system

Abstract

The system according to this embodiment aims to automate crop management based on weather data, enabling efficient watering, temperature control, and notification of harvest timing. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an adjustment unit, and a notification unit. The collection unit collects weather data. The analysis unit analyzes the weather data collected by the collection unit. The adjustment unit performs watering and temperature control based on the analysis results obtained by the analysis unit. The notification unit analyzes growth data based on the environment adjusted by the adjustment unit and notifies the harvest timing.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the management of crops according to the weather is performed manually, and there is a problem that it is difficult to efficiently water, adjust the temperature, and determine the harvesting timing.

[0005] The system according to the embodiment aims to automate the management of crops based on weather data and perform efficient watering, temperature adjustment, and notification of harvesting timing.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, an adjustment unit, and a notification unit. The collection unit collects weather data. The analysis unit analyzes the weather data collected by the collection unit. The adjustment unit performs watering and temperature control based on the analysis results obtained by the analysis unit. The notification unit analyzes growth data based on the environment adjusted by the adjustment unit and notifies the harvest timing. [Effects of the Invention]

[0007] The system according to this embodiment can automate crop management based on weather data, enabling efficient watering, temperature control, and notification of harvest timing. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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) The crop management system according to an embodiment of the present invention is a system that automates crop management using an AI agent. This crop management system collects and analyzes weather data and automatically performs watering, temperature control, and sunlight control appropriate for the crops. It also analyzes crop growth data and notifies the ideal harvest time. For example, the crop management system uses a weather sensor to collect weather data. The weather sensor acquires data such as temperature, humidity, precipitation, and sunshine duration in real time. Next, the crop management system analyzes the collected weather data. An AI agent is used for the analysis, and it understands the weather conditions using data analysis methods and algorithms. For example, in the case of dry weather, the crop management system automatically waters and adjusts humidity. Also, if the temperature is high, it automatically deploys shades to block sunlight in order to lower the temperature. Furthermore, the crop management system analyzes crop growth data and predicts the appropriate time for harvest. For example, it monitors the growth stage of the crop and notifies the appropriate timing for harvest. This allows farmers to harvest at the optimal time. This system allows farmers to efficiently manage their crops without the hassle of responding to daily weather and temperature changes. For example, by automatically watering and adjusting temperatures according to the weather, it can maintain crop quality and increase yields. Furthermore, by notifying farmers of the ideal harvest time, it ensures that they don't miss the optimal harvest period and can achieve maximum yields. In this way, the crop management system enables farmers to efficiently manage their crops and maximize their harvests.

[0029] The crop management system according to this embodiment comprises a collection unit, an analysis unit, an adjustment unit, and a notification unit. The collection unit collects weather data. The collection unit can acquire weather data such as temperature, humidity, precipitation, and sunshine duration in real time using, for example, a weather sensor. The collection unit can acquire weather data in real time using a weather sensor. For example, the collection unit can acquire weather data such as temperature, humidity, precipitation, and sunshine duration in real time using a weather sensor. The collection unit can acquire weather data in real time using a weather sensor. The analysis unit analyzes the weather data collected by the collection unit. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. The adjustment unit performs watering and temperature control based on the analysis results obtained by the analysis unit. The adjustment unit can automatically water and adjust humidity in the event of dry weather. The adjustment unit can automatically water and adjust humidity in the event of dry weather. For example, the adjustment unit can automatically water and adjust humidity in the event of dry weather. The adjustment unit can automatically water and adjust humidity in the event of dry weather. The adjustment unit can automatically deploy shades to block sunlight in order to lower the temperature when the temperature is high. The adjustment unit can automatically deploy shades to block sunlight in order to lower the temperature when the temperature is high. For example, the adjustment unit can automatically deploy shades to block sunlight in order to lower the temperature when the temperature is high. The adjustment unit can automatically deploy shades to block sunlight in order to lower the temperature when the temperature is high. The notification unit analyzes growth data based on the environment adjusted by the adjustment unit and notifies the timing of harvest. The notification unit can monitor the growth stage of crops and predict the optimal time for harvest. For example, the notification unit can monitor the growth stage of crops and predict the optimal time for harvest.As a result, the crop management system according to this embodiment can automate everything from weather data collection to analysis, adjustment, and notification, enabling efficient crop management.

[0030] The data collection unit collects weather data. For example, it uses weather sensors to acquire weather data such as temperature, humidity, precipitation, and sunshine duration in real time. Specifically, weather sensors are installed in various locations on farmland, and these sensors periodically collect data and transmit it to a central database. The weather sensor system consists of multiple sensors, such as temperature sensors, humidity sensors, rain gauges, and pyranometers, each measuring a specific weather element. For example, the temperature sensor measures temperature, the humidity sensor measures the amount of water vapor in the air, the rain gauge measures precipitation, and the pyranometer measures sunshine duration and solar radiation. This data is collected in real time and stored in the database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and adjustment units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes weather data collected by the collection unit. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. Specifically, based on the collected data, the analysis unit identifies factors affecting crop growth and derives optimal cultivation conditions. For example, it analyzes temperature data to make adjustments to maintain a temperature range suitable for specific crops. It also analyzes humidity data to assess the risk of drought and overwatering and determine the appropriate watering timing. Precipitation data is analyzed to supplement watering with natural rainfall and prevent overwatering. Sunshine duration data is analyzed to adjust the deployment and removal of shades to ensure the amount of light crops require. The analysis unit comprehensively analyzes this data and sends instructions to the adjustment unit to provide the optimal environment for crop growth. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical weather data, it can predict weather patterns in specific seasons and regions and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. 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 system.

[0032] The adjustment unit controls watering and temperature based on the analysis results obtained by the analysis unit. For example, in dry weather, the adjustment unit automatically waters and adjusts humidity. Specifically, the adjustment unit receives instructions from the analysis unit and controls the automatic irrigation system installed on the farmland. The automatic irrigation system calculates the required amount of water based on soil moisture sensors and weather data and waters at the appropriate time. The adjustment unit also automatically deploys shades to block sunlight and lower the temperature when it is high. The shades are installed above the farmland and open and close automatically based on temperature sensor data. This prevents crops from receiving excessive sunlight and maintains an appropriate temperature environment. Furthermore, if the humidity is too high, the adjustment unit can activate the ventilation system and take measures to lower the humidity. In this way, the adjustment unit automatically makes adjustments to maintain an optimal environment for crop growth, achieving efficient crop management.

[0033] The notification unit analyzes growth data based on the environment adjusted by the adjustment unit and notifies farmers of the optimal harvest time. Specifically, the notification unit monitors the growth stage of crops and predicts the best time for harvest. The notification unit monitors the growth status of crops in real time using growth sensors and cameras installed in the fields. The growth sensors measure growth indicators such as crop height, leaf color, and fruit size, and analyze this data. The cameras capture images of the crops and evaluate their growth status using image analysis technology. Based on this data, the notification unit determines whether the crops have reached a state suitable for harvest and sends a notification to the farmer. The notification is sent via smartphone apps, email, SMS, etc., allowing farmers to harvest at the appropriate time. Furthermore, the notification unit can also collect post-harvest data and provide feedback for the next cultivation. In this way, the notification unit can accurately grasp the growth status of crops and notify farmers of the optimal harvest time, thereby improving crop quality and yield.

[0034] The data collection unit acquires weather data in real time using weather sensors. For example, the data collection unit acquires weather data such as temperature, humidity, precipitation, and sunshine duration in real time using weather sensors. The data collection unit can acquire weather data in real time using weather sensors. For example, the data collection unit acquires weather data such as temperature, humidity, precipitation, and sunshine duration in real time using weather sensors. The data collection unit can acquire weather data in real time using weather sensors. This allows the data collection unit to grasp the latest weather conditions by acquiring weather data in real time. Real time refers to a state in which the frequency of data updates and delay time are minimized. For example, the data collection unit acquires weather data every minute using weather sensors and updates it in real time. The data collection unit can acquire weather data in real time using weather sensors. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input weather data acquired from weather sensors into a generating AI, and the generating AI can analyze and update the data in real time.

[0035] The analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. This allows the analysis unit to provide an environment suitable for crops by analyzing weather data. Analysis refers to processing data using data analysis methods and algorithms to extract meaningful information. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration to identify optimal environmental conditions for crops. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input weather data into a generating AI, which then analyzes the data to identify the optimal environmental conditions for crops.

[0036] The adjustment unit automatically waters and adjusts humidity in the event of dry weather. The adjustment unit can automatically water and adjust humidity in the event of dry weather. For example, the adjustment unit can automatically water and adjust humidity in the event of dry weather. The adjustment unit can automatically water and adjust humidity in the event of dry weather. This allows the adjustment unit to optimize the growing environment for crops by automatically watering in response to dry weather. Dry weather refers to a state where humidity falls below a certain threshold. For example, the adjustment unit automatically waters and adjusts humidity when humidity is 30% or less. The adjustment unit can automatically water and adjust humidity in the event of dry weather. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input dry weather data into a generating AI, which can then automatically water and adjust humidity.

[0037] The adjustment unit automatically deploys a shade to block sunlight in order to lower the temperature when the temperature is high. For example, the adjustment unit automatically deploys a shade to block sunlight in order to lower the temperature when the temperature is high. The adjustment unit can automatically deploy a shade to block sunlight in order to lower the temperature when the temperature is high. For example, the adjustment unit automatically deploys a shade to block sunlight in order to lower the temperature when the temperature is high. The adjustment unit can automatically deploy a shade to block sunlight in order to lower the temperature when the temperature is high. In this way, the adjustment unit can optimize the growing environment for crops by automatically deploying a shade when the temperature is high. High temperature refers to a state in which the temperature exceeds a certain threshold. For example, the adjustment unit automatically deploys a shade to lower the temperature when the temperature is 30 degrees or higher. The adjustment unit can automatically deploy a shade to block sunlight in order to lower the temperature when the temperature is high. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input high-temperature weather data into a generating AI, which can then automatically deploy the shade to lower the temperature.

[0038] The notification unit monitors the growth stages of crops and predicts the optimal time for harvest. The notification unit can monitor the growth stages of crops and predict the optimal time for harvest. For example, the notification unit can monitor the growth stages of crops and predict the optimal time for harvest. The notification unit can monitor the growth stages of crops and predict the optimal time for harvest. This allows the notification unit to notify the optimal harvest timing by monitoring the growth stages of crops. Growth stages refer to the phases of crop growth, including, for example, germination, growth, and harvest. For example, the notification unit can monitor the growth stages of crops and predict the optimal time for harvest. The notification unit can monitor the growth stages of crops and predict the optimal time for harvest. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input crop growth data into a generating AI, which can then predict and notify the optimal time for harvest.

[0039] The data collection unit optimizes the timing of data collection by referring to past weather patterns when collecting weather data. For example, the data collection unit analyzes past weather data and increases the collection frequency during specific seasons or time periods. The data collection unit can analyze past weather data and increase the collection frequency during specific seasons or time periods. For example, the data collection unit analyzes past weather data and increases the collection frequency during specific seasons or time periods. The data collection unit can analyze past weather data and increase the collection frequency during specific seasons or time periods. Based on past extreme weather data, the data collection unit increases the collection frequency when extreme weather is predicted. Based on past extreme weather data, the data collection unit can increase the collection frequency when extreme weather is predicted. For example, the data collection unit analyzes past extreme weather data and increases the collection frequency when extreme weather is predicted. Based on past extreme weather data, the data collection unit can increase the collection frequency when extreme weather is predicted. Based on past extreme weather data, the data collection unit can increase the collection frequency when extreme weather is predicted. The data collection unit associates past harvest data with weather data, identifies weather patterns that affect harvest, and adjusts the collection timing. The data collection unit associates past harvest data with weather data, identifies weather patterns that affect harvest, and adjusts the collection timing. For example, the data collection unit can associate historical harvest data with weather data, identify weather patterns that affect harvest, and adjust the collection timing. The data collection unit can associate historical harvest data with weather data, identify weather patterns that affect harvest, and adjust the collection timing. This allows the data collection unit to optimize the timing of collection by referring to past weather patterns. Past weather patterns refer to weather fluctuations over a certain period in the past. For example, the data collection unit can analyze weather data from the past 10 years and increase the collection frequency during specific seasons or time periods. When collecting weather data, the data collection unit can optimize the timing of collection by referring to past weather patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input historical weather data into a generating AI, which can analyze the data and optimize the timing of collection.

[0040] The data collection unit selects the types of data that have a significant impact on specific crops when collecting weather data. For example, the data collection unit prioritizes collecting weather elements (temperature, humidity, precipitation, etc.) that have a significant impact on specific crops. The data collection unit can prioritize collecting weather elements (temperature, humidity, precipitation, etc.) that have a significant impact on specific crops. For example, the data collection unit prioritizes collecting weather elements (temperature, humidity, precipitation, etc.) that have a significant impact on specific crops. The data collection unit can prioritize collecting weather elements (temperature, humidity, precipitation, etc.) that have a significant impact on specific crops. The data collection unit changes the types of weather data required according to the growth stage of the crops. The data collection unit can change the types of weather data required according to the growth stage of the crops. For example, the data collection unit changes the types of weather data required according to the growth stage of the crops. The data collection unit can change the types of weather data required according to the growth stage of the crops. The data collection unit collects weather conditions that pose a high risk of outbreaks of specific pests and diseases, providing data for implementing preventive measures. The data collection unit can collect weather conditions that pose a high risk of outbreaks of specific pests and diseases, and provide data for implementing preventive measures. For example, the data collection unit can collect weather conditions that pose a high risk of outbreaks of specific pests and diseases, and provide data for implementing preventive measures. The data collection unit can collect weather conditions that pose a high risk of outbreaks of specific pests and diseases, and provide data for implementing preventive measures. This allows the data collection unit to efficiently collect the necessary data by considering the impact on specific crops. The impact on specific crops refers to weather elements that affect the growth and harvest of crops. For example, the data collection unit prioritizes collecting weather elements that have a significant impact on specific crops (such as temperature, humidity, and precipitation). When collecting weather data, the data collection unit can select the types of data by considering the impact on specific crops. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input weather elements that have a significant impact on specific crops into a generating AI, which can then select and collect the data.

[0041] The data collection unit selects collection points considering geographical characteristics when collecting weather data. For example, the data collection unit selects collection points considering differences in topography and elevation. The data collection unit can select collection points considering differences in topography and elevation. For example, the data collection unit can select collection points considering differences in topography and elevation. The data collection unit can select collection points considering differences in topography and elevation. The data collection unit selects collection points considering the different weather characteristics for each crop cultivation area. The data collection unit can select collection points considering the different weather characteristics for each crop cultivation area. For example, the data collection unit can select collection points considering the different weather characteristics for each crop cultivation area. The data collection unit can select collection points considering the different weather characteristics for each crop cultivation area. The data collection unit analyzes weather patterns for each region and selects the optimal collection points. The data collection unit can analyze weather patterns for each region and select the optimal collection points. For example, the data collection unit analyzes weather patterns for each region and selects the optimal collection points. The data collection unit can analyze weather patterns for each region and select the optimal collection points. This allows the data collection unit to select the optimal collection points by considering geographical characteristics. Geographical characteristics refer to topography and climatic conditions, etc. For example, the data collection unit selects collection points by considering differences in topography and elevation. When collecting weather data, the data collection unit can select collection points by considering geographical characteristics. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input geographical characteristic data into a generating AI, and the generating AI can select collection points.

[0042] The data collection unit changes the type of data it collects according to the growth stage of the crop when collecting weather data. For example, during the germination stage, the data collection unit focuses on collecting temperature and humidity data. The data collection unit can focus on collecting temperature and humidity data during the germination stage. For example, during the germination stage, the data collection unit focuses on collecting temperature and humidity data. The data collection unit can focus on collecting temperature and humidity data during the germination stage. The data collection unit focuses on collecting sunshine duration and precipitation data during the growth stage. The data collection unit can focus on collecting sunshine duration and precipitation data during the growth stage. For example, during the growth stage, the data collection unit focuses on collecting sunshine duration and precipitation data. The data collection unit can focus on collecting sunshine duration and precipitation data during the growth stage. The data collection unit focuses on collecting temperature and precipitation data during the harvest stage. The data collection unit can focus on collecting temperature and precipitation data during the harvest stage. For example, the data collection unit focuses on collecting temperature and precipitation data during the harvest season. The data collection unit can focus on collecting temperature and precipitation data during the harvest season. This allows the data collection unit to efficiently collect the necessary data by changing the type of data collected according to the growth stage of the crop. Growth stages refer to the phases of crop growth, including, for example, germination, growth, and harvest. For example, the data collection unit focuses on collecting temperature and humidity data during the germination stage. The data collection unit can change the type of data collected according to the growth stage of the crop when collecting weather data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input crop growth stage data into a generating AI and change the type of data collected by the generating AI.

[0043] The analysis unit detects anomalies by comparing them with past weather data during analysis. For example, the analysis unit can detect abnormal temperature increases and precipitation fluctuations by comparing them with past weather data. The analysis unit can detect abnormal temperature increases and precipitation fluctuations by comparing them with past weather data. For example, the analysis unit can detect abnormal temperature increases and precipitation fluctuations by comparing them with past weather data. The analysis unit can detect abnormal humidity fluctuations and evaluate their impact on crops. For example, the analysis unit can detect abnormal humidity fluctuations and evaluate their impact on crops. The analysis unit can detect abnormal sunshine duration fluctuations and evaluate their impact on crop growth. The analysis unit can detect abnormal fluctuations in sunshine duration and evaluate their impact on crop growth. This allows the analysis unit to detect anomalies by comparing current data with past weather data. Anomalies refer to data that deviates from the normal range. For example, the analysis unit can detect abnormal temperature increases or precipitation fluctuations by comparing current data with past weather data. The analysis unit can detect anomalies by comparing current data with past weather data during analysis. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input past weather data into a generating AI, which can then detect anomalies.

[0044] The analysis unit optimizes the analysis algorithm during analysis, taking into account the impact on specific crops. For example, the analysis unit prioritizes analyzing weather elements that have a significant impact on specific crops. The analysis unit can prioritize analyzing weather elements that have a significant impact on specific crops. For example, the analysis unit prioritizes analyzing weather elements that have a significant impact on specific crops. The analysis unit can prioritize analyzing weather elements that have a significant impact on specific crops. The analysis unit adjusts the analysis algorithm according to the growth stage of the crops. The analysis unit can adjust the analysis algorithm according to the growth stage of the crops. For example, the analysis unit adjusts the analysis algorithm according to the growth stage of the crops. The analysis unit can adjust the analysis algorithm according to the growth stage of the crops. The analysis unit optimizes the analysis algorithm considering the risk of outbreaks of specific pests and diseases. The analysis unit can optimize the analysis algorithm considering the risk of outbreaks of specific pests and diseases. For example, the analysis unit optimizes the analysis algorithm considering the risk of outbreaks of specific pests and diseases. The analysis unit can optimize the analysis algorithm considering the risk of outbreaks of specific pests and diseases. This allows the analysis unit to optimize its analysis algorithm by considering the impact on specific crops. The impact on specific crops refers to weather elements that affect crop growth and harvest. For example, the analysis unit prioritizes analyzing weather elements that have a significant impact on specific crops. The analysis unit can optimize its analysis algorithm by considering the impact on specific crops during the analysis. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input weather elements that have a significant impact on specific crops into a generating AI, which can then optimize the analysis algorithm.

[0045] The analysis unit modifies the analysis algorithm during analysis, taking into account geographical characteristics. The analysis unit modifies the analysis algorithm, for example, by taking into account differences in topography and elevation. The analysis unit can modify the analysis algorithm, taking into account differences in topography and elevation. For example, the analysis unit modifies the analysis algorithm, taking into account differences in topography and elevation. The analysis unit can modify the analysis algorithm, taking into account differences in topography and elevation. The analysis unit modifies the analysis algorithm, taking into account different weather characteristics for each crop cultivation area. The analysis unit can modify the analysis algorithm, taking into account different weather characteristics for each crop cultivation area. For example, the analysis unit modifies the analysis algorithm, taking into account different weather characteristics for each crop cultivation area. The analysis unit can modify the analysis algorithm, taking into account different weather characteristics for each crop cultivation area. The analysis unit analyzes weather patterns for each region and selects the optimal analysis algorithm. The analysis unit can analyze weather patterns for each region and select the optimal analysis algorithm. For example, the analysis unit analyzes weather patterns for each region and selects the optimal analysis algorithm. The analysis unit analyzes weather patterns for each region and selects the optimal analysis algorithm. This allows the analysis unit to select the optimal analysis algorithm by considering geographical characteristics. Geographical characteristics refer to topography, climatic conditions, etc. For example, the analysis unit modifies the analysis algorithm by considering differences in topography and elevation. The analysis unit can modify the analysis algorithm by considering geographical characteristics during the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input geographical characteristics data into a generating AI, and the generating AI can modify the analysis algorithm.

[0046] The analysis unit changes the focus of its analysis according to the growth stage of the crop. For example, during the germination stage, the analysis unit focuses on analyzing temperature and humidity. The analysis unit can focus on analyzing temperature and humidity during the germination stage. For example, during the germination stage, the analysis unit focuses on analyzing temperature and humidity. The analysis unit can focus on analyzing temperature and humidity during the germination stage. The analysis unit focuses on analyzing sunshine hours and precipitation during the growth stage. The analysis unit can focus on analyzing sunshine hours and precipitation during the growth stage. For example, during the growth stage, the analysis unit focuses on analyzing sunshine hours and precipitation. The analysis unit can focus on analyzing sunshine hours and precipitation during the growth stage. The analysis unit focuses on analyzing temperature and precipitation during the harvest stage. The analysis unit can focus on analyzing temperature and precipitation during the harvest stage. For example, the analysis unit focuses on analyzing temperature and precipitation during the harvest season. This allows the analysis unit to efficiently analyze necessary data by changing its analysis focus according to the crop's growth stage. Growth stages refer to the phases of crop growth, including, for example, germination, growth, and harvest. For example, the analysis unit focuses on analyzing temperature and humidity during the germination stage. The analysis unit can change its analysis focus during analysis according to the crop's growth stage. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input crop growth stage data into a generating AI, which can then change the analysis focus.

[0047] The adjustment unit selects the optimal adjustment method by referring to past adjustment history during adjustment. For example, the adjustment unit analyzes past watering and temperature control history to select the optimal adjustment method. The adjustment unit can analyze past watering and temperature control history to select the optimal adjustment method. For example, the adjustment unit analyzes past watering and temperature control history to select the optimal adjustment method. The adjustment unit can analyze past watering and temperature control history to select the optimal adjustment method. The adjustment unit selects the optimal adjustment method for specific weather conditions from past adjustment history. The adjustment unit can select the optimal adjustment method for specific weather conditions from past adjustment history. For example, the adjustment unit selects the optimal adjustment method for specific weather conditions from past adjustment history. The adjustment unit can select the optimal adjustment method for specific weather conditions from past adjustment history. Based on past adjustment history, the adjustment unit selects the optimal adjustment method for crop growth. Based on past adjustment history, the adjustment unit can select the optimal adjustment method for crop growth. For example, the adjustment unit selects the optimal adjustment method for crop growth based on past adjustment history. The adjustment unit can select the optimal adjustment method for crop growth based on past adjustment history. This allows the adjustment unit to select the optimal adjustment method by referring to past adjustment history. Past adjustment history refers to records of past watering and temperature control. For example, the adjustment unit analyzes past watering and temperature control history to select the optimal adjustment method. The adjustment unit can also select the optimal adjustment method by referring to past adjustment history during adjustment. Some or all of the above processing in the adjustment unit may be performed using AI, or without AI. For example, the adjustment unit can input past adjustment history data into a generating AI, which can then select the optimal adjustment method.

[0048] The adjustment unit optimizes the adjustment method during adjustment, taking into account its impact on specific crops. For example, the adjustment unit prioritizes adjustments based on weather elements that have a significant impact on specific crops. The adjustment unit can prioritize adjustments based on weather elements that have a significant impact on specific crops. For example, the adjustment unit prioritizes adjustments based on weather elements that have a significant impact on specific crops. The adjustment unit can prioritize adjustments based on weather elements that have a significant impact on specific crops. The adjustment unit changes the adjustment method according to the growth stage of the crops. The adjustment unit can change the adjustment method according to the growth stage of the crops. For example, the adjustment unit changes the adjustment method according to the growth stage of the crops. The adjustment unit can change the adjustment method according to the growth stage of the crops. The adjustment unit optimizes the adjustment method, taking into account the risk of outbreaks of specific pests and diseases. The adjustment unit can optimize the adjustment method, taking into account the risk of outbreaks of specific pests and diseases. For example, the adjustment unit optimizes the adjustment method, taking into account the risk of outbreaks of specific pests and diseases. The adjustment unit can optimize the adjustment method, taking into account the risk of outbreaks of specific pests and diseases. This allows the adjustment unit to optimize its adjustment method by considering the impact on specific crops. The impact on specific crops refers to weather elements that affect the growth and harvest of crops. For example, the adjustment unit prioritizes adjusting weather elements that have a significant impact on specific crops. The adjustment unit can optimize its adjustment method by considering the impact on specific crops during the adjustment process. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input weather elements that have a significant impact on specific crops into a generating AI, which can then optimize the adjustment method.

[0049] The adjustment unit modifies the adjustment method when making adjustments, taking into account geographical characteristics. The adjustment unit modifies the adjustment method, for example, by taking into account differences in topography and elevation. The adjustment unit can modify the adjustment method by taking into account differences in topography and elevation. For example, the adjustment unit modifies the adjustment method by taking into account differences in topography and elevation. The adjustment unit can modify the adjustment method by taking into account differences in topography and elevation. The adjustment unit modifies the adjustment method by taking into account different weather characteristics for each crop cultivation area. The adjustment unit can modify the adjustment method by taking into account different weather characteristics for each crop cultivation area. For example, the adjustment unit modifies the adjustment method by taking into account different weather characteristics for each crop cultivation area. The adjustment unit can modify the adjustment method by taking into account different weather characteristics for each crop cultivation area. The adjustment unit analyzes weather patterns for each region and selects the optimal adjustment method. The adjustment unit can analyze weather patterns for each region and select the optimal adjustment method. For example, the adjustment unit analyzes weather patterns for each region and selects the optimal adjustment method. The adjustment unit analyzes weather patterns for each region and selects the optimal adjustment method. This allows the adjustment unit to select the optimal adjustment method by considering geographical characteristics. Geographical characteristics refer to topography, climatic conditions, etc. For example, the adjustment unit changes the adjustment method by considering differences in topography and elevation. The adjustment unit can change the adjustment method by considering geographical characteristics during the adjustment process. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input geographical characteristics data into a generating AI, and the generating AI can change the adjustment method.

[0050] The adjustment unit changes the focus of its adjustments according to the growth stage of the crop. For example, during the germination stage, the adjustment unit focuses on adjusting temperature and humidity. The adjustment unit can focus on adjusting temperature and humidity during the germination stage. For example, during the germination stage, the adjustment unit focuses on adjusting temperature and humidity. The adjustment unit can focus on adjusting temperature and humidity during the germination stage. The adjustment unit focuses on adjusting sunshine hours and rainfall during the growth stage. The adjustment unit can focus on adjusting sunshine hours and rainfall during the growth stage. For example, during the growth stage, the adjustment unit focuses on adjusting sunshine hours and rainfall. The adjustment unit can focus on adjusting sunshine hours and rainfall during the growth stage. The adjustment unit focuses on adjusting temperature and rainfall during the harvest stage. The adjustment unit can focus on adjusting temperature and rainfall during the harvest stage. For example, the adjustment unit focuses on adjusting temperature and rainfall during the harvest season. The adjustment unit can focus on adjusting temperature and rainfall during the harvest season. This allows the adjustment unit to efficiently perform necessary adjustments by changing the focus of adjustments according to the growth stage of the crop. Growth stages refer to the phases of crop growth, including, for example, the germination stage, the growing stage, and the harvest stage. For example, the adjustment unit focuses on adjusting temperature and humidity during the germination stage. The adjustment unit can change the focus of adjustments according to the growth stage of the crop during adjustments. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input crop growth stage data into a generating AI, and the generating AI can change the focus of adjustments.

[0051] The notification unit predicts the optimal harvest timing by referring to past harvest data when a notification is sent. The notification unit can predict the optimal harvest timing by analyzing past harvest data. For example, the notification unit can predict the optimal harvest timing by analyzing past harvest data. The notification unit can predict the optimal harvest timing by analyzing past harvest data. The notification unit can predict the optimal harvest timing by analyzing past harvest data. The notification unit can predict the optimal harvest timing by associating past harvest data with weather data. The notification unit can predict the optimal harvest timing by associating past harvest data with weather data. For example, the notification unit can predict the optimal harvest timing by associating past harvest data with weather data. The notification unit can predict the optimal harvest timing by associating past harvest data with weather data. Based on past harvest data, the notification unit can predict the optimal harvest timing for crop growth. Based on past harvest data, the notification unit can predict the optimal harvest timing for crop growth. For example, the notification unit can predict the optimal harvest timing for crop growth based on past harvest data. Based on past harvest data, the notification unit can predict the optimal harvest timing for crop growth. This allows the notification unit to predict the optimal harvesting timing by referring to past harvest data. Past harvest data refers to records of past harvests. For example, the notification unit analyzes past harvest data to predict the optimal harvesting timing. The notification unit can predict the optimal harvesting timing by referring to past harvest data when sending a notification. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input past harvest data into a generating AI, which can then predict the optimal harvesting timing.

[0052] The notification unit optimizes the notification content when it sends a notification, taking into account its impact on specific crops. For example, the notification unit prioritizes notifying about weather elements that have a significant impact on specific crops. The notification unit can prioritize notifying about weather elements that have a significant impact on specific crops. For example, the notification unit prioritizes notifying about weather elements that have a significant impact on specific crops. The notification unit can prioritize notifying about weather elements that have a significant impact on specific crops. The notification unit changes the notification content according to the growth stage of the crops. The notification unit can change the notification content according to the growth stage of the crops. For example, the notification unit changes the notification content according to the growth stage of the crops. The notification unit can change the notification content according to the growth stage of the crops. The notification unit optimizes the notification content, taking into account the risk of outbreaks of specific pests and diseases. The notification unit can optimize the notification content, taking into account the risk of outbreaks of specific pests and diseases. For example, the notification unit optimizes the notification content, taking into account the risk of outbreaks of specific pests and diseases. The notification unit can optimize the notification content, taking into account the risk of outbreaks of specific pests and diseases. This allows the notification unit to optimize notification content by considering the impact on specific crops. The impact on specific crops refers to weather elements that affect the growth and harvest of crops. For example, the notification unit prioritizes notifying about weather elements that have a significant impact on specific crops. The notification unit can optimize notification content by considering the impact on specific crops when sending notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input weather elements that have a significant impact on specific crops into a generating AI, which can then optimize the notification content.

[0053] The notification unit modifies the notification content when it sends a notification, taking into account geographical characteristics. The notification unit modifies the notification content, for example, by taking into account differences in topography and elevation. The notification unit can modify the notification content, taking into account differences in topography and elevation. For example, the notification unit modifies the notification content, taking into account differences in topography and elevation. The notification unit can modify the notification content, taking into account differences in topography and elevation. The notification unit modifies the notification content, taking into account different weather characteristics for each crop cultivation area. The notification unit can modify the notification content, taking into account different weather characteristics for each crop cultivation area. For example, the notification unit modifies the notification content, taking into account different weather characteristics for each crop cultivation area. The notification unit can modify the notification content, taking into account different weather characteristics for each crop cultivation area. The notification unit analyzes weather patterns for each region and selects the optimal notification content. The notification unit can analyze weather patterns for each region and select the optimal notification content. For example, the notification unit analyzes weather patterns for each region and selects the optimal notification content. The notification unit analyzes weather patterns for each region and selects the optimal notification content. This allows the notification unit to select the optimal notification content by considering geographical characteristics. Geographical characteristics refer to topography, climatic conditions, etc. For example, the notification unit modifies the notification content by considering differences in topography and elevation. The notification unit can modify the notification content by considering geographical characteristics when sending a notification. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input geographical characteristics data into a generating AI, and the generating AI can modify the notification content.

[0054] The notification unit changes the focus of its notifications according to the growth stage of the crop. For example, during the germination stage, the notification unit focuses on notifying about temperature and humidity. The notification unit can focus on notifying about temperature and humidity during the germination stage. For example, during the germination stage, the notification unit focuses on notifying about temperature and humidity. The notification unit can focus on notifying about temperature and humidity during the germination stage. The notification unit focuses on notifying about sunshine hours and rainfall during the growth stage. The notification unit can focus on notifying about sunshine hours and rainfall during the growth stage. For example, during the growth stage, the notification unit focuses on notifying about sunshine hours and rainfall. The notification unit can focus on notifying about sunshine hours and rainfall during the growth stage. The notification unit focuses on notifying about temperature and rainfall during the harvest stage. The notification unit can focus on notifying about temperature and rainfall during the harvest stage. For example, the notification unit can focus on notifying users of temperature and rainfall during the harvest season. This allows the notification unit to efficiently provide necessary notifications by changing the focus of notifications according to the growth stage of the crop. Growth stages refer to the phases of crop growth, including, for example, germination, growth, and harvest. For example, the notification unit can focus on notifying users of temperature and humidity during the germination stage. The notification unit can change the focus of notifications according to the growth stage of the crop when sending notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input crop growth stage data into a generating AI, which can then change the focus of notifications.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The crop management system can also be equipped with soil sensors. These sensors acquire data such as soil moisture content, nutrient levels, and pH values ​​in real time. For example, a soil sensor can measure soil moisture content and automatically water the soil if it is dry. It can also measure soil nutrient levels and add fertilizer as needed. Furthermore, it can measure the soil pH value and adjust it to maintain an appropriate pH level. This allows the crop management system to monitor soil conditions in real time and provide an optimal growing environment.

[0057] The data collection unit can further utilize drones to collect weather data over a wide area. Drones fly across the entire farmland, collecting data such as temperature, humidity, precipitation, and sunshine duration. For example, drones can collect weather data from different areas of the farmland, allowing for an understanding of the environmental conditions in each area. Drones can also photograph crop growth from the air and collect image data. Furthermore, drones can quickly collect data during extreme weather events, enabling measures to be taken to minimize the impact on crops. As a result, the data collection unit can efficiently collect weather data over a wide area and optimize crop management.

[0058] The analysis unit can further predict weather data using machine learning algorithms. These machine learning algorithms learn from past weather data and predict future weather. For example, the analysis unit can use machine learning algorithms to predict the weather for the next week and develop a crop management plan. Furthermore, machine learning algorithms can predict the probability of extreme weather events and allow for proactive countermeasures. In addition, machine learning algorithms can analyze crop growth data and suggest optimal cultivation methods. This enables the analysis unit to predict future weather and manage crops more efficiently.

[0059] The control unit can also be equipped with an automated irrigation system. This automated irrigation system automatically adjusts the timing and amount of watering based on weather and soil data. For example, the control unit can automatically activate the irrigation system when it detects dry weather and low soil moisture levels. It can also stop the irrigation system and activate a drainage system to remove excess water if excessive rainfall is predicted. Furthermore, the irrigation system can adjust the amount and frequency of watering according to the growth stage of specific crops. This allows the control unit to optimize crop water management and achieve efficient irrigation.

[0060] The notification unit can also send notifications to users via a smartphone app. The smartphone app displays crop growth status and weather data in real time, providing users with important information. For example, the notification unit can notify users via the smartphone app when harvest time is approaching. It can also quickly notify users if extreme weather is predicted and provide advice on countermeasures. Furthermore, the smartphone app can provide an interface for users to check the management status of their crops and make necessary adjustments. In this way, the notification unit can provide users with quick and effective notifications and support crop management.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects weather data. The collection unit uses, for example, weather sensors to acquire weather data such as temperature, humidity, precipitation, and sunshine duration in real time. Step 2: The analysis unit analyzes the weather data collected by the collection unit. The analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. Step 3: The adjustment unit performs watering and temperature control based on the analysis results obtained by the analysis unit. For example, it automatically waters and adjusts humidity in dry weather. It also automatically deploys shades to block sunlight and lower the temperature when the temperature is high. Step 4: The notification unit analyzes growth data based on the environment adjusted by the adjustment unit and notifies the harvest timing. For example, it monitors the growth stage of crops and predicts the optimal time for harvest.

[0063] (Example of form 2) The crop management system according to an embodiment of the present invention is a system that automates crop management using an AI agent. This crop management system collects and analyzes weather data and automatically performs watering, temperature control, and sunlight control appropriate for the crops. It also analyzes crop growth data and notifies the ideal harvest time. For example, the crop management system uses a weather sensor to collect weather data. The weather sensor acquires data such as temperature, humidity, precipitation, and sunshine duration in real time. Next, the crop management system analyzes the collected weather data. An AI agent is used for the analysis, and it understands the weather conditions using data analysis methods and algorithms. For example, in the case of dry weather, the crop management system automatically waters and adjusts humidity. Also, if the temperature is high, it automatically deploys shades to block sunlight in order to lower the temperature. Furthermore, the crop management system analyzes crop growth data and predicts the appropriate time for harvest. For example, it monitors the growth stage of the crop and notifies the appropriate timing for harvest. This allows farmers to harvest at the optimal time. This system allows farmers to efficiently manage their crops without the hassle of responding to daily weather and temperature changes. For example, by automatically watering and adjusting temperatures according to the weather, it can maintain crop quality and increase yields. Furthermore, by notifying farmers of the ideal harvest time, it ensures that they don't miss the optimal harvest period and can achieve maximum yields. In this way, the crop management system enables farmers to efficiently manage their crops and maximize their harvests.

[0064] The crop management system according to this embodiment comprises a collection unit, an analysis unit, an adjustment unit, and a notification unit. The collection unit collects weather data. The collection unit can acquire weather data such as temperature, humidity, precipitation, and sunshine duration in real time using, for example, a weather sensor. The collection unit can acquire weather data in real time using a weather sensor. For example, the collection unit can acquire weather data such as temperature, humidity, precipitation, and sunshine duration in real time using a weather sensor. The collection unit can acquire weather data in real time using a weather sensor. The analysis unit analyzes the weather data collected by the collection unit. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. The adjustment unit performs watering and temperature control based on the analysis results obtained by the analysis unit. The adjustment unit can automatically water and adjust humidity in the event of dry weather. The adjustment unit can automatically water and adjust humidity in the event of dry weather. For example, the adjustment unit can automatically water and adjust humidity in the event of dry weather. The adjustment unit can automatically water and adjust humidity in the event of dry weather. The adjustment unit can automatically deploy shades to block sunlight in order to lower the temperature when the temperature is high. The adjustment unit can automatically deploy shades to block sunlight in order to lower the temperature when the temperature is high. For example, the adjustment unit can automatically deploy shades to block sunlight in order to lower the temperature when the temperature is high. The adjustment unit can automatically deploy shades to block sunlight in order to lower the temperature when the temperature is high. The notification unit analyzes growth data based on the environment adjusted by the adjustment unit and notifies the timing of harvest. The notification unit can monitor the growth stage of crops and predict the optimal time for harvest. For example, the notification unit can monitor the growth stage of crops and predict the optimal time for harvest.As a result, the crop management system according to this embodiment can automate everything from weather data collection to analysis, adjustment, and notification, enabling efficient crop management.

[0065] The data collection unit collects weather data. For example, it uses weather sensors to acquire weather data such as temperature, humidity, precipitation, and sunshine duration in real time. Specifically, weather sensors are installed in various locations on farmland, and these sensors periodically collect data and transmit it to a central database. The weather sensor system consists of multiple sensors, such as temperature sensors, humidity sensors, rain gauges, and pyranometers, each measuring a specific weather element. For example, the temperature sensor measures temperature, the humidity sensor measures the amount of water vapor in the air, the rain gauge measures precipitation, and the pyranometer measures sunshine duration and solar radiation. This data is collected in real time and stored in the database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and adjustment units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes weather data collected by the collection unit. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. Specifically, based on the collected data, the analysis unit identifies factors affecting crop growth and derives optimal cultivation conditions. For example, it analyzes temperature data to make adjustments to maintain a temperature range suitable for specific crops. It also analyzes humidity data to assess the risk of drought and overwatering and determine the appropriate watering timing. Precipitation data is analyzed to supplement watering with natural rainfall and prevent overwatering. Sunshine duration data is analyzed to adjust the deployment and removal of shades to ensure the amount of light crops require. The analysis unit comprehensively analyzes this data and sends instructions to the adjustment unit to provide the optimal environment for crop growth. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical weather data, it can predict weather patterns in specific seasons and regions and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. 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 system.

[0067] The adjustment unit controls watering and temperature based on the analysis results obtained by the analysis unit. For example, in dry weather, the adjustment unit automatically waters and adjusts humidity. Specifically, the adjustment unit receives instructions from the analysis unit and controls the automatic irrigation system installed on the farmland. The automatic irrigation system calculates the required amount of water based on soil moisture sensors and weather data and waters at the appropriate time. The adjustment unit also automatically deploys shades to block sunlight and lower the temperature when it is high. The shades are installed above the farmland and open and close automatically based on temperature sensor data. This prevents crops from receiving excessive sunlight and maintains an appropriate temperature environment. Furthermore, if the humidity is too high, the adjustment unit can activate the ventilation system and take measures to lower the humidity. In this way, the adjustment unit automatically makes adjustments to maintain an optimal environment for crop growth, achieving efficient crop management.

[0068] The notification unit analyzes growth data based on the environment adjusted by the adjustment unit and notifies farmers of the optimal harvest time. Specifically, the notification unit monitors the growth stage of crops and predicts the best time for harvest. The notification unit monitors the growth status of crops in real time using growth sensors and cameras installed in the fields. The growth sensors measure growth indicators such as crop height, leaf color, and fruit size, and analyze this data. The cameras capture images of the crops and evaluate their growth status using image analysis technology. Based on this data, the notification unit determines whether the crops have reached a state suitable for harvest and sends a notification to the farmer. The notification is sent via smartphone apps, email, SMS, etc., allowing farmers to harvest at the appropriate time. Furthermore, the notification unit can also collect post-harvest data and provide feedback for the next cultivation. In this way, the notification unit can accurately grasp the growth status of crops and notify farmers of the optimal harvest time, thereby improving crop quality and yield.

[0069] The data collection unit acquires weather data in real time using weather sensors. For example, the data collection unit acquires weather data such as temperature, humidity, precipitation, and sunshine duration in real time using weather sensors. The data collection unit can acquire weather data in real time using weather sensors. For example, the data collection unit acquires weather data such as temperature, humidity, precipitation, and sunshine duration in real time using weather sensors. The data collection unit can acquire weather data in real time using weather sensors. This allows the data collection unit to grasp the latest weather conditions by acquiring weather data in real time. Real time refers to a state in which the frequency of data updates and delay time are minimized. For example, the data collection unit acquires weather data every minute using weather sensors and updates it in real time. The data collection unit can acquire weather data in real time using weather sensors. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input weather data acquired from weather sensors into a generating AI, and the generating AI can analyze and update the data in real time.

[0070] The analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. This allows the analysis unit to provide an environment suitable for crops by analyzing weather data. Analysis refers to processing data using data analysis methods and algorithms to extract meaningful information. For example, the analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration to identify optimal environmental conditions for crops. The analysis unit can analyze weather data such as temperature, humidity, precipitation, and sunshine duration. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input weather data into a generating AI, which then analyzes the data to identify the optimal environmental conditions for crops.

[0071] The adjustment unit automatically waters and adjusts humidity in the event of dry weather. The adjustment unit can automatically water and adjust humidity in the event of dry weather. For example, the adjustment unit can automatically water and adjust humidity in the event of dry weather. The adjustment unit can automatically water and adjust humidity in the event of dry weather. This allows the adjustment unit to optimize the growing environment for crops by automatically watering in response to dry weather. Dry weather refers to a state where humidity falls below a certain threshold. For example, the adjustment unit automatically waters and adjusts humidity when humidity is 30% or less. The adjustment unit can automatically water and adjust humidity in the event of dry weather. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input dry weather data into a generating AI, which can then automatically water and adjust humidity.

[0072] The adjustment unit automatically deploys a shade to block sunlight in order to lower the temperature when the temperature is high. For example, the adjustment unit automatically deploys a shade to block sunlight in order to lower the temperature when the temperature is high. The adjustment unit can automatically deploy a shade to block sunlight in order to lower the temperature when the temperature is high. For example, the adjustment unit automatically deploys a shade to block sunlight in order to lower the temperature when the temperature is high. The adjustment unit can automatically deploy a shade to block sunlight in order to lower the temperature when the temperature is high. In this way, the adjustment unit can optimize the growing environment for crops by automatically deploying a shade when the temperature is high. High temperature refers to a state in which the temperature exceeds a certain threshold. For example, the adjustment unit automatically deploys a shade to lower the temperature when the temperature is 30 degrees or higher. The adjustment unit can automatically deploy a shade to block sunlight in order to lower the temperature when the temperature is high. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input high-temperature weather data into a generating AI, which can then automatically deploy the shade to lower the temperature.

[0073] The notification unit monitors the growth stages of crops and predicts the optimal time for harvest. The notification unit can monitor the growth stages of crops and predict the optimal time for harvest. For example, the notification unit can monitor the growth stages of crops and predict the optimal time for harvest. The notification unit can monitor the growth stages of crops and predict the optimal time for harvest. This allows the notification unit to notify the optimal harvest timing by monitoring the growth stages of crops. Growth stages refer to the phases of crop growth, including, for example, germination, growth, and harvest. For example, the notification unit can monitor the growth stages of crops and predict the optimal time for harvest. The notification unit can monitor the growth stages of crops and predict the optimal time for harvest. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input crop growth data into a generating AI, which can then predict and notify the optimal time for harvest.

[0074] The data collection unit estimates the user's emotions and adjusts the frequency of weather data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit increases the collection frequency to provide more detailed weather data. The data collection unit can increase the collection frequency to provide more detailed weather data if the user is stressed. For example, if the user is stressed, the data collection unit can increase the collection frequency to provide more detailed weather data. The data collection unit can increase the collection frequency to provide more detailed weather data if the user is stressed. The data collection unit can decrease the collection frequency to provide only the minimum necessary data if the user is relaxed. The data collection unit can decrease the collection frequency to provide only the minimum necessary data if the user is relaxed. For example, if the user is relaxed, the data collection unit can decrease the collection frequency to provide only the minimum necessary data if the user is relaxed. The data collection unit can optimize the collection frequency to provide data quickly if the user is in a hurry. For example, if the user is in a hurry, the data collection unit can optimize the collection frequency to provide data quickly. The data collection unit can optimize the collection frequency to provide data quickly when the user is in a hurry. This allows the data collection unit to provide data that meets the user's needs by adjusting the frequency of weather data collection 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI, which can estimate the emotion and adjust the collection frequency.

[0075] The data collection unit optimizes the timing of data collection by referring to past weather patterns when collecting weather data. For example, the data collection unit analyzes past weather data and increases the collection frequency during specific seasons or time periods. The data collection unit can analyze past weather data and increase the collection frequency during specific seasons or time periods. For example, the data collection unit analyzes past weather data and increases the collection frequency during specific seasons or time periods. The data collection unit can analyze past weather data and increase the collection frequency during specific seasons or time periods. Based on past extreme weather data, the data collection unit increases the collection frequency when extreme weather is predicted. Based on past extreme weather data, the data collection unit can increase the collection frequency when extreme weather is predicted. For example, the data collection unit analyzes past extreme weather data and increases the collection frequency when extreme weather is predicted. Based on past extreme weather data, the data collection unit can increase the collection frequency when extreme weather is predicted. Based on past extreme weather data, the data collection unit can increase the collection frequency when extreme weather is predicted. The data collection unit associates past harvest data with weather data, identifies weather patterns that affect harvest, and adjusts the collection timing. The data collection unit associates past harvest data with weather data, identifies weather patterns that affect harvest, and adjusts the collection timing. For example, the data collection unit can associate historical harvest data with weather data, identify weather patterns that affect harvest, and adjust the collection timing. The data collection unit can associate historical harvest data with weather data, identify weather patterns that affect harvest, and adjust the collection timing. This allows the data collection unit to optimize the timing of collection by referring to past weather patterns. Past weather patterns refer to weather fluctuations over a certain period in the past. For example, the data collection unit can analyze weather data from the past 10 years and increase the collection frequency during specific seasons or time periods. When collecting weather data, the data collection unit can optimize the timing of collection by referring to past weather patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input historical weather data into a generating AI, which can analyze the data and optimize the timing of collection.

[0076] The data collection unit selects the types of data that have a significant impact on specific crops when collecting weather data. For example, the data collection unit prioritizes collecting weather elements (temperature, humidity, precipitation, etc.) that have a significant impact on specific crops. The data collection unit can prioritize collecting weather elements (temperature, humidity, precipitation, etc.) that have a significant impact on specific crops. For example, the data collection unit prioritizes collecting weather elements (temperature, humidity, precipitation, etc.) that have a significant impact on specific crops. The data collection unit can prioritize collecting weather elements (temperature, humidity, precipitation, etc.) that have a significant impact on specific crops. The data collection unit changes the types of weather data required according to the growth stage of the crops. The data collection unit can change the types of weather data required according to the growth stage of the crops. For example, the data collection unit changes the types of weather data required according to the growth stage of the crops. The data collection unit can change the types of weather data required according to the growth stage of the crops. The data collection unit collects weather conditions that pose a high risk of outbreaks of specific pests and diseases, providing data for implementing preventive measures. The data collection unit can collect weather conditions that pose a high risk of outbreaks of specific pests and diseases, and provide data for implementing preventive measures. For example, the data collection unit can collect weather conditions that pose a high risk of outbreaks of specific pests and diseases, and provide data for implementing preventive measures. The data collection unit can collect weather conditions that pose a high risk of outbreaks of specific pests and diseases, and provide data for implementing preventive measures. This allows the data collection unit to efficiently collect the necessary data by considering the impact on specific crops. The impact on specific crops refers to weather elements that affect the growth and harvest of crops. For example, the data collection unit prioritizes collecting weather elements that have a significant impact on specific crops (such as temperature, humidity, and precipitation). When collecting weather data, the data collection unit can select the types of data by considering the impact on specific crops. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input weather elements that have a significant impact on specific crops into a generating AI, which can then select and collect the data.

[0077] The data collection unit estimates the user's emotions and determines the priority of weather data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important weather data. The data collection unit can prioritize collecting important weather data if the user is stressed. For example, if the user is stressed, the data collection unit will prioritize collecting important weather data. The data collection unit can prioritize collecting important weather data if the user is stressed. The data collection unit will collect detailed weather data if the user is relaxed. The data collection unit can collect detailed weather data if the user is relaxed. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed weather data. The data collection unit can collect detailed weather data if the user is relaxed. The data collection unit will prioritize weather data that can be collected quickly if the user is in a hurry. The data collection unit can prioritize weather data that can be collected quickly if the user is in a hurry. For example, if the user is in a hurry, the data collection unit will prioritize weather data that can be collected quickly. The data collection unit can prioritize weather data that can be collected quickly if the user is in a hurry. This allows the data collection unit to prioritize weather data according to the user's emotions, thereby providing data that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of weather data to collect.

[0078] The data collection unit selects collection points considering geographical characteristics when collecting weather data. For example, the data collection unit selects collection points considering differences in topography and elevation. The data collection unit can select collection points considering differences in topography and elevation. For example, the data collection unit can select collection points considering differences in topography and elevation. The data collection unit can select collection points considering differences in topography and elevation. The data collection unit selects collection points considering the different weather characteristics for each crop cultivation area. The data collection unit can select collection points considering the different weather characteristics for each crop cultivation area. For example, the data collection unit can select collection points considering the different weather characteristics for each crop cultivation area. The data collection unit can select collection points considering the different weather characteristics for each crop cultivation area. The data collection unit analyzes weather patterns for each region and selects the optimal collection points. The data collection unit can analyze weather patterns for each region and select the optimal collection points. For example, the data collection unit analyzes weather patterns for each region and selects the optimal collection points. The data collection unit can analyze weather patterns for each region and select the optimal collection points. This allows the data collection unit to select the optimal collection points by considering geographical characteristics. Geographical characteristics refer to topography and climatic conditions, etc. For example, the data collection unit selects collection points by considering differences in topography and elevation. When collecting weather data, the data collection unit can select collection points by considering geographical characteristics. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input geographical characteristic data into a generating AI, and the generating AI can select collection points.

[0079] The data collection unit changes the type of data it collects according to the growth stage of the crop when collecting weather data. For example, during the germination stage, the data collection unit focuses on collecting temperature and humidity data. The data collection unit can focus on collecting temperature and humidity data during the germination stage. For example, during the germination stage, the data collection unit focuses on collecting temperature and humidity data. The data collection unit can focus on collecting temperature and humidity data during the germination stage. The data collection unit focuses on collecting sunshine duration and precipitation data during the growth stage. The data collection unit can focus on collecting sunshine duration and precipitation data during the growth stage. For example, during the growth stage, the data collection unit focuses on collecting sunshine duration and precipitation data. The data collection unit can focus on collecting sunshine duration and precipitation data during the growth stage. The data collection unit focuses on collecting temperature and precipitation data during the harvest stage. The data collection unit can focus on collecting temperature and precipitation data during the harvest stage. For example, the data collection unit focuses on collecting temperature and precipitation data during the harvest season. The data collection unit can focus on collecting temperature and precipitation data during the harvest season. This allows the data collection unit to efficiently collect the necessary data by changing the type of data collected according to the growth stage of the crop. Growth stages refer to the phases of crop growth, including, for example, germination, growth, and harvest. For example, the data collection unit focuses on collecting temperature and humidity data during the germination stage. The data collection unit can change the type of data collected according to the growth stage of the crop when collecting weather data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input crop growth stage data into a generating AI and change the type of data collected by the generating AI.

[0080] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. The analysis unit can provide a simple and highly visible display method if the user is nervous. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. The analysis unit can provide a simple and highly visible display method if the user is nervous. The analysis unit provides a display method that includes detailed information if the user is relaxed. The analysis unit can provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information if the user is relaxed. The analysis unit can provide a display method that includes detailed information if the user is relaxed. The analysis unit provides a display method that gets to the point if the user is in a hurry. The analysis unit can provide a display method that gets to the point if the user is in a hurry. For example, if the user is in a hurry, the analysis unit provides a display method that gets to the point if the user is in a hurry. The analysis unit can provide a display method that gets to the point if the user is in a hurry. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions, enabling a user-friendly display. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, the generative AI can estimate emotions, and the display method of the analysis results can be adjusted.

[0081] The analysis unit detects anomalies by comparing them with past weather data during analysis. For example, the analysis unit can detect abnormal temperature increases and precipitation fluctuations by comparing them with past weather data. The analysis unit can detect abnormal temperature increases and precipitation fluctuations by comparing them with past weather data. For example, the analysis unit can detect abnormal temperature increases and precipitation fluctuations by comparing them with past weather data. The analysis unit can detect abnormal humidity fluctuations and evaluate their impact on crops. For example, the analysis unit can detect abnormal humidity fluctuations and evaluate their impact on crops. The analysis unit can detect abnormal sunshine duration fluctuations and evaluate their impact on crop growth. The analysis unit can detect abnormal fluctuations in sunshine duration and evaluate their impact on crop growth. This allows the analysis unit to detect anomalies by comparing current data with past weather data. Anomalies refer to data that deviates from the normal range. For example, the analysis unit can detect abnormal temperature increases or precipitation fluctuations by comparing current data with past weather data. The analysis unit can detect anomalies by comparing current data with past weather data during analysis. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input past weather data into a generating AI, which can then detect anomalies.

[0082] The analysis unit optimizes the analysis algorithm during analysis, taking into account the impact on specific crops. For example, the analysis unit prioritizes analyzing weather elements that have a significant impact on specific crops. The analysis unit can prioritize analyzing weather elements that have a significant impact on specific crops. For example, the analysis unit prioritizes analyzing weather elements that have a significant impact on specific crops. The analysis unit can prioritize analyzing weather elements that have a significant impact on specific crops. The analysis unit adjusts the analysis algorithm according to the growth stage of the crops. The analysis unit can adjust the analysis algorithm according to the growth stage of the crops. For example, the analysis unit adjusts the analysis algorithm according to the growth stage of the crops. The analysis unit can adjust the analysis algorithm according to the growth stage of the crops. The analysis unit optimizes the analysis algorithm considering the risk of outbreaks of specific pests and diseases. The analysis unit can optimize the analysis algorithm considering the risk of outbreaks of specific pests and diseases. For example, the analysis unit optimizes the analysis algorithm considering the risk of outbreaks of specific pests and diseases. The analysis unit can optimize the analysis algorithm considering the risk of outbreaks of specific pests and diseases. This allows the analysis unit to optimize its analysis algorithm by considering the impact on specific crops. The impact on specific crops refers to weather elements that affect crop growth and harvest. For example, the analysis unit prioritizes analyzing weather elements that have a significant impact on specific crops. The analysis unit can optimize its analysis algorithm by considering the impact on specific crops during the analysis. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input weather elements that have a significant impact on specific crops into a generating AI, which can then optimize the analysis algorithm.

[0083] The analysis unit estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple and easy-to-understand analysis results. The analysis unit can provide simple and easy-to-understand analysis results if the user is nervous. For example, if the user is nervous, the analysis unit provides simple and easy-to-understand analysis results. The analysis unit can provide simple and easy-to-understand analysis results if the user is nervous. The analysis unit provides detailed analysis results if the user is relaxed. The analysis unit can provide detailed analysis results if the user is relaxed. For example, if the user is relaxed, the analysis unit provides detailed analysis results. The analysis unit can provide detailed analysis results if the user is relaxed. The analysis unit provides concise analysis results if the user is in a hurry. The analysis unit can provide concise analysis results if the user is in a hurry. This allows the analysis unit to provide users with appropriate information by adjusting the level of detail in the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the level of detail in the analysis results.

[0084] The analysis unit modifies the analysis algorithm during analysis, taking into account geographical characteristics. The analysis unit modifies the analysis algorithm, for example, by taking into account differences in topography and elevation. The analysis unit can modify the analysis algorithm, taking into account differences in topography and elevation. For example, the analysis unit modifies the analysis algorithm, taking into account differences in topography and elevation. The analysis unit can modify the analysis algorithm, taking into account differences in topography and elevation. The analysis unit modifies the analysis algorithm, taking into account different weather characteristics for each crop cultivation area. The analysis unit can modify the analysis algorithm, taking into account different weather characteristics for each crop cultivation area. For example, the analysis unit modifies the analysis algorithm, taking into account different weather characteristics for each crop cultivation area. The analysis unit can modify the analysis algorithm, taking into account different weather characteristics for each crop cultivation area. The analysis unit analyzes weather patterns for each region and selects the optimal analysis algorithm. The analysis unit can analyze weather patterns for each region and select the optimal analysis algorithm. For example, the analysis unit analyzes weather patterns for each region and selects the optimal analysis algorithm. The analysis unit analyzes weather patterns for each region and selects the optimal analysis algorithm. This allows the analysis unit to select the optimal analysis algorithm by considering geographical characteristics. Geographical characteristics refer to topography, climatic conditions, etc. For example, the analysis unit modifies the analysis algorithm by considering differences in topography and elevation. The analysis unit can modify the analysis algorithm by considering geographical characteristics during the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input geographical characteristics data into a generating AI, and the generating AI can modify the analysis algorithm.

[0085] The analysis unit changes the focus of its analysis according to the growth stage of the crop. For example, during the germination stage, the analysis unit focuses on analyzing temperature and humidity. The analysis unit can focus on analyzing temperature and humidity during the germination stage. For example, during the germination stage, the analysis unit focuses on analyzing temperature and humidity. The analysis unit can focus on analyzing temperature and humidity during the germination stage. The analysis unit focuses on analyzing sunshine hours and precipitation during the growth stage. The analysis unit can focus on analyzing sunshine hours and precipitation during the growth stage. For example, during the growth stage, the analysis unit focuses on analyzing sunshine hours and precipitation. The analysis unit can focus on analyzing sunshine hours and precipitation during the growth stage. The analysis unit focuses on analyzing temperature and precipitation during the harvest stage. The analysis unit can focus on analyzing temperature and precipitation during the harvest stage. For example, the analysis unit focuses on analyzing temperature and precipitation during the harvest season. This allows the analysis unit to efficiently analyze necessary data by changing its analysis focus according to the crop's growth stage. Growth stages refer to the phases of crop growth, including, for example, germination, growth, and harvest. For example, the analysis unit focuses on analyzing temperature and humidity during the germination stage. The analysis unit can change its analysis focus during analysis according to the crop's growth stage. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input crop growth stage data into a generating AI, which can then change the analysis focus.

[0086] The adjustment unit estimates the user's emotions and adjusts the frequency of watering and temperature control based on the estimated user emotions. For example, if the user is stressed, the adjustment unit will water and adjust the temperature more frequently to provide a sense of security. The adjustment unit can provide a sense of security by watering and adjusting the temperature more frequently if the user is stressed. For example, if the user is stressed, the adjustment unit will water and adjust the temperature more frequently to provide a sense of security. The adjustment unit can provide a sense of security by watering and adjusting the temperature more frequently if the user is stressed. The adjustment unit will water and adjust the temperature at the minimum necessary frequency if the user is relaxed. The adjustment unit can water and adjust the temperature at the minimum necessary frequency if the user is relaxed. For example, if the user is relaxed, the adjustment unit will water and adjust the temperature at the minimum necessary frequency if the user is relaxed. The adjustment unit can water and adjust the temperature at the minimum necessary frequency if the user is relaxed. The adjustment unit can water and adjust the temperature quickly if the user is in a hurry. The adjustment unit can quickly water and adjust the temperature if the user is in a hurry. For example, the adjustment unit can quickly water or adjust the temperature if the user is in a hurry. This allows the adjustment unit to optimize the crop growing environment by adjusting the frequency of watering and temperature adjustment 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the frequency of watering and temperature adjustment.

[0087] The adjustment unit selects the optimal adjustment method by referring to past adjustment history during adjustment. For example, the adjustment unit analyzes past watering and temperature control history to select the optimal adjustment method. The adjustment unit can analyze past watering and temperature control history to select the optimal adjustment method. For example, the adjustment unit analyzes past watering and temperature control history to select the optimal adjustment method. The adjustment unit can analyze past watering and temperature control history to select the optimal adjustment method. The adjustment unit selects the optimal adjustment method for specific weather conditions from past adjustment history. The adjustment unit can select the optimal adjustment method for specific weather conditions from past adjustment history. For example, the adjustment unit selects the optimal adjustment method for specific weather conditions from past adjustment history. The adjustment unit can select the optimal adjustment method for specific weather conditions from past adjustment history. Based on past adjustment history, the adjustment unit selects the optimal adjustment method for crop growth. Based on past adjustment history, the adjustment unit can select the optimal adjustment method for crop growth. For example, the adjustment unit selects the optimal adjustment method for crop growth based on past adjustment history. The adjustment unit can select the optimal adjustment method for crop growth based on past adjustment history. This allows the adjustment unit to select the optimal adjustment method by referring to past adjustment history. Past adjustment history refers to records of past watering and temperature control. For example, the adjustment unit analyzes past watering and temperature control history to select the optimal adjustment method. The adjustment unit can also select the optimal adjustment method by referring to past adjustment history during adjustment. Some or all of the above processing in the adjustment unit may be performed using AI, or without AI. For example, the adjustment unit can input past adjustment history data into a generating AI, which can then select the optimal adjustment method.

[0088] The adjustment unit optimizes the adjustment method during adjustment, taking into account its impact on specific crops. For example, the adjustment unit prioritizes adjustments based on weather elements that have a significant impact on specific crops. The adjustment unit can prioritize adjustments based on weather elements that have a significant impact on specific crops. For example, the adjustment unit prioritizes adjustments based on weather elements that have a significant impact on specific crops. The adjustment unit can prioritize adjustments based on weather elements that have a significant impact on specific crops. The adjustment unit changes the adjustment method according to the growth stage of the crops. The adjustment unit can change the adjustment method according to the growth stage of the crops. For example, the adjustment unit changes the adjustment method according to the growth stage of the crops. The adjustment unit can change the adjustment method according to the growth stage of the crops. The adjustment unit optimizes the adjustment method, taking into account the risk of outbreaks of specific pests and diseases. The adjustment unit can optimize the adjustment method, taking into account the risk of outbreaks of specific pests and diseases. For example, the adjustment unit optimizes the adjustment method, taking into account the risk of outbreaks of specific pests and diseases. The adjustment unit can optimize the adjustment method, taking into account the risk of outbreaks of specific pests and diseases. This allows the adjustment unit to optimize its adjustment method by considering the impact on specific crops. The impact on specific crops refers to weather elements that affect the growth and harvest of crops. For example, the adjustment unit prioritizes adjusting weather elements that have a significant impact on specific crops. The adjustment unit can optimize its adjustment method by considering the impact on specific crops during the adjustment process. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input weather elements that have a significant impact on specific crops into a generating AI, which can then optimize the adjustment method.

[0089] The adjustment unit estimates the user's emotions and determines the priority of adjustments based on the estimated emotions. For example, if the user is stressed, the adjustment unit will prioritize important adjustments. The adjustment unit can prioritize important adjustments if the user is stressed. For example, if the user is stressed, the adjustment unit will prioritize important adjustments. The adjustment unit can prioritize important adjustments if the user is stressed. The adjustment unit can perform detailed adjustments if the user is relaxed. For example, if the user is relaxed, the adjustment unit will perform detailed adjustments. The adjustment unit can perform detailed adjustments if the user is relaxed. The adjustment unit can perform detailed adjustments if the user is relaxed. The adjustment unit can perform detailed adjustments if the user is in a hurry. The adjustment unit can perform rapid adjustments if the user is in a hurry. For example, if the user is in a hurry, the adjustment unit will perform rapid adjustments. The adjustment unit can perform rapid adjustments if the user is in a hurry. In this way, the adjustment unit can determine the priority of adjustments according to the user's emotions, enabling adjustments that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI, which can then estimate the emotion and determine the priority of the adjustments.

[0090] The adjustment unit modifies the adjustment method when making adjustments, taking into account geographical characteristics. The adjustment unit modifies the adjustment method, for example, by taking into account differences in topography and elevation. The adjustment unit can modify the adjustment method by taking into account differences in topography and elevation. For example, the adjustment unit modifies the adjustment method by taking into account differences in topography and elevation. The adjustment unit can modify the adjustment method by taking into account differences in topography and elevation. The adjustment unit modifies the adjustment method by taking into account different weather characteristics for each crop cultivation area. The adjustment unit can modify the adjustment method by taking into account different weather characteristics for each crop cultivation area. For example, the adjustment unit modifies the adjustment method by taking into account different weather characteristics for each crop cultivation area. The adjustment unit can modify the adjustment method by taking into account different weather characteristics for each crop cultivation area. The adjustment unit analyzes weather patterns for each region and selects the optimal adjustment method. The adjustment unit can analyze weather patterns for each region and select the optimal adjustment method. For example, the adjustment unit analyzes weather patterns for each region and selects the optimal adjustment method. The adjustment unit analyzes weather patterns for each region and selects the optimal adjustment method. This allows the adjustment unit to select the optimal adjustment method by considering geographical characteristics. Geographical characteristics refer to topography, climatic conditions, etc. For example, the adjustment unit changes the adjustment method by considering differences in topography and elevation. The adjustment unit can change the adjustment method by considering geographical characteristics during the adjustment process. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input geographical characteristics data into a generating AI, and the generating AI can change the adjustment method.

[0091] The adjustment unit changes the focus of its adjustments according to the growth stage of the crop. For example, during the germination stage, the adjustment unit focuses on adjusting temperature and humidity. The adjustment unit can focus on adjusting temperature and humidity during the germination stage. For example, during the germination stage, the adjustment unit focuses on adjusting temperature and humidity. The adjustment unit can focus on adjusting temperature and humidity during the germination stage. The adjustment unit focuses on adjusting sunshine hours and rainfall during the growth stage. The adjustment unit can focus on adjusting sunshine hours and rainfall during the growth stage. For example, during the growth stage, the adjustment unit focuses on adjusting sunshine hours and rainfall. The adjustment unit can focus on adjusting sunshine hours and rainfall during the growth stage. The adjustment unit focuses on adjusting temperature and rainfall during the harvest stage. The adjustment unit can focus on adjusting temperature and rainfall during the harvest stage. For example, the adjustment unit focuses on adjusting temperature and rainfall during the harvest season. The adjustment unit can focus on adjusting temperature and rainfall during the harvest season. This allows the adjustment unit to efficiently perform necessary adjustments by changing the focus of adjustments according to the growth stage of the crop. Growth stages refer to the phases of crop growth, including, for example, the germination stage, the growing stage, and the harvest stage. For example, the adjustment unit focuses on adjusting temperature and humidity during the germination stage. The adjustment unit can change the focus of adjustments according to the growth stage of the crop during adjustments. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input crop growth stage data into a generating AI, and the generating AI can change the focus of adjustments.

[0092] The notification unit estimates the user's emotions and adjusts the timing of notifications based on the estimated emotions. For example, if the user is feeling stressed, the notification unit will promptly deliver important notifications. The notification unit can promptly deliver important notifications if the user is feeling stressed. For example, if the user is feeling stressed, the notification unit will promptly deliver important notifications. The notification unit can promptly deliver important notifications if the user is feeling stressed. The notification unit can reduce the frequency of notifications if the user is relaxed. The notification unit can reduce the frequency of notifications if the user is relaxed. For example, if the user is relaxed, the notification unit can reduce the frequency of notifications. The notification unit can reduce the frequency of notifications if the user is relaxed. The notification unit can deliver notifications promptly if the user is in a hurry. For example, if the user is in a hurry, the notification unit will promptly deliver notifications. The notification unit can deliver notifications promptly if the user is in a hurry. In this way, the notification unit can deliver notifications at the optimal time for the user by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the timing of the notification.

[0093] The notification unit predicts the optimal harvest timing by referring to past harvest data when a notification is sent. The notification unit can predict the optimal harvest timing by analyzing past harvest data. For example, the notification unit can predict the optimal harvest timing by analyzing past harvest data. The notification unit can predict the optimal harvest timing by analyzing past harvest data. The notification unit can predict the optimal harvest timing by analyzing past harvest data. The notification unit can predict the optimal harvest timing by associating past harvest data with weather data. The notification unit can predict the optimal harvest timing by associating past harvest data with weather data. For example, the notification unit can predict the optimal harvest timing by associating past harvest data with weather data. The notification unit can predict the optimal harvest timing by associating past harvest data with weather data. Based on past harvest data, the notification unit can predict the optimal harvest timing for crop growth. Based on past harvest data, the notification unit can predict the optimal harvest timing for crop growth. For example, the notification unit can predict the optimal harvest timing for crop growth based on past harvest data. Based on past harvest data, the notification unit can predict the optimal harvest timing for crop growth. This allows the notification unit to predict the optimal harvesting timing by referring to past harvest data. Past harvest data refers to records of past harvests. For example, the notification unit analyzes past harvest data to predict the optimal harvesting timing. The notification unit can predict the optimal harvesting timing by referring to past harvest data when sending a notification. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input past harvest data into a generating AI, which can then predict the optimal harvesting timing.

[0094] The notification unit optimizes the notification content when it sends a notification, taking into account its impact on specific crops. For example, the notification unit prioritizes notifying about weather elements that have a significant impact on specific crops. The notification unit can prioritize notifying about weather elements that have a significant impact on specific crops. For example, the notification unit prioritizes notifying about weather elements that have a significant impact on specific crops. The notification unit can prioritize notifying about weather elements that have a significant impact on specific crops. The notification unit changes the notification content according to the growth stage of the crops. The notification unit can change the notification content according to the growth stage of the crops. For example, the notification unit changes the notification content according to the growth stage of the crops. The notification unit can change the notification content according to the growth stage of the crops. The notification unit optimizes the notification content, taking into account the risk of outbreaks of specific pests and diseases. The notification unit can optimize the notification content, taking into account the risk of outbreaks of specific pests and diseases. For example, the notification unit optimizes the notification content, taking into account the risk of outbreaks of specific pests and diseases. The notification unit can optimize the notification content, taking into account the risk of outbreaks of specific pests and diseases. This allows the notification unit to optimize notification content by considering the impact on specific crops. The impact on specific crops refers to weather elements that affect the growth and harvest of crops. For example, the notification unit prioritizes notifying about weather elements that have a significant impact on specific crops. The notification unit can optimize notification content by considering the impact on specific crops when sending notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input weather elements that have a significant impact on specific crops into a generating AI, which can then optimize the notification content.

[0095] The notification unit estimates the user's emotions and determines notification priorities based on those emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. The notification unit can prioritize important notifications if the user is stressed. For example, if the user is stressed, the notification unit will prioritize important notifications. The notification unit can prioritize important notifications if the user is stressed. The notification unit can provide detailed notifications if the user is relaxed. For example, if the user is relaxed, the notification unit will provide detailed notifications. The notification unit can provide detailed notifications if the user is relaxed. The notification unit can provide detailed notifications if the user is relaxed. The notification unit can provide quick notifications if the user is in a hurry. For example, if the user is in a hurry, the notification unit will provide quick notifications. The notification unit can provide quick notifications if the user is in a hurry. This allows the notification unit to prioritize notifications according to the user's emotions, enabling notifications that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI, which can then estimate the emotion and determine the priority of notifications.

[0096] The notification unit modifies the notification content when it sends a notification, taking into account geographical characteristics. The notification unit modifies the notification content, for example, by taking into account differences in topography and elevation. The notification unit can modify the notification content, taking into account differences in topography and elevation. For example, the notification unit modifies the notification content, taking into account differences in topography and elevation. The notification unit can modify the notification content, taking into account differences in topography and elevation. The notification unit modifies the notification content, taking into account different weather characteristics for each crop cultivation area. The notification unit can modify the notification content, taking into account different weather characteristics for each crop cultivation area. For example, the notification unit modifies the notification content, taking into account different weather characteristics for each crop cultivation area. The notification unit can modify the notification content, taking into account different weather characteristics for each crop cultivation area. The notification unit analyzes weather patterns for each region and selects the optimal notification content. The notification unit can analyze weather patterns for each region and select the optimal notification content. For example, the notification unit analyzes weather patterns for each region and selects the optimal notification content. The notification unit analyzes weather patterns for each region and selects the optimal notification content. This allows the notification unit to select the optimal notification content by considering geographical characteristics. Geographical characteristics refer to topography, climatic conditions, etc. For example, the notification unit modifies the notification content by considering differences in topography and elevation. The notification unit can modify the notification content by considering geographical characteristics when sending a notification. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input geographical characteristics data into a generating AI, and the generating AI can modify the notification content.

[0097] The notification unit changes the focus of its notifications according to the growth stage of the crop. For example, during the germination stage, the notification unit focuses on notifying about temperature and humidity. The notification unit can focus on notifying about temperature and humidity during the germination stage. For example, during the germination stage, the notification unit focuses on notifying about temperature and humidity. The notification unit can focus on notifying about temperature and humidity during the germination stage. The notification unit focuses on notifying about sunshine hours and rainfall during the growth stage. The notification unit can focus on notifying about sunshine hours and rainfall during the growth stage. For example, during the growth stage, the notification unit focuses on notifying about sunshine hours and rainfall. The notification unit can focus on notifying about sunshine hours and rainfall during the growth stage. The notification unit focuses on notifying about temperature and rainfall during the harvest stage. The notification unit can focus on notifying about temperature and rainfall during the harvest stage. For example, the notification unit can focus on notifying users of temperature and rainfall during the harvest season. This allows the notification unit to efficiently provide necessary notifications by changing the focus of notifications according to the growth stage of the crop. Growth stages refer to the phases of crop growth, including, for example, germination, growth, and harvest. For example, the notification unit can focus on notifying users of temperature and humidity during the germination stage. The notification unit can change the focus of notifications according to the growth stage of the crop when sending notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input crop growth stage data into a generating AI, which can then change the focus of notifications.

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

[0099] The crop management system can also be equipped with soil sensors. These sensors acquire data such as soil moisture content, nutrient levels, and pH values ​​in real time. For example, a soil sensor can measure soil moisture content and automatically water the soil if it is dry. It can also measure soil nutrient levels and add fertilizer as needed. Furthermore, it can measure the soil pH value and adjust it to maintain an appropriate pH level. This allows the crop management system to monitor soil conditions in real time and provide an optimal growing environment.

[0100] The data collection unit can further utilize drones to collect weather data over a wide area. Drones fly across the entire farmland, collecting data such as temperature, humidity, precipitation, and sunshine duration. For example, drones can collect weather data from different areas of the farmland, allowing for an understanding of the environmental conditions in each area. Drones can also photograph crop growth from the air and collect image data. Furthermore, drones can quickly collect data during extreme weather events, enabling measures to be taken to minimize the impact on crops. As a result, the data collection unit can efficiently collect weather data over a wide area and optimize crop management.

[0101] The analysis unit can further predict weather data using machine learning algorithms. These machine learning algorithms learn from past weather data and predict future weather. For example, the analysis unit can use machine learning algorithms to predict the weather for the next week and develop a crop management plan. Furthermore, machine learning algorithms can predict the probability of extreme weather events and allow for proactive countermeasures. In addition, machine learning algorithms can analyze crop growth data and suggest optimal cultivation methods. This enables the analysis unit to predict future weather and manage crops more efficiently.

[0102] The control unit can also be equipped with an automated irrigation system. This automated irrigation system automatically adjusts the timing and amount of watering based on weather and soil data. For example, the control unit can automatically activate the irrigation system when it detects dry weather and low soil moisture levels. It can also stop the irrigation system and activate a drainage system to remove excess water if excessive rainfall is predicted. Furthermore, the irrigation system can adjust the amount and frequency of watering according to the growth stage of specific crops. This allows the control unit to optimize crop water management and achieve efficient irrigation.

[0103] The notification unit can also send notifications to users via a smartphone app. The smartphone app displays crop growth status and weather data in real time, providing users with important information. For example, the notification unit can notify users via the smartphone app when harvest time is approaching. It can also quickly notify users if extreme weather is predicted and provide advice on countermeasures. Furthermore, the smartphone app can provide an interface for users to check the management status of their crops and make necessary adjustments. In this way, the notification unit can provide users with quick and effective notifications and support crop management.

[0104] The data collection unit can estimate the user's emotions and adjust the method of collecting weather data based on those emotions. For example, if the user is stressed, the unit can collect detailed weather data to provide the user with a sense of security. If the user is relaxed, the unit can collect only the minimum necessary weather data to reduce the user's burden. Furthermore, if the user is in a hurry, the unit can quickly collect weather data to provide the user with a prompt response. In this way, the data collection unit can adjust the method of collecting weather data according to the user's emotions, enabling the provision of data that meets the user's needs.

[0105] 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 can provide a simple and easy-to-read display method to reduce the user's burden. If the user is relaxed, the analysis unit can provide a display method that includes detailed information, giving the user more information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method to support a quick response. In this way, the analysis unit adjusts the display method of the analysis results according to the user's emotions, making it easy for the user to understand.

[0106] The adjustment unit can estimate the user's emotions and adjust the frequency of watering and temperature control based on those emotions. For example, if the user is stressed, the adjustment unit can provide reassurance by watering and temperature control more frequently. If the user is relaxed, the adjustment unit can reduce the user's burden by watering and temperature control at the minimum necessary frequency. Furthermore, if the user is in a hurry, the adjustment unit can provide a quick response by watering and temperature control quickly. In this way, the adjustment unit can optimize the growing environment for crops by adjusting the frequency of watering and temperature control according to the user's emotions.

[0107] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification unit can quickly deliver important notifications to provide reassurance. If the user is relaxed, the notification unit can reduce the frequency of notifications to lessen the user's burden. Furthermore, if the user is in a hurry, the notification unit can quickly deliver notifications to provide a prompt response. In this way, the notification unit can adjust the timing of notifications according to the user's emotions, delivering notifications at the optimal time for the user.

[0108] The notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is stressed, the notification unit can prioritize important notifications to provide reassurance. If the user is relaxed, the notification unit can provide detailed notifications to give the user more information. Furthermore, if the user is in a hurry, the notification unit can provide quick notifications to allow the user to respond promptly. In this way, the notification unit can prioritize notifications according to the user's emotions, enabling notifications that meet the user's needs.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects weather data. The collection unit uses, for example, weather sensors to acquire weather data such as temperature, humidity, precipitation, and sunshine duration in real time. Step 2: The analysis unit analyzes the weather data collected by the collection unit. The analysis unit analyzes weather data such as temperature, humidity, precipitation, and sunshine duration. Step 3: The adjustment unit performs watering and temperature control based on the analysis results obtained by the analysis unit. For example, it automatically waters and adjusts humidity in dry weather. It also automatically deploys shades to block sunlight and lower the temperature when the temperature is high. Step 4: The notification unit analyzes growth data based on the environment adjusted by the adjustment unit and notifies the harvest timing. For example, it monitors the growth stage of crops and predicts the optimal time for harvest.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires weather data in real time using the weather sensor of the smart device 14. The analysis unit analyzes the weather data collected by the specific processing unit 290 of the data processing unit 12. The adjustment unit performs watering and temperature control based on the analysis results by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the harvest timing by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0128] 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.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires weather data in real time using the weather sensor of the smart glasses 214. The analysis unit analyzes the weather data collected by the specific processing unit 290 of the data processing unit 12. The adjustment unit performs watering and temperature control based on the analysis results by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the harvest timing by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit acquires weather data in real time using the weather sensor of the headset terminal 314. The analysis unit analyzes the weather data collected by the specific processing unit 290 of the data processing unit 12. The adjustment unit performs watering and temperature control based on the analysis results by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the harvest timing by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[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 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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 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).

[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] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires weather data in real time using the weather sensor of the robot 414. The analysis unit analyzes the weather data collected by the specific processing unit 290 of the data processing unit 12. The adjustment unit performs watering and temperature control based on the analysis results by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the harvest timing by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection unit for collecting weather data, An analysis unit for analyzing weather data collected by the aforementioned collection unit, An adjustment unit that controls watering and temperature based on the analysis results obtained from the analysis unit, The system includes a notification unit that analyzes growth data based on the environment adjusted by the adjustment unit and notifies the harvest timing. A system characterized by the following features. (Note 2) The aforementioned collection unit is We acquire weather data in real time using weather sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze weather data including temperature, humidity, precipitation, and sunshine duration. The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, Automatically waters and adjusts humidity in dry weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, When the temperature is high, it automatically deploys a shade to block sunlight and lower the temperature. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Monitor the growth stages of crops and predict the optimal time for harvest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of weather data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting weather data, we optimize the timing of data collection by referring to past weather patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting weather data, select the type of data that will have an impact on specific crops. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of weather data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting weather data, select collection points considering geographical characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting weather data, change the type of data collected according to the growth stage of the crops. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned analysis unit, During analysis, anomalies are detected by comparing them with past weather data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, optimize the analysis algorithm by considering the impact on specific crops. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis algorithm is modified to take geographical characteristics into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the focus of the analysis is changed according to the growth stage of the crop. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, It estimates the user's emotions and adjusts the frequency of watering and temperature control based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, During adjustment, the optimal adjustment method is selected by referring to past adjustment history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, During adjustments, optimize the adjustment method by considering the impact on specific crops. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, During adjustments, the adjustment method will be changed to take geographical characteristics into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, During adjustments, the focus of the adjustments is changed according to the growth stage of the crops. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When a notification is sent, past harvest data is referenced to predict the optimal harvesting timing. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending notifications, the content of the notifications is optimized to take into account the impact on specific crops. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, the content of the notifications will be modified to take geographical characteristics into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the focus of the notifications will be changed according to the growth stage of the crops. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit for collecting weather data, An analysis unit for analyzing weather data collected by the aforementioned collection unit, An adjustment unit that controls watering and temperature based on the analysis results obtained from the analysis unit, The system includes a notification unit that analyzes growth data based on the environment adjusted by the adjustment unit and notifies the harvest timing. A system characterized by the following features.

2. The aforementioned collection unit is We acquire weather data in real time using weather sensors. The system according to feature 1.

3. The aforementioned analysis unit, Analyze weather data including temperature, humidity, precipitation, and sunshine duration. The system according to feature 1.

4. The adjustment unit is, Automatically waters and adjusts humidity in dry weather conditions. The system according to feature 1.

5. The adjustment unit is, When the temperature is high, it automatically deploys a shade to block sunlight and lower the temperature. The system according to feature 1.

6. The aforementioned notification unit, Monitor the growth stages of crops and predict the optimal time for harvest. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of weather data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting weather data, we optimize the timing of data collection by referring to past weather patterns. The system according to feature 1.

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