System

Abstract

An object of the system according to the embodiment is to enable even a person who does not have knowledge about agriculture to identify characteristics of land, diseases of plants, and pests, and to realize stable production of agricultural crops in response to climate change.SOLUTION: A system includes a climate data analysis part, an image analysis part, a land characteristic analysis part, and a harvest prediction part. The climate data analyzer analyzes the climate data. The image analysis part specifies the disease and the harmful insect of the plant based on the weather data analyzed by the weather data analysis part. The land characteristic analysis unit analyzes the characteristics of the land based on the disease or pest of the plant specified by the image analysis unit. The harvest prediction unit predicts a harvest period and a harvest amount of the crop based on the property of the land analyzed by the land property analysis unit.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 technologies have faced challenges in terms of stable agricultural production, as they are difficult to adapt to the characteristics of agricultural land, plant diseases, pest identification, and climate change.

[0005] The system of the embodiment aims to enable even those with no agricultural knowledge to identify soil characteristics, plant diseases, and pests, thereby realizing stable production of agricultural crops that are adapted to climate change. [Means for solving the problem]

[0006] The system according to the embodiment includes a climate data analysis unit, an image analysis unit, a land characteristic analysis unit, and a harvest prediction unit. The climate data analysis unit analyzes climate data. The image analysis unit identifies plant diseases and pests based on the climate data analyzed by the climate data analysis unit. The land characteristic analysis unit analyzes land characteristics based on the plant diseases and pests identified by the image analysis unit. The harvest prediction unit predicts the harvest time and yield of agricultural crops based on the land characteristics analyzed by the land characteristic analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even those with no agricultural knowledge to identify soil characteristics, plant diseases, and pests, thereby enabling stable production of crops that adapt to climate change. [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 utilizes generative AI to solve various problems in agricultural production for individuals and organizations seeking to enter the agricultural business or those promoting agriculture in developing countries. This system analyzes weather data, identifies plant diseases and pests, analyzes soil characteristics, and predicts crop harvest times and yields. This allows even those without agricultural expertise to maintain stable crop yields, potentially reducing barriers to entry into the agricultural business.

[0029] The agricultural support system according to the embodiment includes a climate data analysis unit, an image analysis unit, a land characteristics analysis unit, and a harvest forecasting unit. The climate data analysis unit analyzes climate data. For example, it collects temperature and precipitation data and analyzes climate change patterns. For example, it acquires meteorological satellite data in real time and analyzes fluctuations in temperature and precipitation. The image analysis unit identifies plant diseases and pests based on the climate data analyzed by the climate data analysis unit. For example, it analyzes images of plants taken with a smartphone or drone and identifies the occurrence of diseases and pests. The image analysis unit analyzes images taken with a smartphone or drone and identifies the occurrence of diseases and pests. The land characteristics analysis unit analyzes land characteristics based on the plant diseases and pests identified by the image analysis unit. For example, it analyzes soil quality, moisture content, and sunlight conditions and suggests appropriate crop selection and cultivation methods. The land characteristics analysis unit analyzes soil quality, moisture content, and sunlight conditions and suggests appropriate crop selection and cultivation methods based on the results. The harvest prediction unit predicts the harvest time and yield of agricultural crops based on the characteristics of the land analyzed by the land characteristic analysis unit. For example, the harvest prediction unit analyzes plant growth data and weather data to predict the harvest time and yield. For example, the harvest prediction unit analyzes plant growth data and weather data to predict the harvest time and yield. This enables the agricultural support system according to the embodiment to maintain a stable yield of agricultural crops.

[0030] The climate data analysis unit can propose crop selection and cultivation methods based on fluctuations in temperature and precipitation. For example, the climate data analysis unit analyzes fluctuations in temperature and precipitation and proposes appropriate crop selection and cultivation methods. For example, the generation AI obtains meteorological satellite data in real time and analyzes fluctuations in temperature and precipitation. This makes it possible to propose farming methods that respond to climate change.

[0031] The image analysis unit can analyze images of plants taken with a smartphone or drone to identify the occurrence of diseases or pests. For example, the generative AI can analyze climate data from the past 50 years to identify long-term trends in temperature and precipitation. This enables the early detection of plant diseases and pests.

[0032] The land characteristics analysis unit can analyze soil quality, moisture content, sunlight conditions, etc., and based on that, suggest crop selection and cultivation methods. For example, the generative AI can analyze the stress levels of farmers and suggest farming methods that reduce labor during periods of high stress. This makes it possible to optimize farming methods based on the characteristics of each piece of land.

[0033] The harvest prediction unit can analyze plant growth data and weather data to predict the harvest time and yield. The harvest prediction unit, for example, analyzes plant growth data and weather data to predict the harvest time and yield. For example, the generation AI analyzes plant growth data and weather data to predict the harvest time and yield. This makes it possible to predict and manage the harvest of agricultural crops.

[0034] The climate data analysis unit updates climate data in real time and can propose farming methods that respond to climate change.The climate data analysis unit updates climate data in real time and can propose farming methods that respond to climate change.For example, the generation AI obtains meteorological satellite data in real time and analyzes fluctuations in temperature and precipitation.This makes it possible to propose farming methods that respond to climate change.

[0035] The image analysis unit can integrate images taken at different angles and lighting conditions to improve the accuracy of identifying diseases and pests.The image analysis unit can integrate images taken at different angles and lighting conditions to improve the accuracy of identifying diseases and pests.For example, the generation AI analyzes images taken at different angles and lighting conditions to identify the occurrence of diseases and pests.This improves the accuracy of identifying diseases and pests.

[0036] The land characteristic analysis unit can integrate land characteristic data and past harvest data to propose the optimal farming method. The land characteristic analysis unit can, for example, integrate land characteristic data and past harvest data to propose the optimal farming method. For example, the generative AI analyzes land characteristic data and past harvest data to propose the optimal farming method. This makes it possible to propose the optimal farming method based on the land characteristics and past harvest data.

[0037] The harvest forecasting unit updates the harvest forecast data in real time and can automatically adjust the harvest plan in response to fluctuations in the weather and market. The harvest forecasting unit, for example, updates the harvest forecast data in real time and automatically adjusts the harvest plan in response to fluctuations in the weather and market. For example, the generation AI updates the harvest forecast data in real time and automatically adjusts the harvest plan in response to fluctuations in the weather and market. This makes it possible to automatically adjust the harvest plan in response to fluctuations in the weather and market.

[0038] The climate data analysis unit can compare past climate data with current climate data, analyze long-term climate change patterns, and propose farming methods. The climate data analysis unit, for example, compares past climate data with current climate data, analyzes long-term climate change patterns, and proposes farming methods. For example, the generative AI analyzes past climate data and current climate data to identify long-term climate change patterns. This allows farming methods to be proposed based on long-term climate change patterns.

[0039] The image analysis unit can not only identify plant diseases and pests, but also analyze the progression of the disease and the propagation status of pests, and propose the priority of countermeasures. For example, the image analysis unit can analyze the progression of plant diseases and the propagation status of pests, and propose the priority of countermeasures. For example, the generative AI can analyze the progression of plant diseases and propose early detection and countermeasures. This makes it possible to propose the priority of countermeasures based on the progression and propagation status of diseases and pests.

[0040] The land characteristics analysis unit can also apply optimization of farming methods based on land characteristics to rooftop farms and balcony gardens in urban areas. For example, the land characteristics analysis unit analyzes characteristic data of urban rooftop farms and balcony gardens and suggests optimal crops and cultivation methods. For example, the generative AI analyzes characteristic data of urban rooftop farms and suggests optimal crops and cultivation methods. This makes it possible to suggest optimal farming methods for rooftop farms and balcony gardens in urban areas.

[0041] The harvest prediction unit can not only predict the harvest, but also propose post-harvest storage methods and distribution plans. For example, the harvest prediction unit proposes optimal post-harvest storage methods and distribution plans based on the harvest prediction data. For example, the generation AI proposes optimal post-harvest storage methods based on the harvest prediction data. This makes it possible to propose post-harvest storage methods and distribution plans.

[0042] The climate data analysis unit can apply its proposals for farming methods suited to climate change to urban and indoor farming, optimizing agricultural production in urban areas. For example, the climate data analysis unit analyzes urban climate data and proposes crops and cultivation methods suitable for indoor farming. For example, the generative AI analyzes urban climate data and proposes crops and cultivation methods suitable for indoor farming. This can optimize agricultural production in urban areas.

[0043] The harvest prediction unit can apply harvest prediction and management to other primary industries such as fishing and forestry, enabling comprehensive production management. For example, the harvest prediction unit predicts fishing yields and suggests optimal fishing times and fishing grounds. For example, the generation AI predicts fishing yields and suggests optimal fishing times and fishing grounds. This allows for application to other primary industries such as fishing and forestry.

[0044] The harvest forecasting unit can not only forecast and manage harvests, but also propose post-harvest processing and sales strategies. For example, the harvest forecasting unit proposes post-harvest processing methods and sales strategies based on harvest forecast data. For example, the generation AI proposes post-harvest processing methods based on harvest forecast data. This makes it possible to propose post-harvest processing and sales strategies.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The agricultural support system can further include a voice recognition unit. The voice recognition unit can analyze the voice instructions of the farmer and send instructions to each part of the system. For example, if the farmer verbally commands, "Tell me when it's time to harvest," the harvest prediction unit will provide the analysis results. The voice recognition unit also allows the farmer to operate the system without using their hands while working, thereby improving work efficiency. Furthermore, the voice recognition unit can support multiple languages, enabling international agricultural support.

[0047] The agricultural support system can further be equipped with a drone control unit. The drone control unit can automatically control the drone to monitor farmland and collect data. For example, the drone can fly over farmland periodically and provide the necessary data to the image analysis unit. The drone control unit can also automate the spraying of pesticides and fertilizers. This reduces the burden on farmers and enables efficient agricultural management. Furthermore, the drone control unit can also be equipped with an emergency flight mode to respond quickly to abnormal weather conditions.

[0048] The agricultural support system can also be equipped with a biosensor unit. The biosensor unit can monitor the physiological state of plants in real time and detect abnormalities. For example, it can measure the color and moisture content of plant leaves to detect early signs of disease or stress. The biosensor unit can also measure the nutrient status and moisture content of soil and suggest appropriate timing for fertilization and irrigation. This helps maintain plant health and maximize yields. The biosensor unit can also store data in the cloud, allowing for long-term accumulation and analysis of agricultural data.

[0049] The agricultural support system can further include a water management unit. The water management unit can monitor the moisture content of farmland in real time and propose optimal irrigation schedules. For example, the water management unit can measure the moisture content of the soil and automatically irrigate when necessary. The water management unit can also analyze weather data and adjust irrigation timing based on rainfall forecasts. This enables efficient use of water resources and optimizes crop growth. The water management unit can also include an emergency irrigation mode to respond quickly to abnormal weather conditions.

[0050] The agricultural support system can further be equipped with an energy management unit. The energy management unit can monitor the energy consumption of agricultural machinery and equipment in real time and propose efficient energy usage. For example, the energy management unit can analyze the operating status of agricultural machinery and optimize energy consumption. The energy management unit can also promote the use of renewable energy and make proposals to reduce environmental impact. This can reduce energy costs and protect the environment. Furthermore, the energy management unit can also be equipped with an alert function to detect abnormal energy consumption and respond quickly.

[0051] The agricultural support system can further include a logistics management unit. The logistics management unit can optimize the transportation and storage of harvested products and make suggestions to maintain quality. For example, the logistics management unit can analyze the transportation route of harvested products and suggest the optimal route. The logistics management unit can also monitor the storage environment and manage temperature and humidity. This makes it possible to maintain the quality of harvested products and reduce waste. Furthermore, the logistics management unit can also suggest appropriate shipping timing based on demand forecast data.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The climate data analysis unit analyzes climate data. For example, it collects data on temperature and precipitation and analyzes climate change patterns. The climate data analysis unit acquires meteorological satellite data in real time and analyzes fluctuations in temperature and precipitation. Step 2: The image analysis unit identifies plant diseases and pests based on the climate data analyzed by the climate data analysis unit. For example, it analyzes images of plants taken with a smartphone or drone to identify the occurrence of diseases and pests. Step 3: The land characteristics analysis unit analyzes the land characteristics based on the plant diseases and pests identified by the image analysis unit, such as soil quality, moisture content, and sunlight conditions, and suggests appropriate crop selection and cultivation methods. Step 4: The harvest prediction unit predicts the harvest time and yield of agricultural crops based on the land characteristics analyzed by the land characteristics analysis unit. For example, the harvest time and yield are predicted by analyzing plant growth data and weather data.

[0054] (Example 2) The agricultural support system according to an embodiment of the present invention utilizes generative AI to solve various problems in agricultural production for individuals and organizations seeking to enter the agricultural business or those promoting agriculture in developing countries. This system analyzes weather data, identifies plant diseases and pests, analyzes soil characteristics, and predicts crop harvest times and yields. This allows even those without agricultural expertise to maintain stable crop yields, potentially reducing barriers to entry into the agricultural business.

[0055] The agricultural support system according to the embodiment includes a climate data analysis unit, an image analysis unit, a land characteristics analysis unit, and a harvest forecasting unit. The climate data analysis unit analyzes climate data. For example, it collects temperature and precipitation data and analyzes climate change patterns. For example, it acquires meteorological satellite data in real time and analyzes fluctuations in temperature and precipitation. The image analysis unit identifies plant diseases and pests based on the climate data analyzed by the climate data analysis unit. For example, it analyzes images of plants taken with a smartphone or drone and identifies the occurrence of diseases and pests. The image analysis unit analyzes images taken with a smartphone or drone and identifies the occurrence of diseases and pests. The land characteristics analysis unit analyzes land characteristics based on the plant diseases and pests identified by the image analysis unit. For example, it analyzes soil quality, moisture content, and sunlight conditions and suggests appropriate crop selection and cultivation methods. The land characteristics analysis unit analyzes soil quality, moisture content, and sunlight conditions and suggests appropriate crop selection and cultivation methods based on the results. The harvest prediction unit predicts the harvest time and yield of agricultural crops based on the characteristics of the land analyzed by the land characteristic analysis unit. For example, the harvest prediction unit analyzes plant growth data and weather data to predict the harvest time and yield. For example, the harvest prediction unit analyzes plant growth data and weather data to predict the harvest time and yield. This enables the agricultural support system according to the embodiment to maintain a stable yield of agricultural crops.

[0056] The climate data analysis unit can propose crop selection and cultivation methods based on fluctuations in temperature and precipitation. For example, the climate data analysis unit analyzes fluctuations in temperature and precipitation and proposes appropriate crop selection and cultivation methods. For example, the generation AI obtains meteorological satellite data in real time and analyzes fluctuations in temperature and precipitation. This makes it possible to propose farming methods that respond to climate change.

[0057] The image analysis unit can analyze images of plants taken with a smartphone or drone to identify the occurrence of diseases or pests. For example, the generative AI can analyze climate data from the past 50 years to identify long-term trends in temperature and precipitation. This enables the early detection of plant diseases and pests.

[0058] The land characteristics analysis unit can analyze soil quality, moisture content, sunlight conditions, etc., and based on that, suggest crop selection and cultivation methods. For example, the generative AI can analyze the stress levels of farmers and suggest farming methods that reduce labor during periods of high stress. This makes it possible to optimize farming methods based on the characteristics of each piece of land.

[0059] The harvest prediction unit can analyze plant growth data and weather data to predict the harvest time and yield. The harvest prediction unit, for example, analyzes plant growth data and weather data to predict the harvest time and yield. For example, the generation AI analyzes plant growth data and weather data to predict the harvest time and yield. This makes it possible to predict and manage the harvest of agricultural crops.

[0060] The climate data analysis unit updates climate data in real time and can propose farming methods that respond to climate change.The climate data analysis unit updates climate data in real time and can propose farming methods that respond to climate change.For example, the generation AI obtains meteorological satellite data in real time and analyzes fluctuations in temperature and precipitation.This makes it possible to propose farming methods that respond to climate change.

[0061] The image analysis unit can integrate images taken at different angles and lighting conditions to improve the accuracy of identifying diseases and pests.The image analysis unit can integrate images taken at different angles and lighting conditions to improve the accuracy of identifying diseases and pests.For example, the generation AI analyzes images taken at different angles and lighting conditions to identify the occurrence of diseases and pests.This improves the accuracy of identifying diseases and pests.

[0062] The land characteristic analysis unit can integrate land characteristic data and past harvest data to propose the optimal farming method. The land characteristic analysis unit can, for example, integrate land characteristic data and past harvest data to propose the optimal farming method. For example, the generative AI analyzes land characteristic data and past harvest data to propose the optimal farming method. This makes it possible to propose the optimal farming method based on the land characteristics and past harvest data.

[0063] The harvest forecasting unit updates the harvest forecast data in real time and can automatically adjust the harvest plan in response to fluctuations in the weather and market. The harvest forecasting unit, for example, updates the harvest forecast data in real time and automatically adjusts the harvest plan in response to fluctuations in the weather and market. For example, the generation AI updates the harvest forecast data in real time and automatically adjusts the harvest plan in response to fluctuations in the weather and market. This makes it possible to automatically adjust the harvest plan in response to fluctuations in the weather and market.

[0064] The climate data analysis unit can compare past climate data with current climate data, analyze long-term climate change patterns, and propose farming methods. The climate data analysis unit, for example, compares past climate data with current climate data, analyzes long-term climate change patterns, and proposes farming methods. For example, the generative AI analyzes past climate data and current climate data to identify long-term climate change patterns. This allows farming methods to be proposed based on long-term climate change patterns.

[0065] The image analysis unit can not only identify plant diseases and pests, but also analyze the progression of the disease and the propagation status of pests, and propose the priority of countermeasures. For example, the image analysis unit can analyze the progression of plant diseases and the propagation status of pests, and propose the priority of countermeasures. For example, the generative AI can analyze the progression of plant diseases and propose early detection and countermeasures. This makes it possible to propose the priority of countermeasures based on the progression and propagation status of diseases and pests.

[0066] The land characteristics analysis unit can also apply optimization of farming methods based on land characteristics to rooftop farms and balcony gardens in urban areas. For example, the land characteristics analysis unit analyzes characteristic data of urban rooftop farms and balcony gardens and suggests optimal crops and cultivation methods. For example, the generative AI analyzes characteristic data of urban rooftop farms and suggests optimal crops and cultivation methods. This makes it possible to suggest optimal farming methods for rooftop farms and balcony gardens in urban areas.

[0067] The harvest prediction unit can not only predict the harvest, but also propose post-harvest storage methods and distribution plans. For example, the harvest prediction unit proposes optimal post-harvest storage methods and distribution plans based on the harvest prediction data. For example, the generation AI proposes optimal post-harvest storage methods based on the harvest prediction data. This makes it possible to propose post-harvest storage methods and distribution plans.

[0068] The climate data analysis unit can apply its proposals for farming methods suited to climate change to urban and indoor farming, optimizing agricultural production in urban areas. For example, the climate data analysis unit analyzes urban climate data and proposes crops and cultivation methods suitable for indoor farming. For example, the generative AI analyzes urban climate data and proposes crops and cultivation methods suitable for indoor farming. This can optimize agricultural production in urban areas.

[0069] The harvest prediction unit can apply harvest prediction and management to other primary industries such as fishing and forestry, enabling comprehensive production management. For example, the harvest prediction unit predicts fishing yields and suggests optimal fishing times and fishing grounds. For example, the generation AI predicts fishing yields and suggests optimal fishing times and fishing grounds. This allows for application to other primary industries such as fishing and forestry.

[0070] The harvest forecasting unit can not only forecast and manage harvests, but also propose post-harvest processing and sales strategies. For example, the harvest forecasting unit proposes post-harvest processing methods and sales strategies based on harvest forecast data. For example, the generation AI proposes post-harvest processing methods based on harvest forecast data. This makes it possible to propose post-harvest processing and sales strategies.

[0071] The climate data analysis unit can use the emotion estimation function to analyze the stress levels of farmers and suggest farming methods to reduce stress. The climate data analysis unit can, for example, use the emotion estimation function to analyze the stress levels of farmers and suggest farming methods to reduce stress. For example, the generation AI can analyze the stress levels of farmers and suggest farming methods that reduce labor during times of high stress. This makes it possible to suggest farming methods to reduce stress for farmers.

[0072] The image analysis unit can use the emotion estimation function to analyze the anxiety level of agricultural workers and propose measures to reduce the anxiety. The image analysis unit can, for example, use the emotion estimation function to analyze the anxiety level of agricultural workers and propose measures to reduce the anxiety. For example, the generation AI can analyze the anxiety level of agricultural workers and propose measures to reduce the anxiety. This makes it possible to propose measures to reduce the anxiety of agricultural workers.

[0073] The land characteristic analysis unit can use the emotion estimation function to analyze the satisfaction of farmers and propose farming methods to improve satisfaction. The land characteristic analysis unit can, for example, use the emotion estimation function to analyze the satisfaction of farmers and propose farming methods to improve satisfaction. For example, the generation AI can analyze the satisfaction data of farmers and propose a work schedule that results in high satisfaction. This makes it possible to propose farming methods to improve the satisfaction of farmers.

[0074] The harvest prediction unit can use the emotion estimation function to analyze the expectations of farmers and propose a harvest plan that meets those expectations. The harvest prediction unit, for example, uses the emotion estimation function to analyze the expectations of farmers and propose a harvest plan that meets those expectations. For example, the generation AI analyzes the expectations data of farmers and proposes a harvest plan that meets those expectations. This makes it possible to propose a harvest plan that meets the expectations of farmers.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The agricultural support system can further include a voice recognition unit. The voice recognition unit can analyze the voice instructions of the farmer and send instructions to each part of the system. For example, if the farmer verbally commands, "Tell me when it's time to harvest," the harvest prediction unit will provide the analysis results. The voice recognition unit also allows the farmer to operate the system without using their hands while working, thereby improving work efficiency. Furthermore, the voice recognition unit can support multiple languages, enabling international agricultural support.

[0077] The agricultural support system can further be equipped with a drone control unit. The drone control unit can automatically control the drone to monitor farmland and collect data. For example, the drone can fly over farmland periodically and provide the necessary data to the image analysis unit. The drone control unit can also automate the spraying of pesticides and fertilizers. This reduces the burden on farmers and enables efficient agricultural management. Furthermore, the drone control unit can also be equipped with an emergency flight mode to respond quickly to abnormal weather conditions.

[0078] The agricultural support system can further use the emotion estimation function to analyze farmers' motivation and provide feedback to improve it. For example, if the emotion estimation function determines that a farmer's motivation is declining, the system can provide encouraging messages and success stories. It can also suggest more challenging tasks when a farmer's motivation is high. This helps maintain farmers' motivation and improve work efficiency. Furthermore, the emotion estimation function can analyze farmers' motivation data over the long term and provide optimal feedback to each individual farmer.

[0079] The agricultural support system can also be equipped with a biosensor unit. The biosensor unit can monitor the physiological state of plants in real time and detect abnormalities. For example, it can measure the color and moisture content of plant leaves to detect early signs of disease or stress. The biosensor unit can also measure the nutrient status and moisture content of soil and suggest appropriate timing for fertilization and irrigation. This helps maintain plant health and maximize yields. The biosensor unit can also store data in the cloud, allowing for long-term accumulation and analysis of agricultural data.

[0080] The agricultural support system can also use the emotion estimation function to analyze the fatigue level of farmers and suggest when to take breaks. For example, if the emotion estimation function determines that a farmer is highly fatigued, the system will notify the farmer to take a break. It can also suggest an efficient work schedule when the farmer's fatigue level is low. This helps maintain the health of farmers and improve work efficiency. Furthermore, the emotion estimation function can analyze the fatigue level data of farmers over the long term and provide an optimal break schedule for each farmer.

[0081] The agricultural support system can further include a water management unit. The water management unit can monitor the moisture content of farmland in real time and propose optimal irrigation schedules. For example, the water management unit can measure the moisture content of the soil and automatically irrigate when necessary. The water management unit can also analyze weather data and adjust irrigation timing based on rainfall forecasts. This enables efficient use of water resources and optimizes crop growth. The water management unit can also include an emergency irrigation mode to respond quickly to abnormal weather conditions.

[0082] The agricultural support system can also use the emotion estimation function to analyze the happiness level of farmers and make suggestions for environmental improvements to increase happiness. For example, if the emotion estimation function determines that a farmer's happiness level is low, the system can suggest ways to improve the working environment or refresh them. In addition, when happiness levels are high, the system can suggest team-building activities. This can help maintain the happiness level of farmers and improve work efficiency. Furthermore, the emotion estimation function can analyze the happiness data of farmers over the long term and provide environmental improvement suggestions that are optimal for each individual farmer.

[0083] The agricultural support system can further be equipped with an energy management unit. The energy management unit can monitor the energy consumption of agricultural machinery and equipment in real time and propose efficient energy usage. For example, the energy management unit can analyze the operating status of agricultural machinery and optimize energy consumption. The energy management unit can also promote the use of renewable energy and make proposals to reduce environmental impact. This can reduce energy costs and protect the environment. Furthermore, the energy management unit can also be equipped with an alert function to detect abnormal energy consumption and respond quickly.

[0084] The agricultural support system can also use the emotion estimation function to analyze the communication status of farmers and make suggestions to promote smooth communication. For example, if the emotion estimation function determines that communication between farmers is lacking, the system can suggest opportunities for team meetings and information sharing. In addition, during periods when communication is smooth, the system can make suggestions to improve the efficiency of collaborative work. This can improve communication between farmers and enhance teamwork. Furthermore, the emotion estimation function can also analyze farmers' communication data over the long term and provide optimal communication strategies.

[0085] The agricultural support system can further include a logistics management unit. The logistics management unit can optimize the transportation and storage of harvested products and make suggestions to maintain quality. For example, the logistics management unit can analyze the transportation route of harvested products and suggest the optimal route. The logistics management unit can also monitor the storage environment and manage temperature and humidity. This makes it possible to maintain the quality of harvested products and reduce waste. Furthermore, the logistics management unit can also suggest appropriate shipping timing based on demand forecast data.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The climate data analysis unit analyzes climate data. For example, it collects data on temperature and precipitation and analyzes climate change patterns. The climate data analysis unit acquires meteorological satellite data in real time and analyzes fluctuations in temperature and precipitation. Step 2: The image analysis unit identifies plant diseases and pests based on the climate data analyzed by the climate data analysis unit. For example, it analyzes images of plants taken with a smartphone or drone to identify the occurrence of diseases and pests. Step 3: The land characteristics analysis unit analyzes the land characteristics based on the plant diseases and pests identified by the image analysis unit, such as soil quality, moisture content, and sunlight conditions, and suggests appropriate crop selection and cultivation methods. Step 4: The harvest prediction unit predicts the harvest time and yield of agricultural crops based on the land characteristics analyzed by the land characteristics analysis unit. For example, the harvest time and yield are predicted by analyzing plant growth data and weather data.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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 climate data analysis unit that analyzes climate data; an image analysis unit that identifies plant diseases and pests based on the climate data analyzed by the climate data analysis unit; a land characteristic analysis unit that analyzes land characteristics based on the plant diseases and pests identified by the image analysis unit; a harvest prediction unit that predicts the harvest time and yield of agricultural crops based on the land characteristics analyzed by the land characteristics analysis unit. A system characterized by:

2. The image analysis unit Analyzing images of the plants taken with a smartphone or drone to identify the occurrence of the disease or pest 2. The system of claim 1.

3. The land characteristic analysis unit Analyzes soil quality, moisture content, sunlight conditions, etc., and then suggests crop selection and cultivation methods based on that information.

2. The system of claim 1.

4. The harvest prediction unit Analyzing the growth data and meteorological data of the plants and predicting the harvest time and the harvest amount 2. The system of claim 1.

5. The climate data analysis unit Using emotion estimation, we analyze the stress levels of farmers and propose farming methods to reduce stress.

2. The system of claim 1.

Images