System

Abstract

An object of a system according to an embodiment is to improve the efficiency of agricultural management by analyzing agricultural data and measuring the state and moisture content of soil in real time.SOLUTION: A system includes a data analysis unit, a sensor measurement unit, a prediction unit, and a consulting unit. The data analysis part analyzes the agricultural data. The sensor measurement unit measures the state and moisture content of the soil in real time. The prediction unit predicts future crop production based on past data. The consulting unit provides an optimal solution according to the situation of the farm.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 technology does not adequately analyze agricultural data or measure soil conditions in real time, leaving room for improvement in the efficiency of agricultural management.

[0005] The system according to the embodiment aims to improve the efficiency of agricultural management by analyzing agricultural data and measuring soil conditions and moisture content in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a sensor measurement unit, a prediction unit, and a consulting unit. The data analysis unit analyzes agricultural data. The sensor measurement unit measures soil condition and moisture content in real time. The prediction unit predicts future crop production based on past data. The consulting unit provides optimal solutions according to the farm's situation. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of agricultural management by analyzing agricultural data and measuring soil conditions and moisture content in real time. [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 smart agricultural management system according to an embodiment of the present invention is a system that realizes smart and efficient agricultural management by utilizing generative AI and digital sensors. As a result, the smart agricultural management system is capable of analyzing agricultural data, taking real-time measurements, making future predictions, and providing optimal solutions.

[0029] A smart agriculture management system according to an embodiment includes a data analysis unit, a sensor measurement unit, a prediction unit, and a consulting unit. The data analysis unit analyzes agricultural data. For example, the data analysis unit analyzes crop growth data and weather data to propose optimal fertilization timing and amount. The data analysis unit can also analyze the nutritional status of soil and propose optimal fertilization methods. The data analysis unit can also analyze the risk of pest and disease outbreaks and propose preventive measures. The sensor measurement unit measures the soil condition and moisture content in real time. For example, the sensor measurement unit measures the soil moisture content using a soil sensor and automatically adjusts the irrigation system as needed. The sensor measurement unit can also measure the amount of light using an optical sensor to provide an optimal lighting environment. The sensor measurement unit can also measure the temperature using a temperature sensor to provide an optimal temperature environment. The prediction unit predicts future crop production based on past data. For example, the prediction unit analyzes past weather data to predict weather conditions for the next season. The prediction unit can also analyze past crop growth data to predict crop growth for the next season. The prediction unit can also analyze past pest and disease occurrence data and predict the risk of pest and disease occurrence in the next season. The consulting unit provides optimal solutions according to the farm's situation. For example, the consulting unit analyzes farm data and proposes optimal fertilization and irrigation methods. The consulting unit can also predict the risk of pest and disease occurrence and propose preventive measures. The consulting unit can also analyze the growth status of crops and propose the optimal harvest timing. As a result, the smart agriculture management system according to the embodiment is capable of analyzing agricultural data, taking real-time measurements, making future predictions, and providing optimal solutions.

[0030] The data analysis unit can analyze the nutritional state of the soil and weather data to suggest the optimal timing and amount of fertilization. The data analysis unit, for example, analyzes the nutritional state of the soil and suggests the optimal timing and amount of fertilization. For example, the generation AI analyzes the nutritional state of the soil and suggests the optimal timing and amount of fertilization for nitrogen, phosphorus, and potassium. The data analysis unit also analyzes weather data and suggests the optimal timing of fertilization. For example, the generation AI analyzes weather data and adjusts the timing of fertilization based on rainfall forecasts. The data analysis unit also analyzes crop growth data and suggests the optimal amount of fertilizer to apply. For example, the generation AI analyzes crop growth data and suggests the optimal amount of fertilizer to apply for each growth stage. This makes it possible to optimize crop growth by suggesting the optimal timing and amount of fertilization.

[0031] The sensor measurement unit can measure the moisture content of the soil and automatically adjust the irrigation system as needed. The sensor measurement unit, for example, measures the moisture content of the soil and automatically adjusts the irrigation system as needed. For example, a soil sensor measures the moisture content of the soil in real time, and the generation AI analyzes the data to automatically adjust the irrigation system. The sensor measurement unit also analyzes weather data and automatically adjusts the irrigation system. For example, the generation AI analyzes weather data and adjusts the timing of irrigation based on rainfall forecasts. The sensor measurement unit also analyzes crop growth data and automatically adjusts the irrigation system. For example, the generation AI analyzes crop growth data and adjusts the optimal amount of irrigation for each growth stage. This enables efficient water management by measuring the moisture content of the soil in real time and automatically adjusting the irrigation system.

[0032] The prediction unit can analyze past weather data and predict weather conditions for the next season. The prediction unit, for example, analyzes past weather data and predicts weather conditions for the next season. For example, the generation AI analyzes past weather data and predicts the temperature, precipitation, and wind speed for the next season. The prediction unit also analyzes past crop growth data and predicts crop growth for the next season. For example, the generation AI analyzes past crop growth data and predicts the harvest yield for the next season. The prediction unit also analyzes past pest and disease occurrence data and predicts the risk of pest and disease occurrence for the next season. For example, the generation AI analyzes past pest and disease occurrence data and predicts the risk of pest and disease occurrence for the next season. This allows farmers to make efficient production plans by predicting weather conditions for the next season.

[0033] The consulting department can analyze farm data, predict the risk of pest and disease outbreaks, and propose preventive measures. For example, the consulting department analyzes farm data, predicts the risk of pest and disease outbreaks, and proposes preventive measures. For example, the generation AI analyzes farm data, predicts the risk of specific pest and disease outbreaks, and proposes the use of appropriate pesticides. The consulting department also analyzes weather data and predicts the risk of pest and disease outbreaks. For example, the generation AI analyzes weather data and predicts the risk of pest and disease outbreaks based on fluctuations in temperature and humidity. The consulting department also analyzes crop growth data and predicts the risk of pest and disease outbreaks. For example, the generation AI analyzes crop growth data and predicts the risk of pest and disease outbreaks for each growth stage. This makes it possible to predict the risk of pest and disease outbreaks and propose preventive measures, thereby improving the quality and yield of agricultural crops.

[0034] The data analysis unit can analyze agricultural data and propose optimal management methods for each crop growth stage. For example, the data analysis unit analyzes agricultural data and proposes optimal management methods for each crop growth stage. For example, the generation AI analyzes the amount of nutrients and water needed for each crop growth stage and proposes the optimal timing for fertilization and irrigation. The data analysis unit also analyzes the risk of pests and diseases according to the crop growth stage and proposes preventive measures. For example, the generation AI predicts that certain pests are more likely to occur in the early stages of growth and proposes the use of appropriate pesticides. The data analysis unit also analyzes the optimal temperature and humidity conditions for each crop growth stage and adjusts the environmental control system. For example, the generation AI analyzes that high temperatures and humidity are preferable in the middle stages of growth and automatically adjusts the temperature and humidity. This allows the quality and yield of crops to be improved by proposing optimal management methods for each crop growth stage.

[0035] The data analysis unit analyzes agricultural data and automatically controls agricultural machinery, thereby improving work efficiency. The data analysis unit, for example, analyzes agricultural data and automatically controls agricultural machinery, thereby improving work efficiency. For example, the generation AI analyzes soil conditions and crop growth status to automatically control agricultural machinery such as tractors and combine harvesters. The data analysis unit also analyzes weather data and automatically controls irrigation systems. For example, the generation AI analyzes weather data and adjusts irrigation timing based on rainfall forecasts. The data analysis unit also analyzes crop growth data and automatically controls harvesting machinery. For example, the generation AI analyzes crop growth data, predicts the optimum harvest time, and sets the optimal harvest timing. This allows for automatic control of agricultural machinery and improves work efficiency.

[0036] The data analysis unit can provide data analysis and management methods for urban agriculture and home gardens. The data analysis unit provides, for example, data analysis and management methods for urban agriculture and home gardens. For example, the generation AI proposes the optimal management method for growing crops on building rooftops or balconies for urban agriculture. The data analysis unit also proposes the optimal cultivation method for vegetables and fruits to be grown at home for home gardens. For example, the generation AI analyzes the optimal temperature and humidity conditions for small home greenhouses. The data analysis unit also proposes preventive measures for pests and diseases for urban agriculture and home gardens. For example, the generation AI analyzes preventive measures for specific pests that are likely to occur in urban areas. In this way, by providing data analysis and management methods for urban agriculture and home gardens, agricultural activities in urban areas and at home can be made more efficient.

[0037] The data analysis department can analyze agricultural data, develop agricultural education programs, and train the next generation of farmers. For example, the data analysis department analyzes agricultural data, develops agricultural education programs, and trains the next generation of farmers. For example, the generative AI analyzes agricultural data and develops educational programs customized for the next generation of farmers. The data analysis department also develops practical agricultural training programs based on the analyzed data. For example, the generative AI proposes a curriculum that includes on-farm training. The data analysis department also analyzes agricultural data and develops educational programs that provide specialized knowledge for the next generation of farmers. For example, the generative AI provides teaching materials for learning specialized knowledge about pest prevention and soil management. In this way, by analyzing agricultural data and developing agricultural education programs, the next generation of farmers can be trained.

[0038] The sensor measurement unit can measure microbial activity in real time and evaluate the health of the soil. For example, the sensor measurement unit measures microbial activity in real time and evaluates the health of the soil. For example, a digital sensor measures microbial activity in the soil in real time, and the generation AI analyzes the data to evaluate the health of the soil. The sensor measurement unit also monitors microbial activity in the soil and displays the soil health in real time. For example, it issues an alert if microbial activity decreases. The sensor measurement unit also collects microbial activity data in the soil, and the generation AI analyzes the data to evaluate the health of the soil. For example, it analyzes microbial activity patterns and proposes soil improvement measures. This makes it possible to measure microbial activity in real time, evaluate the health of the soil, and propose soil improvement measures.

[0039] The sensor measurement unit can build a system that automatically optimizes the crop growth environment based on data from digital sensors. The sensor measurement unit, for example, builds a system that automatically optimizes the crop growth environment based on data from digital sensors. For example, digital sensors measure soil conditions and weather data in real time, and build a system that automatically optimizes the crop growth environment based on that data. The sensor measurement unit also measures the amount of light and water in real time, and automatically controls the irrigation system based on that data. For example, it automatically supplies the required amount of water. The sensor measurement unit also measures the nutrient state of the soil in real time, and automatically controls the fertilization system based on that data. For example, it automatically supplies the required nutrients. In this way, the crop growth environment can be automatically optimized, thereby improving the quality and yield of the crops.

[0040] The sensor measurement unit can monitor the health status of animals in the livestock industry in real time. The sensor measurement unit, for example, monitors the health status of animals in the livestock industry in real time. For example, a digital sensor measures the body temperature and heart rate of animals in the livestock industry in real time to monitor their health status. The sensor measurement unit also analyzes the behavioral patterns of animals in real time to evaluate their health status. For example, if abnormal behavior is detected, a notification is sent. The sensor measurement unit also measures the amount of food and water intake of animals in real time to monitor their health status. For example, an alert is issued if intake decreases. In this way, by monitoring the health status of animals in the livestock industry in real time, abnormalities can be detected early and appropriate measures can be taken.

[0041] The sensor measurement unit can provide a system that improves the efficiency of urban green space management and park maintenance based on digital sensor data. The sensor measurement unit provides a system that improves the efficiency of urban green space management and park maintenance based on digital sensor data. For example, a system is provided in which digital sensors measure the soil condition and moisture content of urban green spaces and parks in real time and improve the efficiency of maintenance based on that data. The sensor measurement unit also measures the amount of light and temperature in urban green spaces and parks in real time and provides an environmental control system based on that data. For example, the system automatically adjusts the amount of light and temperature to the optimum level. The sensor measurement unit also monitors the health of plants in urban green spaces and parks in real time and improves the efficiency of maintenance based on that data. For example, the system predicts the occurrence of pests and diseases and proposes preventive measures. This improves the efficiency of urban green space management and park maintenance, making it possible to improve and maintain the urban environment.

[0042] The prediction unit predicts abnormal weather and disasters from past data, thereby reducing agricultural risks. The prediction unit, for example, predicts abnormal weather and disasters from past data, thereby reducing agricultural risks. For example, the generation AI analyzes past weather data and predicts the probability of abnormal weather occurring. The prediction unit also learns past disaster data and predicts disasters that will affect agriculture. For example, the generation AI learns past disaster data and analyzes the risk of earthquakes and typhoons occurring. The prediction unit also integrates past weather data and crop growth data to predict the impact of abnormal weather on crops. For example, the generation AI predicts fluctuations in harvest yields when abnormal weather occurs. In this way, agricultural risks can be reduced by predicting abnormal weather and disasters.

[0043] The prediction unit can propose crop breeding improvements and new cultivation methods based on the learned data. For example, the prediction unit proposes crop breeding improvements and new cultivation methods based on the learned data. For example, the generation AI analyzes past crop growth data and proposes the optimal direction for breeding improvements. The prediction unit also learns past cultivation data and proposes new cultivation methods. For example, the generation AI analyzes the optimal sowing time and thinning method and makes recommendations to farmers. The prediction unit also integrates past weather data and crop growth data to propose breeding improvements and cultivation methods that respond to climate change. For example, the generation AI proposes the development of heat-resistant varieties that can withstand rising temperatures. In this way, agricultural efficiency and yields can be improved by proposing crop breeding improvements and new cultivation methods.

[0044] The prediction unit can predict urban planning and infrastructure development from past data. For example, the prediction unit predicts urban planning and infrastructure development from past data. For example, the generation AI analyzes past urban planning data and predicts future urban planning. The prediction unit also learns past infrastructure development data and predicts future infrastructure development. For example, the generation AI analyzes the risk of deterioration of roads and bridges and proposes optimal development plans. The prediction unit also integrates past urban planning data with weather data to predict urban planning and infrastructure development that responds to climate change. For example, the generation AI proposes urban planning that takes flood risk into account. In this way, predicting urban planning and infrastructure development makes it possible to develop and maintain cities.

[0045] The prediction unit can make predictions and proposals for environmental protection and ecosystem maintenance based on the learned data. The prediction unit makes predictions and proposals for environmental protection and ecosystem maintenance based on the learned data, for example. For example, the generation AI analyzes past environmental data and makes predictions for future environmental protection. The prediction unit also learns past ecosystem data and makes predictions for ecosystem maintenance. For example, the generation AI predicts the habitats of endangered species and proposes conservation measures. The prediction unit also integrates past environmental data and weather data to make predictions for environmental protection and ecosystem maintenance in response to climate change. For example, the generation AI proposes ecosystem protection measures in response to rising temperatures. In this way, predictions and proposals for environmental protection and ecosystem maintenance make sustainable environmental protection possible.

[0046] The consulting department can automatically generate a customized consulting report for each farm. For example, the consulting department automatically generates a customized consulting report for each farm. For example, the generation AI analyzes farm data and automatically generates a customized consulting report. The consulting department also analyzes weather data for the farm and automatically generates a consulting report according to the weather conditions. For example, the generation AI suggests the timing of irrigation and fertilization based on rainfall and temperature. The consulting department also analyzes farm production data and automatically generates a consulting report to improve production efficiency. For example, the generation AI provides specific advice to improve yield and quality. In this way, efficient farm management is possible by automatically generating a customized consulting report for each farm.

[0047] The consulting department can propose an optimization plan for agricultural management based on the analyzed data. The consulting department, for example, proposes an optimization plan for agricultural management based on the analyzed data. For example, the generation AI analyzes the farm's management data and proposes an optimal management plan. The consulting department also analyzes the farm's production data and proposes a management plan to improve production efficiency. For example, the generation AI proposes optimizing work schedules and efficient use of resources. The consulting department also analyzes the farm's market data and proposes a management plan that meets market needs. For example, the generation AI proposes cultivation and sales strategies for crops that are in high demand. In this way, by proposing an optimization plan for agricultural management, it is possible to improve the efficiency and profitability of agricultural management.

[0048] The consulting department can provide consulting services for urban agriculture and home gardens. For example, the consulting department provides consulting services for urban agriculture and home gardens. For example, the generative AI provides consulting services for urban agriculture that are optimal for growing crops on building rooftops and balconies. The consulting department also provides consulting services for home gardens that suggest optimal cultivation methods for vegetables and fruits to be grown at home. For example, the generative AI analyzes the optimal temperature and humidity conditions for small home greenhouses. The consulting department also provides consulting services for urban agriculture and home gardens that suggest preventive measures for pests and diseases. For example, the generative AI analyzes preventive measures for specific pests that are likely to occur in urban areas. In this way, by providing consulting services for urban agriculture and home gardens, it is possible to improve the efficiency of agricultural activities in urban areas and at home.

[0049] The consulting department can hold agriculture-related educational programs and workshops based on the analyzed data. For example, the consulting department holds agriculture-related educational programs and workshops based on the analyzed data. For example, the generative AI analyzes agricultural data, develops customized educational programs for the next generation of farmers, and holds workshops. The consulting department also develops practical agricultural training programs based on the analyzed data and holds workshops. For example, the generative AI proposes a curriculum that includes on-farm training. The consulting department also analyzes agricultural data, develops educational programs that provide specialized knowledge for the next generation of farmers, and holds workshops. For example, the generative AI provides teaching materials for learning specialized knowledge about pest prevention and soil management. In this way, the next generation of farmers can be trained by holding agriculture-related educational programs and workshops.

[0050] The data analysis unit can propose environmentally friendly agricultural techniques and optimal resource utilization methods. For example, the data analysis unit analyzes agricultural data and proposes environmentally friendly agricultural techniques. For example, the generation AI recommends pesticide-free cultivation and the use of organic fertilizer. The data analysis unit also analyzes water resource utilization data and proposes optimal irrigation methods. For example, the generation AI recommends drip irrigation and rainwater reuse. The data analysis unit also analyzes energy consumption data and proposes the use of renewable energy. For example, the generation AI introduces solar power generation or wind power generation to a farm. This makes it possible to achieve sustainable agriculture by proposing environmentally friendly agricultural techniques and optimal resource utilization methods.

[0051] The data analysis unit can build a sustainable agricultural management model based on the analyzed data. The data analysis unit, for example, builds a sustainable agricultural management model based on the analyzed data. For example, the generation AI analyzes agricultural data and proposes a management plan that balances profitability and environmental protection. The data analysis unit also analyzes market data and predicts demand for sustainable agricultural products. For example, the generation AI analyzes market trends for organic agricultural products and fair trade products and proposes a management strategy. The data analysis unit also analyzes production data and proposes sustainable production methods. For example, the generation AI proposes a production plan that incorporates crop rotation and intercropping. In this way, a sustainable agricultural management model can be built that balances environmental protection and profitability.

[0052] The data analysis unit can propose sustainable food production models for urban areas and depopulated areas. The data analysis unit, for example, proposes sustainable food production models for urban areas and depopulated areas. For example, the generative AI analyzes data from urban areas and proposes a sustainable food production model suitable for urban agriculture. The data analysis unit also analyzes data from depopulated areas and proposes a sustainable food production model suitable for depopulated areas. For example, the generative AI proposes an agricultural method that utilizes local resources. The data analysis unit also integrates data from urban and depopulated areas and proposes a sustainable food production model suitable for both areas. For example, the generative AI proposes a model that links consumption in urban areas with production in depopulated areas. In this way, by proposing sustainable food production models for urban and depopulated areas, sustainable agriculture tailored to the region can be realized.

[0053] The data analysis unit can promote sustainable agriculture projects in collaboration with local communities based on the analyzed data. The data analysis unit, for example, promotes sustainable agriculture projects in collaboration with local communities based on the analyzed data. For example, the generation AI analyzes data from local communities and proposes sustainable agriculture projects. The data analysis unit also analyzes local weather data and soil data and proposes sustainable agriculture projects that are suitable for the region. For example, the generation AI proposes crop cultivation methods that are suitable for the local climate. The data analysis unit also analyzes local economic data and proposes sustainable agriculture projects in collaboration with local communities. For example, the generation AI proposes projects to create local jobs. In this way, by promoting sustainable agriculture projects in collaboration with local communities, regional development and sustainable agriculture can be achieved.

[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 data analysis unit analyzes agricultural data and automatically controls agricultural machinery, improving work efficiency. For example, the generation AI analyzes soil conditions and crop growth status to automatically control agricultural machinery such as tractors and combine harvesters. The data analysis unit also analyzes weather data and automatically controls irrigation systems. For example, the generation AI analyzes weather data and adjusts irrigation timing based on rainfall forecasts. The data analysis unit also analyzes crop growth data and automatically controls harvesting machinery. For example, the generation AI analyzes crop growth data, predicts the optimum harvest time, and sets the optimal harvest timing. This allows for automatic control of agricultural machinery and improves work efficiency.

[0056] The data analysis unit can provide data analysis and management methods for urban agriculture and home gardens. For example, the generative AI can propose optimal management methods for growing crops on building rooftops or balconies for urban agriculture. The data analysis unit can also propose optimal cultivation methods for home gardens for growing vegetables and fruits at home. For example, the generative AI can analyze the optimal temperature and humidity conditions for small home greenhouses. The data analysis unit can also propose pest prevention measures for urban agriculture and home gardens. For example, the generative AI can analyze preventive measures for specific pests that are more likely to occur in urban areas. This can improve the efficiency of agricultural activities in urban areas and at home by providing data analysis and management methods for urban agriculture and home gardens.

[0057] The sensor measurement unit can measure microbial activity in real time and evaluate the health of the soil. For example, a digital sensor measures the activity of microorganisms in the soil in real time, and the generation AI analyzes the data to evaluate the health of the soil. The sensor measurement unit also monitors the activity of microorganisms in the soil and displays the soil health in real time. For example, it issues an alert if microbial activity decreases. The sensor measurement unit also collects data on the activity of microorganisms in the soil, and the generation AI analyzes the data to evaluate the health of the soil. For example, it analyzes microbial activity patterns and suggests soil improvement measures. This makes it possible to measure microbial activity in real time, evaluate the health of the soil, and suggest soil improvement measures.

[0058] The sensor measurement unit can build a system that automatically optimizes the growing environment for crops based on data from digital sensors. For example, digital sensors measure soil conditions and weather data in real time, and build a system that automatically optimizes the growing environment for crops based on that data. The sensor measurement unit can also measure the amount of light and water in real time and automatically control the irrigation system based on that data. For example, it can automatically supply the required amount of water. The sensor measurement unit can also measure the nutrient status of the soil in real time and automatically control the fertilization system based on that data. For example, it can automatically supply the required nutrients. This automatically optimizes the growing environment for crops, thereby improving the quality and yield of crops.

[0059] The prediction unit predicts abnormal weather and disasters from past data, reducing agricultural risks. For example, the generation AI analyzes past weather data and predicts the probability of abnormal weather occurring. The prediction unit also learns from past disaster data and predicts disasters that will affect agriculture. For example, the generation AI learns from past disaster data and analyzes the risk of earthquakes and typhoons occurring. The prediction unit also integrates past weather data with crop growth data and predicts the impact of abnormal weather on crops. For example, the generation AI predicts fluctuations in harvest yields when abnormal weather occurs. In this way, agricultural risks can be reduced by predicting abnormal weather and disasters.

[0060] The prediction unit can propose crop breeding improvements and new cultivation methods based on the learned data. For example, the generation AI analyzes past crop growth data and proposes the optimal direction for breeding improvements. The prediction unit also learns from past cultivation data and proposes new cultivation methods. For example, the generation AI analyzes the optimal sowing time and thinning method and makes recommendations to farmers. The prediction unit also integrates past weather data and crop growth data to propose breeding improvements and cultivation methods that respond to climate change. For example, the generation AI proposes the development of heat-resistant varieties that can withstand rising temperatures. This allows for crop breeding improvements and proposals for new cultivation methods, thereby improving agricultural efficiency and yields.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The data analysis unit analyzes agricultural data. For example, it analyzes crop growth data and weather data to suggest optimal fertilization timing and amounts. It can also analyze the nutritional status of the soil and suggest optimal fertilization methods. It can also analyze the risk of pest and disease outbreaks and suggest preventative measures. Step 2: The sensor measurement unit measures the condition and moisture content of the soil in real time. For example, a soil sensor can be used to measure the moisture content of the soil and automatically adjust the irrigation system as needed. A light sensor can also be used to measure the amount of light and provide an optimal lighting environment. A temperature sensor can also be used to measure the temperature and provide an optimal temperature environment. Step 3: The forecasting unit predicts future crop production based on past data. For example, it analyzes past weather data to predict weather conditions for the next season. It can also analyze past crop growth data to predict crop growth for the next season. It can also analyze past pest and disease outbreak data to predict the risk of pest and disease outbreak for the next season. Step 4: The consulting department provides optimal solutions tailored to the farm's situation. For example, they analyze farm data and propose optimal fertilization and irrigation methods. They can also predict the risk of pest and disease outbreaks and propose preventative measures. They can also analyze crop growth and propose optimal harvest timing.

[0063] (Example 2) The smart agricultural management system according to an embodiment of the present invention is a system that realizes smart and efficient agricultural management by utilizing generative AI and digital sensors. As a result, the smart agricultural management system is capable of analyzing agricultural data, taking real-time measurements, making future predictions, and providing optimal solutions.

[0064] A smart agriculture management system according to an embodiment includes a data analysis unit, a sensor measurement unit, a prediction unit, and a consulting unit. The data analysis unit analyzes agricultural data. For example, the data analysis unit analyzes crop growth data and weather data to propose optimal fertilization timing and amount. The data analysis unit can also analyze the nutritional status of soil and propose optimal fertilization methods. The data analysis unit can also analyze the risk of pest and disease outbreaks and propose preventive measures. The sensor measurement unit measures the soil condition and moisture content in real time. For example, the sensor measurement unit measures the soil moisture content using a soil sensor and automatically adjusts the irrigation system as needed. The sensor measurement unit can also measure the amount of light using an optical sensor to provide an optimal lighting environment. The sensor measurement unit can also measure the temperature using a temperature sensor to provide an optimal temperature environment. The prediction unit predicts future crop production based on past data. For example, the prediction unit analyzes past weather data to predict weather conditions for the next season. The prediction unit can also analyze past crop growth data to predict crop growth for the next season. The prediction unit can also analyze past pest and disease occurrence data and predict the risk of pest and disease occurrence in the next season. The consulting unit provides optimal solutions according to the farm's situation. For example, the consulting unit analyzes farm data and proposes optimal fertilization and irrigation methods. The consulting unit can also predict the risk of pest and disease occurrence and propose preventive measures. The consulting unit can also analyze the growth status of crops and propose the optimal harvest timing. As a result, the smart agriculture management system according to the embodiment is capable of analyzing agricultural data, taking real-time measurements, making future predictions, and providing optimal solutions.

[0065] The data analysis unit can analyze the nutritional state of the soil and weather data to suggest the optimal timing and amount of fertilization. The data analysis unit, for example, analyzes the nutritional state of the soil and suggests the optimal timing and amount of fertilization. For example, the generation AI analyzes the nutritional state of the soil and suggests the optimal timing and amount of fertilization for nitrogen, phosphorus, and potassium. The data analysis unit also analyzes weather data and suggests the optimal timing of fertilization. For example, the generation AI analyzes weather data and adjusts the timing of fertilization based on rainfall forecasts. The data analysis unit also analyzes crop growth data and suggests the optimal amount of fertilizer to apply. For example, the generation AI analyzes crop growth data and suggests the optimal amount of fertilizer to apply for each growth stage. This makes it possible to optimize crop growth by suggesting the optimal timing and amount of fertilization.

[0066] The sensor measurement unit can measure the moisture content of the soil and automatically adjust the irrigation system as needed. The sensor measurement unit, for example, measures the moisture content of the soil and automatically adjusts the irrigation system as needed. For example, a soil sensor measures the moisture content of the soil in real time, and the generation AI analyzes the data to automatically adjust the irrigation system. The sensor measurement unit also analyzes weather data and automatically adjusts the irrigation system. For example, the generation AI analyzes weather data and adjusts the timing of irrigation based on rainfall forecasts. The sensor measurement unit also analyzes crop growth data and automatically adjusts the irrigation system. For example, the generation AI analyzes crop growth data and adjusts the optimal amount of irrigation for each growth stage. This enables efficient water management by measuring the moisture content of the soil in real time and automatically adjusting the irrigation system.

[0067] The prediction unit can analyze past weather data and predict weather conditions for the next season. The prediction unit, for example, analyzes past weather data and predicts weather conditions for the next season. For example, the generation AI analyzes past weather data and predicts the temperature, precipitation, and wind speed for the next season. The prediction unit also analyzes past crop growth data and predicts crop growth for the next season. For example, the generation AI analyzes past crop growth data and predicts the harvest yield for the next season. The prediction unit also analyzes past pest and disease occurrence data and predicts the risk of pest and disease occurrence for the next season. For example, the generation AI analyzes past pest and disease occurrence data and predicts the risk of pest and disease occurrence for the next season. This allows farmers to make efficient production plans by predicting weather conditions for the next season.

[0068] The consulting department can analyze farm data, predict the risk of pest and disease outbreaks, and propose preventive measures. For example, the consulting department analyzes farm data, predicts the risk of pest and disease outbreaks, and proposes preventive measures. For example, the generation AI analyzes farm data, predicts the risk of specific pest and disease outbreaks, and proposes the use of appropriate pesticides. The consulting department also analyzes weather data and predicts the risk of pest and disease outbreaks. For example, the generation AI analyzes weather data and predicts the risk of pest and disease outbreaks based on fluctuations in temperature and humidity. The consulting department also analyzes crop growth data and predicts the risk of pest and disease outbreaks. For example, the generation AI analyzes crop growth data and predicts the risk of pest and disease outbreaks for each growth stage. This makes it possible to predict the risk of pest and disease outbreaks and propose preventive measures, thereby improving the quality and yield of agricultural crops.

[0069] The data analysis unit can analyze agricultural data and propose optimal management methods for each crop growth stage. For example, the data analysis unit analyzes agricultural data and proposes optimal management methods for each crop growth stage. For example, the generation AI analyzes the amount of nutrients and water needed for each crop growth stage and proposes the optimal timing for fertilization and irrigation. The data analysis unit also analyzes the risk of pests and diseases according to the crop growth stage and proposes preventive measures. For example, the generation AI predicts that certain pests are more likely to occur in the early stages of growth and proposes the use of appropriate pesticides. The data analysis unit also analyzes the optimal temperature and humidity conditions for each crop growth stage and adjusts the environmental control system. For example, the generation AI analyzes that high temperatures and humidity are preferable in the middle stages of growth and automatically adjusts the temperature and humidity. This allows the quality and yield of crops to be improved by proposing optimal management methods for each crop growth stage.

[0070] The data analysis unit analyzes agricultural data and automatically controls agricultural machinery, thereby improving work efficiency. The data analysis unit, for example, analyzes agricultural data and automatically controls agricultural machinery, thereby improving work efficiency. For example, the generation AI analyzes soil conditions and crop growth status to automatically control agricultural machinery such as tractors and combine harvesters. The data analysis unit also analyzes weather data and automatically controls irrigation systems. For example, the generation AI analyzes weather data and adjusts irrigation timing based on rainfall forecasts. The data analysis unit also analyzes crop growth data and automatically controls harvesting machinery. For example, the generation AI analyzes crop growth data, predicts the optimum harvest time, and sets the optimal harvest timing. This allows for automatic control of agricultural machinery and improves work efficiency.

[0071] The data analysis unit can analyze the emotional state of farmers and suggest agricultural management methods to reduce stress. For example, the data analysis unit analyzes the emotional state of farmers and suggests agricultural management methods to reduce stress. For example, the generation AI analyzes the emotional state of farmers and suggests management methods to reduce workload during times of high stress. The data analysis unit also suggests relaxation methods to reduce stress based on the farmers' emotional data. For example, the generation AI recommends music that is relaxing to listen to in between farm work. The data analysis unit also analyzes the farmers' emotional state and suggests sending support staff during times of high stress. For example, the generation AI arranges for additional manpower during harvest season. In this way, by analyzing the farmers' emotional state and suggesting agricultural management methods to reduce stress, the mental burden on farmers can be reduced.

[0072] The data analysis unit can provide data analysis and management methods for urban agriculture and home gardens. The data analysis unit provides, for example, data analysis and management methods for urban agriculture and home gardens. For example, the generation AI proposes the optimal management method for growing crops on building rooftops or balconies for urban agriculture. The data analysis unit also proposes the optimal cultivation method for vegetables and fruits to be grown at home for home gardens. For example, the generation AI analyzes the optimal temperature and humidity conditions for small home greenhouses. The data analysis unit also proposes preventive measures for pests and diseases for urban agriculture and home gardens. For example, the generation AI analyzes preventive measures for specific pests that are likely to occur in urban areas. In this way, by providing data analysis and management methods for urban agriculture and home gardens, agricultural activities in urban areas and at home can be made more efficient.

[0073] The data analysis department can analyze agricultural data, develop agricultural education programs, and train the next generation of farmers. For example, the data analysis department analyzes agricultural data, develops agricultural education programs, and trains the next generation of farmers. For example, the generative AI analyzes agricultural data and develops educational programs customized for the next generation of farmers. The data analysis department also develops practical agricultural training programs based on the analyzed data. For example, the generative AI proposes a curriculum that includes on-farm training. The data analysis department also analyzes agricultural data and develops educational programs that provide specialized knowledge for the next generation of farmers. For example, the generative AI provides teaching materials for learning specialized knowledge about pest prevention and soil management. In this way, by analyzing agricultural data and developing agricultural education programs, the next generation of farmers can be trained.

[0074] The data analysis unit can provide customized agricultural advice according to the farmer's emotions. For example, the data analysis unit analyzes the farmer's emotional state and provides customized agricultural advice according to the emotion. For example, the generation AI analyzes the farmer's emotional state and provides agricultural advice to elicit positive emotions. The data analysis unit also provides specific advice to reduce stress based on the farmer's emotional data. For example, the generation AI emphasizes the importance of improving work efficiency and rest. The data analysis unit also analyzes the farmer's emotional state and provides customized agricultural advice according to the emotion. For example, if the emotion is negative, the generation AI suggests relaxation methods and support. In this way, by providing customized agricultural advice according to the farmer's emotions, the mental burden on the farmer can be reduced.

[0075] The sensor measurement unit can measure microbial activity in real time and evaluate the health of the soil. For example, the sensor measurement unit measures microbial activity in real time and evaluates the health of the soil. For example, a digital sensor measures microbial activity in the soil in real time, and the generation AI analyzes the data to evaluate the health of the soil. The sensor measurement unit also monitors microbial activity in the soil and displays the soil health in real time. For example, it issues an alert if microbial activity decreases. The sensor measurement unit also collects microbial activity data in the soil, and the generation AI analyzes the data to evaluate the health of the soil. For example, it analyzes microbial activity patterns and proposes soil improvement measures. This makes it possible to measure microbial activity in real time, evaluate the health of the soil, and propose soil improvement measures.

[0076] The sensor measurement unit can build a system that automatically optimizes the crop growth environment based on data from digital sensors. The sensor measurement unit, for example, builds a system that automatically optimizes the crop growth environment based on data from digital sensors. For example, digital sensors measure soil conditions and weather data in real time, and build a system that automatically optimizes the crop growth environment based on that data. The sensor measurement unit also measures the amount of light and water in real time, and automatically controls the irrigation system based on that data. For example, it automatically supplies the required amount of water. The sensor measurement unit also measures the nutrient state of the soil in real time, and automatically controls the fertilization system based on that data. For example, it automatically supplies the required nutrients. In this way, the crop growth environment can be automatically optimized, thereby improving the quality and yield of the crops.

[0077] The sensor measurement unit can provide real-time alerts and notifications according to the farmer's emotional state. For example, a digital sensor can analyze the farmer's emotional state in real time and provide an alert to help the farmer relax if stress levels are high. The sensor measurement unit also provides work advice according to the farmer's emotional state based on the farmer's emotional data. For example, if emotions are negative, it can suggest reducing work. The sensor measurement unit also monitors the farmer's emotional state in real time and provides customized notifications according to the emotion. For example, it can send encouraging messages to elicit positive emotions. This reduces the mental burden on farmers by providing real-time alerts and notifications according to their emotional state.

[0078] The sensor measurement unit can monitor the health status of animals in the livestock industry in real time. The sensor measurement unit, for example, monitors the health status of animals in the livestock industry in real time. For example, a digital sensor measures the body temperature and heart rate of animals in the livestock industry in real time to monitor their health status. The sensor measurement unit also analyzes the behavioral patterns of animals in real time to evaluate their health status. For example, if abnormal behavior is detected, a notification is sent. The sensor measurement unit also measures the amount of food and water intake of animals in real time to monitor their health status. For example, an alert is issued if intake decreases. In this way, by monitoring the health status of animals in the livestock industry in real time, abnormalities can be detected early and appropriate measures can be taken.

[0079] The sensor measurement unit can provide a system that improves the efficiency of urban green space management and park maintenance based on digital sensor data. The sensor measurement unit provides a system that improves the efficiency of urban green space management and park maintenance based on digital sensor data. For example, a system is provided in which digital sensors measure the soil condition and moisture content of urban green spaces and parks in real time and improve the efficiency of maintenance based on that data. The sensor measurement unit also measures the amount of light and temperature in urban green spaces and parks in real time and provides an environmental control system based on that data. For example, the system automatically adjusts the amount of light and temperature to the optimum level. The sensor measurement unit also monitors the health of plants in urban green spaces and parks in real time and improves the efficiency of maintenance based on that data. For example, the system predicts the occurrence of pests and diseases and proposes preventive measures. This improves the efficiency of urban green space management and park maintenance, making it possible to improve and maintain the urban environment.

[0080] The sensor measurement unit can customize the way sensor data is displayed according to the farmer's emotions. For example, the sensor measurement unit analyzes the farmer's emotional state and customizes the way data is displayed according to the emotion. For example, a digital sensor analyzes the farmer's emotional state and, if stress is high, provides a simple, easy-to-understand display. The sensor measurement unit also adjusts the color and font of the data display according to the farmer's emotion based on the farmer's emotional data. For example, bright colors and large fonts are used to elicit positive emotions. The sensor measurement unit also monitors the farmer's emotional state in real time and dynamically adjusts the way data is displayed according to the emotion. For example, if the emotion is negative, the details of the data are omitted and only the main points are displayed. In this way, customizing the way sensor data is displayed according to the farmer's emotions can reduce the mental burden on farmers.

[0081] The prediction unit predicts abnormal weather and disasters from past data, thereby reducing agricultural risks. The prediction unit, for example, predicts abnormal weather and disasters from past data, thereby reducing agricultural risks. For example, the generation AI analyzes past weather data and predicts the probability of abnormal weather occurring. The prediction unit also learns past disaster data and predicts disasters that will affect agriculture. For example, the generation AI learns past disaster data and analyzes the risk of earthquakes and typhoons occurring. The prediction unit also integrates past weather data and crop growth data to predict the impact of abnormal weather on crops. For example, the generation AI predicts fluctuations in harvest yields when abnormal weather occurs. In this way, agricultural risks can be reduced by predicting abnormal weather and disasters.

[0082] The prediction unit can propose crop breeding improvements and new cultivation methods based on the learned data. For example, the prediction unit proposes crop breeding improvements and new cultivation methods based on the learned data. For example, the generation AI analyzes past crop growth data and proposes the optimal direction for breeding improvements. The prediction unit also learns past cultivation data and proposes new cultivation methods. For example, the generation AI analyzes the optimal sowing time and thinning method and makes recommendations to farmers. The prediction unit also integrates past weather data and crop growth data to propose breeding improvements and cultivation methods that respond to climate change. For example, the generation AI proposes the development of heat-resistant varieties that can withstand rising temperatures. In this way, agricultural efficiency and yields can be improved by proposing crop breeding improvements and new cultivation methods.

[0083] The prediction unit can customize future predictions based on the farmer's emotions. For example, the prediction unit analyzes the farmer's emotional state and customizes future predictions based on emotions. For example, the generation AI analyzes the farmer's emotional state and, if stress is high, prioritizes predictions with fewer risks. The prediction unit also provides future prediction scenarios based on the farmer's emotional data. For example, the generation AI presents predictions that include many success stories to elicit positive emotions. The prediction unit also monitors the farmer's emotional state in real time and dynamically customizes future predictions based on emotions. For example, if the generation AI is emotionally negative, it prioritizes predictions for risk avoidance. In this way, by customizing future predictions based on the farmer's emotions, the mental burden on farmers can be reduced.

[0084] The prediction unit can predict urban planning and infrastructure development from past data. For example, the prediction unit predicts urban planning and infrastructure development from past data. For example, the generation AI analyzes past urban planning data and predicts future urban planning. The prediction unit also learns past infrastructure development data and predicts future infrastructure development. For example, the generation AI analyzes the risk of deterioration of roads and bridges and proposes optimal development plans. The prediction unit also integrates past urban planning data with weather data to predict urban planning and infrastructure development that responds to climate change. For example, the generation AI proposes urban planning that takes flood risk into account. In this way, predicting urban planning and infrastructure development makes it possible to develop and maintain cities.

[0085] The prediction unit can make predictions and proposals for environmental protection and ecosystem maintenance based on the learned data. The prediction unit makes predictions and proposals for environmental protection and ecosystem maintenance based on the learned data, for example. For example, the generation AI analyzes past environmental data and makes predictions for future environmental protection. The prediction unit also learns past ecosystem data and makes predictions for ecosystem maintenance. For example, the generation AI predicts the habitats of endangered species and proposes conservation measures. The prediction unit also integrates past environmental data and weather data to make predictions for environmental protection and ecosystem maintenance in response to climate change. For example, the generation AI proposes ecosystem protection measures in response to rising temperatures. In this way, predictions and proposals for environmental protection and ecosystem maintenance make sustainable environmental protection possible.

[0086] The prediction unit can provide future prediction feedback according to the farmer's emotions. For example, the prediction unit analyzes the farmer's emotional state and provides future prediction feedback according to the emotion. For example, the generation AI analyzes the farmer's emotional state and provides feedback including many success stories to elicit positive emotions. The prediction unit also provides future prediction scenarios according to the farmer's emotions based on the farmer's emotional data. For example, if the generation AI is highly stressed, it will prioritize predictions with fewer risks. The prediction unit also monitors the farmer's emotional state in real time and dynamically provides future prediction feedback according to the emotion. For example, if the generation AI is feeling negative, it will provide feedback to avoid risks. In this way, by providing future prediction feedback according to the farmer's emotions, the mental burden on the farmer can be reduced.

[0087] The consulting department can automatically generate a customized consulting report for each farm. For example, the consulting department automatically generates a customized consulting report for each farm. For example, the generation AI analyzes farm data and automatically generates a customized consulting report. The consulting department also analyzes weather data for the farm and automatically generates a consulting report according to the weather conditions. For example, the generation AI suggests the timing of irrigation and fertilization based on rainfall and temperature. The consulting department also analyzes farm production data and automatically generates a consulting report to improve production efficiency. For example, the generation AI provides specific advice to improve yield and quality. In this way, efficient farm management is possible by automatically generating a customized consulting report for each farm.

[0088] The consulting department can propose an optimization plan for agricultural management based on the analyzed data. The consulting department, for example, proposes an optimization plan for agricultural management based on the analyzed data. For example, the generation AI analyzes the farm's management data and proposes an optimal management plan. The consulting department also analyzes the farm's production data and proposes a management plan to improve production efficiency. For example, the generation AI proposes optimizing work schedules and efficient use of resources. The consulting department also analyzes the farm's market data and proposes a management plan that meets market needs. For example, the generation AI proposes cultivation and sales strategies for crops that are in high demand. In this way, by proposing an optimization plan for agricultural management, it is possible to improve the efficiency and profitability of agricultural management.

[0089] The consulting department can adjust the consulting content according to the farmer's emotions. For example, the consulting department analyzes the farmer's emotional state and adjusts the consulting content according to the emotion. For example, the generation AI analyzes the farmer's emotional state and, if stress is high, provides advice to reduce the farmer's workload. The consulting department also customizes the consulting content according to the farmer's emotions based on the farmer's emotional data. For example, the generation AI provides advice that includes many success stories to elicit positive emotions. The consulting department also monitors the farmer's emotional state in real time and dynamically adjusts the consulting content according to the emotion. For example, if the generation AI is feeling negative, it provides advice to avoid risks. In this way, by adjusting the consulting content according to the farmer's emotions, the mental burden on the farmer can be reduced.

[0090] The consulting department can provide consulting services for urban agriculture and home gardens. For example, the consulting department provides consulting services for urban agriculture and home gardens. For example, the generative AI provides consulting services for urban agriculture that are optimal for growing crops on building rooftops and balconies. The consulting department also provides consulting services for home gardens that suggest optimal cultivation methods for vegetables and fruits to be grown at home. For example, the generative AI analyzes the optimal temperature and humidity conditions for small home greenhouses. The consulting department also provides consulting services for urban agriculture and home gardens that suggest preventive measures for pests and diseases. For example, the generative AI analyzes preventive measures for specific pests that are likely to occur in urban areas. In this way, by providing consulting services for urban agriculture and home gardens, it is possible to improve the efficiency of agricultural activities in urban areas and at home.

[0091] The consulting department can hold agriculture-related educational programs and workshops based on the analyzed data. For example, the consulting department holds agriculture-related educational programs and workshops based on the analyzed data. For example, the generative AI analyzes agricultural data, develops customized educational programs for the next generation of farmers, and holds workshops. The consulting department also develops practical agricultural training programs based on the analyzed data and holds workshops. For example, the generative AI proposes a curriculum that includes on-farm training. The consulting department also analyzes agricultural data, develops educational programs that provide specialized knowledge for the next generation of farmers, and holds workshops. For example, the generative AI provides teaching materials for learning specialized knowledge about pest prevention and soil management. In this way, the next generation of farmers can be trained by holding agriculture-related educational programs and workshops.

[0092] The consulting department can customize how consulting services are provided according to the farmer's emotions. For example, the consulting department analyzes the farmer's emotional state and customizes how consulting services are provided according to the emotion. For example, the generation AI analyzes the farmer's emotional state and, if stress is high, provides advice to reduce the farmer's workload. The consulting department also customizes how consulting services are provided according to the farmer's emotions based on the farmer's emotional data. For example, the generation AI provides advice that includes many success stories to elicit positive emotions. The consulting department also monitors the farmer's emotional state in real time and dynamically adjusts how consulting services are provided according to the farmer's emotions. For example, if the generation AI's emotions are negative, it provides advice to avoid risks. In this way, by customizing how consulting services are provided according to the farmer's emotions, the mental burden on the farmer can be reduced.

[0093] The data analysis unit can propose environmentally friendly agricultural techniques and optimal resource utilization methods. For example, the data analysis unit analyzes agricultural data and proposes environmentally friendly agricultural techniques. For example, the generation AI recommends pesticide-free cultivation and the use of organic fertilizer. The data analysis unit also analyzes water resource utilization data and proposes optimal irrigation methods. For example, the generation AI recommends drip irrigation and rainwater reuse. The data analysis unit also analyzes energy consumption data and proposes the use of renewable energy. For example, the generation AI introduces solar power generation or wind power generation to a farm. This makes it possible to achieve sustainable agriculture by proposing environmentally friendly agricultural techniques and optimal resource utilization methods.

[0094] The data analysis unit can build a sustainable agricultural management model based on the analyzed data. The data analysis unit, for example, builds a sustainable agricultural management model based on the analyzed data. For example, the generation AI analyzes agricultural data and proposes a management plan that balances profitability and environmental protection. The data analysis unit also analyzes market data and predicts demand for sustainable agricultural products. For example, the generation AI analyzes market trends for organic agricultural products and fair trade products and proposes a management strategy. The data analysis unit also analyzes production data and proposes sustainable production methods. For example, the generation AI proposes a production plan that incorporates crop rotation and intercropping. In this way, a sustainable agricultural management model can be built that balances environmental protection and profitability.

[0095] The data analysis unit can propose sustainable agricultural techniques based on farmers' emotions. For example, the data analysis unit analyzes the emotional state of farmers and proposes sustainable agricultural techniques based on their emotions. For example, the generation AI analyzes the emotional state of farmers and proposes work methods that cause less stress. The data analysis unit also proposes sustainable agricultural techniques based on the farmers' emotional data, according to their emotions. For example, the generation AI proposes techniques that include many success stories in order to elicit positive emotions. The data analysis unit also monitors the farmers' emotional state in real time and dynamically proposes sustainable agricultural techniques based on their emotions. For example, if the generation AI's emotions are negative, it proposes techniques for risk avoidance. In this way, by proposing sustainable agricultural techniques based on farmers' emotions, it is possible to reduce the mental burden on farmers and achieve sustainable agriculture.

[0096] The data analysis unit can propose sustainable food production models for urban areas and depopulated areas. The data analysis unit, for example, proposes sustainable food production models for urban areas and depopulated areas. For example, the generative AI analyzes data from urban areas and proposes a sustainable food production model suitable for urban agriculture. The data analysis unit also analyzes data from depopulated areas and proposes a sustainable food production model suitable for depopulated areas. For example, the generative AI proposes an agricultural method that utilizes local resources. The data analysis unit also integrates data from urban and depopulated areas and proposes a sustainable food production model suitable for both areas. For example, the generative AI proposes a model that links consumption in urban areas with production in depopulated areas. In this way, by proposing sustainable food production models for urban and depopulated areas, sustainable agriculture tailored to the region can be realized.

[0097] The data analysis unit can promote sustainable agriculture projects in collaboration with local communities based on the analyzed data. The data analysis unit, for example, promotes sustainable agriculture projects in collaboration with local communities based on the analyzed data. For example, the generation AI analyzes data from local communities and proposes sustainable agriculture projects. The data analysis unit also analyzes local weather data and soil data and proposes sustainable agriculture projects that are suitable for the region. For example, the generation AI proposes crop cultivation methods that are suitable for the local climate. The data analysis unit also analyzes local economic data and proposes sustainable agriculture projects in collaboration with local communities. For example, the generation AI proposes projects to create local jobs. In this way, by promoting sustainable agriculture projects in collaboration with local communities, regional development and sustainable agriculture can be achieved.

[0098] The data analysis unit can support the introduction of sustainable agricultural techniques in accordance with the farmers' emotions. For example, the data analysis unit analyzes the farmers' emotional state and supports the introduction of sustainable agricultural techniques in accordance with the emotions. For example, the generation AI analyzes the farmers' emotional state and supports the introduction of techniques that cause less stress. The data analysis unit also supports the introduction of sustainable agricultural techniques in accordance with the farmers' emotions based on the farmers' emotional data. For example, the generation AI supports the introduction of techniques that include many success stories in order to elicit positive emotions. The data analysis unit also monitors the farmers' emotional state in real time and dynamically supports the introduction of sustainable agricultural techniques in accordance with the emotions. For example, if the generation AI's emotions are negative, it supports the introduction of techniques for risk avoidance. In this way, by supporting the introduction of sustainable agricultural techniques in accordance with the farmers' emotions, the mental burden on farmers can be reduced and sustainable agriculture can be achieved.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The data analysis unit analyzes agricultural data and automatically controls agricultural machinery, improving work efficiency. For example, the generation AI analyzes soil conditions and crop growth status to automatically control agricultural machinery such as tractors and combine harvesters. The data analysis unit also analyzes weather data and automatically controls irrigation systems. For example, the generation AI analyzes weather data and adjusts irrigation timing based on rainfall forecasts. The data analysis unit also analyzes crop growth data and automatically controls harvesting machinery. For example, the generation AI analyzes crop growth data, predicts the optimum harvest time, and sets the optimal harvest timing. This allows for automatic control of agricultural machinery and improves work efficiency.

[0101] The data analysis unit can provide data analysis and management methods for urban agriculture and home gardens. For example, the generative AI can propose optimal management methods for growing crops on building rooftops or balconies for urban agriculture. The data analysis unit can also propose optimal cultivation methods for home gardens for growing vegetables and fruits at home. For example, the generative AI can analyze the optimal temperature and humidity conditions for small home greenhouses. The data analysis unit can also propose pest prevention measures for urban agriculture and home gardens. For example, the generative AI can analyze preventive measures for specific pests that are more likely to occur in urban areas. This can improve the efficiency of agricultural activities in urban areas and at home by providing data analysis and management methods for urban agriculture and home gardens.

[0102] The data analysis unit can analyze the emotional state of farmers and suggest agricultural management methods to reduce stress. For example, the generation AI analyzes the emotional state of farmers and suggests management methods to reduce workload during times of high stress. The data analysis unit also suggests relaxation methods to reduce stress based on the farmers' emotional data. For example, the generation AI recommends music that helps farmers relax between farm tasks. The data analysis unit also analyzes the farmers' emotional state and suggests sending support staff during times of high stress. For example, the generation AI arranges for additional manpower during harvest season. In this way, by analyzing the farmers' emotional state and suggesting agricultural management methods to reduce stress, the mental burden on farmers can be reduced.

[0103] The data analysis unit can provide customized agricultural advice based on the farmer's emotions. For example, the generation AI analyzes the farmer's emotional state and provides agricultural advice designed to elicit positive emotions. The data analysis unit also provides specific advice to reduce stress based on the farmer's emotional data. For example, the generation AI emphasizes the importance of improving work efficiency and taking rest. The data analysis unit also analyzes the farmer's emotional state and provides customized agricultural advice based on the farmer's emotions. For example, if the generation AI detects negative emotions, it suggests relaxation methods and support. This allows the farmer's mental burden to be reduced by providing customized agricultural advice based on their emotions.

[0104] The sensor measurement unit can measure microbial activity in real time and evaluate the health of the soil. For example, a digital sensor measures the activity of microorganisms in the soil in real time, and the generation AI analyzes the data to evaluate the health of the soil. The sensor measurement unit also monitors the activity of microorganisms in the soil and displays the soil health in real time. For example, it issues an alert if microbial activity decreases. The sensor measurement unit also collects data on the activity of microorganisms in the soil, and the generation AI analyzes the data to evaluate the health of the soil. For example, it analyzes microbial activity patterns and suggests soil improvement measures. This makes it possible to measure microbial activity in real time, evaluate the health of the soil, and suggest soil improvement measures.

[0105] The sensor measurement unit can build a system that automatically optimizes the growing environment for crops based on data from digital sensors. For example, digital sensors measure soil conditions and weather data in real time, and build a system that automatically optimizes the growing environment for crops based on that data. The sensor measurement unit can also measure the amount of light and water in real time and automatically control the irrigation system based on that data. For example, it can automatically supply the required amount of water. The sensor measurement unit can also measure the nutrient status of the soil in real time and automatically control the fertilization system based on that data. For example, it can automatically supply the required nutrients. This automatically optimizes the growing environment for crops, thereby improving the quality and yield of crops.

[0106] The sensor measurement unit can provide real-time alerts and notifications according to the farmer's emotional state. For example, digital sensors can analyze the farmer's emotional state in real time and provide an alert to relax if stress levels are high. The sensor measurement unit can also provide work advice according to the farmer's emotional state based on the farmer's emotional data. For example, if emotions are negative, it can suggest reducing work. The sensor measurement unit can also monitor the farmer's emotional state in real time and provide customized notifications according to emotions. For example, it can send encouraging messages to elicit positive emotions. This can reduce the mental burden on farmers by providing real-time alerts and notifications according to their emotional state.

[0107] The prediction unit predicts abnormal weather and disasters from past data, reducing agricultural risks. For example, the generation AI analyzes past weather data and predicts the probability of abnormal weather occurring. The prediction unit also learns from past disaster data and predicts disasters that will affect agriculture. For example, the generation AI learns from past disaster data and analyzes the risk of earthquakes and typhoons occurring. The prediction unit also integrates past weather data with crop growth data and predicts the impact of abnormal weather on crops. For example, the generation AI predicts fluctuations in harvest yields when abnormal weather occurs. In this way, agricultural risks can be reduced by predicting abnormal weather and disasters.

[0108] The prediction unit can propose crop breeding improvements and new cultivation methods based on the learned data. For example, the generation AI analyzes past crop growth data and proposes the optimal direction for breeding improvements. The prediction unit also learns from past cultivation data and proposes new cultivation methods. For example, the generation AI analyzes the optimal sowing time and thinning method and makes recommendations to farmers. The prediction unit also integrates past weather data and crop growth data to propose breeding improvements and cultivation methods that respond to climate change. For example, the generation AI proposes the development of heat-resistant varieties that can withstand rising temperatures. This allows for crop breeding improvements and proposals for new cultivation methods, thereby improving agricultural efficiency and yields.

[0109] The prediction unit can customize future predictions based on the farmer's emotions. For example, the generation AI analyzes the farmer's emotional state, and if stress is high, it will prioritize predictions with less risk. The prediction unit also provides future prediction scenarios based on the farmer's emotional data. For example, the generation AI will present predictions that include many success stories to elicit positive emotions. The prediction unit also monitors the farmer's emotional state in real time and dynamically customizes future predictions based on their emotions. For example, if the generation AI is feeling negative, it will prioritize predictions for risk avoidance. In this way, by customizing future predictions based on the farmer's emotions, it is possible to reduce the mental burden on farmers.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The data analysis unit analyzes agricultural data. For example, it analyzes crop growth data and weather data to suggest optimal fertilization timing and amounts. It can also analyze the nutritional status of the soil and suggest optimal fertilization methods. It can also analyze the risk of pest and disease outbreaks and suggest preventative measures. Step 2: The sensor measurement unit measures the condition and moisture content of the soil in real time. For example, a soil sensor can be used to measure the moisture content of the soil and automatically adjust the irrigation system as needed. A light sensor can also be used to measure the amount of light and provide an optimal lighting environment. A temperature sensor can also be used to measure the temperature and provide an optimal temperature environment. Step 3: The forecasting unit predicts future crop production based on past data. For example, it analyzes past weather data to predict weather conditions for the next season. It can also analyze past crop growth data to predict crop growth for the next season. It can also analyze past pest and disease outbreak data to predict the risk of pest and disease outbreak for the next season. Step 4: The consulting department provides optimal solutions tailored to the farm's situation. For example, they analyze farm data and propose optimal fertilization and irrigation methods. They can also predict the risk of pest and disease outbreaks and propose preventative measures. They can also analyze crop growth and propose optimal harvest timing.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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]

[0179] 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. a data analysis unit that analyzes agricultural data; A sensor measurement unit that measures soil condition and moisture content in real time, a prediction unit that predicts future crop production based on past data; A consulting department that provides optimal solutions according to the farm's situation. A system characterized by:

2. The data analysis unit Analyze the nutritional status of the soil and weather data to propose the optimal timing and amount of fertilization.

2. The system of claim 1.

3. The sensor measurement unit Measure the moisture content of the soil and automatically adjust the irrigation system as needed.

2. The system of claim 1.

4. The prediction unit Analyzing past weather data to predict weather conditions for the next season 2. The system of claim 1.

5. The consulting department: Analyze data from the farm, predict the risk of pests and diseases, and propose preventative measures 2. The system of claim 1.

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