System

Abstract

An object of the system according to the embodiment is to collect AI of agricultural environments, analyze and predict the agricultural environments using the collected raw data, and automatically control the environments.SOLUTION: A system includes a data collection unit, an analysis unit, and a control unit. The data collection unit collects data of an agricultural environment using an IoT device. The analyzing unit analyzes and predicts the date collected by the data-collecting unit in a generation AI. The controller automatically controls the environment based on the prediction generated by the analyzer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology relies on intuition and experience for environmental control in agriculture, leaving room for improvement in terms of automation and prediction.

[0005] The system of the embodiment aims to collect data on the agricultural environment, analyze and predict it using generative AI, and automatically control the environment. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a control unit. The data collection unit collects data on the agricultural environment using IoT devices. The analysis unit analyzes and predicts the data collected by the data collection unit using a generation AI. The control unit automatically controls the environment based on the predictions generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect data on the agricultural environment, analyze and predict it using generative AI, and automatically control the environment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The agricultural support system according to an embodiment of the present invention uses IoT devices to collect data on the agricultural environment, and then uses generative AI to analyze and predict the data and automatically control the environment. This allows the agricultural support system to achieve automation and prediction without relying on intuition and experience in agriculture.

[0029] An agricultural support system according to an embodiment includes a data collection unit, an analysis unit, and a control unit. The data collection unit collects data on the agricultural environment using an IoT device. For example, the data collection unit collects sunshine hours using a sensor that measures sunshine hours. The data collection unit can also collect temperature using a temperature sensor. The data collection unit can also collect humidity using a humidity sensor. For example, the data collection unit collects moisture content using a soil sensor. The analysis unit analyzes and predicts the data collected by the data collection unit using a generation AI. For example, the generation AI can analyze the data and make predictions using a text generation AI (e.g., LLM). The generation AI can also analyze the data and make predictions using a multimodal generation AI. The generation AI can also suggest optimal conditions for plant growth based on past data and specialized knowledge. For example, the generation AI can generate advice such as, "Based on the current temperature and humidity, watering is required tomorrow morning." The control unit automatically controls the environment based on the predictions generated by the analysis unit. For example, if the generating AI determines that "artificial lighting is necessary due to a lack of sunlight," the control unit automatically turns on the lights. Also, if the generating AI determines that "watering is necessary due to low soil moisture," the control unit automatically activates the sprinkler system. This allows the agricultural support system according to the embodiment to achieve automation and prediction without relying on intuition and experience in agriculture. For example, the agricultural support system can solve the problems of an aging population and a lack of successors, and by increasing the number of new entrants, the sustainability of agriculture can be improved. Furthermore, precise control based on environmental data can improve the quality and yield of agricultural products.

[0030] The data collection unit includes sensors that measure sunshine hours, temperature, humidity, and moisture content. For example, the data collection unit collects sunshine hours using a sensor that measures sunshine hours. The data collection unit can also collect temperature using a temperature sensor. The data collection unit can also collect humidity using a humidity sensor. For example, the data collection unit collects moisture content using a soil sensor. This allows detailed data on the agricultural environment to be collected.

[0031] The analysis unit can analyze data in real time and instantly detect abnormal values ​​and patterns. For example, the analysis unit can analyze data in real time and instantly detect abnormal values ​​and patterns. For example, the generation AI can analyze data such as temperature, humidity, and moisture content collected from IoT devices in real time and instantly detect abnormal values ​​and patterns. The generation AI can also issue an alert if it detects an abnormality. The generation AI can also make predictions based on abnormal patterns and propose countermeasures. This enables the immediate detection of abnormalities and a rapid response.

[0032] The control unit can automatically control sunlight, watering, and fertilizer application based on the analysis results of the generating AI. For example, the control unit automatically controls sunlight based on the analysis results of the generating AI. For example, if the generating AI determines that "artificial lighting must be used due to a lack of sunlight," the control unit automatically turns on the lighting. The control unit also automatically controls watering based on the analysis results of the generating AI. For example, if the generating AI determines that "watering is necessary due to a lack of soil moisture," the control unit automatically activates a watering device. The control unit also automatically controls fertilizer application based on the analysis results of the generating AI. For example, if the generating AI determines that "nutrients necessary for plant growth are lacking, so fertilizer application is necessary," the control unit automatically activates a fertilizer application device. This allows the agricultural environment to be maintained in optimal conditions.

[0033] The data collection unit includes a sensor that measures the bioelectric potential and growth rate of the plant. For example, the data collection unit collects the bioelectric potential using a sensor that measures the bioelectric potential of the plant. The data collection unit can also collect the growth rate using a sensor that measures the growth rate of the plant. This allows for detailed monitoring of the health and growth status of the plant.

[0034] The data collection unit collects not only data on the agricultural environment but also data on the surrounding ecosystem. For example, the data collection unit collects not only data on the agricultural environment but also data on the surrounding ecosystem. For example, the data collection unit collects ecosystem data using a sensor that monitors insect activity. The data collection unit can also collect ecosystem data using a sensor that collects bird calls. The data collection unit can also collect ecosystem data using a sensor that collects water quality data. This enables management that takes into account the balance between the agricultural environment and the ecosystem.

[0035] The data collection unit collects operational status and maintenance information of agricultural machinery. For example, the data collection unit collects operational status using a sensor that monitors the operational status of the agricultural machinery. The data collection unit can also collect maintenance information using a sensor that collects maintenance information of the agricultural machinery. This can support optimal operation of the agricultural machinery.

[0036] The analysis unit can predict plant diseases based on the data and issue alerts to enable early countermeasures to be taken. The analysis unit, for example, predicts plant diseases based on the data and issue alerts to enable early countermeasures to be taken. For example, the generation AI analyzes collected data and predicts plant diseases. For example, it evaluates the risk of disease occurrence based on fluctuations in temperature and humidity and issues an alert. The generation AI can also suggest countermeasures based on the risk of disease occurrence. This makes it possible to predict plant diseases early and take countermeasures.

[0037] The analysis unit can compare past data with current data and predict the impact of long-term climate change. The analysis unit, for example, compares past data with current data and predicts the impact of long-term climate change. For example, the generation AI compares past data with current data and predicts the impact of long-term climate change. For example, it analyzes fluctuations in temperature and precipitation to evaluate the impact of future climate change. The generation AI can also propose countermeasures based on the impact of climate change. This makes it possible to predict the impact of long-term climate change and take appropriate measures.

[0038] The analysis unit analyzes not only agricultural data but also economic data and market trends, and can propose optimal crop selection and sales strategies. The analysis unit, for example, analyzes not only agricultural data but also economic data and market trends, and proposes optimal crop selection and sales strategies. For example, the generation AI integrates agricultural data and economic data to propose optimal crop selection and sales strategies. For example, it analyzes market prices and demand for crops and proposes optimal cultivation plans. The generation AI can also predict consumer preferences based on market trends and propose sales strategies. This makes it possible to propose optimal crop selection and sales strategies that take economic data and market trends into consideration.

[0039] The analysis unit can compare agricultural data from different regions and propose the optimal agricultural method for each region. The analysis unit, for example, compares agricultural data from different regions and proposes the optimal agricultural method for each region. For example, the generation AI compares agricultural data from different regions and proposes the optimal agricultural method for each region. For example, it analyzes climate and soil conditions and proposes crops and cultivation methods suitable for each region. The generation AI can also propose the optimal cultivation plan based on the agricultural method for each region. This makes it possible to propose the optimal agricultural method for each region.

[0040] The control unit can optimize the operation of the agricultural machinery in real time based on environmental data to improve energy efficiency. The control unit can, for example, optimize the operation of the agricultural machinery in real time based on environmental data to improve energy efficiency. For example, the generative AI analyzes environmental data and optimizes the operation of the agricultural machinery in real time. For example, it adjusts the operating speed of the tractor according to the temperature and humidity. The generative AI can also suggest optimal operating patterns to improve energy efficiency. This makes it possible to optimize the operation of the agricultural machinery and improve energy efficiency.

[0041] The control unit can optimize the amount of pesticides and fertilizers used based on the collected data, thereby minimizing the environmental impact. The control unit can, for example, optimize the amount of pesticides and fertilizers used based on the collected data, thereby minimizing the environmental impact. For example, the generation AI analyzes the collected data and optimizes the amount of pesticides and fertilizers used. For example, it evaluates the risk of disease outbreaks and uses the minimum amount of pesticide necessary. The generation AI can also make suggestions for optimizing the amount of fertilizers used and minimizing the environmental impact. This makes it possible to optimize the amount of pesticides and fertilizers used and minimize the environmental impact.

[0042] The control unit can perform comprehensive environmental control by taking into account not only the agricultural environment but also surrounding meteorological and geological data. The control unit can perform comprehensive environmental control by taking into account not only the agricultural environment but also surrounding meteorological and geological data. For example, the generation AI can integrate agricultural environment data with surrounding meteorological data to perform comprehensive environmental control. For example, it can adjust watering schedules based on weather forecasts. The generation AI can also suggest optimal cultivation methods based on geological data. This makes it possible to perform comprehensive environmental control by taking into account the agricultural environment and surrounding meteorological and geological data.

[0043] The control unit can not only control the agricultural environment, but also manage the health of agricultural workers and optimize the working environment. For example, the control unit can not only control the agricultural environment, but also manage the health of agricultural workers and optimize the working environment. For example, the generative AI analyzes agricultural environment data and supports the health management of agricultural workers. For example, it can adjust the temperature and humidity of the working environment to provide a comfortable working environment. The generative AI can also suggest the optimal working environment based on the health data of agricultural workers. This allows not only control of the agricultural environment, but also management of the health of agricultural workers and optimization of the working environment.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The agricultural support system also includes a voice recognition unit. The voice recognition unit recognizes voice commands from farmers and allows them to operate the system by voice. For example, if a farmer commands, "Start watering," the voice recognition unit recognizes the command and transmits it to the control unit, which activates the watering device. The voice recognition unit can also provide answers to questions from farmers using a generation AI. For example, if a farmer asks, "What is the current soil moisture level?" the voice recognition unit recognizes the question and the generation AI analyzes the data and provides an answer. This allows farmers to operate the system without using their hands, improving work efficiency.

[0046] The agricultural support system also includes a drone control unit. The drone control unit uses drones to collect data on the agricultural environment and can spray pesticides and fertilizers as needed. For example, the drone control unit monitors the growth status of crops using a camera mounted on the drone and sends the data to the data collection unit. The drone control unit can also automatically fly the drone and spray pesticides and fertilizers based on the analysis results of the generating AI. This allows for efficient management of large areas of farmland.

[0047] The agricultural support system further includes an energy management unit. The energy management unit can monitor and optimize energy consumption in the agricultural environment. For example, the energy management unit can monitor the power generation output of solar power generation systems and wind power generation systems to understand energy usage in real time. The energy management unit can also make suggestions for optimizing energy consumption based on the analysis results of the generative AI. This can improve the energy efficiency of the agricultural environment and achieve sustainable agriculture.

[0048] The agricultural support system also includes a logistics management unit, which optimizes the logistics of harvested crops and delivers them to the market efficiently. For example, the logistics management unit monitors the quantity and quality of harvested crops and proposes the optimal transportation route. The logistics management unit can also forecast demand based on the analysis results of the generative AI and supply crops to the market at the appropriate time. This enables efficient logistics while maintaining the freshness of crops.

[0049] The agricultural support system also includes a budget management unit, which manages the budget for agricultural management and can propose optimal capital allocation. For example, the budget management unit uses collected data to monitor agricultural management income and expenditures in real time and identify budget surpluses and shortfalls. The budget management unit can also propose optimal capital allocation based on the analysis results of the generation AI, improving management efficiency. This can lead to the soundness of agricultural management.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection unit uses IoT devices to collect data on the agricultural environment. For example, it uses a sensor that measures sunlight hours, a temperature sensor, a humidity sensor, and a soil sensor to collect data on sunlight hours, temperature, humidity, and water content, respectively. Step 2: The analysis unit uses generation AI to analyze and predict the data collected by the data collection unit. For example, it uses text generation AI (LLM) or multimodal generation AI to analyze the data and make predictions. It can also suggest optimal conditions for plant growth based on past data and specialized knowledge. Step 3: The control unit automatically controls the environment based on the predictions generated by the analysis unit. For example, if the generation AI determines that "artificial lighting is necessary due to a lack of sunlight," it will automatically turn on the lights. Or, if it determines that "watering is necessary due to a decrease in soil moisture," it will automatically activate the sprinkler system.

[0052] (Example 2) The agricultural support system according to an embodiment of the present invention uses IoT devices to collect data on the agricultural environment, and then uses generative AI to analyze and predict the data and automatically control the environment. This allows the agricultural support system to achieve automation and prediction without relying on intuition and experience in agriculture.

[0053] An agricultural support system according to an embodiment includes a data collection unit, an analysis unit, and a control unit. The data collection unit collects data on the agricultural environment using an IoT device. For example, the data collection unit collects sunshine hours using a sensor that measures sunshine hours. The data collection unit can also collect temperature using a temperature sensor. The data collection unit can also collect humidity using a humidity sensor. For example, the data collection unit collects moisture content using a soil sensor. The analysis unit analyzes and predicts the data collected by the data collection unit using a generation AI. For example, the generation AI can analyze the data and make predictions using a text generation AI (e.g., LLM). The generation AI can also analyze the data and make predictions using a multimodal generation AI. The generation AI can also suggest optimal conditions for plant growth based on past data and specialized knowledge. For example, the generation AI can generate advice such as, "Based on the current temperature and humidity, watering is required tomorrow morning." The control unit automatically controls the environment based on the predictions generated by the analysis unit. For example, if the generating AI determines that "artificial lighting is necessary due to a lack of sunlight," the control unit automatically turns on the lights. Also, if the generating AI determines that "watering is necessary due to low soil moisture," the control unit automatically activates the sprinkler system. This allows the agricultural support system according to the embodiment to achieve automation and prediction without relying on intuition and experience in agriculture. For example, the agricultural support system can solve the problems of an aging population and a lack of successors, and by increasing the number of new entrants, the sustainability of agriculture can be improved. Furthermore, precise control based on environmental data can improve the quality and yield of agricultural products.

[0054] The data collection unit includes sensors that measure sunshine hours, temperature, humidity, and moisture content. For example, the data collection unit collects sunshine hours using a sensor that measures sunshine hours. The data collection unit can also collect temperature using a temperature sensor. The data collection unit can also collect humidity using a humidity sensor. For example, the data collection unit collects moisture content using a soil sensor. This allows detailed data on the agricultural environment to be collected.

[0055] The analysis unit can analyze data in real time and instantly detect abnormal values ​​and patterns. For example, the analysis unit can analyze data in real time and instantly detect abnormal values ​​and patterns. For example, the generation AI can analyze data such as temperature, humidity, and moisture content collected from IoT devices in real time and instantly detect abnormal values ​​and patterns. The generation AI can also issue an alert if it detects an abnormality. The generation AI can also make predictions based on abnormal patterns and propose countermeasures. This enables the immediate detection of abnormalities and a rapid response.

[0056] The control unit can automatically control sunlight, watering, and fertilizer application based on the analysis results of the generating AI. For example, the control unit automatically controls sunlight based on the analysis results of the generating AI. For example, if the generating AI determines that "artificial lighting must be used due to a lack of sunlight," the control unit automatically turns on the lighting. The control unit also automatically controls watering based on the analysis results of the generating AI. For example, if the generating AI determines that "watering is necessary due to a lack of soil moisture," the control unit automatically activates a watering device. The control unit also automatically controls fertilizer application based on the analysis results of the generating AI. For example, if the generating AI determines that "nutrients necessary for plant growth are lacking, so fertilizer application is necessary," the control unit automatically activates a fertilizer application device. This allows the agricultural environment to be maintained in optimal conditions.

[0057] The data collection unit includes a sensor that measures the bioelectric potential and growth rate of the plant. For example, the data collection unit collects the bioelectric potential using a sensor that measures the bioelectric potential of the plant. The data collection unit can also collect the growth rate using a sensor that measures the growth rate of the plant. This allows for detailed monitoring of the health and growth status of the plant.

[0058] The data collection unit collects emotion data of the farmer and evaluates the stress level using the emotion estimation function. The data collection unit, for example, collects emotion data of the farmer. For example, the emotion estimation function is used to collect facial expression data of the farmer and evaluate the stress level. The data collection unit can also collect voice data of the farmer and evaluate the stress level using the emotion estimation function. The data collection unit can also collect biometric data of the farmer (heart rate and electrodermal activity) and evaluate the stress level using the emotion estimation function. This makes it possible to monitor the stress level of the farmer and take appropriate measures.

[0059] The data collection unit collects not only data on the agricultural environment but also data on the surrounding ecosystem. For example, the data collection unit collects not only data on the agricultural environment but also data on the surrounding ecosystem. For example, the data collection unit collects ecosystem data using a sensor that monitors insect activity. The data collection unit can also collect ecosystem data using a sensor that collects bird calls. The data collection unit can also collect ecosystem data using a sensor that collects water quality data. This enables management that takes into account the balance between the agricultural environment and the ecosystem.

[0060] The data collection unit collects operational status and maintenance information of agricultural machinery. For example, the data collection unit collects operational status using a sensor that monitors the operational status of the agricultural machinery. The data collection unit can also collect maintenance information using a sensor that collects maintenance information of the agricultural machinery. This can support optimal operation of the agricultural machinery.

[0061] The data collection unit has the farmer wear a wearable device equipped with an emotion estimation function and collects emotion data while working. For example, the data collection unit has the farmer wear a wearable device equipped with an emotion estimation function and collects emotion data while working. For example, the wearable device collects heart rate and facial expression data, and evaluates the stress level using the emotion estimation function. The data collection unit can also have the wearable device collect voice data and evaluate the stress level using the emotion estimation function. The data collection unit can also have the wearable device collect biometric data (heart rate and electrodermal activity) and evaluate the stress level using the emotion estimation function. In this way, emotion data of the farmer can be collected and feedback can be provided to improve work efficiency.

[0062] The analysis unit can predict plant diseases based on the data and issue alerts to enable early countermeasures to be taken. The analysis unit, for example, predicts plant diseases based on the data and issue alerts to enable early countermeasures to be taken. For example, the generation AI analyzes collected data and predicts plant diseases. For example, it evaluates the risk of disease occurrence based on fluctuations in temperature and humidity and issues an alert. The generation AI can also suggest countermeasures based on the risk of disease occurrence. This makes it possible to predict plant diseases early and take countermeasures.

[0063] The analysis unit can compare past data with current data and predict the impact of long-term climate change. The analysis unit, for example, compares past data with current data and predicts the impact of long-term climate change. For example, the generation AI compares past data with current data and predicts the impact of long-term climate change. For example, it analyzes fluctuations in temperature and precipitation to evaluate the impact of future climate change. The generation AI can also propose countermeasures based on the impact of climate change. This makes it possible to predict the impact of long-term climate change and take appropriate measures.

[0064] The analysis unit can use the emotion estimation function to analyze the emotion data of farmers and evaluate the impact of emotional fluctuations on the efficiency of agricultural work. For example, the analysis unit can use the emotion estimation function to analyze the emotion data of farmers and evaluate the impact of emotional fluctuations on the efficiency of agricultural work. For example, the generation AI can use the emotion estimation function to collect emotion data of farmers and evaluate the impact of emotional fluctuations on the efficiency of agricultural work. For example, it can analyze work efficiency when stress levels are high. The generation AI can also suggest countermeasures based on the impact of emotional fluctuations on the efficiency of agricultural work. This makes it possible to evaluate the impact of emotional fluctuations of farmers on the efficiency of agricultural work and take appropriate countermeasures.

[0065] The analysis unit analyzes not only agricultural data but also economic data and market trends, and can propose optimal crop selection and sales strategies. The analysis unit, for example, analyzes not only agricultural data but also economic data and market trends, and proposes optimal crop selection and sales strategies. For example, the generation AI integrates agricultural data and economic data to propose optimal crop selection and sales strategies. For example, it analyzes market prices and demand for crops and proposes optimal cultivation plans. The generation AI can also predict consumer preferences based on market trends and propose sales strategies. This makes it possible to propose optimal crop selection and sales strategies that take economic data and market trends into consideration.

[0066] The analysis unit can compare agricultural data from different regions and propose the optimal agricultural method for each region. The analysis unit, for example, compares agricultural data from different regions and proposes the optimal agricultural method for each region. For example, the generation AI compares agricultural data from different regions and proposes the optimal agricultural method for each region. For example, it analyzes climate and soil conditions and proposes crops and cultivation methods suitable for each region. The generation AI can also propose the optimal cultivation plan based on the agricultural method for each region. This makes it possible to propose the optimal agricultural method for each region.

[0067] The analysis unit can use the emotion estimation function to collect consumer emotion data and develop crop cultivation and sales strategies based on consumer preferences. The analysis unit, for example, uses the emotion estimation function to collect consumer emotion data and develop crop cultivation and sales strategies based on consumer preferences. For example, the generation AI can use the emotion estimation function to collect consumer emotion data and develop crop cultivation and sales strategies based on consumer preferences. For example, it can analyze consumers' emotional responses and select popular crops. The generation AI can also propose optimal cultivation plans and sales strategies based on consumer preferences. This makes it possible to develop crop cultivation and sales strategies based on consumer preferences.

[0068] The control unit can optimize the operation of the agricultural machinery in real time based on environmental data to improve energy efficiency. The control unit can, for example, optimize the operation of the agricultural machinery in real time based on environmental data to improve energy efficiency. For example, the generative AI analyzes environmental data and optimizes the operation of the agricultural machinery in real time. For example, it adjusts the operating speed of the tractor according to the temperature and humidity. The generative AI can also suggest optimal operating patterns to improve energy efficiency. This makes it possible to optimize the operation of the agricultural machinery and improve energy efficiency.

[0069] The control unit can optimize the amount of pesticides and fertilizers used based on the collected data, thereby minimizing the environmental impact. The control unit can, for example, optimize the amount of pesticides and fertilizers used based on the collected data, thereby minimizing the environmental impact. For example, the generation AI analyzes the collected data and optimizes the amount of pesticides and fertilizers used. For example, it evaluates the risk of disease outbreaks and uses the minimum amount of pesticide necessary. The generation AI can also make suggestions for optimizing the amount of fertilizers used and minimizing the environmental impact. This makes it possible to optimize the amount of pesticides and fertilizers used and minimize the environmental impact.

[0070] The control unit can use the emotion estimation function to automatically adjust the work schedule according to the emotional state of the farmer, maximizing work efficiency. The control unit can, for example, use the emotion estimation function to automatically adjust the work schedule according to the emotional state of the farmer, maximizing work efficiency. For example, the generation AI can use the emotion estimation function to monitor the emotional state of the farmer in real time and automatically adjust the work schedule. For example, it can suggest a break when the stress level is high. The generation AI can also suggest an optimal work schedule according to the emotional state. This makes it possible to automatically adjust the work schedule according to the emotional state of the farmer, maximizing work efficiency.

[0071] The control unit can perform comprehensive environmental control by taking into account not only the agricultural environment but also surrounding meteorological and geological data. The control unit can perform comprehensive environmental control by taking into account not only the agricultural environment but also surrounding meteorological and geological data. For example, the generation AI can integrate agricultural environment data with surrounding meteorological data to perform comprehensive environmental control. For example, it can adjust watering schedules based on weather forecasts. The generation AI can also suggest optimal cultivation methods based on geological data. This makes it possible to perform comprehensive environmental control by taking into account the agricultural environment and surrounding meteorological and geological data.

[0072] The control unit can not only control the agricultural environment, but also manage the health of agricultural workers and optimize the working environment. For example, the control unit can not only control the agricultural environment, but also manage the health of agricultural workers and optimize the working environment. For example, the generative AI analyzes agricultural environment data and supports the health management of agricultural workers. For example, it can adjust the temperature and humidity of the working environment to provide a comfortable working environment. The generative AI can also suggest the optimal working environment based on the health data of agricultural workers. This allows not only control of the agricultural environment, but also management of the health of agricultural workers and optimization of the working environment.

[0073] The control unit can use the emotion estimation function to make suggestions for improving the working environment based on the emotion data of agricultural workers, thereby improving worker satisfaction. The control unit can, for example, use the emotion estimation function to make suggestions for improving the working environment based on the emotion data of agricultural workers, thereby improving worker satisfaction. For example, the generation AI can use the emotion estimation function to collect emotion data of agricultural workers and make suggestions for improving the working environment. For example, it can suggest ways to relax when stress levels are high. The generation AI can also suggest the optimal working environment based on the emotion data. This makes it possible to make suggestions for improving the working environment based on the emotion data of agricultural workers, thereby improving worker satisfaction.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The agricultural support system also includes a voice recognition unit. The voice recognition unit recognizes voice commands from farmers and allows them to operate the system by voice. For example, if a farmer commands, "Start watering," the voice recognition unit recognizes the command and transmits it to the control unit, which activates the watering device. The voice recognition unit can also provide answers to questions from farmers using a generation AI. For example, if a farmer asks, "What is the current soil moisture level?" the voice recognition unit recognizes the question and the generation AI analyzes the data and provides an answer. This allows farmers to operate the system without using their hands, improving work efficiency.

[0076] The agricultural support system also includes a drone control unit. The drone control unit uses drones to collect data on the agricultural environment and can spray pesticides and fertilizers as needed. For example, the drone control unit monitors the growth status of crops using a camera mounted on the drone and sends the data to the data collection unit. The drone control unit can also automatically fly the drone and spray pesticides and fertilizers based on the analysis results of the generating AI. This allows for efficient management of large areas of farmland.

[0077] The agricultural support system further includes an energy management unit. The energy management unit can monitor and optimize energy consumption in the agricultural environment. For example, the energy management unit can monitor the power generation output of solar power generation systems and wind power generation systems to understand energy usage in real time. The energy management unit can also make suggestions for optimizing energy consumption based on the analysis results of the generative AI. This can improve the energy efficiency of the agricultural environment and achieve sustainable agriculture.

[0078] The agricultural support system also includes a logistics management unit, which optimizes the logistics of harvested crops and delivers them to the market efficiently. For example, the logistics management unit monitors the quantity and quality of harvested crops and proposes the optimal transportation route. The logistics management unit can also forecast demand based on the analysis results of the generative AI and supply crops to the market at the appropriate time. This enables efficient logistics while maintaining the freshness of crops.

[0079] The agricultural support system also includes a budget management unit, which manages the budget for agricultural management and can propose optimal capital allocation. For example, the budget management unit uses collected data to monitor agricultural management income and expenditures in real time and identify budget surpluses and shortfalls. The budget management unit can also propose optimal capital allocation based on the analysis results of the generation AI, improving management efficiency. This can lead to the soundness of agricultural management.

[0080] The agricultural support system can also use emotion estimation to evaluate the motivation of farmers and provide appropriate feedback. For example, the emotion estimation function can be used to collect facial expressions and voice data of farmers to evaluate fluctuations in motivation. The emotion estimation function can also be used to monitor the stress level of farmers and provide appropriate feedback. For example, it can suggest relaxation methods when stress is high and provide encouraging messages when motivation is low. This can help maintain the motivation of farmers and improve work efficiency.

[0081] The agricultural support system can also use the emotion estimation function to adjust work schedules based on the emotional data of farmers. For example, the emotion estimation function can be used to collect emotional data from farmers and evaluate their stress levels and fatigue. The emotion estimation function can also be used to suggest work schedules based on the emotional state of farmers. For example, it can suggest breaks when stress is high and assign light work when fatigue is accumulating. This helps maintain the health of farmers and maximize work efficiency.

[0082] The agricultural support system can also use the emotion estimation function to optimize the work environment based on the emotional data of farmers. For example, the emotion estimation function can be used to collect emotional data from farmers and make suggestions for improving the work environment. The emotion estimation function can also be used to adjust the work environment according to the emotional state of the farmer. For example, the system can suggest playing relaxing music when stress levels are high, and adjust the temperature and humidity of the work environment when fatigue is building up. This allows the work environment to be optimized according to the emotional state of the farmer, improving work efficiency.

[0083] The agricultural support system can also use the emotion estimation function to manage the health of farmers based on their emotional data. For example, the emotion estimation function can be used to collect emotional data on farmers and evaluate their health status. The emotion estimation function can also be used to monitor the stress and fatigue levels of farmers and make health management suggestions. For example, when stress is high, the system can suggest relaxation methods, and when fatigue is accumulating, it can suggest rest. This can help maintain the health of farmers and improve work efficiency.

[0084] The agricultural support system can also use the emotion estimation function to make suggestions for improving communication based on the emotional data of farmers. For example, the emotion estimation function can be used to collect emotional data from farmers and make suggestions for improving communication. The emotion estimation function can also be used to suggest communication methods that suit the emotional state of farmers. For example, when stress is high, the system can suggest a communication method that helps farmers relax, and when motivation is low, it can provide an encouraging message. This makes it possible to improve communication according to the emotional state of farmers and improve work efficiency.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The data collection unit uses IoT devices to collect data on the agricultural environment. For example, it uses a sensor that measures sunlight hours, a temperature sensor, a humidity sensor, and a soil sensor to collect data on sunlight hours, temperature, humidity, and water content, respectively. Step 2: The analysis unit uses generation AI to analyze and predict the data collected by the data collection unit. For example, it uses text generation AI (LLM) or multimodal generation AI to analyze the data and make predictions. It can also suggest optimal conditions for plant growth based on past data and specialized knowledge. Step 3: The control unit automatically controls the environment based on the predictions generated by the analysis unit. For example, if the generation AI determines that "artificial lighting is necessary due to a lack of sunlight," it will automatically turn on the lights. Or, if it determines that "watering is necessary due to a decrease in soil moisture," it will automatically activate the sprinkler system.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0154] 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 data collection unit that collects data on the agricultural environment using IoT devices; an analysis unit that analyzes and predicts the data collected by the data collection unit using a generation AI; a control unit that automatically controls the environment based on the predictions generated by the analysis unit. A system characterized by:

2. The data collection unit Includes sensors to measure sunlight, temperature, humidity, and moisture content 2. The system of claim 1.

3. The analysis unit Analyze the data in real time to instantly detect outliers and patterns 2. The system of claim 1.

4. The control unit Based on the analysis results of the generated AI, sunlight, watering, and fertilizer application are automatically controlled.

2. The system of claim 1.

5. The data collection unit Collect data on the surrounding ecosystem as well as the agricultural environment.

2. The system of claim 1.

6. The analysis unit Based on this data, plant diseases are predicted and alerts are issued to take early countermeasures.

2. The system of claim 1.

7. The control unit Optimize agricultural machinery operation in real time based on environmental data to improve energy efficiency 2. The system of claim 1.

8. The data collection unit Collecting emotional data from farmers and assessing their stress levels 2. The system of claim 1.

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