system

Abstract

The system according to this embodiment aims to analyze the growth status of crops in real time and automatically perform appropriate irrigation and fertilization. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a planning unit, and an execution unit. The collection unit collects data from at least one of a plurality of sensors and drones. The analysis unit analyzes the data collected by the collection unit and analyzes the growth status of crops in real time. The planning unit plans appropriate irrigation and fertilization based on the analysis results obtained by the analysis unit. The execution unit automatically carries out irrigation and fertilization based on the plan made by the planning unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to grasp the growth state of crops in real time and perform appropriate irrigation and fertilization, and there is room for improvement.

[0005] [[ID=​​​​​​The system according to the embodiment comprises a data collection unit, an analysis unit, a planning unit, and an execution unit. The data collection unit collects data from at least one of a plurality of sensors and drones. The analysis unit analyzes the data collected by the data collection unit and analyzes the growth status of crops in real time. The planning unit plans appropriate irrigation and fertilization based on the analysis results obtained by the analysis unit. The execution unit automatically carries out irrigation and fertilization based on the plan made by the planning unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the growth status of crops in real time and automatically perform appropriate irrigation and fertilization. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An agricultural management system according to an embodiment of the present invention is a system that uses an AI agent to analyze the growth status of crops in real time, and plans and automatically executes optimal irrigation and fertilization. This agricultural management system collects data from multiple sensors and drones, and the AI ​​agent analyzes this data to analyze the growth status of crops in real time. For example, it collects data such as soil moisture, temperature, nutrient content, and the color and shape of crop leaves. This data is transmitted to the AI ​​agent, which analyzes the collected data and analyzes the growth status of crops in real time. For example, if the soil moisture is low, the AI ​​agent will determine that irrigation is necessary. Also, if the color of the crop leaves has changed, it may indicate a nutrient deficiency, and therefore the AI ​​agent will determine that fertilization is necessary. Furthermore, the AI ​​agent also takes weather forecasts and market trends into consideration to plan optimal irrigation and fertilization. For example, if rain is expected according to the weather forecast, irrigation can be refrained from. Also, considering market trends, the cultivation of high-value crops can be prioritized. Finally, irrigation and fertilization are automatically executed based on the plan created by the AI ​​agent. For example, drones can automatically irrigate, and sensors can determine the optimal timing for fertilization and apply fertilizer automatically. This system allows farmers to constantly monitor crop growth and determine the optimal timing for water and fertilizer supply. It also enables comprehensive agricultural management that takes weather fluctuations and market trends into account, leading to increased efficiency and automation of operations. This eliminates excesses and deficiencies in irrigation and fertilization, leading to more stable yields and improved crop quality. As a result, the agricultural management system can analyze crop growth in real time, plan optimal irrigation and fertilization, and execute them automatically.

[0029] The agricultural management system according to this embodiment comprises a data collection unit, an analysis unit, a planning unit, and an execution unit. The data collection unit collects data from at least one of a plurality of sensors and drones. The data collection unit collects data using, for example, a temperature sensor, a humidity sensor, or a drone equipped with a multispectral camera. The data collection unit collects data such as soil moisture, temperature, nutrient content, and the color and shape of crop leaves. The data collection unit can also collect data over a wide area using, for example, a drone. The analysis unit analyzes the data collected by the data collection unit and analyzes the growth status of crops in real time. The analysis unit analyzes the data using, for example, AI and evaluates the growth status of crops. The analysis unit determines, for example, that irrigation is necessary if the soil moisture is low. The analysis unit also determines that fertilization is necessary if the color of crop leaves has changed, as this may indicate a nutrient deficiency. The planning unit plans the optimal irrigation and fertilization based on the analysis results obtained by the analysis unit. The planning unit plans considering, for example, weather forecast data and market trend data. The planning unit can, for example, refrain from irrigation if rain is predicted based on weather forecasts. The planning unit can also prioritize the cultivation of high-value crops, taking market trends into consideration. The execution unit automatically carries out irrigation and fertilization based on the plan developed by the planning unit. The execution unit can, for example, use drones to automatically perform irrigation. The execution unit can, for example, use sensors to determine the timing of fertilization and carry it out automatically. The execution unit can also, for example, monitor environmental data in real time to correct any excess or deficiency in irrigation or fertilization, and modify the plan as needed. As a result, the agricultural management system according to this embodiment can analyze the growth status of crops in real time, plan and automatically execute optimal irrigation and fertilization.

[0030] The data collection unit collects data from at least one of several sensors and / or drones. For example, the unit uses temperature sensors, humidity sensors, and drones equipped with multispectral cameras to collect data. Specifically, temperature sensors measure soil and air temperature, and humidity sensors measure soil and air humidity. These sensors may be ground-based or mounted on drones to collect data while flying over a wide area. Multispectral cameras capture detailed images of crop leaf color and shape, providing data to understand crop health and growth. For example, a change in leaf color may indicate pest or disease outbreaks or nutrient deficiencies. The data collection unit collects this data in real time and transmits it to a central database. Furthermore, the data collection unit is equipped with sensors to measure soil nutrient content, allowing it to measure the concentrations of key nutrients such as nitrogen, phosphorus, and potassium. This enables the data collection unit to gain a detailed understanding of the environmental conditions necessary for crop growth and provide fundamental data for appropriate management. The data collection unit can also collect data over a wide area using drones. Drones can fly across farmland, collecting data using sensors and cameras, allowing for a comprehensive overview of crop conditions across a wide area. This enables the data collection unit to efficiently and effectively gather data, improving the overall performance of the agricultural management system.

[0031] The analysis unit analyzes the data collected by the collection unit to analyze the crop's growth status in real time. For example, the analysis unit uses AI to analyze the data and evaluate the crop's growth status. Specifically, the AI ​​evaluates the optimal environmental conditions for crop growth based on the collected temperature, humidity, and nutrient data. For example, if the soil moisture is low, the AI ​​will determine that irrigation is necessary and calculate the appropriate amount of irrigation. Also, if the color of the crop's leaves has changed, the AI ​​will determine that fertilization is necessary as this may indicate a nutrient deficiency. The AI ​​uses image recognition technology to analyze data from a multispectral camera and evaluate the health of the crop. For example, it can detect changes in leaf color and shape, enabling early detection of pest and disease outbreaks and nutrient deficiencies. Furthermore, the analysis unit can also utilize historical data and statistical information to evaluate long-term growth trends and risks. For example, based on historical weather data and harvest data, it can predict the growth patterns of specific crops and propose optimal management methods. As a result, the analysis unit can quickly and accurately analyze the collected data and understand the crop's growth status in real time. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire agricultural management system.

[0032] The planning department develops optimal irrigation and fertilization plans based on the analysis results obtained by the analysis department. For example, the planning department considers weather forecast data and market trend data when developing plans. Specifically, based on weather forecast data, it predicts future rainfall and temperature fluctuations and adjusts the timing and amount of irrigation. For instance, if rain is expected based on the weather forecast, irrigation can be reduced. The planning department can also prioritize the cultivation of high-value crops by considering market trends. Based on market trend data, it can predict periods of high demand and price fluctuations, enabling the development of optimal planting plans. Furthermore, the planning department develops optimal fertilization plans considering the crop growth cycle and harvest time. For example, by supplying necessary nutrients at the appropriate time according to the crop's growth stage, it can maintain crop health and maximize yields. By integrating this data and developing optimal irrigation and fertilization plans, the planning department can improve the overall efficiency and effectiveness of the agricultural management system. Additionally, the planning department can develop long-term agricultural plans based on past data and experience. For example, based on past harvest data and weather data, it can propose optimal cultivation times and management methods for specific crops. This allows the planning department to address not only short-term management but also long-term agricultural planning, thereby improving the stability and sustainability of agricultural management.

[0033] The execution unit automatically carries out irrigation and fertilization based on the plan developed by the planning unit. For example, the execution unit can use drones to automatically perform irrigation. Specifically, the drone flies over the irrigation area set by the planning unit and can accurately spray the required amount of water. The drone uses GPS to determine its precise location and irrigates efficiently. In addition, the drone is equipped with sensors and can monitor environmental data in real time while irrigating. This prevents excessive or insufficient irrigation and maintains the health of the crops. For example, the execution unit uses sensors to determine the timing of fertilization and carries out fertilization automatically. Specifically, it can monitor the nutrient content of the soil in real time and supply the necessary nutrients at the appropriate time. This efficiently supplies the nutrients necessary for crop growth and maximizes yields. Furthermore, the execution unit can monitor environmental data in real time to correct excessive or insufficient irrigation and fertilization and modify the plan as needed. For example, it can flexibly adjust the irrigation and fertilization plan in response to sudden weather changes or unexpected environmental fluctuations. This allows the execution department to accurately carry out the plans developed by the planning department, maintaining the health of the crops while improving the overall efficiency and effectiveness of the agricultural management system.

[0034] The forecasting unit can collect weather forecast data and provide it to the planning unit. For example, the forecasting unit collects weather forecast data such as temperature, precipitation, and wind speed. The forecasting unit can perform weather forecasts using, for example, weather satellite data and ground observation data. The forecasting unit can also analyze the weather forecast data using, for example, AI, and provide it to the planning unit. This allows for the development of optimal irrigation and fertilization plans based on the weather forecast data.

[0035] The Market Analysis Department can collect market trend data and provide it to the Planning Department. For example, the Market Analysis Department collects market trend data such as price trends, demand forecasts, and supply conditions. The Market Analysis Department can analyze market trends using, for example, economic data and market research data. The Market Analysis Department can also analyze market trend data using, for example, AI and provide it to the Planning Department. This allows for the development of optimal irrigation and fertilization plans based on market trend data.

[0036] The planning department can develop appropriate irrigation and fertilization plans based on weather forecast data and market trend data. For example, the planning department can adjust the timing of irrigation based on weather forecast data. For example, the planning department can prioritize the cultivation of high-value crops based on market trend data. For example, the planning department can use AI to analyze weather forecast data and market trend data to develop optimal irrigation and fertilization plans. This allows for more accurate planning by considering weather forecast data and market trend data.

[0037] The execution unit can perform irrigation automatically using drones. For example, the execution unit can efficiently irrigate large areas using drones. For example, the execution unit can optimize the drone's flight path to improve irrigation efficiency. For example, the execution unit can control the drone's flight path using AI to perform irrigation automatically. In this way, irrigation can be automated and performed efficiently by using drones.

[0038] The execution unit can determine the timing of fertilization using sensors and perform fertilization automatically. For example, the execution unit can measure the nutrient content of the soil with sensors and determine the timing of fertilization. For example, the execution unit can analyze sensor data using AI and determine the timing of fertilization. For example, the execution unit can monitor the timing of fertilization in real time using sensors and perform fertilization automatically. In this way, by using sensors, the timing of fertilization can be accurately determined and fertilization can be performed automatically.

[0039] The service department can provide farmers with the action plans developed by the planning department. For example, the service department can provide the action plans to farmers through web or mobile applications. Alternatively, the service department can send the action plans to farmers via email. The service department can also provide farmers with printed copies of the action plans. This allows for more efficient agricultural management by providing farmers with concrete action plans.

[0040] The data collection unit can dynamically change the type and frequency of data collected according to the crop's growth stage. For example, when the crop is in the germination stage, the unit frequently collects soil moisture and temperature. When the crop is in the growth stage, the unit periodically collects leaf color and shape. When the crop is in the harvest stage, the unit focuses on collecting nutrient content. This optimizes data collection according to the crop's growth stage, enabling efficient data collection.

[0041] The data collection unit can integrate data from different sensors and drones and perform filtering to ensure data consistency. For example, the data collection unit can integrate humidity data from different sensors and calculate an average value. For example, the data collection unit can analyze image data from drones and remove outliers. For example, the data collection unit can compare sensor and drone data and filter out inconsistent data. By ensuring data consistency, reliable data analysis becomes possible.

[0042] The data collection unit can optimize its collection range by considering geographical and climatic conditions during data collection. For example, in high-altitude areas, the unit will focus on collecting temperature and humidity data. In arid regions, for example, the unit will frequently collect soil moisture content data. During the rainy season, for example, the unit will collect rainfall and soil drainage data. This allows for efficient data collection by considering geographical and climatic conditions.

[0043] The data collection unit can customize its collection methods according to the type and variety of crop. For example, in the case of tomatoes, the unit focuses on collecting leaf color and shape. For example, in the case of wheat, the unit frequently collects soil nutrient content. For example, in the case of grapes, the unit regularly collects fruit sugar content and acidity. By customizing the collection method according to the type and variety of crop, appropriate data collection becomes possible.

[0044] The analysis unit can detect anomalies by comparing current data with past data during analysis and identify the cause of the anomaly. For example, the analysis unit can detect abnormally low humidity values ​​compared with past humidity data and identify insufficient irrigation. For example, the analysis unit can detect abnormally high temperature values ​​compared with past temperature data and identify excessive sunlight. For example, the analysis unit can detect abnormally low nutrient values ​​compared with past nutrient data and identify insufficient fertilization. In this way, by comparing current data with past data, anomalies can be detected and their causes can be identified.

[0045] The analysis unit can predict future growth using a crop growth model during analysis. For example, the analysis unit can predict the harvest time based on the crop growth model. For example, the analysis unit can predict the required amount of irrigation based on the crop growth model. For example, the analysis unit can predict the required amount of fertilizer based on the crop growth model. In this way, future growth can be predicted by using a crop growth model.

[0046] The analysis unit can compare the growth patterns of different crops during analysis and identify optimal growth conditions. For example, the analysis unit can compare the growth patterns of tomatoes and wheat to identify optimal irrigation conditions. For example, the analysis unit can compare the growth patterns of grapes and apples to identify optimal fertilization conditions. For example, the analysis unit can compare the growth patterns of different crops to identify common optimal growth conditions. In this way, by comparing the growth patterns of different crops, optimal growth conditions can be identified.

[0047] The analysis unit can improve the accuracy of its analysis by integrating environmental and meteorological data during the analysis process. For example, the analysis unit can integrate environmental and meteorological data to identify the optimal timing for irrigation. For example, the analysis unit can integrate environmental and meteorological data to identify the optimal timing for fertilization. For example, the analysis unit can integrate environmental and meteorological data to improve the accuracy of crop growth predictions. In this way, the accuracy of the analysis is improved by integrating environmental and meteorological data.

[0048] The planning department can formulate the optimal plan by referring to past successes and failures during the planning stage. For example, the planning department can formulate the optimal irrigation plan based on past successes. For example, the planning department can formulate a plan to improve fertilization based on past failures. For example, the planning department can formulate the optimal agricultural plan by comprehensively analyzing past cases. In this way, the optimal plan can be formulated by referring to past cases.

[0049] The planning department can simulate multiple scenarios during the planning stage and select the optimal scenario. For example, the planning department can simulate different irrigation scenarios and select the optimal one. For example, the planning department can simulate different fertilization scenarios and select the optimal one. For example, the planning department can simulate different weather scenarios and select the optimal agricultural plan. In this way, by simulating multiple scenarios, the optimal scenario can be selected.

[0050] The planning department can integrate cultivation plans for different crops during the planning stage to optimize the overall plan. For example, the planning department can integrate tomato and wheat cultivation plans to formulate an optimal irrigation plan. For example, the planning department can integrate grape and apple cultivation plans to formulate an optimal fertilization plan. For example, the planning department can integrate cultivation plans for different crops to formulate an optimal overall agricultural plan. In this way, by integrating cultivation plans for different crops, an optimal overall agricultural plan can be formulated.

[0051] The planning department can adjust plans when formulating them, taking into account local agricultural policies and regulations. For example, the planning department can adjust irrigation plans considering local water resource management policies. For example, the planning department can adjust fertilization plans considering local fertilizer use regulations. For example, the planning department can adjust the overall agricultural plan considering local agricultural support policies. This allows for the formulation of appropriate plans by taking local agricultural policies and regulations into consideration.

[0052] The execution unit can monitor environmental data in real time during execution and modify the plan as needed. For example, if soil moisture changes rapidly during execution, the execution unit will modify the irrigation plan. For example, if the temperature rises unexpectedly during execution, the execution unit will modify the fertilization plan. For example, if the weather changes suddenly during execution, the execution unit will modify the overall execution plan. This allows for timely plan modifications by monitoring environmental data in real time.

[0053] The execution unit can coordinate the operation of multiple drones and sensors during execution to perform efficient irrigation and fertilization. For example, the execution unit can coordinate the operation of multiple drones to efficiently irrigate a wide area. For example, the execution unit can coordinate the operation of multiple sensors to optimize the timing of fertilization. For example, the execution unit can link drones and sensors to achieve efficient agricultural work. In this way, efficient irrigation and fertilization become possible by coordinating the operation of multiple drones and sensors.

[0054] The execution unit can customize its execution method according to the growth stage of different crops during execution. For example, during the germination stage of tomatoes, the execution unit can customize the appropriate irrigation method. For example, during the growth stage of wheat, the execution unit can customize the appropriate fertilization method. For example, during the harvest stage of grapes, the execution unit can customize the appropriate harvesting method. This allows for appropriate irrigation and fertilization by customizing the execution method according to the growth stage of different crops.

[0055] The implementation unit can adjust the implementation plan during implementation, taking into account the local water resources and fertilizer supply situation. For example, if local water resources are insufficient, the implementation unit will adjust the irrigation plan. For example, if local fertilizer supply is unstable, the implementation unit will adjust the fertilization plan. For example, the implementation unit will optimize the implementation plan by comprehensively considering the local resource situation. In this way, an appropriate implementation plan can be formulated by taking into account the local water resources and fertilizer supply situation.

[0056] The forecasting unit can improve its forecast accuracy by referring to past weather data during the forecasting process. For example, the forecasting unit can improve the accuracy of rainfall forecasts based on past weather data. For example, the forecasting unit can improve the accuracy of temperature forecasts based on past weather data. For example, the forecasting unit can improve the accuracy of wind speed forecasts based on past weather data. In this way, forecast accuracy can be improved by referring to past weather data.

[0057] The forecasting unit can improve the reliability of its forecasts by combining different weather models during the forecasting process. For example, the forecasting unit can improve the reliability of its rainfall forecasts by combining different weather models. For example, the forecasting unit can improve the reliability of its temperature forecasts by combining different weather models. For example, the forecasting unit can improve the reliability of its wind speed forecasts by combining different weather models. In this way, the reliability of forecasts can be improved by combining different weather models.

[0058] The forecasting unit can customize its forecasting model by considering local weather patterns during the forecasting process. For example, the forecasting unit can customize the rainfall forecasting model by considering local weather patterns. For example, the forecasting unit can customize the temperature forecasting model by considering local weather patterns. For example, the forecasting unit can customize the wind speed forecasting model by considering local weather patterns. This allows the forecasting model to be optimized by considering local weather patterns.

[0059] The forecasting unit can improve the accuracy of its forecasts by integrating different weather data sources during the forecasting process. For example, the forecasting unit can improve the accuracy of its rainfall forecast by integrating different weather data sources. For example, the forecasting unit can improve the accuracy of its temperature forecast by integrating different weather data sources. For example, the forecasting unit can improve the accuracy of its wind speed forecast by integrating different weather data sources. In this way, the accuracy of the forecast can be improved by integrating different weather data sources.

[0060] The market analysis department can predict market trends by referring to historical market data during market analysis. For example, the market analysis department can forecast crop demand based on historical market data. For example, the market analysis department can forecast price fluctuations based on historical market data. For example, the market analysis department can forecast supply volumes based on historical market data. In this way, market trends can be predicted by referring to historical market data.

[0061] The market analysis department can enhance the reliability of its analysis by combining different market models. For example, the market analysis department can enhance the reliability of demand forecasts by combining different market models. For example, the market analysis department can enhance the reliability of price fluctuation forecasts by combining different market models. For example, the market analysis department can enhance the reliability of supply volume forecasts by combining different market models. In this way, the reliability of the analysis can be enhanced by combining different market models.

[0062] The market analysis department can customize its analysis models by taking regional market trends into consideration during market analysis. For example, the market analysis department can customize demand forecasting models by taking regional market trends into consideration. For example, the market analysis department can customize price fluctuation forecasting models by taking regional market trends into consideration. For example, the market analysis department can customize supply volume forecasting models by taking regional market trends into consideration. In this way, the analysis models can be optimized by taking regional market trends into consideration.

[0063] The market analysis department can improve the accuracy of its analysis by integrating different market data sources. For example, the market analysis department can improve the accuracy of demand forecasting by integrating different market data sources. For example, the market analysis department can improve the accuracy of price fluctuation forecasting by integrating different market data sources. For example, the market analysis department can improve the accuracy of supply volume forecasting by integrating different market data sources. In this way, the accuracy of analysis can be improved by integrating different market data sources.

[0064] The service provider can provide the optimal plan by referring to the user's past behavioral history when providing an action plan. For example, the service provider can provide the optimal irrigation plan based on the user's past behavioral history. For example, the service provider can provide the optimal fertilization plan based on the user's past behavioral history. For example, the service provider can provide the optimal agricultural plan based on the user's past behavioral history. In this way, the service provider can provide the optimal plan by referring to the user's past behavioral history.

[0065] The service provider can present multiple action plans when providing them, allowing the user to choose. For example, the service provider can present multiple irrigation plans to the user and allow them to select the optimal plan. For example, the service provider can present multiple fertilization plans to the user and allow them to select the optimal plan. For example, the service provider can present multiple agricultural plans to the user and allow them to select the optimal plan. In this way, by presenting multiple plans, the user can choose the optimal plan.

[0066] The planning department can adjust action plans to take into account local agricultural policies and regulations. For example, it can adjust irrigation plans to take into account local water resource management policies, fertilization plans to take into account local fertilizer use regulations, and overall agricultural plans to take into account local agricultural support policies. This allows for the provision of appropriate plans by considering local agricultural policies and regulations.

[0067] The service provider can select the optimal display method when providing an action plan, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking into account the user's device information.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The data collection unit can monitor not only the growth status of crops but also changes in the surrounding ecosystem. For example, it can monitor pest outbreaks and suggest appropriate control measures if pests are increasing. It can also monitor the growth status of surrounding plants and detect the presence of competing plants that may affect crops. Furthermore, it can monitor microbial activity in the soil and assess the health of the soil. This enables agricultural management that considers not only crop growth but also the entire surrounding ecosystem.

[0070] The analysis unit can consider the genetic information of crops when analyzing their growth status. For example, it can check whether a specific gene is being expressed and make growth predictions based on the results. Furthermore, the analysis unit can propose ways to optimize crop growth using gene editing technology. In addition, it can support the selection of crops with high disease resistance and environmental adaptability based on genetic information. This enables advanced agricultural management utilizing genetic information.

[0071] The planning department can develop plans that take into account the efficient use of resources necessary for crop growth. For example, it can optimize the timing and amount of irrigation to maximize the efficiency of water resource use. It can also develop fertilization plans to meet crop nutritional requirements while minimizing fertilizer use. Furthermore, it can optimize the operating schedule of agricultural machinery to reduce energy consumption. This enables sustainable agricultural management through the efficient use of resources.

[0072] The execution unit can not only automate agricultural tasks but also incorporate functions to improve safety. For example, it can detect obstacles during the operation of drones or agricultural machinery and automatically avoid them. It can also track the location of workers in real time and issue warnings if dangerous situations arise. Furthermore, it can evaluate the safety of the work environment and modify the work plan as needed. This allows for the promotion of automation while ensuring the safety of agricultural work.

[0073] The service provider can offer farmers not only feedback on crop growth and farming plans, but also educational content. For example, it can provide video tutorials on crop cultivation methods and pest and disease control. It can also regularly provide information on the latest agricultural technologies and research findings. Furthermore, it can offer a community platform to facilitate information exchange among farmers. This helps improve farmers' knowledge and skills, leading to increased efficiency and higher quality in agriculture.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The collection unit collects data from at least one of several sensors and / or drones. For example, it may use temperature sensors, humidity sensors, or a drone equipped with a multispectral camera to collect data such as soil moisture, temperature, nutrient content, and the color and shape of crop leaves. Drones can also be used to collect data over a wide area. Step 2: The analysis unit analyzes the data collected by the collection unit and analyzes the growth status of the crops in real time. For example, by using AI to analyze the data, it determines that irrigation is necessary if the soil moisture is low, and that fertilization is necessary if the color of the crop leaves has changed, as this may indicate a nutrient deficiency. Step 3: The planning department develops an optimal irrigation and fertilization plan based on the analysis results obtained by the analysis department. For example, the plan can take into account weather forecast data and market trend data, and irrigation can be reduced if rain is expected based on the weather forecast, and the cultivation of high-value crops can be prioritized based on market trends. Step 4: The execution unit automatically carries out irrigation and fertilization based on the plan developed by the planning unit. For example, it may use drones to automatically irrigate, and sensors to determine the timing of fertilization and carry it out automatically. It can also monitor environmental data in real time to correct any excess or deficiency in irrigation or fertilization, and modify the plan as needed.

[0076] (Example of form 2) An agricultural management system according to an embodiment of the present invention is a system that uses an AI agent to analyze the growth status of crops in real time, and plans and automatically executes optimal irrigation and fertilization. This agricultural management system collects data from multiple sensors and drones, and the AI ​​agent analyzes this data to analyze the growth status of crops in real time. For example, it collects data such as soil moisture, temperature, nutrient content, and the color and shape of crop leaves. This data is transmitted to the AI ​​agent, which analyzes the collected data and analyzes the growth status of crops in real time. For example, if the soil moisture is low, the AI ​​agent will determine that irrigation is necessary. Also, if the color of the crop leaves has changed, it may indicate a nutrient deficiency, and therefore the AI ​​agent will determine that fertilization is necessary. Furthermore, the AI ​​agent also takes weather forecasts and market trends into consideration to plan optimal irrigation and fertilization. For example, if rain is expected according to the weather forecast, irrigation can be refrained from. Also, considering market trends, the cultivation of high-value crops can be prioritized. Finally, irrigation and fertilization are automatically executed based on the plan created by the AI ​​agent. For example, drones can automatically irrigate, and sensors can determine the optimal timing for fertilization and apply fertilizer automatically. This system allows farmers to constantly monitor crop growth and determine the optimal timing for water and fertilizer supply. It also enables comprehensive agricultural management that takes weather fluctuations and market trends into account, leading to increased efficiency and automation of operations. This eliminates excesses and deficiencies in irrigation and fertilization, leading to more stable yields and improved crop quality. As a result, the agricultural management system can analyze crop growth in real time, plan optimal irrigation and fertilization, and execute them automatically.

[0077] The agricultural management system according to this embodiment comprises a data collection unit, an analysis unit, a planning unit, and an execution unit. The data collection unit collects data from at least one of a plurality of sensors and drones. The data collection unit collects data using, for example, a temperature sensor, a humidity sensor, or a drone equipped with a multispectral camera. The data collection unit collects data such as soil moisture, temperature, nutrient content, and the color and shape of crop leaves. The data collection unit can also collect data over a wide area using, for example, a drone. The analysis unit analyzes the data collected by the data collection unit and analyzes the growth status of crops in real time. The analysis unit analyzes the data using, for example, AI and evaluates the growth status of crops. The analysis unit determines, for example, that irrigation is necessary if the soil moisture is low. The analysis unit also determines that fertilization is necessary if the color of crop leaves has changed, as this may indicate a nutrient deficiency. The planning unit plans the optimal irrigation and fertilization based on the analysis results obtained by the analysis unit. The planning unit plans considering, for example, weather forecast data and market trend data. The planning unit can, for example, refrain from irrigation if rain is predicted based on weather forecasts. The planning unit can also prioritize the cultivation of high-value crops, taking market trends into consideration. The execution unit automatically carries out irrigation and fertilization based on the plan developed by the planning unit. The execution unit can, for example, use drones to automatically perform irrigation. The execution unit can, for example, use sensors to determine the timing of fertilization and carry it out automatically. The execution unit can also, for example, monitor environmental data in real time to correct any excess or deficiency in irrigation or fertilization, and modify the plan as needed. As a result, the agricultural management system according to this embodiment can analyze the growth status of crops in real time, plan and automatically execute optimal irrigation and fertilization.

[0078] The data collection unit collects data from at least one of several sensors and / or drones. For example, the unit uses temperature sensors, humidity sensors, and drones equipped with multispectral cameras to collect data. Specifically, temperature sensors measure soil and air temperature, and humidity sensors measure soil and air humidity. These sensors may be ground-based or mounted on drones to collect data while flying over a wide area. Multispectral cameras capture detailed images of crop leaf color and shape, providing data to understand crop health and growth. For example, a change in leaf color may indicate pest or disease outbreaks or nutrient deficiencies. The data collection unit collects this data in real time and transmits it to a central database. Furthermore, the data collection unit is equipped with sensors to measure soil nutrient content, allowing it to measure the concentrations of key nutrients such as nitrogen, phosphorus, and potassium. This enables the data collection unit to gain a detailed understanding of the environmental conditions necessary for crop growth and provide fundamental data for appropriate management. The data collection unit can also collect data over a wide area using drones. Drones can fly across farmland, collecting data using sensors and cameras, allowing for a comprehensive overview of crop conditions across a wide area. This enables the data collection unit to efficiently and effectively gather data, improving the overall performance of the agricultural management system.

[0079] The analysis unit analyzes the data collected by the collection unit to analyze the crop's growth status in real time. For example, the analysis unit uses AI to analyze the data and evaluate the crop's growth status. Specifically, the AI ​​evaluates the optimal environmental conditions for crop growth based on the collected temperature, humidity, and nutrient data. For example, if the soil moisture is low, the AI ​​will determine that irrigation is necessary and calculate the appropriate amount of irrigation. Also, if the color of the crop's leaves has changed, the AI ​​will determine that fertilization is necessary as this may indicate a nutrient deficiency. The AI ​​uses image recognition technology to analyze data from a multispectral camera and evaluate the health of the crop. For example, it can detect changes in leaf color and shape, enabling early detection of pest and disease outbreaks and nutrient deficiencies. Furthermore, the analysis unit can also utilize historical data and statistical information to evaluate long-term growth trends and risks. For example, based on historical weather data and harvest data, it can predict the growth patterns of specific crops and propose optimal management methods. As a result, the analysis unit can quickly and accurately analyze the collected data and understand the crop's growth status in real time. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire agricultural management system.

[0080] The planning department develops optimal irrigation and fertilization plans based on the analysis results obtained by the analysis department. For example, the planning department considers weather forecast data and market trend data when developing plans. Specifically, based on weather forecast data, it predicts future rainfall and temperature fluctuations and adjusts the timing and amount of irrigation. For instance, if rain is expected based on the weather forecast, irrigation can be reduced. The planning department can also prioritize the cultivation of high-value crops by considering market trends. Based on market trend data, it can predict periods of high demand and price fluctuations, enabling the development of optimal planting plans. Furthermore, the planning department develops optimal fertilization plans considering the crop growth cycle and harvest time. For example, by supplying necessary nutrients at the appropriate time according to the crop's growth stage, it can maintain crop health and maximize yields. By integrating this data and developing optimal irrigation and fertilization plans, the planning department can improve the overall efficiency and effectiveness of the agricultural management system. Additionally, the planning department can develop long-term agricultural plans based on past data and experience. For example, based on past harvest data and weather data, it can propose optimal cultivation times and management methods for specific crops. This allows the planning department to address not only short-term management but also long-term agricultural planning, thereby improving the stability and sustainability of agricultural management.

[0081] The execution unit automatically carries out irrigation and fertilization based on the plan developed by the planning unit. For example, the execution unit can use drones to automatically perform irrigation. Specifically, the drone flies over the irrigation area set by the planning unit and can accurately spray the required amount of water. The drone uses GPS to determine its precise location and irrigates efficiently. In addition, the drone is equipped with sensors and can monitor environmental data in real time while irrigating. This prevents excessive or insufficient irrigation and maintains the health of the crops. For example, the execution unit uses sensors to determine the timing of fertilization and carries out fertilization automatically. Specifically, it can monitor the nutrient content of the soil in real time and supply the necessary nutrients at the appropriate time. This efficiently supplies the nutrients necessary for crop growth and maximizes yields. Furthermore, the execution unit can monitor environmental data in real time to correct excessive or insufficient irrigation and fertilization and modify the plan as needed. For example, it can flexibly adjust the irrigation and fertilization plan in response to sudden weather changes or unexpected environmental fluctuations. This allows the execution department to accurately carry out the plans developed by the planning department, maintaining the health of the crops while improving the overall efficiency and effectiveness of the agricultural management system.

[0082] The forecasting unit can collect weather forecast data and provide it to the planning unit. For example, the forecasting unit collects weather forecast data such as temperature, precipitation, and wind speed. The forecasting unit can perform weather forecasts using, for example, weather satellite data and ground observation data. The forecasting unit can also analyze the weather forecast data using, for example, AI, and provide it to the planning unit. This allows for the development of optimal irrigation and fertilization plans based on the weather forecast data.

[0083] The Market Analysis Department can collect market trend data and provide it to the Planning Department. For example, the Market Analysis Department collects market trend data such as price trends, demand forecasts, and supply conditions. The Market Analysis Department can analyze market trends using, for example, economic data and market research data. The Market Analysis Department can also analyze market trend data using, for example, AI and provide it to the Planning Department. This allows for the development of optimal irrigation and fertilization plans based on market trend data.

[0084] The planning department can develop appropriate irrigation and fertilization plans based on weather forecast data and market trend data. For example, the planning department can adjust the timing of irrigation based on weather forecast data. For example, the planning department can prioritize the cultivation of high-value crops based on market trend data. For example, the planning department can use AI to analyze weather forecast data and market trend data to develop optimal irrigation and fertilization plans. This allows for more accurate planning by considering weather forecast data and market trend data.

[0085] The execution unit can perform irrigation automatically using drones. For example, the execution unit can efficiently irrigate large areas using drones. For example, the execution unit can optimize the drone's flight path to improve irrigation efficiency. For example, the execution unit can control the drone's flight path using AI to perform irrigation automatically. In this way, irrigation can be automated and performed efficiently by using drones.

[0086] The execution unit can determine the timing of fertilization using sensors and perform fertilization automatically. For example, the execution unit can measure the nutrient content of the soil with sensors and determine the timing of fertilization. For example, the execution unit can analyze sensor data using AI and determine the timing of fertilization. For example, the execution unit can monitor the timing of fertilization in real time using sensors and perform fertilization automatically. In this way, by using sensors, the timing of fertilization can be accurately determined and fertilization can be performed automatically.

[0087] The service department can provide farmers with the action plans developed by the planning department. For example, the service department can provide the action plans to farmers through web or mobile applications. Alternatively, the service department can send the action plans to farmers via email. The service department can also provide farmers with printed copies of the action plans. This allows for more efficient agricultural management by providing farmers with concrete action plans.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will reduce the frequency of data collection and refrain from sending notifications. For example, if the user is relaxed, the data collection unit will collect more detailed data and increase notifications. For example, if the user is in a hurry, the data collection unit will prioritize collecting only important data and send notifications quickly. This reduces the burden on the user by adjusting the timing of data collection according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The data collection unit can dynamically change the type and frequency of data collected according to the crop's growth stage. For example, when the crop is in the germination stage, the unit frequently collects soil moisture and temperature. When the crop is in the growth stage, the unit periodically collects leaf color and shape. When the crop is in the harvest stage, the unit focuses on collecting nutrient content. This optimizes data collection according to the crop's growth stage, enabling efficient data collection.

[0090] The data collection unit can integrate data from different sensors and drones and perform filtering to ensure data consistency. For example, the data collection unit can integrate humidity data from different sensors and calculate an average value. For example, the data collection unit can analyze image data from drones and remove outliers. For example, the data collection unit can compare sensor and drone data and filter out inconsistent data. By ensuring data consistency, reliable data analysis becomes possible.

[0091] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a hurry, the data collection unit will prioritize data that can be collected quickly. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The data collection unit can optimize its collection range by considering geographical and climatic conditions during data collection. For example, in high-altitude areas, the unit will focus on collecting temperature and humidity data. In arid regions, for example, the unit will frequently collect soil moisture content data. During the rainy season, for example, the unit will collect rainfall and soil drainage data. This allows for efficient data collection by considering geographical and climatic conditions.

[0093] The data collection unit can customize its collection methods according to the type and variety of crop. For example, in the case of tomatoes, the unit focuses on collecting leaf color and shape. For example, in the case of wheat, the unit frequently collects soil nutrient content. For example, in the case of grapes, the unit regularly collects fruit sugar content and acidity. By customizing the collection method according to the type and variety of crop, appropriate data collection becomes possible.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The analysis unit can detect anomalies by comparing current data with past data during analysis and identify the cause of the anomaly. For example, the analysis unit can detect abnormally low humidity values ​​compared with past humidity data and identify insufficient irrigation. For example, the analysis unit can detect abnormally high temperature values ​​compared with past temperature data and identify excessive sunlight. For example, the analysis unit can detect abnormally low nutrient values ​​compared with past nutrient data and identify insufficient fertilization. In this way, by comparing current data with past data, anomalies can be detected and their causes can be identified.

[0096] The analysis unit can predict future growth using a crop growth model during analysis. For example, the analysis unit can predict the harvest time based on the crop growth model. For example, the analysis unit can predict the required amount of irrigation based on the crop growth model. For example, the analysis unit can predict the required amount of fertilizer based on the crop growth model. In this way, future growth can be predicted by using a crop growth model.

[0097] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying only the most important analysis results. If the user is relaxed, the analysis unit will prioritize displaying detailed analysis results. If the user is in a hurry, the analysis unit will prioritize analysis results that can be quickly reviewed. This allows for the prioritization of important analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The analysis unit can compare the growth patterns of different crops during analysis and identify optimal growth conditions. For example, the analysis unit can compare the growth patterns of tomatoes and wheat to identify optimal irrigation conditions. For example, the analysis unit can compare the growth patterns of grapes and apples to identify optimal fertilization conditions. For example, the analysis unit can compare the growth patterns of different crops to identify common optimal growth conditions. In this way, by comparing the growth patterns of different crops, optimal growth conditions can be identified.

[0099] The analysis unit can improve the accuracy of its analysis by integrating environmental and meteorological data during the analysis process. For example, the analysis unit can integrate environmental and meteorological data to identify the optimal timing for irrigation. For example, the analysis unit can integrate environmental and meteorological data to identify the optimal timing for fertilization. For example, the analysis unit can integrate environmental and meteorological data to improve the accuracy of crop growth predictions. In this way, the accuracy of the analysis is improved by integrating environmental and meteorological data.

[0100] The planning unit can estimate the user's emotions and adjust the level of detail in the plan based on the estimated emotions. For example, if the user is stressed, the planning unit will provide a simple and easy-to-understand plan. If the user is relaxed, the planning unit will provide a plan with detailed information. If the user is in a hurry, the planning unit will provide a plan that gets straight to the point. By adjusting the level of detail in the plan according to the user's emotions, the system can provide a plan that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The planning department can formulate the optimal plan by referring to past successes and failures during the planning stage. For example, the planning department can formulate the optimal irrigation plan based on past successes. For example, the planning department can formulate a plan to improve fertilization based on past failures. For example, the planning department can formulate the optimal agricultural plan by comprehensively analyzing past cases. In this way, the optimal plan can be formulated by referring to past cases.

[0102] The planning department can simulate multiple scenarios during the planning stage and select the optimal scenario. For example, the planning department can simulate different irrigation scenarios and select the optimal one. For example, the planning department can simulate different fertilization scenarios and select the optimal one. For example, the planning department can simulate different weather scenarios and select the optimal agricultural plan. In this way, by simulating multiple scenarios, the optimal scenario can be selected.

[0103] The planning unit can estimate the user's emotions and prioritize plans based on those emotions. For example, if the user is stressed, the planning unit will prioritize displaying only important plans. If the user is relaxed, the planning unit will prioritize displaying detailed plans. If the user is in a hurry, the planning unit will prioritize plans that can be quickly reviewed. This allows important plans to be displayed preferentially by prioritizing them according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The planning department can integrate cultivation plans for different crops during the planning stage to optimize the overall plan. For example, the planning department can integrate tomato and wheat cultivation plans to formulate an optimal irrigation plan. For example, the planning department can integrate grape and apple cultivation plans to formulate an optimal fertilization plan. For example, the planning department can integrate cultivation plans for different crops to formulate an optimal overall agricultural plan. In this way, by integrating cultivation plans for different crops, an optimal overall agricultural plan can be formulated.

[0105] The planning department can adjust plans when formulating them, taking into account local agricultural policies and regulations. For example, the planning department can adjust irrigation plans considering local water resource management policies. For example, the planning department can adjust fertilization plans considering local fertilizer use regulations. For example, the planning department can adjust the overall agricultural plan considering local agricultural support policies. This allows for the formulation of appropriate plans by taking local agricultural policies and regulations into consideration.

[0106] The execution unit can estimate the user's emotions and adjust the timing of executions based on the estimated emotions. For example, if the user is stressed, the execution unit will reduce the frequency of executions and refrain from sending notifications. For example, if the user is relaxed, the execution unit will provide a detailed execution plan and increase notifications. For example, if the user is in a hurry, the execution unit will prioritize only important executions and send notifications quickly. This reduces the user's burden by adjusting the timing of executions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The execution unit can monitor environmental data in real time during execution and modify the plan as needed. For example, if soil moisture changes rapidly during execution, the execution unit will modify the irrigation plan. For example, if the temperature rises unexpectedly during execution, the execution unit will modify the fertilization plan. For example, if the weather changes suddenly during execution, the execution unit will modify the overall execution plan. This allows for timely plan modifications by monitoring environmental data in real time.

[0108] The execution unit can coordinate the operation of multiple drones and sensors during execution to perform efficient irrigation and fertilization. For example, the execution unit can coordinate the operation of multiple drones to efficiently irrigate a wide area. For example, the execution unit can coordinate the operation of multiple sensors to optimize the timing of fertilization. For example, the execution unit can link drones and sensors to achieve efficient agricultural work. In this way, efficient irrigation and fertilization become possible by coordinating the operation of multiple drones and sensors.

[0109] The execution unit can estimate the user's emotions and determine the priority of executions based on the estimated emotions. For example, if the user is stressed, the execution unit will prioritize only important executions. For example, if the user is relaxed, the execution unit will prioritize detailed execution plans. For example, if the user is in a hurry, the execution unit will prioritize plans that can be executed quickly. In this way, by determining the priority of executions according to the user's emotions, important executions can be prioritized. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The execution unit can customize its execution method according to the growth stage of different crops during execution. For example, during the germination stage of tomatoes, the execution unit can customize the appropriate irrigation method. For example, during the growth stage of wheat, the execution unit can customize the appropriate fertilization method. For example, during the harvest stage of grapes, the execution unit can customize the appropriate harvesting method. This allows for appropriate irrigation and fertilization by customizing the execution method according to the growth stage of different crops.

[0111] The implementation unit can adjust the implementation plan during implementation, taking into account the local water resources and fertilizer supply situation. For example, if local water resources are insufficient, the implementation unit will adjust the irrigation plan. For example, if local fertilizer supply is unstable, the implementation unit will adjust the fertilization plan. For example, the implementation unit will optimize the implementation plan by comprehensively considering the local resource situation. In this way, an appropriate implementation plan can be formulated by taking into account the local water resources and fertilizer supply situation.

[0112] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display method. For example, if the user is relaxed, the prediction unit provides a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit provides a display method that gets straight to the point. By adjusting the display method of the prediction results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The forecasting unit can improve its forecast accuracy by referring to past weather data during the forecasting process. For example, the forecasting unit can improve the accuracy of rainfall forecasts based on past weather data. For example, the forecasting unit can improve the accuracy of temperature forecasts based on past weather data. For example, the forecasting unit can improve the accuracy of wind speed forecasts based on past weather data. In this way, forecast accuracy can be improved by referring to past weather data.

[0114] The forecasting unit can improve the reliability of its forecasts by combining different weather models during the forecasting process. For example, the forecasting unit can improve the reliability of its rainfall forecasts by combining different weather models. For example, the forecasting unit can improve the reliability of its temperature forecasts by combining different weather models. For example, the forecasting unit can improve the reliability of its wind speed forecasts by combining different weather models. In this way, the reliability of forecasts can be improved by combining different weather models.

[0115] The prediction unit can estimate the user's emotions and prioritize prediction results based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize displaying only important prediction results. If the user is relaxed, the prediction unit will prioritize displaying detailed prediction results. If the user is in a hurry, the prediction unit will prioritize prediction results that can be quickly reviewed. This allows for the prioritization of important prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The forecasting unit can customize its forecasting model by considering local weather patterns during the forecasting process. For example, the forecasting unit can customize the rainfall forecasting model by considering local weather patterns. For example, the forecasting unit can customize the temperature forecasting model by considering local weather patterns. For example, the forecasting unit can customize the wind speed forecasting model by considering local weather patterns. This allows the forecasting model to be optimized by considering local weather patterns.

[0117] The forecasting unit can improve the accuracy of its forecasts by integrating different weather data sources during the forecasting process. For example, the forecasting unit can improve the accuracy of its rainfall forecast by integrating different weather data sources. For example, the forecasting unit can improve the accuracy of its temperature forecast by integrating different weather data sources. For example, the forecasting unit can improve the accuracy of its wind speed forecast by integrating different weather data sources. In this way, the accuracy of the forecast can be improved by integrating different weather data sources.

[0118] The market analysis department can estimate user emotions and adjust the display method of market analysis results based on the estimated user emotions. For example, if the user is stressed, the market analysis department provides a simple and highly visible display method. For example, if the user is relaxed, the market analysis department provides a display method that includes detailed information. For example, if the user is in a hurry, the market analysis department provides a display method that gets straight to the point. By adjusting the display method of market analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The market analysis department can predict market trends by referring to historical market data during market analysis. For example, the market analysis department can forecast crop demand based on historical market data. For example, the market analysis department can forecast price fluctuations based on historical market data. For example, the market analysis department can forecast supply volumes based on historical market data. In this way, market trends can be predicted by referring to historical market data.

[0120] The market analysis department can enhance the reliability of its analysis by combining different market models. For example, the market analysis department can enhance the reliability of demand forecasts by combining different market models. For example, the market analysis department can enhance the reliability of price fluctuation forecasts by combining different market models. For example, the market analysis department can enhance the reliability of supply volume forecasts by combining different market models. In this way, the reliability of the analysis can be enhanced by combining different market models.

[0121] The market analysis department can estimate the user's emotions and prioritize market analysis results based on those emotions. For example, if the user is stressed, the market analysis department will prioritize displaying only the most important market analysis results. If the user is relaxed, the market analysis department will prioritize displaying detailed market analysis results. If the user is in a hurry, the market analysis department will prioritize market analysis results that can be quickly reviewed. This allows for the prioritization of important market analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The market analysis department can customize its analysis models by taking regional market trends into consideration during market analysis. For example, the market analysis department can customize demand forecasting models by taking regional market trends into consideration. For example, the market analysis department can customize price fluctuation forecasting models by taking regional market trends into consideration. For example, the market analysis department can customize supply volume forecasting models by taking regional market trends into consideration. In this way, the analysis models can be optimized by taking regional market trends into consideration.

[0123] The market analysis department can improve the accuracy of its analysis by integrating different market data sources. For example, the market analysis department can improve the accuracy of demand forecasting by integrating different market data sources. For example, the market analysis department can improve the accuracy of price fluctuation forecasting by integrating different market data sources. For example, the market analysis department can improve the accuracy of supply volume forecasting by integrating different market data sources. In this way, the accuracy of analysis can be improved by integrating different market data sources.

[0124] The service provider can estimate the user's emotions and adjust how the action plan is displayed based on those emotions. For example, if the user is nervous, the service provider provides a simple and highly visible display. For example, if the user is relaxed, the service provider provides a display that includes detailed information. For example, if the user is in a hurry, the service provider provides a display that gets straight to the point. By adjusting how the action plan is displayed according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The service provider can provide the optimal plan by referring to the user's past behavioral history when providing an action plan. For example, the service provider can provide the optimal irrigation plan based on the user's past behavioral history. For example, the service provider can provide the optimal fertilization plan based on the user's past behavioral history. For example, the service provider can provide the optimal agricultural plan based on the user's past behavioral history. In this way, the service provider can provide the optimal plan by referring to the user's past behavioral history.

[0126] The service provider can present multiple action plans when providing them, allowing the user to choose. For example, the service provider can present multiple irrigation plans to the user and allow them to select the optimal plan. For example, the service provider can present multiple fertilization plans to the user and allow them to select the optimal plan. For example, the service provider can present multiple agricultural plans to the user and allow them to select the optimal plan. In this way, by presenting multiple plans, the user can choose the optimal plan.

[0127] The service provider can estimate the user's emotions and prioritize action plans based on those emotions. For example, if the user is stressed, the service provider will prioritize displaying only important action plans. For example, if the user is relaxed, the service provider will prioritize displaying detailed action plans. For example, if the user is in a hurry, the service provider will prioritize action plans that can be quickly reviewed. This allows the service provider to prioritize important action plans by determining their priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0128] The planning department can adjust action plans to take into account local agricultural policies and regulations. For example, it can adjust irrigation plans to take into account local water resource management policies, fertilization plans to take into account local fertilizer use regulations, and overall agricultural plans to take into account local agricultural support policies. This allows for the provision of appropriate plans by considering local agricultural policies and regulations.

[0129] The service provider can select the optimal display method when providing an action plan, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking into account the user's device information.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] The data collection unit can monitor not only the growth status of crops but also changes in the surrounding ecosystem. For example, it can monitor pest outbreaks and suggest appropriate control measures if pests are increasing. It can also monitor the growth status of surrounding plants and detect the presence of competing plants that may affect crops. Furthermore, it can monitor microbial activity in the soil and assess the health of the soil. This enables agricultural management that considers not only crop growth but also the entire surrounding ecosystem.

[0132] The analysis unit can consider the genetic information of crops when analyzing their growth status. For example, it can check whether a specific gene is being expressed and make growth predictions based on the results. Furthermore, the analysis unit can propose ways to optimize crop growth using gene editing technology. In addition, it can support the selection of crops with high disease resistance and environmental adaptability based on genetic information. This enables advanced agricultural management utilizing genetic information.

[0133] The planning department can develop plans that take into account the efficient use of resources necessary for crop growth. For example, it can optimize the timing and amount of irrigation to maximize the efficiency of water resource use. It can also develop fertilization plans to meet crop nutritional requirements while minimizing fertilizer use. Furthermore, it can optimize the operating schedule of agricultural machinery to reduce energy consumption. This enables sustainable agricultural management through the efficient use of resources.

[0134] The execution unit can not only automate agricultural tasks but also incorporate functions to improve safety. For example, it can detect obstacles during the operation of drones or agricultural machinery and automatically avoid them. It can also track the location of workers in real time and issue warnings if dangerous situations arise. Furthermore, it can evaluate the safety of the work environment and modify the work plan as needed. This allows for the promotion of automation while ensuring the safety of agricultural work.

[0135] The service provider can offer farmers not only feedback on crop growth and farming plans, but also educational content. For example, it can provide video tutorials on crop cultivation methods and pest and disease control. It can also regularly provide information on the latest agricultural technologies and research findings. Furthermore, it can offer a community platform to facilitate information exchange among farmers. This helps improve farmers' knowledge and skills, leading to increased efficiency and higher quality in agriculture.

[0136] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced and notifications can be kept to a minimum. Conversely, if the user is relaxed, more detailed data can be collected and notifications increased. Furthermore, if the user is in a hurry, only important data can be prioritized and notifications can be sent quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection according to their emotions.

[0137] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0138] The planning department can estimate the user's emotions and adjust the level of detail in the plan based on those estimates. For example, if the user is stressed, a simple and highly visual plan can be provided. If the user is relaxed, a plan with more detailed information can be provided. Furthermore, if the user is in a hurry, a plan that gets straight to the point can be provided. By adjusting the level of detail in the plan according to the user's emotions, a plan that is easy for the user to understand can be provided.

[0139] The execution unit can estimate the user's emotions and adjust the timing of executions based on those emotions. For example, if the user is stressed, the frequency of executions can be reduced and notifications can be kept to a minimum. If the user is relaxed, a detailed execution plan can be provided and notifications increased. Furthermore, if the user is in a hurry, only important executions can be prioritized and notifications can be sent quickly. In this way, the user's burden can be reduced by adjusting the timing of executions according to their emotions.

[0140] The system can estimate the user's emotions and adjust how the action plan is displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. By adjusting the display method of the action plan according to the user's emotions, a user-friendly display can be achieved.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The collection unit collects data from at least one of several sensors and / or drones. For example, it may use temperature sensors, humidity sensors, or a drone equipped with a multispectral camera to collect data such as soil moisture, temperature, nutrient content, and the color and shape of crop leaves. Drones can also be used to collect data over a wide area. Step 2: The analysis unit analyzes the data collected by the collection unit and analyzes the growth status of the crops in real time. For example, by using AI to analyze the data, it determines that irrigation is necessary if the soil moisture is low, and that fertilization is necessary if the color of the crop leaves has changed, as this may indicate a nutrient deficiency. Step 3: The planning department develops an optimal irrigation and fertilization plan based on the analysis results obtained by the analysis department. For example, the plan can take into account weather forecast data and market trend data, and irrigation can be reduced if rain is expected based on the weather forecast, and the cultivation of high-value crops can be prioritized based on market trends. Step 4: The execution unit automatically carries out irrigation and fertilization based on the plan developed by the planning unit. For example, it may use drones to automatically irrigate, and sensors to determine the timing of fertilization and carry it out automatically. It can also monitor environmental data in real time to correct any excess or deficiency in irrigation or fertilization, and modify the plan as needed.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0145] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, planning unit, and execution unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the smart device 14, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to analyze the growth status of crops in real time. The planning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and plans the optimal irrigation and fertilization based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the smart device 14, and automatically executes irrigation and fertilization based on the plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the data collection unit, analysis unit, planning unit, and execution unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and sensors of the smart glasses 214 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to analyze the growth status of crops in real time. The planning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and plans the optimal irrigation and fertilization based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the smart glasses 214, and automatically executes irrigation and fertilization based on the plan. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0167] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the data collection unit, analysis unit, planning unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and sensors of the headset terminal 314, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to analyze the growth status of crops in real time. The planning unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, which plans the optimal irrigation and fertilization based on the analysis results. The execution unit is implemented in real time by the control unit 46A of the headset terminal 314, which automatically executes irrigation and fertilization based on the plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0181] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0183] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0185] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0186] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0187] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0188] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0189] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0190] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0191] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0192] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0193] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0194] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0195] Each of the multiple elements described above, including the collection unit, analysis unit, planning unit, and execution unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the robot 414 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to analyze the growth status of crops in real time. The planning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and plans the optimal irrigation and fertilization based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the robot 414, and automatically executes irrigation and fertilization based on the plan. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0196] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0198] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0199] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0200] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0204] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0205] 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.

[0206] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0207] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0208] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0209] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0211] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0212] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0213] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0214] (Note 1) A data collection unit that collects data from at least one of multiple sensors and drones, The data collected by the aforementioned collection unit is analyzed by an analysis unit that analyzes the growth status of crops in real time, A planning unit that plans appropriate irrigation and fertilization based on the analysis results obtained by the aforementioned analysis unit, The system includes an execution unit that automatically performs irrigation and fertilization based on a plan created by the aforementioned planning unit. A system characterized by the following features. (Note 2) The system includes a forecasting unit that collects weather forecast data and provides it to the planning unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) The facility includes a market analysis department that collects market trend data and provides it to the planning department. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning department, Develop appropriate irrigation and fertilization plans based on weather forecast data and market trend data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, Automated irrigation using drones The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, The system uses sensors to determine the optimal timing for fertilization and automatically applies fertilizer. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, The planning department provides farmers with the action plan it has developed. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The type and frequency of data collected are dynamically changed according to the crop's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Data from different sensors and drones is integrated and filtered to ensure data consistency. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, optimize the collection range by considering geographical and climatic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, customize the collection method according to the type and variety of crop. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, anomalies are detected by comparing them with past data, and the cause of the anomalies is identified. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, crop growth models are used to predict future growth. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, we compare the growth patterns of different crops to identify the optimal growth conditions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, environmental and meteorological data are integrated to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, Estimate the user's emotions and adjust the level of detail in the plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, When formulating a plan, refer to past success stories and failures to develop the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, During the planning stage, multiple scenarios are simulated, and the optimal scenario is selected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, It estimates user sentiment and prioritizes plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planning department, When planning, integrate cultivation plans for different crops and optimize the overall process. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned planning department, When formulating a plan, adjust it to take into account local agricultural policies and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 26) The execution unit is, It estimates the user's emotions and adjusts the timing of execution based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, During execution, monitor environmental data in real time and modify the plan as needed. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, During execution, multiple drones and sensors will work in coordination to perform efficient irrigation and fertilization. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, During execution, the execution method is customized according to the growth stage of different crops. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, During implementation, the implementation plan will be adjusted to take into account the local water resources and fertilizer supply situation. The system described in Appendix 1, characterized by the features described herein. (Note 32) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The prediction unit, When making predictions, historical weather data is referenced to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 34) The prediction unit, When making forecasts, different weather models are combined to improve the reliability of the forecast. The system described in Appendix 1, characterized by the features described herein. (Note 35) The prediction unit, It estimates the user's emotions and prioritizes the prediction results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The prediction unit, When making predictions, customize the forecasting model to take into account local weather patterns. The system described in Appendix 1, characterized by the features described herein. (Note 37) The prediction unit, When making forecasts, we integrate different weather data sources to improve forecast accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned market analysis department, It estimates user sentiment and adjusts how market analysis results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned market analysis department, When conducting market analysis, historical market data is used to predict market trends. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned market analysis department, When conducting market analysis, combining different market models can enhance the reliability of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned market analysis department, We estimate user sentiment and prioritize market analysis results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned market analysis department, When conducting market analysis, customize the analysis model by taking regional market trends into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned market analysis department, When conducting market analysis, integrating different market data sources improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned supply unit is, It estimates the user's emotions and adjusts how action plans are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned supply unit is, When providing an action plan, we refer to the user's past behavioral history to provide the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned supply unit is, When providing an action plan, offer multiple options so that users can choose the one they prefer. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned supply unit is, Estimate user emotions and prioritize action plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned supply unit is, When providing an action plan, adjust the plan to take into account local agricultural policies and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned supply unit is, When providing an action plan, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0215] 10, 210, 310, 410 data processing systems 12 data processing devices 14 smart devices 214 smart glasses 314 headset-type terminals 414 robots

Claims

1. A data collection unit that collects data from at least one of multiple sensors and drones, The data collected by the aforementioned collection unit is analyzed by an analysis unit that analyzes the growth status of crops in real time, A planning unit that plans appropriate irrigation and fertilization based on the analysis results obtained by the aforementioned analysis unit, The system includes an execution unit that automatically performs irrigation and fertilization based on a plan created by the aforementioned planning unit. A system characterized by the following features.

2. The system includes a forecasting unit that collects weather forecast data and provides it to the planning unit. The system according to feature 1.

3. The facility includes a market analysis department that collects market trend data and provides it to the planning department. The system according to feature 1.

4. The aforementioned planning department, Develop appropriate irrigation and fertilization plans based on weather forecast data and market trend data. The system according to feature 1.

5. The execution unit is, Automated irrigation using drones The system according to feature 1.

6. The execution unit is, The system uses sensors to determine the optimal timing for fertilization and automatically applies fertilizer. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The type and frequency of data collected are dynamically changed according to the crop's growth stage. The system according to feature 1.

9. The aforementioned collection unit is Data from different sensors and drones is integrated and filtered to ensure data consistency. The system according to feature 1.

Images