system

Abstract

The system according to this embodiment aims to propose an optimal cultivation method based on the growth status of crops and environmental data, and to automate agricultural work. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an automation unit. The collection unit uses sensors to collect data on the growth status and environment of agricultural crops. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The automation unit automates agricultural work based on the cultivation method proposed by the proposal unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately propose optimal cultivation methods based on crop growth conditions and environmental data, nor do they fully automate agricultural work, leaving room for improvement.

[0005] The system according to this embodiment aims to propose an optimal cultivation method based on the growth status of crops and environmental data, and to automate agricultural work. [Means for solving the problem]

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an automation unit. The data collection unit collects data on crop growth and environmental conditions using sensors. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The automation unit automates agricultural work based on the cultivation method proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal cultivation methods based on the growth status of agricultural crops and environmental data, and automate agricultural work. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The smart agriculture system according to an embodiment of the present invention uses e-kakashi. This system incorporates a generative AI to enhance its brain function, resulting in higher yields and environmental conservation. The system uses sensors to collect data on crop growth and environmental conditions. The generative AI analyzes the collected data and proposes optimal cultivation methods. Furthermore, agricultural work is automated based on the proposed cultivation methods. This mechanism improves yields and contributes to environmental conservation. For example, the system collects data such as soil humidity, temperature, and sunlight in real time. This allows for accurate understanding of crop growth conditions. The generative AI then analyzes the collected data. Based on the collected data, the generative AI determines the optimal conditions for crop growth. For example, if soil humidity is low, it proposes the appropriate amount of irrigation. Furthermore, if temperature is too high, it proposes measures such as installing shade. Furthermore, agricultural work is automated based on the proposed cultivation methods. For example, it can automatically install irrigation systems and shades. This improves agricultural efficiency and increases yields. This system not only improves yields but also contributes to environmental conservation. For example, by suggesting the appropriate amount of irrigation, it is possible to prevent the waste of water resources. In addition, by installing shade, it is possible to prevent crops from being exposed to high temperatures, thereby reducing the burden on the environment. In this way, the smart agriculture system can accurately grasp the growth status of crops, suggest optimal cultivation methods, and automate agricultural work, thereby increasing yields and contributing to environmental conservation.

[0029] The smart agriculture system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an automation unit. The collection unit uses sensors to collect data on the growth status and environment of agricultural crops. The collection unit collects data such as soil humidity, temperature, and amount of sunlight in real time. The collection unit can measure the amount of moisture in the soil using a soil humidity sensor, for example. The collection unit can also measure the temperature around the crops using a temperature sensor. The collection unit can also measure the amount of sunlight received by the crops using a sunlight sensor. The analysis unit analyzes the data collected by the collection unit. The analysis unit determines optimal conditions for growing crops based on the collected data, for example, using a generative AI. For example, the analysis unit can determine an appropriate amount of irrigation when soil humidity is low. The analysis unit can also determine measures such as installing shades when temperatures are too high. The proposal unit proposes an optimal cultivation method based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, use a generation AI to suggest an optimal irrigation amount and a shade installation method for growing agricultural crops. For example, the suggestion unit can suggest an appropriate irrigation amount when soil humidity is low. Furthermore, the suggestion unit can suggest measures such as installing a shade when the temperature is too high. The automation unit automates agricultural work based on the cultivation method suggested by the suggestion unit. For example, the automation unit can automatically install an irrigation system and a shade. For example, the automation unit can automatically control the irrigation system to provide an appropriate amount of irrigation. Furthermore, the automation unit can automatically install a shade to prevent agricultural crops from being exposed to high temperatures. As a result, the smart agriculture system according to the embodiment can accurately grasp the growth status of agricultural crops, suggest an optimal cultivation method, and automate agricultural work, thereby increasing yields and contributing to environmental conservation.

[0030] The data collection unit can collect data on soil humidity, temperature, and sunlight in real time. For example, the unit can measure soil moisture content in real time using a soil humidity sensor. For instance, the unit can install a soil humidity sensor underground and continuously measure soil moisture content. The unit can also measure the temperature around crops in real time using a temperature sensor. For example, the unit can install a temperature sensor near crops and continuously measure the ambient temperature. Furthermore, the unit can measure the amount of sunlight received by crops in real time using a sunlight sensor. For example, the unit can install a sunlight sensor above crops and continuously measure sunlight. This allows the data collection unit to accurately understand the growth status of crops by collecting data on soil humidity, temperature, and sunlight in real time.

[0031] The analysis unit can determine the optimal conditions for crop growth based on the collected data. For example, the analysis unit can use generative AI to determine the optimal conditions for crop growth based on the collected data. For example, if the soil moisture is low, the analysis unit can determine the appropriate amount of irrigation. Also, if the temperature is too high, the analysis unit can determine countermeasures such as installing shade. Furthermore, if there is insufficient sunlight, the analysis unit can determine countermeasures such as installing additional lighting. In this way, the analysis unit can optimize crop growth by determining the optimal conditions for crop growth based on the collected data.

[0032] The suggestion unit can suggest an appropriate irrigation amount when the soil humidity is low. The suggestion unit can suggest an appropriate irrigation amount when the soil humidity is low, for example, by using a generative AI. For example, the suggestion unit can calculate an appropriate irrigation amount based on the soil's water retention capacity and the type of plant. The suggestion unit can also adjust the irrigation amount in real time based on data obtained from a soil humidity sensor. Furthermore, the suggestion unit can refer to past irrigation data and suggest an optimal irrigation schedule. As a result, the suggestion unit can optimize crop growth by suggesting an appropriate irrigation amount when the soil humidity is low.

[0033] The suggestion unit can suggest measures such as installing shades when temperatures are high. The suggestion unit can suggest measures such as installing shades when temperatures are high, for example, using a generation AI. For example, the suggestion unit can suggest where and how to install shades based on data obtained from a temperature sensor. Furthermore, when temperatures are too high, the suggestion unit can suggest not only the installation of shades but also the introduction of a cooling system. Furthermore, the suggestion unit can refer to past temperature data and suggest an optimal shade installation schedule. As a result, the suggestion unit can optimize crop growth by suggesting measures such as installing shades when temperatures are high.

[0034] The automation unit can automatically install an irrigation system or a shade. For example, the automation unit can automatically control the irrigation system and provide an appropriate amount of irrigation. For example, the automation unit can automatically control the irrigation system and provide an appropriate amount of irrigation based on data obtained from a soil moisture sensor. The automation unit can also automatically install shades to prevent agricultural crops from being exposed to high temperatures. For example, the automation unit can automatically install shades based on data obtained from a temperature sensor to prevent agricultural crops from being exposed to high temperatures. Furthermore, the automation unit can automate not only the installation of irrigation systems or shades, but also the fertilization system. For example, the automation unit can measure the nutrient status of the soil with a sensor and automatically apply an appropriate amount of fertilizer. In this way, the automation unit can automatically install irrigation systems or shades, thereby improving the efficiency of agricultural work and increasing yields.

[0035] The collection unit can analyze past collected data and dynamically change the optimal sensor placement. The collection unit can, for example, use generative AI to analyze past collected data and dynamically change the optimal sensor placement. For example, if past data shows that data collection in a specific area is insufficient, the collection unit can add sensors to that area. Also, if past data shows that data collection is concentrated in a specific time period, the collection unit can change the placement of sensors during that time period. Furthermore, if past data shows that data collection under specific environmental conditions is important, the collection unit can adjust the placement of sensors to suit those conditions. This allows the collection unit to improve the accuracy of data collection by analyzing past data and dynamically changing the placement of sensors.

[0036] The data collection unit can integrate and analyze data from multiple sensors to improve the accuracy of the collected data. For example, the data collection unit can use generative AI to integrate and analyze data from multiple sensors to improve the accuracy of the collected data. For example, the data collection unit can collect soil moisture data from multiple sensors and calculate the average value to improve accuracy. The data collection unit can also collect temperature data from multiple sensors and exclude outliers to obtain accurate data. Furthermore, the data collection unit can collect solar radiation data from multiple sensors, integrate and analyze it to obtain more detailed environmental information. In this way, the data collection unit can improve the accuracy of the data by integrating and analyzing data from multiple sensors.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the farm during data collection. For example, the data collection unit can use generative AI to prioritize the collection of highly relevant data by considering the geographical location information of the farm during data collection. For example, if data collection is important in a particular area of ​​the farm, the data collection unit can prioritize the collection of data from that area. Furthermore, the data collection unit can prioritize the collection of data under specific environmental conditions based on the geographical location information of the farm. In addition, the data collection unit can prioritize the collection of data related to specific crops by considering the geographical location information of the farm. As a result, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the farm during data collection.

[0038] The collection unit can simultaneously collect data on the farm's surrounding environment during collection and use it for analysis. The collection unit can simultaneously collect data on the farm's surrounding environment (e.g., weather data) during collection and use it for analysis, for example, using a generation AI. For example, the collection unit can collect weather data on the farm and analyze it in association with the growth status of crops. The collection unit can also collect data on the farm's surrounding environment and identify factors that affect crop growth. Furthermore, the collection unit can collect weather data on the farm and use it for analysis to propose appropriate cultivation methods. In this way, the collection unit can obtain more detailed environmental information by simultaneously collecting data on the farm's surrounding environment and using it for analysis.

[0039] During analysis, the analysis unit can detect abnormal values ​​by comparing with past data and identify the cause of the abnormality. During analysis, the analysis unit can detect abnormal values ​​by comparing with past data and identify the cause of the abnormality, for example, using generative AI. For example, the analysis unit can detect abnormal humidity values ​​by comparing with past data and identify the cause. Also, the analysis unit can detect abnormal temperature values ​​by comparing with past data and identify the cause. Furthermore, the analysis unit can detect abnormal amounts of sunlight by comparing with past data and identify the cause. As a result, the analysis unit can detect abnormal values ​​by comparing with past data and identify the cause of the abnormality, enabling early detection of problems.

[0040] The analysis unit can apply different analysis algorithms during analysis to improve the reliability of the results. For example, the analysis unit can use generative AI to apply different analysis algorithms during analysis to improve the reliability of the results. For example, the analysis unit can apply different analysis algorithms and compare the analysis results of humidity data to improve reliability. The analysis unit can also apply different analysis algorithms and compare the analysis results of temperature data to improve reliability. Furthermore, the analysis unit can apply different analysis algorithms and compare the analysis results of solar radiation data to improve reliability. In this way, the analysis unit can improve the reliability of the analysis results by applying different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the time of submission of collected data during analysis. The analysis unit can determine the priority of analysis based on the time of submission of collected data during analysis, for example, using a generation AI. For example, the analysis unit can prioritize analysis of data that was submitted early in the collected data. Also, the analysis unit can postpone analysis of data that was submitted late in the collected data. Furthermore, the analysis unit can prioritize analysis of important data based on the time of submission of collected data. In this way, the analysis unit can prioritize analysis of important data by determining the priority of analysis based on the time of submission of collected data.

[0042] The analysis unit can improve the accuracy of the analysis results by referring to related literature data during analysis. The analysis unit can improve the accuracy of the analysis results by referring to related literature data during analysis, for example, using a generation AI. For example, the analysis unit can improve the accuracy of the analysis results of humidity data by referring to related literature data. The analysis unit can also improve the accuracy of the analysis results of temperature data by referring to related literature data. Furthermore, the analysis unit can improve the accuracy of the analysis results of solar radiation data by referring to related literature data. In this way, the analysis unit can improve the accuracy of the analysis results by referring to related literature data.

[0043] The proposal unit can apply different proposal algorithms depending on the type and growth stage of the crop when making a proposal. The proposal unit can apply different proposal algorithms depending on the type and growth stage of the crop when making a proposal, for example, by using a generation AI. For example, the proposal unit can propose an optimal irrigation amount depending on the type of crop. Also, the proposal unit can propose an appropriate cultivation method depending on the growth stage of the crop. Furthermore, the proposal unit can propose an optimal fertilization method depending on the type and growth stage of the crop. This enables the proposal unit to make optimal proposals depending on the type and growth stage of the crop.

[0044] The proposal unit can refer to past proposal history when making a proposal and make the optimal proposal. The proposal unit can refer to past proposal history when making a proposal, for example, using a generation AI. For example, the proposal unit can refer to past proposal history to propose the optimal irrigation amount. The proposal unit can also refer to past proposal history to propose an appropriate cultivation method. Furthermore, the proposal unit can refer to past proposal history to propose the optimal fertilization method. This allows the proposal unit to make more accurate proposals by referring to past proposal history.

[0045] The proposal unit can make the optimal proposal by taking into consideration the geographical location information of the farm when making the proposal. The proposal unit can make the optimal proposal by taking into consideration the geographical location information of the farm when making the proposal, for example, by using a generation AI. For example, the proposal unit can propose an optimal irrigation amount based on the geographical location information of the farm. The proposal unit can also propose an appropriate cultivation method based on the geographical location information of the farm. Furthermore, the proposal unit can propose an optimal fertilization method based on the geographical location information of the farm. As a result, the proposal unit can make the optimal proposal by taking into consideration the geographical location information of the farm, thereby proposing a more effective cultivation method.

[0046] The proposal unit can make economically optimal suggestions by referring to market price data for agricultural products when making suggestions. For example, the proposal unit can use generation AI to refer to market price data for agricultural products when making suggestions. For example, the proposal unit can refer to market price data for agricultural products and suggest the optimal harvest time. The proposal unit can also refer to market price data for agricultural products and suggest appropriate cultivation methods. Furthermore, the proposal unit can refer to market price data for agricultural products and suggest the optimal fertilization method. In this way, the proposal unit can make economically optimal suggestions by referring to market price data for agricultural products.

[0047] The automation unit can monitor the progress of agricultural work in real time during automation and make adjustments as needed. The automation unit can monitor the progress of agricultural work in real time during automation and make adjustments as needed, for example, using generative AI. For example, the automation unit can monitor the progress of an irrigation system in real time and adjust the amount of water as needed. The automation unit can also monitor the installation status of shades in real time and adjust their positions as needed. Furthermore, the automation unit can monitor the progress of fertilization work in real time and adjust the amount of fertilizer as needed. This allows the automation unit to monitor the progress of agricultural work in real time and make adjustments as needed, thereby improving the accuracy of the work.

[0048] The automation unit can immediately notify if an abnormality occurs during automation and propose countermeasures. The automation unit can immediately notify if an abnormality occurs during automation and propose countermeasures, for example, by using generative AI. For example, the automation unit can immediately notify if an abnormality occurs in an irrigation system and propose appropriate countermeasures. The automation unit can also immediately notify if an abnormality occurs in shade installation and propose appropriate countermeasures. Furthermore, the automation unit can immediately notify if an abnormality occurs in fertilization work and propose appropriate countermeasures. As a result, the automation unit can immediately notify if an abnormality occurs and propose countermeasures, enabling a rapid response.

[0049] The automation unit can select the optimal automation method while considering the geographical location information of the farm during automation. For example, the automation unit can use a generating AI to select the optimal automation method while considering the geographical location information of the farm during automation. For example, the automation unit can select the optimal irrigation system based on the geographical location information of the farm. The automation unit can also select an appropriate method for installing shades based on the geographical location information of the farm. Furthermore, the automation unit can select the optimal fertilization method based on the geographical location information of the farm. As a result, by selecting the optimal automation method while considering the geographical location information of the farm, the automation unit enables efficient farm work.

[0050] The automation unit can monitor the growth status of crops in real time during automation and modify the automation settings as needed. For example, the automation unit can use generational AI to monitor the growth status of crops in real time during automation and modify the automation settings as needed. For example, the automation unit can monitor the growth status of crops in real time and adjust the amount of irrigation as needed. The automation unit can also monitor the growth status of crops in real time and adjust the position of shades as needed. Furthermore, the automation unit can monitor the growth status of crops in real time and adjust the amount of fertilizer as needed. In this way, the automation unit can optimize crop growth by monitoring the growth status of crops in real time and modifying the automation settings as needed.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can use a drone to collect data from the air to understand the growth status of agricultural crops. For example, the collection unit can use a camera mounted on the drone to collect image data of the growth status of the crops. The collection unit can also use a multispectral sensor mounted on the drone to collect data for evaluating the health status of the crops. Furthermore, the collection unit can use the drone to efficiently monitor a wide area of ​​farmland and identify abnormal areas. This allows the collection unit to understand the growth status of the crops in more detail by collecting data from the air using the drone.

[0053] The analysis unit can predict the risk of pest and disease outbreaks in agricultural crops based on the collected data. For example, the analysis unit can analyze data on soil humidity, temperature, and sunlight intensity to identify conditions that are likely to cause pest and disease outbreaks. The analysis unit can also refer to past pest and disease outbreak data and compare it with current environmental conditions to assess the risk. Furthermore, if there is a high risk of pest and disease outbreaks, the analysis unit can suggest appropriate control methods. In this way, the analysis unit can predict the risk of pest and disease outbreaks in agricultural crops and take appropriate measures to maintain the health of the crops.

[0054] The proposal unit can propose a fertilization plan to optimize the balance of nutrients necessary for the growth of agricultural crops. For example, the proposal unit can analyze the nutritional state of the soil and identify the necessary nutrients. The proposal unit can also propose the optimal amount and timing of fertilization depending on the type of agricultural crop and the growth stage. Furthermore, the proposal unit can refer to past fertilization data and propose an effective fertilization method. As a result, the proposal unit can promote the growth of agricultural crops by proposing a fertilization plan to optimize the nutritional balance of the crops.

[0055] The automated unit can monitor the progress of agricultural work in real time and dynamically adjust the work schedule as needed. For example, the automated unit can monitor the progress of irrigation work and adjust the irrigation schedule according to changes in weather. It can also monitor the progress of fertilization work and adjust the fertilization schedule according to the soil's nutrient status. Furthermore, it can monitor the progress of harvesting work and adjust the harvesting schedule according to the maturity of the crops. In this way, the automated unit can improve work efficiency by monitoring the progress of agricultural work in real time and dynamically adjusting the schedule.

[0056] The data collection unit can analyze past collected data and dynamically change the optimal sensor placement. For example, if the data collection unit finds that data collection is insufficient in a particular area based on past data, it can add sensors to that area. Furthermore, if the data collection unit finds that data collection is concentrated during a specific time period based on past data, it can change the sensor placement to match that time period. In addition, if the data collection unit finds that data collection under specific environmental conditions is important based on past data, it can adjust the sensor placement to suit those conditions. In this way, the data collection unit can improve the accuracy of data collection by analyzing past data and dynamically changing the sensor placement.

[0057] The analysis unit can improve the reliability of the results by applying different analysis algorithms during the analysis. For example, the analysis unit can improve reliability by applying different analysis algorithms and comparing the analysis results of humidity data. Furthermore, the analysis unit can improve reliability by applying different analysis algorithms and comparing the analysis results of temperature data. In addition, the analysis unit can improve reliability by applying different analysis algorithms and comparing the analysis results of solar irradiance data. Thus, the analysis unit can improve the reliability of the analysis results by applying different analysis algorithms.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit uses sensors to collect data on the growth status of crops and environmental data. Specifically, it collects data on soil humidity, temperature, amount of sunlight, and other data in real time. For example, it uses a soil humidity sensor to measure the amount of moisture in the soil, a temperature sensor to measure the temperature around the crops, and a sunlight sensor to measure the amount of sunlight the crops receive. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, using generative AI, it determines the optimal conditions for growing crops based on the collected data. Specifically, it determines the appropriate amount of irrigation if the soil humidity is low, and determines measures such as installing shade if the temperature is too high. Step 3: The proposal unit proposes optimal cultivation methods based on the analysis results obtained by the analysis unit. For example, it uses generative AI to propose the optimal amount of irrigation and the method of installing shade for crop growth. Specifically, it suggests the appropriate amount of irrigation when soil humidity is low, and suggests measures such as installing shade when temperatures are too high. Step 4: The automation unit automates agricultural work based on the cultivation method proposed by the proposal unit. For example, it automatically installs irrigation systems and shades. Specifically, it automatically controls the irrigation system to provide the appropriate amount of irrigation and automatically installs shades to protect crops from high temperatures.

[0060] (Example 2) The smart agriculture system according to an embodiment of the present invention uses e-kakashi. This system incorporates a generative AI to enhance its brain function, resulting in higher yields and environmental conservation. The system uses sensors to collect data on crop growth and environmental conditions. The generative AI analyzes the collected data and proposes optimal cultivation methods. Furthermore, agricultural work is automated based on the proposed cultivation methods. This mechanism improves yields and contributes to environmental conservation. For example, the system collects data such as soil humidity, temperature, and sunlight in real time. This allows for accurate understanding of crop growth conditions. The generative AI then analyzes the collected data. Based on the collected data, the generative AI determines the optimal conditions for crop growth. For example, if soil humidity is low, it proposes the appropriate amount of irrigation. Furthermore, if temperature is too high, it proposes measures such as installing shade. Furthermore, agricultural work is automated based on the proposed cultivation methods. For example, it can automatically install irrigation systems and shades. This improves agricultural efficiency and increases yields. This system not only improves yields but also contributes to environmental conservation. For example, by suggesting the appropriate amount of irrigation, it is possible to prevent the waste of water resources. In addition, by installing shade, it is possible to prevent crops from being exposed to high temperatures, thereby reducing the burden on the environment. In this way, the smart agriculture system can accurately grasp the growth status of crops, suggest optimal cultivation methods, and automate agricultural work, thereby increasing yields and contributing to environmental conservation.

[0061] The smart agriculture system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an automation unit. The collection unit uses sensors to collect data on the growth status and environment of agricultural crops. The collection unit collects data such as soil humidity, temperature, and amount of sunlight in real time. The collection unit can measure the amount of moisture in the soil using a soil humidity sensor, for example. The collection unit can also measure the temperature around the crops using a temperature sensor. The collection unit can also measure the amount of sunlight received by the crops using a sunlight sensor. The analysis unit analyzes the data collected by the collection unit. The analysis unit determines optimal conditions for growing crops based on the collected data, for example, using a generative AI. For example, the analysis unit can determine an appropriate amount of irrigation when soil humidity is low. The analysis unit can also determine measures such as installing shades when temperatures are too high. The proposal unit proposes an optimal cultivation method based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, use a generation AI to suggest an optimal irrigation amount and a shade installation method for growing agricultural crops. For example, the suggestion unit can suggest an appropriate irrigation amount when soil humidity is low. Furthermore, the suggestion unit can suggest measures such as installing a shade when the temperature is too high. The automation unit automates agricultural work based on the cultivation method suggested by the suggestion unit. For example, the automation unit can automatically install an irrigation system and a shade. For example, the automation unit can automatically control the irrigation system to provide an appropriate amount of irrigation. Furthermore, the automation unit can automatically install a shade to prevent agricultural crops from being exposed to high temperatures. As a result, the smart agriculture system according to the embodiment can accurately grasp the growth status of agricultural crops, suggest an optimal cultivation method, and automate agricultural work, thereby increasing yields and contributing to environmental conservation.

[0062] The data collection unit can collect data on soil humidity, temperature, and sunlight in real time. For example, the unit can measure soil moisture content in real time using a soil humidity sensor. For instance, the unit can install a soil humidity sensor underground and continuously measure soil moisture content. The unit can also measure the temperature around crops in real time using a temperature sensor. For example, the unit can install a temperature sensor near crops and continuously measure the ambient temperature. Furthermore, the unit can measure the amount of sunlight received by crops in real time using a sunlight sensor. For example, the unit can install a sunlight sensor above crops and continuously measure sunlight. This allows the data collection unit to accurately understand the growth status of crops by collecting data on soil humidity, temperature, and sunlight in real time.

[0063] The analysis unit can determine the optimal conditions for crop growth based on the collected data. For example, the analysis unit can use generative AI to determine the optimal conditions for crop growth based on the collected data. For example, if the soil moisture is low, the analysis unit can determine the appropriate amount of irrigation. Also, if the temperature is too high, the analysis unit can determine countermeasures such as installing shade. Furthermore, if there is insufficient sunlight, the analysis unit can determine countermeasures such as installing additional lighting. In this way, the analysis unit can optimize crop growth by determining the optimal conditions for crop growth based on the collected data.

[0064] The suggestion unit can suggest an appropriate irrigation amount when the soil humidity is low. The suggestion unit can suggest an appropriate irrigation amount when the soil humidity is low, for example, by using a generative AI. For example, the suggestion unit can calculate an appropriate irrigation amount based on the soil's water retention capacity and the type of plant. The suggestion unit can also adjust the irrigation amount in real time based on data obtained from a soil humidity sensor. Furthermore, the suggestion unit can refer to past irrigation data and suggest an optimal irrigation schedule. As a result, the suggestion unit can optimize crop growth by suggesting an appropriate irrigation amount when the soil humidity is low.

[0065] The suggestion unit can suggest measures such as installing shades when temperatures are high. The suggestion unit can suggest measures such as installing shades when temperatures are high, for example, using a generation AI. For example, the suggestion unit can suggest where and how to install shades based on data obtained from a temperature sensor. Furthermore, when temperatures are too high, the suggestion unit can suggest not only the installation of shades but also the introduction of a cooling system. Furthermore, the suggestion unit can refer to past temperature data and suggest an optimal shade installation schedule. As a result, the suggestion unit can optimize crop growth by suggesting measures such as installing shades when temperatures are high.

[0066] The automation unit can automatically install an irrigation system or a shade. For example, the automation unit can automatically control the irrigation system and provide an appropriate amount of irrigation. For example, the automation unit can automatically control the irrigation system and provide an appropriate amount of irrigation based on data obtained from a soil moisture sensor. The automation unit can also automatically install shades to prevent agricultural crops from being exposed to high temperatures. For example, the automation unit can automatically install shades based on data obtained from a temperature sensor to prevent agricultural crops from being exposed to high temperatures. Furthermore, the automation unit can automate not only the installation of irrigation systems or shades, but also the fertilization system. For example, the automation unit can measure the nutrient status of the soil with a sensor and automatically apply an appropriate amount of fertilizer. In this way, the automation unit can automatically install irrigation systems or shades, thereby improving the efficiency of agricultural work and increasing yields.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can use generative AI to estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For instance, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only the most important data and perform rapid analysis. In this way, the data collection unit can reduce the user's burden by adjusting the timing 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The collection unit can analyze past collected data and dynamically change the optimal sensor placement. The collection unit can, for example, use generative AI to analyze past collected data and dynamically change the optimal sensor placement. For example, if past data shows that data collection in a specific area is insufficient, the collection unit can add sensors to that area. Also, if past data shows that data collection is concentrated in a specific time period, the collection unit can change the placement of sensors during that time period. Furthermore, if past data shows that data collection under specific environmental conditions is important, the collection unit can adjust the placement of sensors to suit those conditions. This allows the collection unit to improve the accuracy of data collection by analyzing past data and dynamically changing the placement of sensors.

[0069] The data collection unit can integrate and analyze data from multiple sensors to improve the accuracy of the collected data. For example, the data collection unit can use generative AI to integrate and analyze data from multiple sensors to improve the accuracy of the collected data. For example, the data collection unit can collect soil moisture data from multiple sensors and calculate the average value to improve accuracy. The data collection unit can also collect temperature data from multiple sensors and exclude outliers to obtain accurate data. Furthermore, the data collection unit can collect solar radiation data from multiple sensors, integrate and analyze it to obtain more detailed environmental information. In this way, the data collection unit can improve the accuracy of the data by integrating and analyzing data from multiple sensors.

[0070] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions using, for example, a generation AI and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data, thereby reducing the user's burden. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data, thereby improving the accuracy of analysis. Furthermore, when the user is in a hurry, the collection unit prioritizes collecting data that can be collected quickly, allowing for quick analysis. In this way, the collection unit can reduce the user's burden by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the farm during data collection. For example, the data collection unit can use generative AI to prioritize the collection of highly relevant data by considering the geographical location information of the farm during data collection. For example, if data collection is important in a particular area of ​​the farm, the data collection unit can prioritize the collection of data from that area. Furthermore, the data collection unit can prioritize the collection of data under specific environmental conditions based on the geographical location information of the farm. In addition, the data collection unit can prioritize the collection of data related to specific crops by considering the geographical location information of the farm. As a result, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the farm during data collection.

[0072] The collection unit can simultaneously collect data on the farm's surrounding environment during collection and use it for analysis. The collection unit can simultaneously collect data on the farm's surrounding environment (e.g., weather data) during collection and use it for analysis, for example, using a generation AI. For example, the collection unit can collect weather data on the farm and analyze it in association with the growth status of crops. The collection unit can also collect data on the farm's surrounding environment and identify factors that affect crop growth. Furthermore, the collection unit can collect weather data on the farm and use it for analysis to propose appropriate cultivation methods. In this way, the collection unit can obtain more detailed environmental information by simultaneously collecting data on the farm's surrounding environment and using it for analysis.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, a generation AI and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions, thereby enabling a display that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] During analysis, the analysis unit can detect abnormal values ​​by comparing with past data and identify the cause of the abnormality. During analysis, the analysis unit can detect abnormal values ​​by comparing with past data and identify the cause of the abnormality, for example, using generative AI. For example, the analysis unit can detect abnormal humidity values ​​by comparing with past data and identify the cause. Also, the analysis unit can detect abnormal temperature values ​​by comparing with past data and identify the cause. Furthermore, the analysis unit can detect abnormal amounts of sunlight by comparing with past data and identify the cause. As a result, the analysis unit can detect abnormal values ​​by comparing with past data and identify the cause of the abnormality, enabling early detection of problems.

[0075] The analysis unit can apply different analysis algorithms during analysis to improve the reliability of the results. For example, the analysis unit can use generative AI to apply different analysis algorithms during analysis to improve the reliability of the results. For example, the analysis unit can apply different analysis algorithms and compare the analysis results of humidity data to improve reliability. The analysis unit can also apply different analysis algorithms and compare the analysis results of temperature data to improve reliability. Furthermore, the analysis unit can apply different analysis algorithms and compare the analysis results of solar radiation data to improve reliability. In this way, the analysis unit can improve the reliability of the analysis results by applying different analysis algorithms.

[0076] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, a generation AI and adjust the level of detail of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can provide an analysis result that includes detailed information. If the user is in a hurry, the analysis unit can provide an analysis result that focuses on the main points. In this way, the analysis unit can adjust the level of detail of the analysis result according to the user's emotions, thereby providing an appropriate amount of information for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The analysis unit can determine the priority of analysis based on the time of submission of collected data during analysis. The analysis unit can determine the priority of analysis based on the time of submission of collected data during analysis, for example, using a generation AI. For example, the analysis unit can prioritize analysis of data that was submitted early in the collected data. Also, the analysis unit can postpone analysis of data that was submitted late in the collected data. Furthermore, the analysis unit can prioritize analysis of important data based on the time of submission of collected data. In this way, the analysis unit can prioritize analysis of important data by determining the priority of analysis based on the time of submission of collected data.

[0078] The analysis unit can improve the accuracy of the analysis results by referring to related literature data during analysis. The analysis unit can improve the accuracy of the analysis results by referring to related literature data during analysis, for example, using a generation AI. For example, the analysis unit can improve the accuracy of the analysis results of humidity data by referring to related literature data. The analysis unit can also improve the accuracy of the analysis results of temperature data by referring to related literature data. Furthermore, the analysis unit can improve the accuracy of the analysis results of solar radiation data by referring to related literature data. In this way, the analysis unit can improve the accuracy of the analysis results by referring to related literature data.

[0079] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, it can use generative AI to estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For instance, if the user is stressed, the suggestion function can provide simple, easy-to-understand suggestions. If the user is relaxed, it can provide suggestions with more detailed information. Furthermore, if the user is in a hurry, it can provide concise suggestions. This allows the suggestion function to provide user-friendly suggestions by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The proposal unit can apply different proposal algorithms depending on the type and growth stage of the crop when making a proposal. The proposal unit can apply different proposal algorithms depending on the type and growth stage of the crop when making a proposal, for example, by using a generation AI. For example, the proposal unit can propose an optimal irrigation amount depending on the type of crop. Also, the proposal unit can propose an appropriate cultivation method depending on the growth stage of the crop. Furthermore, the proposal unit can propose an optimal fertilization method depending on the type and growth stage of the crop. This enables the proposal unit to make optimal proposals depending on the type and growth stage of the crop.

[0081] The proposal unit can refer to past proposal history when making a proposal and make the optimal proposal. The proposal unit can refer to past proposal history when making a proposal, for example, using a generation AI. For example, the proposal unit can refer to past proposal history to propose the optimal irrigation amount. The proposal unit can also refer to past proposal history to propose an appropriate cultivation method. Furthermore, the proposal unit can refer to past proposal history to propose the optimal fertilization method. This allows the proposal unit to make more accurate proposals by referring to past proposal history.

[0082] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. The suggestion unit can estimate the user's emotions using, for example, a generation AI and determine the priority of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize only important suggestions. Furthermore, if the user is relaxed, the suggestion unit can prioritize detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions that can be implemented quickly. In this way, the suggestion unit can reduce the burden on the user by determining the priority of suggestions according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The proposal unit can make the optimal proposal by taking into consideration the geographical location information of the farm when making the proposal. The proposal unit can make the optimal proposal by taking into consideration the geographical location information of the farm when making the proposal, for example, by using a generation AI. For example, the proposal unit can propose an optimal irrigation amount based on the geographical location information of the farm. The proposal unit can also propose an appropriate cultivation method based on the geographical location information of the farm. Furthermore, the proposal unit can propose an optimal fertilization method based on the geographical location information of the farm. As a result, the proposal unit can make the optimal proposal by taking into consideration the geographical location information of the farm, thereby proposing a more effective cultivation method.

[0084] The proposal unit can make economically optimal suggestions by referring to market price data for agricultural products when making suggestions. For example, the proposal unit can use generation AI to refer to market price data for agricultural products when making suggestions. For example, the proposal unit can refer to market price data for agricultural products and suggest the optimal harvest time. The proposal unit can also refer to market price data for agricultural products and suggest appropriate cultivation methods. Furthermore, the proposal unit can refer to market price data for agricultural products and suggest the optimal fertilization method. In this way, the proposal unit can make economically optimal suggestions by referring to market price data for agricultural products.

[0085] The automation unit can estimate the user's emotions and adjust the timing of automation based on the estimated user emotions. The automation unit can estimate the user's emotions using, for example, a generation AI and adjust the timing of automation based on the estimated user emotions. For example, if the user is feeling stressed, the automation unit can reduce the frequency of automation to reduce the user's burden. Also, if the user is relaxed, the automation unit can increase the frequency of automation to automate detailed tasks. Furthermore, if the user is in a hurry, the automation unit can prioritize and quickly execute only important tasks. In this way, the automation unit can reduce the user's burden by adjusting the timing of automation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The automation unit can monitor the progress of agricultural work in real time during automation and make adjustments as needed. The automation unit can monitor the progress of agricultural work in real time during automation and make adjustments as needed, for example, using generative AI. For example, the automation unit can monitor the progress of an irrigation system in real time and adjust the amount of water as needed. The automation unit can also monitor the installation status of shades in real time and adjust their positions as needed. Furthermore, the automation unit can monitor the progress of fertilization work in real time and adjust the amount of fertilizer as needed. This allows the automation unit to monitor the progress of agricultural work in real time and make adjustments as needed, thereby improving the accuracy of the work.

[0087] The automation unit can immediately notify if an abnormality occurs during automation and propose countermeasures. The automation unit can immediately notify if an abnormality occurs during automation and propose countermeasures, for example, by using generative AI. For example, the automation unit can immediately notify if an abnormality occurs in an irrigation system and propose appropriate countermeasures. The automation unit can also immediately notify if an abnormality occurs in shade installation and propose appropriate countermeasures. Furthermore, the automation unit can immediately notify if an abnormality occurs in fertilization work and propose appropriate countermeasures. As a result, the automation unit can immediately notify if an abnormality occurs and propose countermeasures, enabling a rapid response.

[0088] The automation unit can estimate a user's emotions and determine automation priorities based on the estimated user emotions. The automation unit can estimate a user's emotions using, for example, a generation AI and determine automation priorities based on the estimated user emotions. For example, when a user is feeling stressed, the automation unit can prioritize automating only important tasks to reduce the user's burden. Furthermore, when a user is relaxed, the automation unit can prioritize automating detailed tasks to improve efficiency. Furthermore, when a user is in a hurry, the automation unit can prioritize automating tasks that can be performed quickly. In this way, the automation unit can reduce the user's burden by determining automation priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The automation unit can select the optimal automation method while considering the geographical location information of the farm during automation. For example, the automation unit can use a generating AI to select the optimal automation method while considering the geographical location information of the farm during automation. For example, the automation unit can select the optimal irrigation system based on the geographical location information of the farm. The automation unit can also select an appropriate method for installing shades based on the geographical location information of the farm. Furthermore, the automation unit can select the optimal fertilization method based on the geographical location information of the farm. As a result, by selecting the optimal automation method while considering the geographical location information of the farm, the automation unit enables efficient farm work.

[0090] The automation unit can monitor the growth status of crops in real time during automation and modify the automation settings as needed. For example, the automation unit can use generational AI to monitor the growth status of crops in real time during automation and modify the automation settings as needed. For example, the automation unit can monitor the growth status of crops in real time and adjust the amount of irrigation as needed. The automation unit can also monitor the growth status of crops in real time and adjust the position of shades as needed. Furthermore, the automation unit can monitor the growth status of crops in real time and adjust the amount of fertilizer as needed. In this way, the automation unit can optimize crop growth by monitoring the growth status of crops in real time and modifying the automation settings as needed. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and automation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data on crop growth and environmental conditions using the sensors of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the generated AI analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes an optimal cultivation method. The automation unit is implemented, for example, by the control unit 46A of the smart device 14, and automates farm work based on the proposed cultivation method. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and automation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data on crop growth and environmental conditions using the sensors of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and the generated AI analyzes the collected data. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes an optimal cultivation method. The automation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and automates farm work based on the proposed cultivation method. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and automation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data on crop growth and environmental conditions using the sensors of the headset terminal 314. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and the generated AI analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes an optimal cultivation method. The automation unit is implemented by, for example, the control unit 46A of the headset terminal 314, and automates farm work based on the proposed cultivation method. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and automation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on the growth status and environment of agricultural crops using sensors of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal cultivation method. The automation unit is realized, for example, by the control unit 46A of the robot 414, and automates agricultural work based on the proposed cultivation method.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The collection unit can use a drone to collect data from the air to understand the growth status of agricultural crops. For example, the collection unit can use a camera mounted on the drone to collect image data of the growth status of the crops. The collection unit can also use a multispectral sensor mounted on the drone to collect data for evaluating the health status of the crops. Furthermore, the collection unit can use the drone to efficiently monitor a wide area of ​​farmland and identify abnormal areas. This allows the collection unit to understand the growth status of the crops in more detail by collecting data from the air using the drone.

[0093] The analysis unit can predict the risk of pest and disease outbreaks in agricultural crops based on the collected data. For example, the analysis unit can analyze data on soil humidity, temperature, and sunlight intensity to identify conditions that are likely to cause pest and disease outbreaks. The analysis unit can also refer to past pest and disease outbreak data and compare it with current environmental conditions to assess the risk. Furthermore, if there is a high risk of pest and disease outbreaks, the analysis unit can suggest appropriate control methods. In this way, the analysis unit can predict the risk of pest and disease outbreaks in agricultural crops and take appropriate measures to maintain the health of the crops.

[0094] The proposal unit can propose a fertilization plan to optimize the balance of nutrients necessary for the growth of agricultural crops. For example, the proposal unit can analyze the nutritional state of the soil and identify the necessary nutrients. The proposal unit can also propose the optimal amount and timing of fertilization depending on the type of agricultural crop and the growth stage. Furthermore, the proposal unit can refer to past fertilization data and propose an effective fertilization method. As a result, the proposal unit can promote the growth of agricultural crops by proposing a fertilization plan to optimize the nutritional balance of the crops.

[0095] The automated unit can monitor the progress of agricultural work in real time and dynamically adjust the work schedule as needed. For example, the automated unit can monitor the progress of irrigation work and adjust the irrigation schedule according to changes in weather. It can also monitor the progress of fertilization work and adjust the fertilization schedule according to the soil's nutrient status. Furthermore, it can monitor the progress of harvesting work and adjust the harvesting schedule according to the maturity of the crops. In this way, the automated unit can improve work efficiency by monitoring the progress of agricultural work in real time and dynamically adjusting the schedule.

[0096] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data and perform analysis quickly. In this way, the collection unit can reduce the burden on the user by adjusting the frequency of data collection according to the user's emotions.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions, making it easier for the user to understand.

[0098] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion function can provide simple and easily understandable suggestions. If the user is relaxed, it can provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can provide suggestions that get straight to the point. In this way, the suggestion function can provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions according to the user's emotions.

[0099] The automation unit can estimate the user's emotions and adjust the timing of automation based on those emotions. For example, if the user is stressed, the automation unit can reduce the frequency of automation to lessen the user's burden. Conversely, if the user is relaxed, the automation unit can increase the frequency of automation and automate more detailed tasks. Furthermore, if the user is in a hurry, the automation unit can prioritize and quickly automate only the most important tasks. In this way, the automation unit can reduce the user's burden by adjusting the timing of automation according to the user's emotions.

[0100] The data collection unit can analyze past collected data and dynamically change the optimal sensor placement. For example, if the data collection unit finds that data collection is insufficient in a particular area based on past data, it can add sensors to that area. Furthermore, if the data collection unit finds that data collection is concentrated during a specific time period based on past data, it can change the sensor placement to match that time period. In addition, if the data collection unit finds that data collection under specific environmental conditions is important based on past data, it can adjust the sensor placement to suit those conditions. In this way, the data collection unit can improve the accuracy of data collection by analyzing past data and dynamically changing the sensor placement.

[0101] The analysis unit can improve the reliability of the results by applying different analysis algorithms during the analysis. For example, the analysis unit can improve reliability by applying different analysis algorithms and comparing the analysis results of humidity data. Furthermore, the analysis unit can improve reliability by applying different analysis algorithms and comparing the analysis results of temperature data. In addition, the analysis unit can improve reliability by applying different analysis algorithms and comparing the analysis results of solar irradiance data. Thus, the analysis unit can improve the reliability of the analysis results by applying different analysis algorithms.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit uses sensors to collect data on the growth status of crops and environmental data. Specifically, it collects data on soil humidity, temperature, amount of sunlight, and other data in real time. For example, it uses a soil humidity sensor to measure the amount of moisture in the soil, a temperature sensor to measure the temperature around the crops, and a sunlight sensor to measure the amount of sunlight the crops receive. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, using generative AI, it determines the optimal conditions for growing crops based on the collected data. Specifically, it determines the appropriate amount of irrigation if the soil humidity is low, and determines measures such as installing shade if the temperature is too high. Step 3: The proposal unit proposes optimal cultivation methods based on the analysis results obtained by the analysis unit. For example, it uses generative AI to propose the optimal amount of irrigation and the method of installing shade for crop growth. Specifically, it suggests the appropriate amount of irrigation when soil humidity is low, and suggests measures such as installing shade when temperatures are too high. Step 4: The automation unit automates agricultural work based on the cultivation method proposed by the proposal unit. For example, it automatically installs irrigation systems and shades. Specifically, it automatically controls the irrigation system to provide the appropriate amount of irrigation and automatically installs shades to protect crops from high temperatures.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data on the growth status of agricultural crops and environmental data using sensors; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes a cultivation method based on the analysis results obtained by the analysis unit; an automation unit that automates agricultural work based on the cultivation method proposed by the proposal unit. A system characterized by:

2. The collecting unit Collecting real-time data on soil moisture, temperature, and sunlight 2. The system of claim 1.

3. The analysis unit Determine the optimal conditions for growing crops based on collected data 2. The system of claim 1.

4. The proposal unit Suggests appropriate irrigation amounts when soil moisture is low 2. The system of claim 1.

5. The proposal unit If the temperature is high, we suggest installing shades.

2. The system of claim 1.

6. The automation unit Automate the installation of irrigation systems and shades 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyzes past collected data and dynamically changes sensor placement to optimize it 2. The system of claim 1.

Images