System

Abstract

An object of a system according to an embodiment is to analyze soil data, weather information, and sensor data and propose an optimal irrigation schedule.SOLUTION: A system according to an embodiment includes a soil-data-analyzing unit, a weather-information-analyzing unit, a sensor-data-analyzing unit, an irrigation-schedule-proposing unit, a agricultural land use plan-supporting unit, and an irrigation-infrastructure-optimizing unit. The soil data analysis unit analyzes the soil data. The weather information analysis unit analyzes weather information. The sensor data analysis unit analyzes the sensor data. The irrigation schedule proposal unit proposes an optimal irrigation schedule on the basis of analysis results of the soil data analysis unit, the weather information analysis unit, and the sensor data analysis unit. The agricultural land use plan support unit supports a use plan of the agricultural land. The irrigation infrastructure optimization unit optimizes the irrigation infrastructure.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 soil data, weather information, and sensor data in an integrated manner to propose optimal irrigation schedules, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze soil data, weather information, and sensor data to propose an optimal irrigation schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a soil data analysis unit, a weather information analysis unit, a sensor data analysis unit, an irrigation schedule proposal unit, a farmland use planning support unit, and an irrigation infrastructure optimization unit. The soil data analysis unit analyzes soil data. The weather information analysis unit analyzes weather information. The sensor data analysis unit analyzes sensor data. The irrigation schedule proposal unit proposes an optimal irrigation schedule based on the analysis results of the soil data analysis unit, the weather information analysis unit, and the sensor data analysis unit. The farmland use planning support unit supports farmland use planning. The irrigation infrastructure optimization unit optimizes irrigation infrastructure. [Effects of the Invention]

[0007] The system according to the embodiment can analyze soil data, weather information, and sensor data to propose an optimal irrigation schedule. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 water agriculture optimization and irrigation management system according to an embodiment of the present invention uses AI to analyze soil data and weather information, propose optimal irrigation schedules and water amounts, and use sensor data to monitor crop water demand and reduce water waste. As a result, the water agriculture optimization and irrigation management system can improve crop yield and quality.

[0029] The water agriculture optimization and irrigation management system according to the embodiment includes a soil data analysis unit, a weather information analysis unit, a sensor data analysis unit, an irrigation schedule proposal unit, a farmland use planning support unit, and an irrigation infrastructure optimization unit. The soil data analysis unit analyzes soil data. For example, the soil data analysis unit collects and analyzes data such as soil humidity, temperature, and nutrient content. The soil data analysis unit can also analyze soil physical and chemical properties. For example, it analyzes soil particle size distribution and organic matter content. The weather information analysis unit analyzes weather information. For example, the weather information analysis unit collects and analyzes weather information such as rainfall, temperature, and wind speed. The weather information analysis unit can also analyze weather forecast data. For example, it predicts future weather conditions based on the weather forecast data. The sensor data analysis unit analyzes sensor data. For example, the sensor data analysis unit collects and analyzes humidity and temperature data in real time from sensors installed in the soil. The sensor data analysis unit can also monitor physiological responses of crops. For example, the irrigation schedule proposal unit monitors the color and growth rate of crop leaves. The irrigation schedule proposal unit proposes an optimal irrigation schedule based on the analysis results of the soil data analysis unit, the weather information analysis unit, and the sensor data analysis unit. For example, the irrigation schedule proposal unit proposes different irrigation schedules for each crop growth stage. The irrigation schedule proposal unit can also propose irrigation schedules taking into account the characteristics of different crops. The agricultural land use planning support unit supports agricultural land use plans. For example, the agricultural land use planning support unit analyzes the topography, soil characteristics, and weather conditions of the agricultural land to propose optimal crop placement and cultivation methods. The agricultural land use planning support unit can also propose optimal irrigation methods specific to each region by comparing data from different regions and climate zones. The irrigation infrastructure optimization unit optimizes irrigation infrastructure. For example, the irrigation infrastructure optimization unit analyzes the placement of irrigation systems, piping design, pump operation schedules, etc. to propose optimal irrigation infrastructure. The irrigation infrastructure optimization unit can also integrate with other agricultural data to support comprehensive agricultural management. As a result, the water agriculture optimization and irrigation management system according to the embodiment can realize water agriculture optimization and irrigation management, and improve crop yield and quality.For example, generative AI can analyze soil data and weather information to suggest optimal irrigation schedules and water amounts. It can also use sensor data to monitor crop water demand and reduce water waste. Furthermore, generative AI can assist with land use planning and irrigation infrastructure optimization.

[0030] The soil data analysis unit can collect and analyze data such as soil humidity, temperature, and nutrient content. The soil data analysis unit uses, for example, a sensor to measure soil humidity. For example, a soil humidity sensor is installed in the soil to collect humidity data in real time. The soil data analysis unit also uses a temperature sensor to measure soil temperature. For example, a subsurface temperature sensor is installed in the soil to collect temperature data. The soil data analysis unit also performs chemical analysis to measure the nutrient content of the soil. For example, soil samples are collected and analyzed in a laboratory for the content of nutrients such as nitrogen, phosphorus, and potassium. This allows for the collection and analysis of detailed soil data, making it possible to propose more accurate irrigation schedules.

[0031] The weather information analysis unit can collect and analyze weather information such as rainfall, temperature, and wind speed. The weather information analysis unit uses, for example, a rain gauge to measure rainfall. For example, a rain gauge is installed and rainfall data is collected. The weather information analysis unit also uses a thermometer to measure temperature. For example, a thermometer is installed in a weather station and temperature data is collected. The weather information analysis unit also uses an anemometer to measure wind speed. For example, an anemometer is installed and wind speed data is collected. This allows for detailed analysis of the weather information to improve the accuracy of irrigation schedules.

[0032] The sensor data analysis unit can collect and analyze humidity and temperature data in real time from sensors installed in the soil. The sensor data analysis unit, for example, uses a soil humidity sensor to collect soil humidity data in real time. For example, a soil humidity sensor is installed in the soil to collect humidity data. The sensor data analysis unit also uses a subsurface temperature sensor to collect soil temperature data in real time. For example, a subsurface temperature sensor is installed in the soil to collect temperature data. The sensor data analysis unit also uses sensors to monitor the physiological responses of crops. For example, a camera is used to monitor the color and growth rate of crop leaves. This makes it possible to accurately determine the water demand of crops by analyzing sensor data in real time.

[0033] The irrigation schedule proposal unit can propose different irrigation schedules for each growth stage of the crop. For example, the irrigation schedule proposal unit proposes an irrigation schedule that supplies small amounts of water frequently during the germination period. For example, a small amount of water is supplied daily during the germination period. The irrigation schedule proposal unit also proposes an irrigation schedule that increases the amount of water during the growth period. For example, a large amount of water is supplied once a week during the growth period. The irrigation schedule proposal unit also proposes an irrigation schedule that reduces the amount of water during the harvest period. For example, a small amount of water is supplied once a week during the harvest period. This makes it possible to provide an optimal irrigation schedule according to the growth stage of the crop.

[0034] The agricultural land use planning support department can analyze the topography, soil characteristics, weather conditions, etc. of agricultural land and propose optimal crop placement and cultivation methods. For example, the agricultural land use planning support department collects topographical data to analyze the topography of agricultural land. For example, it analyzes the slope and elevation of agricultural land based on the topographical data. The agricultural land use planning support department also collects soil data to analyze soil characteristics. For example, it analyzes soil pH and permeability. The agricultural land use planning support department also collects meteorological data to analyze meteorological conditions. For example, it analyzes rainfall and temperature. This makes it possible to maximize the efficiency of agricultural land use and improve crop yields and quality.

[0035] The irrigation infrastructure optimization unit can analyze the layout of irrigation systems, piping design, pump operation schedules, etc., and propose optimal irrigation infrastructure. For example, the irrigation infrastructure optimization unit collects topographical data to analyze the layout of irrigation systems. For example, the irrigation infrastructure optimization unit proposes the optimal layout of irrigation systems based on the topographical data. The irrigation infrastructure optimization unit also collects piping data to analyze piping design. For example, the piping diameter and material are analyzed. The irrigation infrastructure optimization unit also collects pump data to analyze pump operation schedules. For example, the pump operation time and frequency are analyzed. This makes it possible to improve the efficiency of irrigation infrastructure and ensure effective use of water resources.

[0036] The soil data analysis unit can compare data with past soil data to detect abnormal variations. The soil data analysis unit can, for example, analyze soil data from the past 10 years to detect abnormal variations. For example, it can detect abnormal humidity and temperature variations based on past data. The soil data analysis unit also performs statistical analysis to detect abnormal variations. For example, it can use an outlier detection algorithm to identify abnormal data points. The soil data analysis unit also uses machine learning models to detect abnormal variations. For example, it can train an anomaly detection model to predict abnormal variations. This makes it possible to detect abnormal variations early by comparing with past data and propose appropriate countermeasures.

[0037] The weather information analysis unit can predict abnormal weather based on past weather data. For example, the weather information analysis unit analyzes weather data from the past 10 years to predict abnormal weather. For example, it predicts abnormal rainfall and temperature fluctuations based on past data. The weather information analysis unit also performs statistical analysis to predict abnormal weather. For example, it uses an anomaly detection algorithm to identify abnormal weather patterns. The weather information analysis unit also uses machine learning models to predict abnormal weather. For example, it trains an abnormal weather prediction model to predict abnormal weather. In this way, by predicting abnormal weather based on past weather data, it can warn farmers in advance and suggest appropriate countermeasures.

[0038] The irrigation schedule proposal unit can propose an optimal irrigation schedule for each crop by taking into account the characteristics of different crops. For example, the irrigation schedule proposal unit proposes an irrigation schedule according to the different water demands of corn and tomatoes. For example, a large amount of water is supplied to corn once a week, and a small amount of water is supplied to tomatoes daily. The irrigation schedule proposal unit can also propose irrigation schedules according to the growth stages of different crops. For example, small amounts of water are supplied frequently to corn during the germination period, and the amount of water is increased during the growth period. In this way, an optimal irrigation schedule can be provided for each crop by taking into account the characteristics of different crops.

[0039] The agricultural land use planning support department can compare data from different regions and climate zones and propose the optimal irrigation method specific to the region. For example, in arid regions, the agricultural land use planning support department can propose methods for efficient water use. For example, in arid regions, it can propose drip irrigation systems. The agricultural land use planning support department can also propose methods for draining excess water in humid regions. For example, in humid regions, it can propose drainage systems. The agricultural land use planning support department can also propose the optimal irrigation method based on data from different climate zones. For example, in tropical regions, it can propose irrigation methods to deal with frequent rainfall. This makes it possible to provide the optimal irrigation method by taking into account data specific to the region.

[0040] The irrigation infrastructure optimization unit can be integrated with other agricultural-related data to support comprehensive agricultural management. For example, the irrigation infrastructure optimization unit can be integrated with pest and disease information to support comprehensive agricultural management. For example, it can predict pests and diseases that are likely to occur under specific weather conditions and propose countermeasures. The irrigation infrastructure optimization unit can also be integrated with soil data to support comprehensive agricultural management. For example, it can propose appropriate fertilization when there is a nutrient deficiency in the soil. The irrigation infrastructure optimization unit can also be integrated with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, by integrating with other agricultural-related data, it can support comprehensive agricultural management.

[0041] The irrigation schedule proposal unit can integrate the results of the analysis of soil data and meteorological information with other agricultural-related data to support comprehensive agricultural management. For example, the irrigation schedule proposal unit analyzes soil data and meteorological information and integrates it with pest and disease information to support comprehensive agricultural management. For example, it predicts pests and diseases that are likely to occur under specific weather conditions and proposes countermeasures. The irrigation schedule proposal unit can also analyze soil data and meteorological information and integrate it with nutrient data to support comprehensive agricultural management. For example, it can propose appropriate fertilization when soil nutrients are lacking. The irrigation schedule proposal unit can also analyze soil data and meteorological information and integrate it with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, the soil data and meteorological information can be integrated with other agricultural-related data to support comprehensive agricultural management.

[0042] The irrigation schedule proposal unit can compare data from different regions and climate zones and propose an optimal irrigation method specific to the region. For example, in arid regions, the irrigation schedule proposal unit proposes a method for efficient water use. For example, in arid regions, it proposes a drip irrigation system. The irrigation schedule proposal unit can also propose a method for draining excess water in humid regions. For example, it proposes a drainage system in humid regions. The irrigation schedule proposal unit can also propose an optimal irrigation method based on data from different climate zones. For example, in tropical regions, it proposes an irrigation method to deal with frequent rainfall. In this way, the optimal irrigation method can be provided by taking region-specific data into consideration.

[0043] The sensor data analysis unit can integrate data from different sensors to grasp overall water demand. The sensor data analysis unit, for example, integrates data from a drone-mounted camera and an underground sensor to grasp overall water demand. For example, it analyzes images of crops taken by a drone and humidity data from an underground sensor. The sensor data analysis unit can also integrate data from different types of sensors. For example, it can integrate and analyze data from a temperature sensor and a humidity sensor. The sensor data analysis unit can also integrate data from sensors installed in different locations. For example, it can integrate and analyze data from a surface sensor and an underground sensor. In this way, by integrating data from different sensors, it is possible to accurately grasp overall water demand.

[0044] The sensor data analysis unit can integrate the data with other environmental data to support comprehensive agricultural management. For example, the sensor data analysis unit can integrate the data with soil nutrient data to support comprehensive agricultural management. For example, it can suggest appropriate fertilization when soil nutrients are lacking. The sensor data analysis unit can also integrate the data with data on the occurrence of pests and diseases to support comprehensive agricultural management. For example, it can predict pests and diseases that are likely to occur under specific weather conditions and suggest countermeasures. The sensor data analysis unit can also integrate the data with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, by integrating the data with other environmental data, it can support comprehensive agricultural management.

[0045] The irrigation schedule proposal unit updates data in real time and can always provide the latest irrigation schedule. The irrigation schedule proposal unit, for example, collects soil data and weather information in real time and always provides the latest irrigation schedule. For example, it automatically adjusts the irrigation schedule in response to changes in weather conditions. The irrigation schedule proposal unit can also analyze sensor data in real time and update the irrigation schedule. For example, it adjusts the irrigation schedule in response to changes in crop water demand. In this way, by updating data in real time, it is possible to always provide the latest irrigation schedule.

[0046] The irrigation schedule proposal unit can propose an optimal irrigation schedule for each crop by taking into account the characteristics of different crops. For example, the irrigation schedule proposal unit proposes an irrigation schedule according to the different water demands of corn and tomatoes. For example, a large amount of water is supplied to corn once a week, and a small amount of water is supplied to tomatoes daily. The irrigation schedule proposal unit can also propose irrigation schedules according to the growth stages of different crops. For example, small amounts of water are supplied frequently to corn during the germination period, and the amount of water is increased during the growth period. In this way, an optimal irrigation schedule can be provided for each crop by taking into account the characteristics of different crops.

[0047] The irrigation schedule proposal unit can work in conjunction with other agricultural machinery to build an automated irrigation system. The irrigation schedule proposal unit can work in conjunction with, for example, tractors and drones to build an automated irrigation system. For example, the AI ​​generates a schedule for tractors to automatically irrigate. The irrigation schedule proposal unit can also work in conjunction with other agricultural machinery to optimize the irrigation schedule. For example, it can adjust the irrigation schedule based on images of crops taken by a drone. This makes it possible to build an automated irrigation system by working in conjunction with other agricultural machinery.

[0048] The irrigation schedule proposal unit can propose an irrigation schedule that can be applied to different agricultural techniques. The irrigation schedule proposal unit proposes an irrigation schedule that can be applied to, for example, organic farming. For example, in organic farming that does not use chemical fertilizers, the irrigation schedule proposal unit proposes an irrigation method that takes into account the natural nutrient circulation in the soil. The irrigation schedule proposal unit can also propose an irrigation schedule that can be applied to hydroponic cultivation. For example, the irrigation schedule proposal unit proposes an irrigation method that takes into account the management method of the nutrient solution. This makes it possible to provide an irrigation schedule that can be applied to different agricultural techniques.

[0049] The sensor data analysis unit monitors the physiological responses of the crops, enabling a more accurate understanding of water demand. The sensor data analysis unit, for example, uses a camera to monitor the color of the crop leaves. For example, a camera is installed to monitor changes in the color of the crop leaves. The sensor data analysis unit also uses a growth sensor to monitor the growth rate of the crops. For example, a growth sensor is installed to monitor the growth rate of the crops in real time. The sensor data analysis unit also uses a biosensor to monitor the physiological responses of the crops. For example, a biosensor is installed to monitor the physiological responses of the crops in real time. In this way, by monitoring the physiological responses of the crops, a more accurate understanding of water demand can be achieved.

[0050] The sensor data analysis unit can integrate data from different sensors to grasp overall water demand. The sensor data analysis unit, for example, integrates data from a drone-mounted camera and an underground sensor to grasp overall water demand. For example, it analyzes images of crops taken by a drone and humidity data from an underground sensor. The sensor data analysis unit can also integrate data from different types of sensors. For example, it can integrate and analyze data from a temperature sensor and a humidity sensor. The sensor data analysis unit can also integrate data from sensors installed in different locations. For example, it can integrate and analyze data from a surface sensor and an underground sensor. In this way, by integrating data from different sensors, it is possible to accurately grasp overall water demand.

[0051] The sensor data analysis unit can integrate the data with other environmental data to support comprehensive agricultural management. For example, the sensor data analysis unit can integrate the data with soil nutrient data to support comprehensive agricultural management. For example, it can suggest appropriate fertilization when soil nutrients are lacking. The sensor data analysis unit can also integrate the data with data on the occurrence of pests and diseases to support comprehensive agricultural management. For example, it can predict pests and diseases that are likely to occur under specific weather conditions and suggest countermeasures. The sensor data analysis unit can also integrate the data with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, by integrating the data with other environmental data, it can support comprehensive agricultural management.

[0052] The irrigation schedule proposal unit updates data in real time and can always provide the latest irrigation schedule. The irrigation schedule proposal unit, for example, collects soil data and weather information in real time and always provides the latest irrigation schedule. For example, it automatically adjusts the irrigation schedule in response to changes in weather conditions. The irrigation schedule proposal unit can also analyze sensor data in real time and update the irrigation schedule. For example, it adjusts the irrigation schedule in response to changes in crop water demand. In this way, by updating data in real time, it is possible to always provide the latest irrigation schedule.

[0053] The irrigation schedule proposal unit can propose an optimal irrigation schedule for each crop by taking into account the characteristics of different crops. For example, the irrigation schedule proposal unit proposes an irrigation schedule according to the different water demands of corn and tomatoes. For example, a large amount of water is supplied to corn once a week, and a small amount of water is supplied to tomatoes daily. The irrigation schedule proposal unit can also propose irrigation schedules according to the growth stages of different crops. For example, small amounts of water are supplied frequently to corn during the germination period, and the amount of water is increased during the growth period. In this way, an optimal irrigation schedule can be provided for each crop by taking into account the characteristics of different crops.

[0054] The irrigation schedule proposal unit can work in conjunction with other agricultural machinery to build an automated irrigation system. The irrigation schedule proposal unit can work in conjunction with, for example, tractors and drones to build an automated irrigation system. For example, the AI ​​generates a schedule for tractors to automatically irrigate. The irrigation schedule proposal unit can also work in conjunction with other agricultural machinery to optimize the irrigation schedule. For example, it can adjust the irrigation schedule based on images of crops taken by a drone. This makes it possible to build an automated irrigation system by working in conjunction with other agricultural machinery.

[0055] The irrigation schedule proposal unit can propose an irrigation schedule that can be applied to different agricultural techniques. The irrigation schedule proposal unit proposes an irrigation schedule that can be applied to, for example, organic farming. For example, in organic farming that does not use chemical fertilizers, the irrigation schedule proposal unit proposes an irrigation method that takes into account the natural nutrient circulation in the soil. The irrigation schedule proposal unit can also propose an irrigation schedule that can be applied to hydroponic cultivation. For example, the irrigation schedule proposal unit proposes an irrigation method that takes into account the management method of the nutrient solution. This makes it possible to provide an irrigation schedule that can be applied to different agricultural techniques.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The irrigation schedule proposal unit can work in conjunction with other agricultural machinery to build an automated irrigation system. For example, it can work in conjunction with tractors and drones to build an automated irrigation system. For example, the AI ​​can propose a schedule for tractors to automatically irrigate. The irrigation schedule proposal unit can also work in conjunction with other agricultural machinery to optimize irrigation schedules. For example, it can adjust the irrigation schedule based on images of crops taken by drones. This makes it possible to build an automated irrigation system by working in conjunction with other agricultural machinery.

[0058] The irrigation schedule proposal unit can propose an irrigation schedule that can be applied to different agricultural techniques. For example, it proposes an irrigation schedule that can be applied to organic farming. For example, it proposes an irrigation method that takes into account the natural nutrient circulation in the soil in organic farming that does not use chemical fertilizers. The irrigation schedule proposal unit can also propose an irrigation schedule that can be applied to hydroponic cultivation. For example, it proposes an irrigation method that takes into account the management method of the nutrient solution. This makes it possible to provide an irrigation schedule that can be applied to different agricultural techniques.

[0059] The sensor data analysis unit can integrate data from different sensors to determine overall water demand. For example, it can integrate data from a drone-mounted camera and an underground sensor to determine overall water demand. For example, it can analyze images of crops taken by a drone and humidity data from an underground sensor. The sensor data analysis unit can also integrate data from different types of sensors. For example, it can integrate and analyze data from a temperature sensor and a humidity sensor. The sensor data analysis unit can also integrate data from sensors installed in different locations. For example, it can integrate and analyze data from a surface sensor and an underground sensor. In this way, by integrating data from different sensors, it is possible to accurately determine overall water demand.

[0060] The sensor data analysis unit can integrate the data with other environmental data to support comprehensive agricultural management. For example, it can integrate the data with soil nutrient data to support comprehensive agricultural management. For example, it can suggest appropriate fertilization when soil nutrients are lacking. The sensor data analysis unit can also integrate the data with data on pest and disease occurrence status to support comprehensive agricultural management. For example, it can predict pests and diseases that are likely to occur under specific weather conditions and suggest countermeasures. The sensor data analysis unit can also integrate the data with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, by integrating the data with other environmental data, it can support comprehensive agricultural management.

[0061] The irrigation schedule proposal unit updates data in real time and can always provide the latest irrigation schedule. For example, it collects soil data and weather information in real time and always provides the latest irrigation schedule. For example, it automatically adjusts the irrigation schedule in response to changes in weather conditions. The irrigation schedule proposal unit can also analyze sensor data in real time and update the irrigation schedule. For example, it adjusts the irrigation schedule in response to changes in crop water demand. In this way, by updating data in real time, it is possible to always provide the latest irrigation schedule.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The soil data analysis unit analyzes soil data, such as soil moisture, temperature, nutrient content, and physical and chemical properties (particle size distribution, organic matter content, etc.). Step 2: The weather information analysis unit analyzes weather information. For example, it collects and analyzes weather information such as rainfall, temperature, and wind speed. It also predicts future weather conditions based on weather forecast data. Step 3: The sensor data analysis unit analyzes the sensor data. For example, it collects and analyzes humidity and temperature data in real time from sensors installed in the soil. It also monitors the physiological responses of the crops (leaf color, growth rate, etc.). Step 4: The irrigation schedule proposal unit proposes an optimal irrigation schedule based on the analysis results of the soil data analysis unit, meteorological information analysis unit, and sensor data analysis unit. For example, it proposes different irrigation schedules for each stage of crop growth and irrigation schedules that take into account the characteristics of different crops. Step 5: The Agricultural Land Use Planning Support Department supports agricultural land use planning. For example, it analyzes the topography, soil characteristics, and weather conditions of the farmland to propose optimal crop placement and cultivation methods. It also compares data from different regions and climate zones to propose optimal irrigation methods specific to each region. Step 6: The Irrigation Infrastructure Optimization Unit optimizes irrigation infrastructure. For example, it analyzes the layout of irrigation systems, the design of pipes, and pump operation schedules, and proposes optimal irrigation infrastructure. It also integrates other agricultural data to support comprehensive agricultural management.

[0064] (Example 2) The water agriculture optimization and irrigation management system according to an embodiment of the present invention uses AI to analyze soil data and weather information, propose optimal irrigation schedules and water amounts, and use sensor data to monitor crop water demand and reduce water waste. As a result, the water agriculture optimization and irrigation management system can improve crop yield and quality.

[0065] The water agriculture optimization and irrigation management system according to the embodiment includes a soil data analysis unit, a weather information analysis unit, a sensor data analysis unit, an irrigation schedule proposal unit, a farmland use planning support unit, and an irrigation infrastructure optimization unit. The soil data analysis unit analyzes soil data. For example, the soil data analysis unit collects and analyzes data such as soil humidity, temperature, and nutrient content. The soil data analysis unit can also analyze soil physical and chemical properties. For example, it analyzes soil particle size distribution and organic matter content. The weather information analysis unit analyzes weather information. For example, the weather information analysis unit collects and analyzes weather information such as rainfall, temperature, and wind speed. The weather information analysis unit can also analyze weather forecast data. For example, it predicts future weather conditions based on the weather forecast data. The sensor data analysis unit analyzes sensor data. For example, the sensor data analysis unit collects and analyzes humidity and temperature data in real time from sensors installed in the soil. The sensor data analysis unit can also monitor physiological responses of crops. For example, the irrigation schedule proposal unit monitors the color and growth rate of crop leaves. The irrigation schedule proposal unit proposes an optimal irrigation schedule based on the analysis results of the soil data analysis unit, the weather information analysis unit, and the sensor data analysis unit. For example, the irrigation schedule proposal unit proposes different irrigation schedules for each crop growth stage. The irrigation schedule proposal unit can also propose irrigation schedules taking into account the characteristics of different crops. The agricultural land use planning support unit supports agricultural land use plans. For example, the agricultural land use planning support unit analyzes the topography, soil characteristics, and weather conditions of the agricultural land to propose optimal crop placement and cultivation methods. The agricultural land use planning support unit can also propose optimal irrigation methods specific to each region by comparing data from different regions and climate zones. The irrigation infrastructure optimization unit optimizes irrigation infrastructure. For example, the irrigation infrastructure optimization unit analyzes the placement of irrigation systems, piping design, pump operation schedules, etc. to propose optimal irrigation infrastructure. The irrigation infrastructure optimization unit can also integrate with other agricultural data to support comprehensive agricultural management. As a result, the water agriculture optimization and irrigation management system according to the embodiment can realize water agriculture optimization and irrigation management, and improve crop yield and quality.For example, generative AI can analyze soil data and weather information to suggest optimal irrigation schedules and water amounts. It can also use sensor data to monitor crop water demand and reduce water waste. Furthermore, generative AI can assist with land use planning and irrigation infrastructure optimization.

[0066] The soil data analysis unit can collect and analyze data such as soil humidity, temperature, and nutrient content. The soil data analysis unit uses, for example, a sensor to measure soil humidity. For example, a soil humidity sensor is installed in the soil to collect humidity data in real time. The soil data analysis unit also uses a temperature sensor to measure soil temperature. For example, a subsurface temperature sensor is installed in the soil to collect temperature data. The soil data analysis unit also performs chemical analysis to measure the nutrient content of the soil. For example, soil samples are collected and analyzed in a laboratory for the content of nutrients such as nitrogen, phosphorus, and potassium. This allows for the collection and analysis of detailed soil data, making it possible to propose more accurate irrigation schedules.

[0067] The weather information analysis unit can collect and analyze weather information such as rainfall, temperature, and wind speed. The weather information analysis unit uses, for example, a rain gauge to measure rainfall. For example, a rain gauge is installed and rainfall data is collected. The weather information analysis unit also uses a thermometer to measure temperature. For example, a thermometer is installed in a weather station and temperature data is collected. The weather information analysis unit also uses an anemometer to measure wind speed. For example, an anemometer is installed and wind speed data is collected. This allows for detailed analysis of the weather information to improve the accuracy of irrigation schedules.

[0068] The sensor data analysis unit can collect and analyze humidity and temperature data in real time from sensors installed in the soil. The sensor data analysis unit, for example, uses a soil humidity sensor to collect soil humidity data in real time. For example, a soil humidity sensor is installed in the soil to collect humidity data. The sensor data analysis unit also uses a subsurface temperature sensor to collect soil temperature data in real time. For example, a subsurface temperature sensor is installed in the soil to collect temperature data. The sensor data analysis unit also uses sensors to monitor the physiological responses of crops. For example, a camera is used to monitor the color and growth rate of crop leaves. This makes it possible to accurately determine the water demand of crops by analyzing sensor data in real time.

[0069] The irrigation schedule proposal unit can propose different irrigation schedules for each growth stage of the crop. For example, the irrigation schedule proposal unit proposes an irrigation schedule that supplies small amounts of water frequently during the germination period. For example, a small amount of water is supplied daily during the germination period. The irrigation schedule proposal unit also proposes an irrigation schedule that increases the amount of water during the growth period. For example, a large amount of water is supplied once a week during the growth period. The irrigation schedule proposal unit also proposes an irrigation schedule that reduces the amount of water during the harvest period. For example, a small amount of water is supplied once a week during the harvest period. This makes it possible to provide an optimal irrigation schedule according to the growth stage of the crop.

[0070] The agricultural land use planning support department can analyze the topography, soil characteristics, weather conditions, etc. of agricultural land and propose optimal crop placement and cultivation methods. For example, the agricultural land use planning support department collects topographical data to analyze the topography of agricultural land. For example, it analyzes the slope and elevation of agricultural land based on the topographical data. The agricultural land use planning support department also collects soil data to analyze soil characteristics. For example, it analyzes soil pH and permeability. The agricultural land use planning support department also collects meteorological data to analyze meteorological conditions. For example, it analyzes rainfall and temperature. This makes it possible to maximize the efficiency of agricultural land use and improve crop yields and quality.

[0071] The irrigation infrastructure optimization unit can analyze the layout of irrigation systems, piping design, pump operation schedules, etc., and propose optimal irrigation infrastructure. For example, the irrigation infrastructure optimization unit collects topographical data to analyze the layout of irrigation systems. For example, the irrigation infrastructure optimization unit proposes the optimal layout of irrigation systems based on the topographical data. The irrigation infrastructure optimization unit also collects piping data to analyze piping design. For example, the piping diameter and material are analyzed. The irrigation infrastructure optimization unit also collects pump data to analyze pump operation schedules. For example, the pump operation time and frequency are analyzed. This makes it possible to improve the efficiency of irrigation infrastructure and ensure effective use of water resources.

[0072] The soil data analysis unit can compare data with past soil data to detect abnormal variations. The soil data analysis unit can, for example, analyze soil data from the past 10 years to detect abnormal variations. For example, it can detect abnormal humidity and temperature variations based on past data. The soil data analysis unit also performs statistical analysis to detect abnormal variations. For example, it can use an outlier detection algorithm to identify abnormal data points. The soil data analysis unit also uses machine learning models to detect abnormal variations. For example, it can train an anomaly detection model to predict abnormal variations. This makes it possible to detect abnormal variations early by comparing with past data and propose appropriate countermeasures.

[0073] The weather information analysis unit can predict abnormal weather based on past weather data. For example, the weather information analysis unit analyzes weather data from the past 10 years to predict abnormal weather. For example, it predicts abnormal rainfall and temperature fluctuations based on past data. The weather information analysis unit also performs statistical analysis to predict abnormal weather. For example, it uses an anomaly detection algorithm to identify abnormal weather patterns. The weather information analysis unit also uses machine learning models to predict abnormal weather. For example, it trains an abnormal weather prediction model to predict abnormal weather. In this way, by predicting abnormal weather based on past weather data, it can warn farmers in advance and suggest appropriate countermeasures.

[0074] The irrigation schedule proposal unit can propose an optimal irrigation schedule for each crop by taking into account the characteristics of different crops. For example, the irrigation schedule proposal unit proposes an irrigation schedule according to the different water demands of corn and tomatoes. For example, a large amount of water is supplied to corn once a week, and a small amount of water is supplied to tomatoes daily. The irrigation schedule proposal unit can also propose irrigation schedules according to the growth stages of different crops. For example, small amounts of water are supplied frequently to corn during the germination period, and the amount of water is increased during the growth period. In this way, an optimal irrigation schedule can be provided for each crop by taking into account the characteristics of different crops.

[0075] The agricultural land use planning support department can compare data from different regions and climate zones and propose the optimal irrigation method specific to the region. For example, in arid regions, the agricultural land use planning support department can propose methods for efficient water use. For example, in arid regions, it can propose drip irrigation systems. The agricultural land use planning support department can also propose methods for draining excess water in humid regions. For example, in humid regions, it can propose drainage systems. The agricultural land use planning support department can also propose the optimal irrigation method based on data from different climate zones. For example, in tropical regions, it can propose irrigation methods to deal with frequent rainfall. This makes it possible to provide the optimal irrigation method by taking into account data specific to the region.

[0076] The irrigation infrastructure optimization unit can be integrated with other agricultural-related data to support comprehensive agricultural management. For example, the irrigation infrastructure optimization unit can be integrated with pest and disease information to support comprehensive agricultural management. For example, it can predict pests and diseases that are likely to occur under specific weather conditions and propose countermeasures. The irrigation infrastructure optimization unit can also be integrated with soil data to support comprehensive agricultural management. For example, it can propose appropriate fertilization when there is a nutrient deficiency in the soil. The irrigation infrastructure optimization unit can also be integrated with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, by integrating with other agricultural-related data, it can support comprehensive agricultural management.

[0077] The irrigation schedule proposal unit can use the emotion estimation function to analyze the emotions of farmers and propose a low-stress irrigation schedule. The irrigation schedule proposal unit, for example, uses the emotion estimation function to collect emotion data of farmers and propose a low-stress irrigation schedule. For example, it sets an irrigation schedule that avoids time periods when farmers feel stressed. The irrigation schedule proposal unit can also propose an irrigation schedule that takes into account the satisfaction of farmers. For example, irrigation is performed during time periods when farmers are most satisfied. In this way, a low-stress irrigation schedule can be provided by taking into account the emotions of farmers.

[0078] The irrigation schedule proposal unit can integrate the results of the analysis of soil data and meteorological information with other agricultural-related data to support comprehensive agricultural management. For example, the irrigation schedule proposal unit analyzes soil data and meteorological information and integrates it with pest and disease information to support comprehensive agricultural management. For example, it predicts pests and diseases that are likely to occur under specific weather conditions and proposes countermeasures. The irrigation schedule proposal unit can also analyze soil data and meteorological information and integrate it with nutrient data to support comprehensive agricultural management. For example, it can propose appropriate fertilization when soil nutrients are lacking. The irrigation schedule proposal unit can also analyze soil data and meteorological information and integrate it with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, the soil data and meteorological information can be integrated with other agricultural-related data to support comprehensive agricultural management.

[0079] The irrigation schedule proposal unit can compare data from different regions and climate zones and propose an optimal irrigation method specific to the region. For example, in arid regions, the irrigation schedule proposal unit proposes a method for efficient water use. For example, in arid regions, it proposes a drip irrigation system. The irrigation schedule proposal unit can also propose a method for draining excess water in humid regions. For example, it proposes a drainage system in humid regions. The irrigation schedule proposal unit can also propose an optimal irrigation method based on data from different climate zones. For example, in tropical regions, it proposes an irrigation method to deal with frequent rainfall. In this way, the optimal irrigation method can be provided by taking region-specific data into consideration.

[0080] The irrigation schedule proposal unit uses the emotion estimation function to propose an irrigation schedule that will most satisfy the farmer, thereby improving work efficiency. The irrigation schedule proposal unit, for example, uses the emotion estimation function to collect emotion data from the farmer and proposes an irrigation schedule that will most satisfy the farmer. For example, irrigation is performed during times when the farmer is most satisfied. The irrigation schedule proposal unit can also propose an irrigation schedule that reduces stress for the farmer. For example, an irrigation schedule is set that avoids times when the farmer is stressed. This makes it possible to improve work efficiency by taking the farmer's satisfaction into consideration.

[0081] The sensor data analysis unit can integrate data from different sensors to grasp overall water demand. The sensor data analysis unit, for example, integrates data from a drone-mounted camera and an underground sensor to grasp overall water demand. For example, it analyzes images of crops taken by a drone and humidity data from an underground sensor. The sensor data analysis unit can also integrate data from different types of sensors. For example, it can integrate and analyze data from a temperature sensor and a humidity sensor. The sensor data analysis unit can also integrate data from sensors installed in different locations. For example, it can integrate and analyze data from a surface sensor and an underground sensor. In this way, by integrating data from different sensors, it is possible to accurately grasp overall water demand.

[0082] The sensor data analysis unit can use the emotion estimation function to analyze the emotions of agricultural workers and propose a low-stress water demand monitoring method. The sensor data analysis unit, for example, uses the emotion estimation function to collect emotion data of agricultural workers and propose a low-stress water demand monitoring method. For example, it sets a monitoring method that avoids time periods when agricultural workers feel stressed. The sensor data analysis unit can also propose a water demand monitoring method that takes into account the satisfaction of agricultural workers. For example, it performs monitoring during time periods when agricultural workers are most satisfied. In this way, by taking into account the emotions of agricultural workers, it is possible to provide a low-stress water demand monitoring method.

[0083] The sensor data analysis unit can integrate the data with other environmental data to support comprehensive agricultural management. For example, the sensor data analysis unit can integrate the data with soil nutrient data to support comprehensive agricultural management. For example, it can suggest appropriate fertilization when soil nutrients are lacking. The sensor data analysis unit can also integrate the data with data on the occurrence of pests and diseases to support comprehensive agricultural management. For example, it can predict pests and diseases that are likely to occur under specific weather conditions and suggest countermeasures. The sensor data analysis unit can also integrate the data with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, by integrating the data with other environmental data, it can support comprehensive agricultural management.

[0084] The sensor data analysis unit uses the emotion estimation function to propose a water demand monitoring method that will most satisfy farmers, thereby improving work efficiency. The sensor data analysis unit, for example, uses the emotion estimation function to collect emotion data from farmers and propose a water demand monitoring method that will most satisfy them. For example, monitoring is performed during times when farmers are most satisfied. The sensor data analysis unit can also propose a water demand monitoring method that reduces stress for farmers. For example, a monitoring method is set that avoids times when farmers feel stressed. This makes it possible to improve work efficiency by taking farmers' satisfaction into consideration.

[0085] The irrigation schedule proposal unit updates data in real time and can always provide the latest irrigation schedule. The irrigation schedule proposal unit, for example, collects soil data and weather information in real time and always provides the latest irrigation schedule. For example, it automatically adjusts the irrigation schedule in response to changes in weather conditions. The irrigation schedule proposal unit can also analyze sensor data in real time and update the irrigation schedule. For example, it adjusts the irrigation schedule in response to changes in crop water demand. In this way, by updating data in real time, it is possible to always provide the latest irrigation schedule.

[0086] The irrigation schedule proposal unit can propose an optimal irrigation schedule for each crop by taking into account the characteristics of different crops. For example, the irrigation schedule proposal unit proposes an irrigation schedule according to the different water demands of corn and tomatoes. For example, a large amount of water is supplied to corn once a week, and a small amount of water is supplied to tomatoes daily. The irrigation schedule proposal unit can also propose irrigation schedules according to the growth stages of different crops. For example, small amounts of water are supplied frequently to corn during the germination period, and the amount of water is increased during the growth period. In this way, an optimal irrigation schedule can be provided for each crop by taking into account the characteristics of different crops.

[0087] The irrigation schedule proposal unit can use the emotion estimation function to analyze the emotions of farmers and propose an irrigation schedule that reduces their workload. The irrigation schedule proposal unit, for example, uses the emotion estimation function to collect emotional data on farmers and propose an irrigation schedule that reduces their workload. For example, it sets an irrigation schedule that avoids time periods when farmers feel stressed. The irrigation schedule proposal unit can also propose an irrigation schedule that takes into account the satisfaction of farmers. For example, it performs irrigation during time periods when farmers are most satisfied. In this way, an irrigation schedule that reduces their workload can be provided by taking into account the emotions of farmers.

[0088] The irrigation schedule proposal unit can work in conjunction with other agricultural machinery to build an automated irrigation system. The irrigation schedule proposal unit can work in conjunction with, for example, tractors and drones to build an automated irrigation system. For example, the AI ​​generates a schedule for tractors to automatically irrigate. The irrigation schedule proposal unit can also work in conjunction with other agricultural machinery to optimize the irrigation schedule. For example, it can adjust the irrigation schedule based on images of crops taken by a drone. This makes it possible to build an automated irrigation system by working in conjunction with other agricultural machinery.

[0089] The irrigation schedule proposal unit can propose an irrigation schedule that can be applied to different agricultural techniques. The irrigation schedule proposal unit proposes an irrigation schedule that can be applied to, for example, organic farming. For example, in organic farming that does not use chemical fertilizers, the irrigation schedule proposal unit proposes an irrigation method that takes into account the natural nutrient circulation in the soil. The irrigation schedule proposal unit can also propose an irrigation schedule that can be applied to hydroponic cultivation. For example, the irrigation schedule proposal unit proposes an irrigation method that takes into account the management method of the nutrient solution. This makes it possible to provide an irrigation schedule that can be applied to different agricultural techniques.

[0090] The irrigation schedule proposal unit uses the emotion estimation function to propose an irrigation schedule that allows farmers to work with the greatest peace of mind, thereby improving the working environment. The irrigation schedule proposal unit, for example, uses the emotion estimation function to collect emotion data from farmers and proposes an irrigation schedule that allows farmers to work with the greatest peace of mind. For example, irrigation is performed during times when farmers feel safe. The irrigation schedule proposal unit can also propose an irrigation schedule that reduces stress for farmers. For example, an irrigation schedule is set that avoids times when farmers feel stressed. This makes it possible to improve the working environment by taking into account the farmers' sense of security.

[0091] The sensor data analysis unit monitors the physiological responses of the crops, enabling a more accurate understanding of water demand. The sensor data analysis unit, for example, uses a camera to monitor the color of the crop leaves. For example, a camera is installed to monitor changes in the color of the crop leaves. The sensor data analysis unit also uses a growth sensor to monitor the growth rate of the crops. For example, a growth sensor is installed to monitor the growth rate of the crops in real time. The sensor data analysis unit also uses a biosensor to monitor the physiological responses of the crops. For example, a biosensor is installed to monitor the physiological responses of the crops in real time. In this way, by monitoring the physiological responses of the crops, a more accurate understanding of water demand can be achieved.

[0092] The sensor data analysis unit can integrate data from different sensors to grasp overall water demand. The sensor data analysis unit, for example, integrates data from a drone-mounted camera and an underground sensor to grasp overall water demand. For example, it analyzes images of crops taken by a drone and humidity data from an underground sensor. The sensor data analysis unit can also integrate data from different types of sensors. For example, it can integrate and analyze data from a temperature sensor and a humidity sensor. The sensor data analysis unit can also integrate data from sensors installed in different locations. For example, it can integrate and analyze data from a surface sensor and an underground sensor. In this way, by integrating data from different sensors, it is possible to accurately grasp overall water demand.

[0093] The sensor data analysis unit can use the emotion estimation function to analyze the emotions of agricultural workers and propose a low-stress water demand monitoring method. The sensor data analysis unit, for example, uses the emotion estimation function to collect emotion data of agricultural workers and propose a low-stress water demand monitoring method. For example, it sets a monitoring method that avoids time periods when agricultural workers feel stressed. The sensor data analysis unit can also propose a water demand monitoring method that takes into account the satisfaction of agricultural workers. For example, it performs monitoring during time periods when agricultural workers are most satisfied. In this way, by taking into account the emotions of agricultural workers, it is possible to provide a low-stress water demand monitoring method.

[0094] The sensor data analysis unit can integrate the data with other environmental data to support comprehensive agricultural management. For example, the sensor data analysis unit can integrate the data with soil nutrient data to support comprehensive agricultural management. For example, it can suggest appropriate fertilization when soil nutrients are lacking. The sensor data analysis unit can also integrate the data with data on the occurrence of pests and diseases to support comprehensive agricultural management. For example, it can predict pests and diseases that are likely to occur under specific weather conditions and suggest countermeasures. The sensor data analysis unit can also integrate the data with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, by integrating the data with other environmental data, it can support comprehensive agricultural management.

[0095] The sensor data analysis unit uses the emotion estimation function to propose a water demand monitoring method that will most satisfy farmers, thereby improving work efficiency. The sensor data analysis unit, for example, uses the emotion estimation function to collect emotion data from farmers and propose a water demand monitoring method that will most satisfy them. For example, monitoring is performed during times when farmers are most satisfied. The sensor data analysis unit can also propose a water demand monitoring method that reduces stress for farmers. For example, a monitoring method is set that avoids times when farmers feel stressed. This makes it possible to improve work efficiency by taking farmers' satisfaction into consideration.

[0096] The irrigation schedule proposal unit updates data in real time and can always provide the latest irrigation schedule. The irrigation schedule proposal unit, for example, collects soil data and weather information in real time and always provides the latest irrigation schedule. For example, it automatically adjusts the irrigation schedule in response to changes in weather conditions. The irrigation schedule proposal unit can also analyze sensor data in real time and update the irrigation schedule. For example, it adjusts the irrigation schedule in response to changes in crop water demand. In this way, by updating data in real time, it is possible to always provide the latest irrigation schedule.

[0097] The irrigation schedule proposal unit can propose an optimal irrigation schedule for each crop by taking into account the characteristics of different crops. For example, the irrigation schedule proposal unit proposes an irrigation schedule according to the different water demands of corn and tomatoes. For example, a large amount of water is supplied to corn once a week, and a small amount of water is supplied to tomatoes daily. The irrigation schedule proposal unit can also propose irrigation schedules according to the growth stages of different crops. For example, small amounts of water are supplied frequently to corn during the germination period, and the amount of water is increased during the growth period. In this way, an optimal irrigation schedule can be provided for each crop by taking into account the characteristics of different crops.

[0098] The irrigation schedule proposal unit can use the emotion estimation function to analyze the emotions of farmers and propose an irrigation schedule that reduces their workload. The irrigation schedule proposal unit, for example, uses the emotion estimation function to collect emotional data on farmers and propose an irrigation schedule that reduces their workload. For example, it sets an irrigation schedule that avoids time periods when farmers feel stressed. The irrigation schedule proposal unit can also propose an irrigation schedule that takes into account the satisfaction of farmers. For example, it performs irrigation during time periods when farmers are most satisfied. In this way, an irrigation schedule that reduces their workload can be provided by taking into account the emotions of farmers.

[0099] The irrigation schedule proposal unit can work in conjunction with other agricultural machinery to build an automated irrigation system. The irrigation schedule proposal unit can work in conjunction with, for example, tractors and drones to build an automated irrigation system. For example, the AI ​​generates a schedule for tractors to automatically irrigate. The irrigation schedule proposal unit can also work in conjunction with other agricultural machinery to optimize the irrigation schedule. For example, it can adjust the irrigation schedule based on images of crops taken by a drone. This makes it possible to build an automated irrigation system by working in conjunction with other agricultural machinery.

[0100] The irrigation schedule proposal unit can propose an irrigation schedule that can be applied to different agricultural techniques. The irrigation schedule proposal unit proposes an irrigation schedule that can be applied to, for example, organic farming. For example, in organic farming that does not use chemical fertilizers, the irrigation schedule proposal unit proposes an irrigation method that takes into account the natural nutrient circulation in the soil. The irrigation schedule proposal unit can also propose an irrigation schedule that can be applied to hydroponic cultivation. For example, the irrigation schedule proposal unit proposes an irrigation method that takes into account the management method of the nutrient solution. This makes it possible to provide an irrigation schedule that can be applied to different agricultural techniques.

[0101] The irrigation schedule proposal unit uses the emotion estimation function to propose an irrigation schedule that allows farmers to work with the greatest peace of mind, thereby improving the working environment. The irrigation schedule proposal unit, for example, uses the emotion estimation function to collect emotion data from farmers and proposes an irrigation schedule that allows farmers to work with the greatest peace of mind. For example, irrigation is performed during times when farmers feel safe. The irrigation schedule proposal unit can also propose an irrigation schedule that reduces stress for farmers. For example, an irrigation schedule is set that avoids times when farmers feel stressed. This makes it possible to improve the working environment by taking into account the farmers' sense of security.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The irrigation schedule proposal unit can use the emotion estimation function to analyze the emotions of farmers and propose a low-stress irrigation schedule. For example, the emotion estimation function can be used to collect emotional data on farmers and propose a low-stress irrigation schedule. For example, an irrigation schedule can be set that avoids time periods when farmers feel stressed. The irrigation schedule proposal unit can also propose an irrigation schedule that takes into account the satisfaction of farmers. For example, irrigation can be performed during time periods when farmers feel most satisfied. In this way, a low-stress irrigation schedule can be provided by taking into account the emotions of farmers.

[0104] The irrigation schedule proposal unit can work in conjunction with other agricultural machinery to build an automated irrigation system. For example, it can work in conjunction with tractors and drones to build an automated irrigation system. For example, the AI ​​can propose a schedule for tractors to automatically irrigate. The irrigation schedule proposal unit can also work in conjunction with other agricultural machinery to optimize irrigation schedules. For example, it can adjust the irrigation schedule based on images of crops taken by drones. This makes it possible to build an automated irrigation system by working in conjunction with other agricultural machinery.

[0105] The irrigation schedule proposal unit can use the emotion estimation function to propose an irrigation schedule that will most satisfy farmers, thereby improving work efficiency. For example, the emotion estimation function can be used to collect emotion data from farmers and propose an irrigation schedule that will most satisfy them. For example, irrigation can be performed during times when farmers are most satisfied. The irrigation schedule proposal unit can also propose an irrigation schedule that reduces stress for farmers. For example, an irrigation schedule can be set that avoids times when farmers feel stressed. This can improve work efficiency by taking farmers' satisfaction into consideration.

[0106] The irrigation schedule proposal unit can propose an irrigation schedule that can be applied to different agricultural techniques. For example, it proposes an irrigation schedule that can be applied to organic farming. For example, it proposes an irrigation method that takes into account the natural nutrient circulation in the soil in organic farming that does not use chemical fertilizers. The irrigation schedule proposal unit can also propose an irrigation schedule that can be applied to hydroponic cultivation. For example, it proposes an irrigation method that takes into account the management method of the nutrient solution. This makes it possible to provide an irrigation schedule that can be applied to different agricultural techniques.

[0107] The irrigation schedule proposal unit can use the emotion estimation function to propose an irrigation schedule that allows farmers to work with the greatest peace of mind, thereby improving the working environment. For example, the emotion estimation function can be used to collect emotional data on farmers and propose an irrigation schedule that allows them to work with the greatest peace of mind. For example, irrigation can be performed during times when farmers feel safe. The irrigation schedule proposal unit can also propose an irrigation schedule that reduces stress for farmers. For example, an irrigation schedule can be set that avoids times when farmers feel stressed. This can improve the working environment by taking into account the farmers' sense of security.

[0108] The sensor data analysis unit can integrate data from different sensors to determine overall water demand. For example, it can integrate data from a drone-mounted camera and an underground sensor to determine overall water demand. For example, it can analyze images of crops taken by a drone and humidity data from an underground sensor. The sensor data analysis unit can also integrate data from different types of sensors. For example, it can integrate and analyze data from a temperature sensor and a humidity sensor. The sensor data analysis unit can also integrate data from sensors installed in different locations. For example, it can integrate and analyze data from a surface sensor and an underground sensor. In this way, by integrating data from different sensors, it is possible to accurately determine overall water demand.

[0109] The sensor data analysis unit can use the emotion estimation function to analyze the emotions of farmers and propose a low-stress water demand monitoring method. For example, the emotion estimation function can be used to collect emotion data from farmers and propose a low-stress water demand monitoring method. For example, a monitoring method can be set that avoids time periods when farmers feel stressed. The sensor data analysis unit can also propose a water demand monitoring method that takes into account the satisfaction of farmers. For example, monitoring can be performed during time periods when farmers are most satisfied. In this way, a low-stress water demand monitoring method can be provided by taking into account the emotions of farmers.

[0110] The sensor data analysis unit can integrate the data with other environmental data to support comprehensive agricultural management. For example, it can integrate the data with soil nutrient data to support comprehensive agricultural management. For example, it can suggest appropriate fertilization when soil nutrients are lacking. The sensor data analysis unit can also integrate the data with data on pest and disease occurrence status to support comprehensive agricultural management. For example, it can predict pests and diseases that are likely to occur under specific weather conditions and suggest countermeasures. The sensor data analysis unit can also integrate the data with weather data to support comprehensive agricultural management. For example, it can adjust irrigation schedules based on predictions of abnormal weather. In this way, by integrating the data with other environmental data, it can support comprehensive agricultural management.

[0111] The irrigation schedule proposal unit updates data in real time and can always provide the latest irrigation schedule. For example, it collects soil data and weather information in real time and always provides the latest irrigation schedule. For example, it automatically adjusts the irrigation schedule in response to changes in weather conditions. The irrigation schedule proposal unit can also analyze sensor data in real time and update the irrigation schedule. For example, it adjusts the irrigation schedule in response to changes in crop water demand. In this way, by updating data in real time, it is possible to always provide the latest irrigation schedule.

[0112] The irrigation schedule proposal unit can use the emotion estimation function to analyze the emotions of farmers and propose an irrigation schedule that reduces their workload. For example, the emotion estimation function can be used to collect emotional data on farmers and propose an irrigation schedule that reduces their workload. For example, an irrigation schedule can be set that avoids times when farmers feel stressed. The irrigation schedule proposal unit can also propose an irrigation schedule that takes into account the satisfaction of farmers. For example, irrigation can be performed during times when farmers are most satisfied. In this way, an irrigation schedule that reduces their workload can be provided by taking into account the emotions of farmers.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The soil data analysis unit analyzes soil data, such as soil moisture, temperature, nutrient content, and physical and chemical properties (particle size distribution, organic matter content, etc.). Step 2: The weather information analysis unit analyzes weather information. For example, it collects and analyzes weather information such as rainfall, temperature, and wind speed. It also predicts future weather conditions based on weather forecast data. Step 3: The sensor data analysis unit analyzes the sensor data. For example, it collects and analyzes humidity and temperature data in real time from sensors installed in the soil. It also monitors the physiological responses of the crops (leaf color, growth rate, etc.). Step 4: The irrigation schedule proposal unit proposes an optimal irrigation schedule based on the analysis results of the soil data analysis unit, meteorological information analysis unit, and sensor data analysis unit. For example, it proposes different irrigation schedules for each stage of crop growth and irrigation schedules that take into account the characteristics of different crops. Step 5: The Agricultural Land Use Planning Support Department supports agricultural land use planning. For example, it analyzes the topography, soil characteristics, and weather conditions of the farmland to propose optimal crop placement and cultivation methods. It also compares data from different regions and climate zones to propose optimal irrigation methods specific to each region. Step 6: The Irrigation Infrastructure Optimization Unit optimizes irrigation infrastructure. For example, it analyzes the layout of irrigation systems, the design of pipes, and pump operation schedules, and proposes optimal irrigation infrastructure. It also integrates other agricultural data to support comprehensive agricultural management.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0159] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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]

[0182] 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 soil data analysis unit that analyzes soil data; a weather information analysis unit that analyzes weather information; a sensor data analysis unit that analyzes the sensor data; an irrigation schedule proposal unit that proposes an optimal irrigation schedule based on the analysis results of the soil data analysis unit, the weather information analysis unit, and the sensor data analysis unit; The Agricultural Land Use Planning Support Department, which supports agricultural land use plans; an irrigation infrastructure optimization unit that optimizes irrigation infrastructure; A system characterized by:

2. The soil data analysis unit Collect and analyze data such as soil moisture, temperature, and nutrient content 2. The system of claim 1.

3. The weather information analysis unit Collect and analyze weather information such as rainfall, temperature, and wind speed 2. The system of claim 1.

4. The sensor data analysis unit Collect and analyze humidity and temperature data in real time from sensors installed in the soil 2. The system of claim 1.

5. The irrigation schedule proposal unit Analyzing farmers' emotions and proposing less stressful irrigation schedules 2. The system of claim 1.

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