system

Abstract

The system according to this embodiment aims to collect and analyze environmental data in agriculture, provide optimal cultivation advice, and automatically control the environment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a control unit. The collection unit collects data such as sunshine duration, temperature, humidity, and moisture content. The analysis unit analyzes the data collected by the collection unit and provides optimal cultivation advice. The control unit automatically controls the environment based on the analysis results obtained by the analysis unit.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the collection and analysis of environmental data in agriculture have not been sufficiently carried out, and it is difficult to provide efficient growth advice and environmental control.

[0005] The system according to the embodiment aims to collect and analyze environmental data in agriculture, provide optimal growth advice, and automatically control the environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a control unit. The collection unit collects data such as sunshine duration, temperature, humidity, and moisture content. The analysis unit analyzes the data collected by the collection unit and provides optimal cultivation advice. The control unit automatically controls the environment based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze environmental data in agriculture, provide optimal cultivation advice, and automatically control the environment. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An agricultural support system according to an embodiment of the present invention is a system that utilizes IoT and generative AI to improve the efficiency and automation of agriculture. The agricultural support system collects data such as sunshine duration, temperature, humidity, and moisture content using IoT devices, and the generative AI analyzes the collected data to provide optimal cultivation advice. Furthermore, it automatically controls the environment (sunlight, watering, fertilizer application, etc.) based on the analysis results of the generative AI. For example, the agricultural support system installs various sensors and acquires data in real time. For example, by installing temperature sensors and humidity sensors in the field and measuring sunshine duration and moisture content, the growth status of crops can be understood. Next, the agricultural support system analyzes the collected data using generative AI. Based on the collected data, the generative AI predicts the growth status of crops and provides optimal cultivation advice. For example, the generative AI can analyze temperature and humidity data and propose appropriate watering timing and fertilizer amounts. This can be expected to improve crop quality and increase yields. Furthermore, the agricultural support system automatically controls the environment based on the analysis results of the generative AI. For example, the watering system can be automatically activated based on the watering timing proposed by the generative AI. Furthermore, the shading system can be adjusted according to the amount of sunlight. This optimizes the growing environment for crops and enables efficient agriculture. This system makes agriculture automated and predictable, helping to solve the problems of an aging workforce and a shortage of successors. Even inexperienced individuals can easily enter agriculture, improving its sustainability. For example, by utilizing IoT devices and generative AI, even those without specialized agricultural knowledge can grow crops while receiving appropriate cultivation advice. In addition, the automation of heavy labor allows elderly people to continue farming without burden. In this way, agricultural support systems can achieve efficiency and automation in agriculture, solving the problems of an aging workforce and a shortage of successors.

[0029] The agricultural support system according to this embodiment comprises a data collection unit, an analysis unit, and a control unit. The data collection unit collects data such as sunshine duration, temperature, humidity, and moisture content. The data collection unit collects data such as sunshine duration, temperature, humidity, and moisture content using various sensors, for example. The data collection unit can, for example, install temperature sensors and humidity sensors in a field to measure sunshine duration and moisture content. The data collection unit can also, for example, measure soil moisture content using a soil moisture sensor. The data collection unit can also, for example, measure sunshine duration using a light sensor. The analysis unit analyzes the data collected by the data collection unit and provides optimal cultivation advice. The analysis unit analyzes the data collected using, for example, a generative AI. The generative AI predicts the growth status of crops based on the collected data and provides optimal cultivation advice. For example, the generative AI can analyze temperature and humidity data and propose appropriate watering timing and fertilizer amounts. The generative AI can also predict the growth status of crops based on the collected data and provide optimal cultivation advice. For example, the generating AI can analyze temperature and humidity data and propose appropriate watering timing and fertilizer amounts. The control unit automatically controls the environment based on the analysis results obtained by the analysis unit. The control unit can, for example, automatically activate the watering system based on the watering timing proposed by the generating AI. The control unit can also, for example, automatically activate the watering system based on the watering timing proposed by the generating AI. Furthermore, the control unit can adjust the shading system according to the amount of sunlight. The control unit can, for example, adjust the shading system according to the amount of sunlight. As a result, the agricultural support system according to this embodiment can achieve efficiency and automation in agriculture and solve the problems of an aging workforce and a shortage of successors.

[0030] The data collection unit collects data such as sunshine duration, temperature, humidity, and moisture content. For example, the unit uses various sensors to collect data such as sunshine duration, temperature, humidity, and moisture content. Specifically, temperature and humidity sensors can be installed in fields to measure sunshine duration and moisture content. Temperature sensors measure surface and underground temperatures in real time and transmit the data to a central database. Humidity sensors measure humidity in the air, providing data to understand the humidity environment suitable for crop growth. Soil moisture sensors measure soil moisture content, allowing for an understanding of how much water crop roots are absorbing. Light sensors measure sunshine duration, providing data to determine how much light crops are receiving. These sensors can collect data using wireless communication technology and transmit it to a central database. Furthermore, the data collection unit can also collect data over a wide area using drones and autonomous vehicles. Drones photograph the entire field from above and collect image data. Autonomous vehicles patrol the fields, collecting data using sensors and transmitting it to a central database in real time. This allows the data collection unit to collect a wide range of data using diverse devices and methods, contributing to increased efficiency and accuracy in agriculture.

[0031] The analysis department analyzes the data collected by the collection department and provides optimal cultivation advice. For example, the analysis department uses generative AI to analyze the collected data. Based on the collected data, the generative AI predicts the growth status of crops and provides optimal cultivation advice. Specifically, the generative AI can analyze temperature and humidity data and suggest appropriate watering timing and fertilizer amounts. The generative AI can also predict future weather conditions based on past data and weather forecasts and provide cultivation advice accordingly. For example, the generative AI combines past temperature data and weather forecasts to predict future temperature fluctuations and suggests adjustments to watering and fertilizer accordingly. Furthermore, the generative AI can suggest optimal management methods according to the growth stage of the crop. For example, if the crop is in the growth stage, the generative AI suggests the appropriate type and amount of fertilizer, and if it is approaching harvest time, it suggests the timing of harvest. In addition, the generative AI can predict the risk of pest and disease outbreaks and provide advice for taking early countermeasures. As a result, the analysis department can highly analyze the collected data and provide specific and practical cultivation advice to agricultural workers.

[0032] The control unit automatically controls the environment based on the analysis results obtained by the analysis unit. For example, the control unit automatically activates the watering system based on the watering timing proposed by the generating AI. Specifically, the control unit receives data from the collection unit and instructions from the analysis unit and activates the watering system at the appropriate time. For example, if the soil moisture sensor drops below a certain moisture level, the control unit automatically activates the watering system and supplies the necessary amount of water. The control unit can also adjust the shading system according to the amount of sunlight. For example, if the amount of sunlight is too long, the control unit automatically activates the shading system to prevent crops from receiving excessive sunlight. Furthermore, the control unit can control the temperature control system and ventilation system based on temperature and humidity data. For example, if the temperature is too high, the control unit automatically activates the cooling system to adjust it to an appropriate temperature. Also, if the humidity is too low, the control unit automatically activates the humidification system to maintain an appropriate humidity level. In this way, the control unit automatically maintains an environment that is optimal for crop growth, enabling increased efficiency and automation in agriculture. Furthermore, the control unit is equipped with an anomaly detection function, which immediately notifies the user if an abnormality occurs in the sensors or system, enabling a rapid response. This allows the control unit to improve the efficiency and automation of agriculture, helping to address challenges such as an aging workforce and a shortage of successors.

[0033] The data collection unit can collect data such as sunshine duration, temperature, humidity, and moisture content using various sensors. For example, the data collection unit can collect temperature data using a temperature sensor. The data collection unit can also collect humidity data using a humidity sensor. The data collection unit can also collect soil moisture content using a soil moisture sensor. This allows for accurate data collection by using various sensors. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input temperature data acquired by the temperature sensor into a generating AI and have the generating AI perform analysis of the temperature data.

[0034] The analysis unit can predict crop growth based on collected data and provide optimal cultivation advice. For example, the analysis unit analyzes the collected data using a generative AI. The generative AI predicts crop growth based on the collected data and provides optimal cultivation advice. For example, the generative AI can analyze temperature and humidity data and suggest appropriate watering timing and fertilizer amounts. Furthermore, the generative AI can predict crop growth based on collected data and provide optimal cultivation advice. For example, the generative AI can analyze temperature and humidity data and suggest appropriate watering timing and fertilizer amounts. This allows for the prediction of crop growth and the provision of optimal cultivation advice, which is expected to improve crop quality and increase yields. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without one. For example, the analysis unit can input collected data into a generative AI and have the generative AI predict crop growth.

[0035] The control unit can automatically activate the watering system based on the watering timing proposed by the generating AI. The control unit can, for example, automatically activate the watering system based on the watering timing proposed by the generating AI. The control unit can, for example, automatically activate the watering system based on the watering timing proposed by the generating AI. The control unit can also adjust the shading system according to the amount of sunlight. The control unit can, for example, adjust the shading system according to the amount of sunlight. This enables efficient watering by automatically activating the watering system based on the proposals of the generating AI. Some or all of the above processing in the control unit may be performed using the generating AI, for example, or without the generating AI. For example, the control unit can activate the watering system using an AI model that takes the watering timing proposed by the generating AI as input and outputs the operation of the watering system.

[0036] The control unit can adjust the shading system according to the amount of sunlight. The control unit can, for example, adjust the shading system according to the amount of sunlight. The control unit can, for example, adjust the shading system according to the amount of sunlight. This allows for the optimization of the crop growing environment by adjusting the shading system according to the amount of sunlight. Some or all of the above-described processes in the control unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the control unit can input sunlight data into a generating AI and have the generating AI perform the adjustment of the shading system.

[0037] The analysis unit can analyze temperature and humidity data and propose appropriate watering timings and fertilizer amounts. For example, the analysis unit can use a generating AI to analyze temperature and humidity data. The generating AI proposes appropriate watering timings and fertilizer amounts based on the temperature and humidity data. For example, the generating AI can analyze temperature and humidity data and propose appropriate watering timings and fertilizer amounts. The generating AI can also propose appropriate watering timings and fertilizer amounts based on temperature and humidity data. For example, the generating AI can analyze temperature and humidity data and propose appropriate watering timings and fertilizer amounts. Thus, by analyzing temperature and humidity data, appropriate watering timings and fertilizer amounts can be proposed. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or without one. For example, the analysis unit can input temperature and humidity data into a generating AI and have the generating AI propose appropriate watering timings and fertilizer amounts.

[0038] The data collection unit can dynamically change the type and accuracy of the data it collects according to the growth stage of different crops. For example, when a crop is in the germination stage, the data collection unit can collect temperature and humidity data with high accuracy. When a crop is in the growth stage, the data collection unit can focus on collecting data on sunshine hours and moisture content. When a crop is in the harvest stage, the data collection unit can collect all data in a balanced manner and determine the optimal harvest timing. By optimizing data collection according to the growth stage of the crop, more accurate data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the changes in the type and accuracy of data collection according to the growth stage of the crop.

[0039] The data collection unit can ensure data reliability by arranging multiple redundant sensors in case of failure. For example, the data collection unit can place multiple temperature sensors in different locations in the field so that data can be acquired even if one fails. For example, the data collection unit can install multiple humidity sensors so that if one fails, the other sensors can compensate for the data. For example, the data collection unit can make the sunlight sensors redundant so that accurate sunlight data can be acquired even if one fails. In this way, data reliability can be ensured by arranging sensors with redundancy. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from multiple sensors into a generating AI and have the generating AI perform the task of ensuring data reliability.

[0040] The data collection unit can share the collected data with other agricultural systems to achieve comprehensive agricultural management. For example, the data collection unit can share collected temperature data with other agricultural management systems to perform comprehensive environmental control. For example, the data collection unit can share humidity data and work with other systems to determine the optimal watering timing. For example, the data collection unit can share sunlight data and work with other systems to adjust the shading system. This enables comprehensive agricultural management by sharing data with other agricultural systems. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the collected data into a generating AI and have the generating AI perform data sharing with other agricultural systems.

[0041] The data collection unit can automatically back up the collected data to cloud storage, ensuring data security. For example, the data collection unit can automatically back up collected temperature data to cloud storage. For example, the data collection unit can periodically save humidity data to cloud storage, ensuring data security. For example, the data collection unit can back up sunlight data to cloud storage in real time, preventing data loss. This ensures data security by backing up to cloud storage. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the collected data into a generating AI and have the generating AI perform the backup to cloud storage.

[0042] The analysis unit can refer to past data to predict the occurrence of extreme weather events and pest infestations and issue warnings in advance. For example, the analysis unit can predict the occurrence of extreme weather events and issue warnings based on past temperature data. For example, the analysis unit can predict the occurrence of pest infestations and issue warnings based on past humidity data. For example, the analysis unit can predict the occurrence of extreme weather events and issue warnings based on past sunshine data. In this way, by referring to past data, it is possible to predict the occurrence of extreme weather events and pest infestations and issue warnings in advance. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input past data into a generation AI and have the generation AI perform predictions of extreme weather events and pest infestations.

[0043] The analysis unit can customize optimal cultivation advice according to the characteristics of different crops. For example, the analysis unit can provide optimal cultivation advice according to the characteristics of tomatoes. For example, the analysis unit can provide optimal cultivation advice according to the characteristics of strawberries. For example, the analysis unit can provide optimal cultivation advice according to the characteristics of lettuce. By providing cultivation advice tailored to the characteristics of each crop, it is expected that crop quality will improve. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input crop characteristic data into a generating AI and have the generating AI perform the customization of optimal cultivation advice.

[0044] The analysis unit can refer to other agricultural data (market prices, demand forecasts, etc.) and provide optimal cultivation advice from an economic perspective. For example, the analysis unit can suggest the optimal harvest timing based on market price data. For example, the analysis unit can suggest the optimal planting plan based on demand forecast data. For example, the analysis unit can suggest the optimal amount of fertilizer to use based on economic data. This allows for more efficient agricultural management by providing optimal cultivation advice from an economic perspective. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input other agricultural data into a generative AI and have the generative AI provide optimal cultivation advice from an economic perspective.

[0045] The control unit can dynamically change environmental control parameters according to the growth stage of different crops. For example, when a crop is in the germination stage, the control unit can control temperature and humidity parameters with high precision. For example, when a crop is in the growth stage, the control unit can focus on controlling sunlight hours and water content parameters. For example, when a crop is in the harvest stage, the control unit can control all parameters in a balanced manner to determine the optimal harvest timing. This makes optimal environmental control possible by changing the environmental control parameters according to the growth stage of the crop. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can have a generative AI perform the changes to environmental control parameters according to the growth stage of the crop.

[0046] The control unit can ensure the reliability of environmental control by arranging multiple redundant control systems in case of failure. For example, the control unit can place multiple temperature control systems in different locations in the field so that environmental control can be performed even in the event of a failure. For example, the control unit can install multiple humidity control systems so that if one fails, the other systems can compensate for the failure of the other. For example, the control unit can make the sunlight control system redundant so that accurate environmental control can be performed even in the event of a failure. In this way, the reliability of environmental control can be ensured by the redundant control systems. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the control unit can input data acquired from multiple control systems into a generative AI and have the generative AI perform the task of ensuring the reliability of environmental control.

[0047] The control unit can work in conjunction with other agricultural systems to achieve comprehensive environmental control. For example, the control unit can work with a temperature control system in conjunction with other agricultural management systems to perform comprehensive environmental control. For example, the control unit can work with a humidity control system in conjunction with other systems to perform optimal environmental control. For example, the control unit can work with a sunlight control system in conjunction with other systems to adjust the shading system. This enables comprehensive environmental control by working in conjunction with other agricultural systems. Some or all of the above-described processes in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can have a generative AI perform data sharing with other agricultural systems.

[0048] The control unit can automatically back up collected data to cloud storage to ensure data security. For example, the control unit can automatically back up collected temperature data to cloud storage. For example, the control unit can periodically save humidity data to cloud storage to ensure data security. For example, the control unit can back up sunlight data to cloud storage in real time to prevent data loss. In this way, data security can be ensured by backing up to cloud storage. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the control unit can input collected data into a generative AI and have the generative AI perform the backup to cloud storage.

[0049] The analysis unit can analyze collected data in conjunction with other agricultural systems to achieve comprehensive agricultural management. For example, the analysis unit can share collected temperature data with other agricultural management systems to perform comprehensive environmental control. For example, the analysis unit can share humidity data and work with other systems to determine the optimal watering timing. For example, the analysis unit can share sunlight data and work with other systems to adjust shading systems. This enables comprehensive agricultural management by analyzing data in conjunction with other agricultural systems. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input collected data into a generative AI and have the generative AI perform data sharing with other agricultural systems.

[0050] The control unit can dynamically change environmental control parameters according to the growth stage of different crops. For example, when a crop is in the germination stage, the control unit can control temperature and humidity parameters with high precision. For example, when a crop is in the growth stage, the control unit can focus on controlling sunlight hours and water content parameters. For example, when a crop is in the harvest stage, the control unit can control all parameters in a balanced manner to determine the optimal harvest timing. This makes optimal environmental control possible by changing the environmental control parameters according to the growth stage of the crop. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can have a generative AI perform the changes to environmental control parameters according to the growth stage of the crop.

[0051] The control unit can ensure the reliability of environmental control by arranging multiple redundant control systems in case of failure. For example, the control unit can place multiple temperature control systems in different locations in the field so that environmental control can be performed even in the event of a failure. For example, the control unit can install multiple humidity control systems so that if one fails, the other systems can compensate for the failure of the other. For example, the control unit can make the sunlight control system redundant so that accurate environmental control can be performed even in the event of a failure. In this way, the reliability of environmental control can be ensured by the redundant control systems. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the control unit can input data acquired from multiple control systems into a generative AI and have the generative AI perform the task of ensuring the reliability of environmental control.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The agricultural support system can also be equipped with a forecasting unit. Based on data obtained from the data collection unit, the forecasting unit can predict future weather conditions and crop growth. For example, the forecasting unit can combine past and current weather data to predict future weather fluctuations. It can also predict harvest time and yield based on crop growth data. Furthermore, the forecasting unit can predict the risk of pest and disease outbreaks and take preventative measures. This allows the agricultural support system to improve the efficiency and stability of agriculture by predicting future risks and taking appropriate countermeasures.

[0054] The agricultural support system can also be equipped with a notification unit. This unit is responsible for notifying the user of information from the analysis and control units. For example, the notification unit can inform the user of the appropriate watering timing and fertilizer amounts. It can also provide the user with environmental control results and forecast information from the prediction unit. Furthermore, the notification unit can warn the user of the risk of extreme weather or pest and disease outbreaks. This allows the user to obtain necessary information in real time, enabling a rapid response.

[0055] The agricultural support system can also be equipped with a learning unit. The learning unit learns to improve the overall system performance based on data obtained from the data collection and analysis units. For example, the learning unit can optimize the algorithms of the generative AI using historical data to provide more accurate predictions and advice. Furthermore, the learning unit can improve the system's usability based on user operation history and feedback. In addition, the learning unit can adapt to the introduction of new sensors and devices, increasing the system's flexibility. This allows the agricultural support system to continuously evolve and achieve higher performance.

[0056] The agricultural support system can also be equipped with a data visualization unit. This unit is responsible for visually displaying data obtained from the collection and analysis units. For example, it can display temperature and humidity data as graphs and charts, allowing users to understand them intuitively. It can also display crop growth status as a 3D model. Furthermore, it can display forecast information from the forecasting unit on a map, enabling users to visually grasp future risks. This allows users to utilize data more effectively and make appropriate decisions.

[0057] The agricultural support system can also be equipped with an energy management unit. This unit plays a role in optimizing the overall energy consumption of the system. For example, it can adjust the operation of the collection and control units to occur at energy-efficient times. Furthermore, the energy management unit can utilize renewable energy sources such as solar and wind power to reduce the system's energy consumption. In addition, the energy management unit can monitor energy consumption data in real time and issue warnings if an anomaly occurs. This enables the agricultural support system to achieve sustainable energy management and reduce its environmental impact.

[0058] The agricultural support system can also include a quality control department. This department is responsible for managing crop quality based on data obtained from the data collection and analysis departments. For example, the quality control department can evaluate crop quality based on temperature and humidity data and take necessary measures. It can also predict harvest time and yield, and propose the optimal harvest timing. Furthermore, the quality control department can assess the risk of pest and disease outbreaks and take preventative measures. This allows the agricultural support system to maintain high crop quality and increase yields.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects data such as sunshine duration, temperature, humidity, and moisture content. The data collection unit uses various sensors to collect this data. For example, temperature and humidity sensors can be installed in a field to measure sunshine duration and moisture content. Soil moisture content can also be measured using a soil moisture sensor, and sunshine duration can be measured using a light sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and provides optimal cultivation advice. The analysis unit uses generative AI to analyze the collected data and predict the growth status of the crops. For example, the generative AI can analyze temperature and humidity data and suggest appropriate watering timing and fertilizer amounts. Step 3: The control unit automatically controls the environment based on the analysis results obtained by the analysis unit. For example, it can automatically activate the watering system based on the watering timing suggested by the generating AI. It can also adjust the shading system according to the amount of sunlight.

[0061] (Example of form 2) An agricultural support system according to an embodiment of the present invention is a system that utilizes IoT and generative AI to improve the efficiency and automation of agriculture. The agricultural support system collects data such as sunshine duration, temperature, humidity, and moisture content using IoT devices, and the generative AI analyzes the collected data to provide optimal cultivation advice. Furthermore, it automatically controls the environment (sunlight, watering, fertilizer application, etc.) based on the analysis results of the generative AI. For example, the agricultural support system installs various sensors and acquires data in real time. For example, by installing temperature sensors and humidity sensors in the field and measuring sunshine duration and moisture content, the growth status of crops can be understood. Next, the agricultural support system analyzes the collected data using generative AI. Based on the collected data, the generative AI predicts the growth status of crops and provides optimal cultivation advice. For example, the generative AI can analyze temperature and humidity data and propose appropriate watering timing and fertilizer amounts. This can be expected to improve crop quality and increase yields. Furthermore, the agricultural support system automatically controls the environment based on the analysis results of the generative AI. For example, the watering system can be automatically activated based on the watering timing proposed by the generative AI. Furthermore, the shading system can be adjusted according to the amount of sunlight. This optimizes the growing environment for crops and enables efficient agriculture. This system makes agriculture automated and predictable, helping to solve the problems of an aging workforce and a shortage of successors. Even inexperienced individuals can easily enter agriculture, improving its sustainability. For example, by utilizing IoT devices and generative AI, even those without specialized agricultural knowledge can grow crops while receiving appropriate cultivation advice. In addition, the automation of heavy labor allows elderly people to continue farming without burden. In this way, agricultural support systems can achieve efficiency and automation in agriculture, solving the problems of an aging workforce and a shortage of successors.

[0062] The agricultural support system according to this embodiment comprises a data collection unit, an analysis unit, and a control unit. The data collection unit collects data such as sunshine duration, temperature, humidity, and moisture content. The data collection unit collects data such as sunshine duration, temperature, humidity, and moisture content using various sensors, for example. The data collection unit can, for example, install temperature sensors and humidity sensors in a field to measure sunshine duration and moisture content. The data collection unit can also, for example, measure soil moisture content using a soil moisture sensor. The data collection unit can also, for example, measure sunshine duration using a light sensor. The analysis unit analyzes the data collected by the data collection unit and provides optimal cultivation advice. The analysis unit analyzes the data collected using, for example, a generative AI. The generative AI predicts the growth status of crops based on the collected data and provides optimal cultivation advice. For example, the generative AI can analyze temperature and humidity data and propose appropriate watering timing and fertilizer amounts. The generative AI can also predict the growth status of crops based on the collected data and provide optimal cultivation advice. For example, the generating AI can analyze temperature and humidity data and propose appropriate watering timing and fertilizer amounts. The control unit automatically controls the environment based on the analysis results obtained by the analysis unit. The control unit can, for example, automatically activate the watering system based on the watering timing proposed by the generating AI. The control unit can also, for example, automatically activate the watering system based on the watering timing proposed by the generating AI. Furthermore, the control unit can adjust the shading system according to the amount of sunlight. The control unit can, for example, adjust the shading system according to the amount of sunlight. As a result, the agricultural support system according to this embodiment can achieve efficiency and automation in agriculture and solve the problems of an aging workforce and a shortage of successors.

[0063] The data collection unit collects data such as sunshine duration, temperature, humidity, and moisture content. For example, the unit uses various sensors to collect data such as sunshine duration, temperature, humidity, and moisture content. Specifically, temperature and humidity sensors can be installed in fields to measure sunshine duration and moisture content. Temperature sensors measure surface and underground temperatures in real time and transmit the data to a central database. Humidity sensors measure humidity in the air, providing data to understand the humidity environment suitable for crop growth. Soil moisture sensors measure soil moisture content, allowing for an understanding of how much water crop roots are absorbing. Light sensors measure sunshine duration, providing data to determine how much light crops are receiving. These sensors can collect data using wireless communication technology and transmit it to a central database. Furthermore, the data collection unit can also collect data over a wide area using drones and autonomous vehicles. Drones photograph the entire field from above and collect image data. Autonomous vehicles patrol the fields, collecting data using sensors and transmitting it to a central database in real time. This allows the data collection unit to collect a wide range of data using diverse devices and methods, contributing to increased efficiency and accuracy in agriculture.

[0064] The analysis department analyzes the data collected by the collection department and provides optimal cultivation advice. For example, the analysis department uses generative AI to analyze the collected data. Based on the collected data, the generative AI predicts the growth status of crops and provides optimal cultivation advice. Specifically, the generative AI can analyze temperature and humidity data and suggest appropriate watering timing and fertilizer amounts. The generative AI can also predict future weather conditions based on past data and weather forecasts and provide cultivation advice accordingly. For example, the generative AI combines past temperature data and weather forecasts to predict future temperature fluctuations and suggests adjustments to watering and fertilizer accordingly. Furthermore, the generative AI can suggest optimal management methods according to the growth stage of the crop. For example, if the crop is in the growth stage, the generative AI suggests the appropriate type and amount of fertilizer, and if it is approaching harvest time, it suggests the timing of harvest. In addition, the generative AI can predict the risk of pest and disease outbreaks and provide advice for taking early countermeasures. As a result, the analysis department can highly analyze the collected data and provide specific and practical cultivation advice to agricultural workers.

[0065] The control unit automatically controls the environment based on the analysis results obtained by the analysis unit. For example, the control unit automatically activates the watering system based on the watering timing proposed by the generating AI. Specifically, the control unit receives data from the collection unit and instructions from the analysis unit and activates the watering system at the appropriate time. For example, if the soil moisture sensor drops below a certain moisture level, the control unit automatically activates the watering system and supplies the necessary amount of water. The control unit can also adjust the shading system according to the amount of sunlight. For example, if the amount of sunlight is too long, the control unit automatically activates the shading system to prevent crops from receiving excessive sunlight. Furthermore, the control unit can control the temperature control system and ventilation system based on temperature and humidity data. For example, if the temperature is too high, the control unit automatically activates the cooling system to adjust it to an appropriate temperature. Also, if the humidity is too low, the control unit automatically activates the humidification system to maintain an appropriate humidity level. In this way, the control unit automatically maintains an environment that is optimal for crop growth, enabling increased efficiency and automation in agriculture. Furthermore, the control unit is equipped with an anomaly detection function, which immediately notifies the user if an abnormality occurs in the sensors or system, enabling a rapid response. This allows the control unit to improve the efficiency and automation of agriculture, helping to address challenges such as an aging workforce and a shortage of successors.

[0066] The data collection unit can collect data such as sunshine duration, temperature, humidity, and moisture content using various sensors. For example, the data collection unit can collect temperature data using a temperature sensor. The data collection unit can also collect humidity data using a humidity sensor. The data collection unit can also collect soil moisture content using a soil moisture sensor. This allows for accurate data collection by using various sensors. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input temperature data acquired by the temperature sensor into a generating AI and have the generating AI perform analysis of the temperature data.

[0067] The analysis unit can predict crop growth based on collected data and provide optimal cultivation advice. For example, the analysis unit analyzes the collected data using a generative AI. The generative AI predicts crop growth based on the collected data and provides optimal cultivation advice. For example, the generative AI can analyze temperature and humidity data and suggest appropriate watering timing and fertilizer amounts. Furthermore, the generative AI can predict crop growth based on collected data and provide optimal cultivation advice. For example, the generative AI can analyze temperature and humidity data and suggest appropriate watering timing and fertilizer amounts. This allows for the prediction of crop growth and the provision of optimal cultivation advice, which is expected to improve crop quality and increase yields. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without one. For example, the analysis unit can input collected data into a generative AI and have the generative AI predict crop growth.

[0068] The control unit can automatically activate the watering system based on the watering timing proposed by the generating AI. The control unit can, for example, automatically activate the watering system based on the watering timing proposed by the generating AI. The control unit can, for example, automatically activate the watering system based on the watering timing proposed by the generating AI. The control unit can also adjust the shading system according to the amount of sunlight. The control unit can, for example, adjust the shading system according to the amount of sunlight. This enables efficient watering by automatically activating the watering system based on the proposals of the generating AI. Some or all of the above processing in the control unit may be performed using the generating AI, for example, or without the generating AI. For example, the control unit can activate the watering system using an AI model that takes the watering timing proposed by the generating AI as input and outputs the operation of the watering system.

[0069] The control unit can adjust the shading system according to the amount of sunlight. The control unit can, for example, adjust the shading system according to the amount of sunlight. The control unit can, for example, adjust the shading system according to the amount of sunlight. This allows for the optimization of the crop growing environment by adjusting the shading system according to the amount of sunlight. Some or all of the above-described processes in the control unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the control unit can input sunlight data into a generating AI and have the generating AI perform the adjustment of the shading system.

[0070] The analysis unit can analyze temperature and humidity data and propose appropriate watering timings and fertilizer amounts. For example, the analysis unit can use a generating AI to analyze temperature and humidity data. The generating AI proposes appropriate watering timings and fertilizer amounts based on the temperature and humidity data. For example, the generating AI can analyze temperature and humidity data and propose appropriate watering timings and fertilizer amounts. The generating AI can also propose appropriate watering timings and fertilizer amounts based on temperature and humidity data. For example, the generating AI can analyze temperature and humidity data and propose appropriate watering timings and fertilizer amounts. Thus, by analyzing temperature and humidity data, appropriate watering timings and fertilizer amounts can be proposed. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or without one. For example, the analysis unit can input temperature and humidity data into a generating AI and have the generating AI propose appropriate watering timings and fertilizer amounts.

[0071] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the system load. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to obtain more detailed data. For example, if the user is in a hurry, the data collection unit can optimize the frequency of data collection to quickly collect only the minimum necessary data. This reduces the system load by adjusting the frequency of 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-described processes in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the frequency of data collection.

[0072] The data collection unit can dynamically change the type and accuracy of the data it collects according to the growth stage of different crops. For example, when a crop is in the germination stage, the data collection unit can collect temperature and humidity data with high accuracy. When a crop is in the growth stage, the data collection unit can focus on collecting data on sunshine hours and moisture content. When a crop is in the harvest stage, the data collection unit can collect all data in a balanced manner and determine the optimal harvest timing. By optimizing data collection according to the growth stage of the crop, more accurate data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the changes in the type and accuracy of data collection according to the growth stage of the crop.

[0073] The data collection unit can ensure data reliability by arranging multiple redundant sensors in case of failure. For example, the data collection unit can place multiple temperature sensors in different locations in the field so that data can be acquired even if one fails. For example, the data collection unit can install multiple humidity sensors so that if one fails, the other sensors can compensate for the data. For example, the data collection unit can make the sunlight sensors redundant so that accurate sunlight data can be acquired even if one fails. In this way, data reliability can be ensured by arranging sensors with redundancy. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from multiple sensors into a generating AI and have the generating AI perform the task of ensuring data reliability.

[0074] The data collection unit can share the collected data with other agricultural systems to achieve comprehensive agricultural management. For example, the data collection unit can share collected temperature data with other agricultural management systems to perform comprehensive environmental control. For example, the data collection unit can share humidity data and work with other systems to determine the optimal watering timing. For example, the data collection unit can share sunlight data and work with other systems to adjust the shading system. This enables comprehensive agricultural management by sharing data with other agricultural systems. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the collected data into a generating AI and have the generating AI perform data sharing with other agricultural systems.

[0075] The data collection unit can automatically back up the collected data to cloud storage, ensuring data security. For example, the data collection unit can automatically back up collected temperature data to cloud storage. For example, the data collection unit can periodically save humidity data to cloud storage, ensuring data security. For example, the data collection unit can back up sunlight data to cloud storage in real time, preventing data loss. This ensures data security by backing up to cloud storage. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the collected data into a generating AI and have the generating AI perform the backup to cloud storage.

[0076] The analysis unit can estimate the user's emotions and adjust the way training advice is expressed based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide concise and easy-to-understand advice. For example, if the user is relaxed, the analysis unit can provide detailed advice. For example, if the user is in a hurry, the analysis unit can provide concise and quick advice. By adjusting the way training advice is expressed according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the way training advice is expressed.

[0077] The analysis unit can refer to past data to predict the occurrence of extreme weather events and pest infestations and issue warnings in advance. For example, the analysis unit can predict the occurrence of extreme weather events and issue warnings based on past temperature data. For example, the analysis unit can predict the occurrence of pest infestations and issue warnings based on past humidity data. For example, the analysis unit can predict the occurrence of extreme weather events and issue warnings based on past sunshine data. In this way, by referring to past data, it is possible to predict the occurrence of extreme weather events and pest infestations and issue warnings in advance. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input past data into a generation AI and have the generation AI perform predictions of extreme weather events and pest infestations.

[0078] The analysis unit can customize optimal cultivation advice according to the characteristics of different crops. For example, the analysis unit can provide optimal cultivation advice according to the characteristics of tomatoes. For example, the analysis unit can provide optimal cultivation advice according to the characteristics of strawberries. For example, the analysis unit can provide optimal cultivation advice according to the characteristics of lettuce. By providing cultivation advice tailored to the characteristics of each crop, it is expected that crop quality will improve. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input crop characteristic data into a generating AI and have the generating AI perform the customization of optimal cultivation advice.

[0079] The analysis unit can refer to other agricultural data (market prices, demand forecasts, etc.) and provide optimal cultivation advice from an economic perspective. For example, the analysis unit can suggest the optimal harvest timing based on market price data. For example, the analysis unit can suggest the optimal planting plan based on demand forecast data. For example, the analysis unit can suggest the optimal amount of fertilizer to use based on economic data. This allows for more efficient agricultural management by providing optimal cultivation advice from an economic perspective. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input other agricultural data into a generative AI and have the generative AI provide optimal cultivation advice from an economic perspective.

[0080] The control unit can estimate the user's emotions and adjust the timing of environmental control based on the estimated emotions. For example, if the user is tense, the control unit can delay the timing of environmental control. For example, if the user is relaxed, the control unit can speed up the timing of environmental control. For example, if the user is in a hurry, the control unit can optimize the timing of environmental control. This allows for more appropriate environmental control by adjusting the timing of environmental control according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the control unit may be performed using a generative AI, or not using a generative AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI adjust the timing of environmental control.

[0081] The control unit can dynamically change environmental control parameters according to the growth stage of different crops. For example, when a crop is in the germination stage, the control unit can control temperature and humidity parameters with high precision. For example, when a crop is in the growth stage, the control unit can focus on controlling sunlight hours and water content parameters. For example, when a crop is in the harvest stage, the control unit can control all parameters in a balanced manner to determine the optimal harvest timing. This makes optimal environmental control possible by changing the environmental control parameters according to the growth stage of the crop. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can have a generative AI perform the changes to environmental control parameters according to the growth stage of the crop.

[0082] The control unit can ensure the reliability of environmental control by arranging multiple redundant control systems in case of failure. For example, the control unit can place multiple temperature control systems in different locations in the field so that environmental control can be performed even in the event of a failure. For example, the control unit can install multiple humidity control systems so that if one fails, the other systems can compensate for the failure of the other. For example, the control unit can make the sunlight control system redundant so that accurate environmental control can be performed even in the event of a failure. In this way, the reliability of environmental control can be ensured by the redundant control systems. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the control unit can input data acquired from multiple control systems into a generative AI and have the generative AI perform the task of ensuring the reliability of environmental control.

[0083] The control unit can work in conjunction with other agricultural systems to achieve comprehensive environmental control. For example, the control unit can work with a temperature control system in conjunction with other agricultural management systems to perform comprehensive environmental control. For example, the control unit can work with a humidity control system in conjunction with other systems to perform optimal environmental control. For example, the control unit can work with a sunlight control system in conjunction with other systems to adjust the shading system. This enables comprehensive environmental control by working in conjunction with other agricultural systems. Some or all of the above-described processes in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can have a generative AI perform data sharing with other agricultural systems.

[0084] The control unit can automatically back up collected data to cloud storage to ensure data security. For example, the control unit can automatically back up collected temperature data to cloud storage. For example, the control unit can periodically save humidity data to cloud storage to ensure data security. For example, the control unit can back up sunlight data to cloud storage in real time to prevent data loss. In this way, data security can be ensured by backing up to cloud storage. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the control unit can input collected data into a generative AI and have the generative AI perform the backup to cloud storage.

[0085] The analysis unit can analyze collected data in conjunction with other agricultural systems to achieve comprehensive agricultural management. For example, the analysis unit can share collected temperature data with other agricultural management systems to perform comprehensive environmental control. For example, the analysis unit can share humidity data and work with other systems to determine the optimal watering timing. For example, the analysis unit can share sunlight data and work with other systems to adjust shading systems. This enables comprehensive agricultural management by analyzing data in conjunction with other agricultural systems. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input collected data into a generative AI and have the generative AI perform data sharing with other agricultural systems.

[0086] The control unit can estimate the user's emotions and determine the priority of environmental control based on the estimated emotions. For example, if the user is stressed, the control unit will prioritize only the most important environmental controls. If the user is relaxed, the control unit can balance all environmental controls. If the user is in a hurry, the control unit can quickly perform the most important environmental controls. This allows for more appropriate environmental control by determining the priority of environmental controls 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 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 control unit may be performed using a generative AI, or not. For example, the control unit can input user emotion data into a generative AI and have the generative AI determine the priority of environmental control.

[0087] The control unit can dynamically change environmental control parameters according to the growth stage of different crops. For example, when a crop is in the germination stage, the control unit can control temperature and humidity parameters with high precision. For example, when a crop is in the growth stage, the control unit can focus on controlling sunlight hours and water content parameters. For example, when a crop is in the harvest stage, the control unit can control all parameters in a balanced manner to determine the optimal harvest timing. This makes optimal environmental control possible by changing the environmental control parameters according to the growth stage of the crop. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can have a generative AI perform the changes to environmental control parameters according to the growth stage of the crop.

[0088] The control unit can ensure the reliability of environmental control by arranging multiple redundant control systems in case of failure. For example, the control unit can place multiple temperature control systems in different locations in the field so that environmental control can be performed even in the event of a failure. For example, the control unit can install multiple humidity control systems so that if one fails, the other systems can compensate for the failure of the other. For example, the control unit can make the sunlight control system redundant so that accurate environmental control can be performed even in the event of a failure. In this way, the reliability of environmental control can be ensured by the redundant control systems. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the control unit can input data acquired from multiple control systems into a generative AI and have the generative AI perform the task of ensuring the reliability of environmental control.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The agricultural support system can also be equipped with a forecasting unit. Based on data obtained from the data collection unit, the forecasting unit can predict future weather conditions and crop growth. For example, the forecasting unit can combine past and current weather data to predict future weather fluctuations. It can also predict harvest time and yield based on crop growth data. Furthermore, the forecasting unit can predict the risk of pest and disease outbreaks and take preventative measures. This allows the agricultural support system to improve the efficiency and stability of agriculture by predicting future risks and taking appropriate countermeasures.

[0091] The agricultural support system can also be equipped with a notification unit. This unit is responsible for notifying the user of information from the analysis and control units. For example, the notification unit can inform the user of the appropriate watering timing and fertilizer amounts. It can also provide the user with environmental control results and forecast information from the prediction unit. Furthermore, the notification unit can warn the user of the risk of extreme weather or pest and disease outbreaks. This allows the user to obtain necessary information in real time, enabling a rapid response.

[0092] The agricultural support system can also be equipped with a learning unit. The learning unit learns to improve the overall system performance based on data obtained from the data collection and analysis units. For example, the learning unit can optimize the algorithms of the generative AI using historical data to provide more accurate predictions and advice. Furthermore, the learning unit can improve the system's usability based on user operation history and feedback. In addition, the learning unit can adapt to the introduction of new sensors and devices, increasing the system's flexibility. This allows the agricultural support system to continuously evolve and achieve higher performance.

[0093] The agricultural support system can also be equipped with an emotion estimation unit. This unit estimates the user's emotions and adjusts the system's operation based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can reduce the frequency of system notifications, thereby alleviating the user's burden. If the user is relaxed, the emotion estimation unit can provide detailed advice. Furthermore, if the user is in a hurry, the emotion estimation unit can quickly provide only the essential information. This allows for flexible responses tailored to the user's emotions, improving the system's usability.

[0094] The agricultural support system can also be equipped with a communication unit. This unit plays a role in facilitating interaction between the user and the system. For example, the communication unit can use speech recognition technology to understand the user's voice instructions and provide appropriate responses. It can also incorporate chatbot functionality to answer user questions in real time. Furthermore, the communication unit can estimate the user's emotions and respond accordingly. This allows users to engage in more efficient farming through natural dialogue with the system.

[0095] The agricultural support system can also be equipped with a data visualization unit. This unit is responsible for visually displaying data obtained from the collection and analysis units. For example, it can display temperature and humidity data as graphs and charts, allowing users to understand them intuitively. It can also display crop growth status as a 3D model. Furthermore, it can display forecast information from the forecasting unit on a map, enabling users to visually grasp future risks. This allows users to utilize data more effectively and make appropriate decisions.

[0096] The agricultural support system can also be equipped with a remote control unit. This unit allows users to operate the system remotely. For example, the remote control unit can be used with a smartphone or tablet to control the field environment. Furthermore, the remote control unit can allow users to check data in real time and perform necessary operations via the internet. In addition, the remote control unit can estimate the user's emotions and provide emotionally appropriate operational support. This enables users to farm efficiently, regardless of their location.

[0097] The agricultural support system can also be equipped with an energy management unit. This unit plays a role in optimizing the overall energy consumption of the system. For example, it can adjust the operation of the collection and control units to occur at energy-efficient times. Furthermore, the energy management unit can utilize renewable energy sources such as solar and wind power to reduce the system's energy consumption. In addition, the energy management unit can monitor energy consumption data in real time and issue warnings if an anomaly occurs. This enables the agricultural support system to achieve sustainable energy management and reduce its environmental impact.

[0098] The agricultural support system can also include a quality control department. This department is responsible for managing crop quality based on data obtained from the data collection and analysis departments. For example, the quality control department can evaluate crop quality based on temperature and humidity data and take necessary measures. It can also predict harvest time and yield, and propose the optimal harvest timing. Furthermore, the quality control department can assess the risk of pest and disease outbreaks and take preventative measures. This allows the agricultural support system to maintain high crop quality and increase yields.

[0099] The agricultural support system can also be equipped with an emotional feedback unit. This unit estimates the user's emotions and adjusts the system's operation based on those estimates. For example, if the user is stressed, the emotional feedback unit can reduce the frequency of system notifications, thereby alleviating the user's burden. Conversely, if the user is relaxed, the emotional feedback unit can provide detailed advice. Furthermore, if the user is in a hurry, the emotional feedback unit can quickly provide only the essential information. This allows for flexible responses tailored to the user's emotions, improving the system's usability.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The data collection unit collects data such as sunshine duration, temperature, humidity, and moisture content. The data collection unit uses various sensors to collect this data. For example, temperature and humidity sensors can be installed in a field to measure sunshine duration and moisture content. Soil moisture content can also be measured using a soil moisture sensor, and sunshine duration can be measured using a light sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and provides optimal cultivation advice. The analysis unit uses generative AI to analyze the collected data and predict the growth status of the crops. For example, the generative AI can analyze temperature and humidity data and suggest appropriate watering timing and fertilizer amounts. Step 3: The control unit automatically controls the environment based on the analysis results obtained by the analysis unit. For example, it can automatically activate the watering system based on the watering timing suggested by the generating AI. It can also adjust the shading system according to the amount of sunlight.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the collection unit, analysis unit, and control unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data such as sunshine duration, temperature, humidity, and moisture content using various sensors on the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using generated AI to provide optimal cultivation advice. The control unit is implemented in the specific processing unit 290 of the data processing unit 12, and automatically controls the environment based on the analysis results of the generated AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the collection unit, analysis unit, and control 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 collects data such as sunshine duration, temperature, humidity, and moisture content using various sensors in the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI and provides optimal cultivation advice. The control unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which automatically controls the environment based on the analysis results of the generated AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the collection unit, analysis unit, and control unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data such as sunshine duration, temperature, humidity, and moisture content using various sensors on the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using generated AI to provide optimal cultivation advice. The control unit is implemented in the specific processing unit 290 of the data processing unit 12, and automatically controls the environment based on the analysis results of the generated AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, and control unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data such as sunshine duration, temperature, humidity, and moisture content using various sensors on the robot 414. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using a generating AI and provides optimal cultivation advice. The control unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which automatically controls the environment based on the analysis results of the generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) A data collection unit that collects data such as sunshine duration, temperature, humidity, and moisture content, The data collected by the aforementioned collection unit is analyzed by the analysis unit, which provides optimal training advice. The system includes a control unit that automatically controls the environment based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Various sensors are used to collect data such as sunshine duration, temperature, humidity, and moisture content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Based on the collected data, we predict crop growth and provide optimal cultivation advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The control unit, The watering system will automatically activate based on the watering timing suggested by the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The control unit, The shading system is adjusted according to the amount of sunlight. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is It analyzes temperature and humidity data to suggest appropriate watering timing and fertilizer amounts. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The type and accuracy of the data collected are dynamically changed according to the growth stage of different crops. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is To ensure data reliability, multiple redundant sensors are deployed in case of failure. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is By linking and sharing the collected data with other agricultural systems, comprehensive agricultural management can be achieved. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The collected data is automatically backed up to cloud storage to ensure data security. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is The system estimates the user's emotions and adjusts the way training advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is By referring to past data, it predicts extreme weather events and pest outbreaks and issues warnings in advance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Customize optimal growing advice according to the characteristics of different crops. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is By referencing other agricultural data, we provide optimal cultivation advice from an economic perspective. The system described in Appendix 1, characterized by the features described herein. (Note 16) The control unit, It estimates the user's emotions and adjusts the timing of environmental control based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The control unit, Dynamically change environmental control parameters according to the growth stage of different crops. The system described in Appendix 1, characterized by the features described herein. (Note 18) The control unit, To ensure the reliability of environmental control, multiple redundant control systems are deployed in case of failure. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, By integrating with other agricultural systems, comprehensive environmental control can be achieved. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, The collected data is automatically backed up to cloud storage to ensure data security. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is By analyzing the collected data in conjunction with other agricultural systems, comprehensive agricultural management can be achieved. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, It estimates the user's emotions and determines the priority of environmental control based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, Dynamically change environmental control parameters according to the growth stage of different crops. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, To ensure the reliability of environmental control, multiple redundant control systems are deployed in case of failure. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 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 data collection unit that collects data such as sunshine duration, temperature, humidity, and moisture content, The data collected by the aforementioned collection unit is analyzed by the analysis unit, which provides optimal training advice. The system includes a control unit that automatically controls the environment based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Various sensors are used to collect data such as sunshine duration, temperature, humidity, and moisture content. The system according to feature 1.

3. The aforementioned analysis unit is Based on the collected data, we predict crop growth and provide optimal cultivation advice. The system according to feature 1.

4. The control unit, The watering system is automatically activated based on the watering timing proposed by the generating AI. The system according to feature 1.

5. The control unit, The shading system is adjusted according to the amount of sunlight. The system according to feature 1.

6. The aforementioned analysis unit is It analyzes temperature and humidity data to suggest appropriate watering timing and fertilizer amounts. The system according to feature 1.

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

8. The aforementioned collection unit is The type and accuracy of the data collected are dynamically changed according to the growth stage of different crops. The system according to feature 1.

9. The aforementioned collection unit is To ensure data reliability, multiple redundant sensors are deployed in case of failure. The system according to feature 1.

10. The aforementioned collection unit is By linking and sharing the collected data with other agricultural systems, comprehensive agricultural management can be achieved. The system according to feature 1.

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