based on the emotional state of the farmworker. For example, the environment adjustment unit monitors the emotional state of the farmworker and adjusts the work environment to relax the farmworker if stress levels are high. For example, the environment adjustment unit plays music or adjusts the lighting. In addition, a system is constructed that uses an emotion estimation function to adjust the work environment according to the farmworker's emotional state. For example, if positive emotions are strong, the environment adjustment unit adjusts the work environment to be more lively. Furthermore, by adjusting the work environment based on the farmworker's emotional state, the environment adjustment unit can improve work efficiency. In this way, adjusting the work environment based on the farmworker's emotional state can improve the farmworker's health and work efficiency.

[0103] The agricultural support system can further include a communication support unit that provides communication support based on the emotional state of the farmworker. For example, the communication support unit monitors the emotional state of the farmworker and suggests a communication method that helps the farmworker relax if stress levels are high. For example, a system can be constructed that uses an emotion estimation function to suggest a communication method based on the farmworker's emotional state. For example, proactive communication can be recommended if positive emotions are strong, and communication that helps the farmworker relax if negative emotions are strong. Furthermore, by providing communication support based on the farmworker's emotional state, the communication support unit can reduce the farmworker's stress and improve work efficiency. In this way, by providing communication support based on the farmworker's emotional state, it is possible to improve the farmworker's health and work efficiency.

[0104] The agricultural support system can further include a performance evaluation unit that evaluates the work performance of agricultural workers. For example, the performance evaluation unit evaluates work performance by recording the work content and work time of agricultural workers and analyzing them using generative AI. For example, it can evaluate work efficiency based on past work data and suggest areas for improvement. The performance evaluation unit can also visualize the work performance of agricultural workers, allowing the progress of work to be confirmed in real time. This makes it possible to improve the efficiency of agricultural work by evaluating the work performance of agricultural workers and making suggestions to improve work efficiency.

[0105] The agricultural support system can further include a schedule adjustment unit that adjusts the work schedule based on the emotional state of the farmworker. For example, the schedule adjustment unit monitors the emotional state of the farmworker and adjusts the work schedule if the worker is highly stressed. For example, a system can be constructed that adjusts the work schedule according to the emotional state of the farmworker using an emotion estimation function. For example, if the worker has strong positive emotions, concentrated work can be recommended, and if the worker has strong negative emotions, light work can be recommended. Furthermore, the schedule adjustment unit can improve work efficiency by adjusting the work schedule based on the worker's emotional state. In this way, adjusting the work schedule based on the worker's emotional state can improve the worker's health and work efficiency.

[0106] The agricultural support system can further include an environmental monitoring unit that monitors the working environment of agricultural workers. For example, the environmental monitoring unit measures the temperature and humidity of the working environment of agricultural workers in real time and makes suggestions for maintaining an appropriate working environment. For example, if the temperature or humidity is not appropriate, it proposes appropriate measures. The environmental monitoring unit can also visualize the working environment of agricultural workers and propose improvements to the working environment. In this way, by monitoring the working environment of agricultural workers and making suggestions for maintaining an appropriate working environment, it is possible to improve the health and work efficiency of agricultural workers.

[0107] The agricultural support system can further include a performance evaluation unit that evaluates the work performance based on the emotional state of the farmworker. For example, the performance evaluation unit monitors the emotional state of the farmworker and evaluates the work performance if stress levels are high. For example, an emotion estimation function can be used to build a system that evaluates the work performance according to the emotional state of the farmworker. For example, a high evaluation can be given if positive emotions are strong, and suggestions for improvement can be made if negative emotions are strong. Furthermore, the performance evaluation unit can improve work efficiency by evaluating the work performance based on the emotional state of the farmworker. This makes it possible to improve the health and work efficiency of the farmworker by evaluating the work performance based on the emotional state of the farmworker.

[0108] The agricultural support system can further include an environmental adjustment unit that adjusts the working environment of the farmworker. For example, the environmental adjustment unit measures the temperature and humidity of the farmworker's working environment in real time and makes suggestions for maintaining an appropriate working environment. For example, if the temperature or humidity is not appropriate, it proposes appropriate measures. The environmental adjustment unit can also visualize the farmworker's working environment and propose improvements to the working environment. This allows the system to adjust the farmworker's working environment and make suggestions for maintaining an appropriate working environment, thereby improving the farmworker's health and work efficiency.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: IoT devices monitor the status of the farm in real time. For example, IoT devices can use soil and temperature sensors to monitor soil moisture levels, nutrient status, and temperature fluctuations. IoT devices can also use cameras and drones to monitor the status of the entire farm. Step 2: The data management unit centrally manages the data collected from the IoT devices. For example, the data management unit can store the data using cloud storage and enable real-time monitoring of the farm's status via a dashboard. The data management unit can also enable data to be viewed and manipulated using a smartphone or tablet. Step 3: The generating AI analyzes the data managed by the data management unit. For example, the generating AI uses machine learning and deep learning to analyze temperature, humidity, and soil conditions, and suggests optimal irrigation and fertilization timing. The generating AI can also predict crop growth and warn of disease. Step 4: The advice provider provides optimal crop management advice based on the data analyzed by the generative AI. For example, the advice provider may provide advice on irrigation and fertilization timing, disease prevention measures, etc. The advice provider can also provide advice via smartphones or tablets.

[0111] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] 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.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] 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.

[0170] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. IoT devices that monitor the farm situation in real time, a data management unit that centrally manages data collected from the IoT devices; A generation AI that analyzes the data managed by the data management unit; and an advice providing unit that provides optimal crop management advice based on the data analyzed by the generating AI. A system characterized by:

2. The IoT device is A soil sensor that measures the moisture content and nutrient status of the soil, a temperature sensor for monitoring temperature fluctuations; 2. The system of claim 1.

3. The data management unit Providing a dashboard that allows users to check farm conditions in real time using a smartphone or tablet 2. The system of claim 1.

4. The generated AI is Analyzes temperature, humidity, and soil conditions to suggest optimal irrigation and fertilization timing 2. The system of claim 1.

5. The generated AI is Predicting disease risk from temperature and humidity fluctuations and taking early action 2. The system of claim 1.

Images