System

Abstract

An object of a system according to an embodiment is to automatically generate and execute an optimal agricultural chemical spraying plan in consideration of topography and climate.SOLUTION: A system according to an embodiment includes a terrain data collection part, a weather data collection part, an analysis part, a spraying plan generation part, and a drone control part. The terrain data collection unit collects terrain data. The climate data collection part collects climate data. The analysis unit analyzes the data collected by the terrain data collection unit and the weather data collection unit. The spraying plan generating portion generates an optimal spraying plan based on the data analyzed by the analyzing portion. The drone controlling portion controls the drone based on the spreading plan generated by the spreading plan generating portion.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 has faced the challenge of making it difficult to automatically generate and execute optimal pesticide spraying plans that take into account topography and climate.

[0005] The system according to the embodiment aims to automatically generate and execute an optimal pesticide spraying plan that takes into account the topography and climate. [Means for solving the problem]

[0006] A system according to an embodiment includes a terrain data collection unit, a climate data collection unit, an analysis unit, a spraying plan generation unit, and a drone control unit. The terrain data collection unit collects terrain data. The climate data collection unit collects climate data. The analysis unit analyzes the data collected by the terrain data collection unit and the climate data collection unit. The spraying plan generation unit generates an optimal spraying plan based on the data analyzed by the analysis unit. The drone control unit controls the drone based on the spraying plan generated by the spraying plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and execute an optimal pesticide spraying plan that takes into account the terrain and climate. [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 automatic pesticide spraying system according to the embodiment of the present invention calculates the topography and climate and automatically sprays pesticides on crops. This allows the automatic pesticide spraying system to address the aging population problem and reduce agricultural work.

[0029] The automatic pesticide spraying system according to the embodiment includes a topographical data collection unit, a climatic data collection unit, an analysis unit, a spraying plan generation unit, and a drone control unit. The topographical data collection unit collects topographical data. For example, the topographical data collection unit can collect topographical data from drones or satellites. The topographical data collection unit can also collect topographical gradient and elevation data. The climatic data collection unit collects climatic data. For example, the climatic data collection unit can collect climatic data such as temperature, humidity, and precipitation. The climatic data collection unit can also acquire weather forecast data in real time. The analysis unit analyzes the data collected by the topographical data collection unit and the climatic data collection unit. For example, the analysis unit statistically analyzes the topographical data and climatic data to generate basic data for creating an optimal spraying plan. The analysis unit can also analyze the data using a machine learning algorithm. The spraying plan generation unit generates an optimal spraying plan based on the data analyzed by the analysis unit. For example, the spraying plan generation unit calculates the timing, amount, and range of spraying. The spraying plan generation unit can also generate a spraying plan using the generation AI. The drone control unit controls the drone based on the spraying plan generated by the spraying plan generation unit. For example, the drone control unit sets the drone's flight route and adjusts the flight altitude. The drone control unit can also operate the spraying device. This allows the automatic pesticide spraying system according to the embodiment to optimally spray pesticides based on topographical and climate data. For example, the automatic pesticide spraying system can fly the drone along the optimal route calculated by the generation AI, even in terraced fields or complex rice paddies, and evenly spray pesticides.

[0030] The terrain data collection unit can collect terrain data in real time from drones or satellites, and the climate data collection unit can obtain weather forecast data in real time. The terrain data collection unit, for example, collects terrain data in real time from drones or satellites, and the AI ​​immediately analyzes the data. For example, the drone can send the terrain and climate data it collects during flight to the cloud, and the AI ​​can update the optimal spraying plan in real time based on that data. This allows the system to obtain terrain data and weather forecast data in real time and create the optimal spraying plan.

[0031] In addition to topographical data, the analysis unit can also analyze the nutrient state and moisture content of the soil. For example, the analysis unit uses a soil sensor to measure the nutrient state and moisture content of the soil in real time along with the topographical data, and sends that data to the AI. For example, the soil sensor measures the concentrations of nitrogen, phosphorus, and potassium, and the AI ​​uses that data to create an optimal spraying plan. This allows for the creation of a more precise spraying plan by analyzing the nutrient state and moisture content of the soil in addition to the topographical data.

[0032] The dispersal plan generation unit can also be applied to disaster prediction and disaster prevention planning using topographical data and climate data. The dispersal plan generation unit, for example, analyzes topographical data and climate data to build a system that predicts the risk of floods and landslides. For example, it simulates water flow based on topographical data and predicts rainfall based on climate data to assess flood risk. This allows the use of topographical data and climate data to be applied to disaster prediction and disaster prevention planning.

[0033] The drone control unit can optimize the drone's flight route and minimize battery consumption. The drone control unit can, for example, develop an algorithm to optimize the drone's flight route and minimize battery consumption. For example, it can calculate the route that will spray pesticides efficiently and in the shortest distance. This allows the drone's flight route to be optimized and battery consumption to be minimized.

[0034] The drone control unit can automatically adjust the type and amount of pesticide to be sprayed according to the growth stage of the crop. For example, the drone control unit can monitor the growth stage of the crop in real time and build a system that automatically adjusts the type and amount of pesticide to be sprayed. For example, a camera mounted on the drone can photograph the growth state of the crop, which is analyzed by AI to adjust the type and amount of pesticide. This makes it possible to automatically adjust the type and amount of pesticide to be sprayed according to the growth stage of the crop.

[0035] The drone control unit can also apply drone-based pesticide spraying to the spraying of fertilizer and water. For example, the drone can be loaded with fertilizer and water, and the same algorithm can be used to calculate the optimal spray route. This allows drone-based pesticide spraying to be applied to the spraying of fertilizer and water.

[0036] The drone control unit can increase the number of sensors installed on the drone and monitor the health of crops in real time. For example, the drone control unit can build a system that installs multiple sensors on the drone and monitors the health of crops in real time. For example, it can install a camera, temperature sensor, and humidity sensor to comprehensively analyze the condition of the crops. This allows the number of sensors installed on the drone to increase and the health of crops to be monitored in real time.

[0037] The drone control unit can analyze wide-area terrain data and update spraying routes in real time. The drone control unit, for example, builds a system that collects wide-area terrain data in real time and updates optimal spraying routes. For example, it analyzes wide-area terrain based on data from drones and satellites and calculates optimal spraying routes. This makes it possible to analyze wide-area terrain data and update optimal spraying routes in real time.

[0038] The drone control unit has developed a system that allows multiple drones to operate in coordination, making it possible to spray over a wide area.The drone control unit has developed a system that allows multiple drones to operate in coordination, making it possible to spray over a wide area.For example, the drones can communicate with each other and share the optimal spraying route.This allows multiple drones to operate in coordination, making it possible to spray over a wide area.

[0039] The drone control unit can use AI to automatically generate rice paddy art designs, allowing farmers to choose from them. For example, the drone control unit can build a system where AI automatically generates rice paddy art designs, allowing farmers to choose from them. For example, AI can generate multiple designs, and farmers can select their preferred design. This makes it possible for farmers to choose from rice paddy art designs automatically generated by AI.

[0040] The drone control unit improves the drone's flight accuracy, allowing it to create rice field art with more detailed designs. The drone control unit, for example, develops algorithms that improve the drone's flight accuracy, allowing it to create rice field art with more detailed designs. For example, improving GPS accuracy allows it to draw finer designs more accurately. This improves the drone's flight accuracy, allowing it to create rice field art with more detailed designs.

[0041] The drone control unit can create rice paddy art designs that reflect local traditions and culture. The drone control unit, for example, builds a system that creates rice paddy art designs that reflect local traditions and culture. For example, it creates designs themed around local festivals and events. This allows the rice paddy art designs to reflect local traditions and culture.

[0042] The drone control unit can apply the rice paddy art design to other crops and flower fields and utilize them as a tourist resource. The drone control unit can, for example, apply rice paddy art techniques to other crops and flower fields and build a system to utilize them as a tourist resource. For example, art can be drawn in flower fields to attract tourists. This allows the rice paddy art design to be applied to other crops and flower fields and utilized as a tourist resource.

[0043] The drone control unit can analyze spray data over a wide area and use it to protect the environment and maintain the ecosystem. The drone control unit can, for example, analyze spray data over a wide area and build a system that is useful for environmental protection. For example, the drone control unit can evaluate the impact on the environment based on the spray data and create an optimal spray plan. This allows the analysis of spray data over a wide area to be useful for protecting the environment and maintaining the ecosystem.

[0044] The drone control unit can also apply wide-area spraying to urban greening and park management. The drone control unit, for example, applies a wide-area spraying system to urban greening. For example, a drone is used to spray fertilizer and water on green spaces and parks in urban areas. This allows wide-area spraying to be applied to urban greening and park management.

[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 automatic pesticide spraying system can further include a crop growth prediction unit. The crop growth prediction unit predicts crop growth based on past climate data and soil data. For example, the crop growth prediction unit analyzes past data such as temperature, precipitation, and sunshine hours to predict crop growth patterns. The crop growth prediction unit can also predict crop growth taking into account the nutritional state and moisture content of the soil. This allows the automatic pesticide spraying system to create an optimal spraying plan based on the crop growth prediction.

[0047] The automatic pesticide spraying system can further include a pest detection unit. The pest detection unit detects whether pests have infested crops. For example, the pest detection unit photographs crops with a camera mounted on a drone and detects the presence of pests using image analysis technology. The pest detection unit can also identify the type and number of pests and create an optimal pesticide spraying plan based on that information. This allows the automatic pesticide spraying system to spray the appropriate pesticide depending on the pest infestation situation.

[0048] The automatic pesticide spraying system can further include a crop health assessment unit. The crop health assessment unit assesses the health of the crop and adjusts the pesticide spraying plan based on the results. For example, the crop health assessment unit may use a sensor mounted on the drone to measure the color and shape of the crop's leaves and assess their health. The crop health assessment unit may also comprehensively assess the health of the crop, taking into account the nutrient status and moisture content of the soil. This allows the automatic pesticide spraying system to create an optimal pesticide spraying plan based on the health of the crop.

[0049] The automatic pesticide spraying system can further include a harvest prediction unit. The harvest prediction unit predicts the crop harvest time and adjusts the pesticide spraying plan based on that information. For example, the harvest prediction unit analyzes past weather data and crop growth data to predict the harvest time. The harvest prediction unit can also more accurately predict the harvest time by taking into account the health and growth rate of the crop. This allows the automatic pesticide spraying system to create an optimal pesticide spraying plan according to the harvest time.

[0050] The automatic pesticide spraying system can further include a crop quality evaluation unit. The crop quality evaluation unit evaluates the quality of harvested crops and improves the pesticide spraying plan based on the results. For example, the crop quality evaluation unit measures the size, color, and shape of harvested crops and evaluates their quality. The crop quality evaluation unit can also comprehensively evaluate the quality, taking into account the storage conditions after harvest and market evaluation. This allows the automatic pesticide spraying system to continuously improve the pesticide spraying plan in order to improve crop quality.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The terrain data collection unit collects terrain data. For example, the terrain data collection unit can collect terrain data from a drone or a satellite. The terrain data collection unit can also collect terrain gradient and elevation data. Step 2: The climate data collection unit collects climate data. For example, the climate data collection unit can collect climate data such as temperature, humidity, and precipitation. The climate data collection unit can also obtain weather forecast data in real time. Step 3: The analysis unit analyzes the data collected by the topographical data collection unit and the climatic data collection unit. For example, the analysis unit performs statistical analysis of the topographical data and climatic data to generate basic data for developing an optimal spraying plan. The analysis unit can also analyze the data using machine learning algorithms. Step 4: The spraying plan generation unit generates an optimal spraying plan based on the data analyzed by the analysis unit. For example, the spraying plan generation unit calculates the timing, amount, and range of spraying. The spraying plan generation unit can also generate a spraying plan using generation AI. Step 5: The drone control unit controls the drone based on the spraying plan generated by the spraying plan generation unit. For example, the drone control unit sets the drone's flight route and adjusts the flight altitude. The drone control unit can also operate the spraying device.

[0053] (Example 2) The automatic pesticide spraying system according to the embodiment of the present invention calculates the topography and climate and automatically sprays pesticides on crops. This allows the automatic pesticide spraying system to address the aging population problem and reduce agricultural work.

[0054] The automatic pesticide spraying system according to the embodiment includes a topographical data collection unit, a climatic data collection unit, an analysis unit, a spraying plan generation unit, and a drone control unit. The topographical data collection unit collects topographical data. For example, the topographical data collection unit can collect topographical data from drones or satellites. The topographical data collection unit can also collect topographical gradient and elevation data. The climatic data collection unit collects climatic data. For example, the climatic data collection unit can collect climatic data such as temperature, humidity, and precipitation. The climatic data collection unit can also acquire weather forecast data in real time. The analysis unit analyzes the data collected by the topographical data collection unit and the climatic data collection unit. For example, the analysis unit statistically analyzes the topographical data and climatic data to generate basic data for creating an optimal spraying plan. The analysis unit can also analyze the data using a machine learning algorithm. The spraying plan generation unit generates an optimal spraying plan based on the data analyzed by the analysis unit. For example, the spraying plan generation unit calculates the timing, amount, and range of spraying. The spraying plan generation unit can also generate a spraying plan using the generation AI. The drone control unit controls the drone based on the spraying plan generated by the spraying plan generation unit. For example, the drone control unit sets the drone's flight route and adjusts the flight altitude. The drone control unit can also operate the spraying device. This allows the automatic pesticide spraying system according to the embodiment to optimally spray pesticides based on topographical and climate data. For example, the automatic pesticide spraying system can fly the drone along the optimal route calculated by the generation AI, even in terraced fields or complex rice paddies, and evenly spray pesticides.

[0055] The terrain data collection unit can collect terrain data in real time from drones or satellites, and the climate data collection unit can obtain weather forecast data in real time. The terrain data collection unit, for example, collects terrain data in real time from drones or satellites, and the AI ​​immediately analyzes the data. For example, the drone can send the terrain and climate data it collects during flight to the cloud, and the AI ​​can update the optimal spraying plan in real time based on that data. This allows the system to obtain terrain data and weather forecast data in real time and create the optimal spraying plan.

[0056] In addition to topographical data, the analysis unit can also analyze the nutrient state and moisture content of the soil. For example, the analysis unit uses a soil sensor to measure the nutrient state and moisture content of the soil in real time along with the topographical data, and sends that data to the AI. For example, the soil sensor measures the concentrations of nitrogen, phosphorus, and potassium, and the AI ​​uses that data to create an optimal spraying plan. This allows for the creation of a more precise spraying plan by analyzing the nutrient state and moisture content of the soil in addition to the topographical data.

[0057] The dispersal plan generation unit can also be applied to disaster prediction and disaster prevention planning using topographical data and climate data. The dispersal plan generation unit, for example, analyzes topographical data and climate data to build a system that predicts the risk of floods and landslides. For example, it simulates water flow based on topographical data and predicts rainfall based on climate data to assess flood risk. This allows the use of topographical data and climate data to be applied to disaster prediction and disaster prevention planning.

[0058] The drone control unit can optimize the drone's flight route and minimize battery consumption. The drone control unit can, for example, develop an algorithm to optimize the drone's flight route and minimize battery consumption. For example, it can calculate the route that will spray pesticides efficiently and in the shortest distance. This allows the drone's flight route to be optimized and battery consumption to be minimized.

[0059] The drone control unit can automatically adjust the type and amount of pesticide to be sprayed according to the growth stage of the crop. For example, the drone control unit can monitor the growth stage of the crop in real time and build a system that automatically adjusts the type and amount of pesticide to be sprayed. For example, a camera mounted on the drone can photograph the growth state of the crop, which is analyzed by AI to adjust the type and amount of pesticide. This makes it possible to automatically adjust the type and amount of pesticide to be sprayed according to the growth stage of the crop.

[0060] The drone control unit can also apply drone-based pesticide spraying to the spraying of fertilizer and water. For example, the drone can be loaded with fertilizer and water, and the same algorithm can be used to calculate the optimal spray route. This allows drone-based pesticide spraying to be applied to the spraying of fertilizer and water.

[0061] The drone control unit can increase the number of sensors installed on the drone and monitor the health of crops in real time. For example, the drone control unit can build a system that installs multiple sensors on the drone and monitors the health of crops in real time. For example, it can install a camera, temperature sensor, and humidity sensor to comprehensively analyze the condition of the crops. This allows the number of sensors installed on the drone to increase and the health of crops to be monitored in real time.

[0062] The drone control unit can analyze wide-area terrain data and update spraying routes in real time. The drone control unit, for example, builds a system that collects wide-area terrain data in real time and updates optimal spraying routes. For example, it analyzes wide-area terrain based on data from drones and satellites and calculates optimal spraying routes. This makes it possible to analyze wide-area terrain data and update optimal spraying routes in real time.

[0063] The drone control unit has developed a system that allows multiple drones to operate in coordination, making it possible to spray over a wide area.The drone control unit has developed a system that allows multiple drones to operate in coordination, making it possible to spray over a wide area.For example, the drones can communicate with each other and share the optimal spraying route.This allows multiple drones to operate in coordination, making it possible to spray over a wide area.

[0064] The drone control unit can use AI to automatically generate rice paddy art designs, allowing farmers to choose from them. For example, the drone control unit can build a system where AI automatically generates rice paddy art designs, allowing farmers to choose from them. For example, AI can generate multiple designs, and farmers can select their preferred design. This makes it possible for farmers to choose from rice paddy art designs automatically generated by AI.

[0065] The drone control unit improves the drone's flight accuracy, allowing it to create rice field art with more detailed designs. The drone control unit, for example, develops algorithms that improve the drone's flight accuracy, allowing it to create rice field art with more detailed designs. For example, improving GPS accuracy allows it to draw finer designs more accurately. This improves the drone's flight accuracy, allowing it to create rice field art with more detailed designs.

[0066] The drone control unit can create rice paddy art designs that reflect local traditions and culture. The drone control unit, for example, builds a system that creates rice paddy art designs that reflect local traditions and culture. For example, it creates designs themed around local festivals and events. This allows the rice paddy art designs to reflect local traditions and culture.

[0067] The drone control unit can apply the rice paddy art design to other crops and flower fields and utilize them as a tourist resource. The drone control unit can, for example, apply rice paddy art techniques to other crops and flower fields and build a system to utilize them as a tourist resource. For example, art can be drawn in flower fields to attract tourists. This allows the rice paddy art design to be applied to other crops and flower fields and utilized as a tourist resource.

[0068] The drone control unit can adjust the design of the rice field art to one that will impress tourists the most. The drone control unit can, for example, monitor the emotional state of tourists in real time and build a system that suggests the most moving design. For example, it measures heart rate and galvanic skin response and suggests designs that will impress tourists the most. This makes it possible to adjust the design of the rice field art to one that will impress tourists the most.

[0069] The drone control unit can analyze spray data over a wide area and use it to protect the environment and maintain the ecosystem. The drone control unit can, for example, analyze spray data over a wide area and build a system that is useful for environmental protection. For example, the drone control unit can evaluate the impact on the environment based on the spray data and create an optimal spray plan. This allows the analysis of spray data over a wide area to be useful for protecting the environment and maintaining the ecosystem.

[0070] The drone control unit can analyze farmers' emotions regarding wide-area spraying work and propose a work plan that is less stressful. The drone control unit can, for example, build a system that monitors farmers' emotional states in real time and analyzes their emotions regarding wide-area spraying work. For example, it measures heart rate and skin galvanic response and proposes a work plan that is less stressful. This makes it possible to analyze farmers' emotions regarding wide-area spraying work and propose a work plan that is less stressful.

[0071] The drone control unit can also apply wide-area spraying to urban greening and park management. The drone control unit, for example, applies a wide-area spraying system to urban greening. For example, a drone is used to spray fertilizer and water on green spaces and parks in urban areas. This allows wide-area spraying to be applied to urban greening and park management.

[0072] The drone control unit can use the emotion estimation function to propose a spraying pattern that will satisfy the farmer most. For example, the drone control unit can monitor the emotional state of the farmer in real time and build a system that proposes a spraying pattern that will satisfy the farmer most. For example, it can measure the farmer's heart rate and electrodermal response and propose a spraying pattern that will satisfy the farmer most. This makes it possible to use the emotion estimation function to propose a spraying pattern that will satisfy the farmer most.

[0073] The drone control unit can use the emotion estimation function to suggest the most comfortable spraying timing for farmers. For example, the drone control unit can monitor the emotional state of farmers in real time and build a system that suggests the most comfortable spraying timing. For example, it can measure heart rate and galvanic skin response and suggest the timing when stress is low. This allows the emotion estimation function to suggest the most comfortable spraying timing for farmers.

[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 automatic pesticide spraying system can further include a crop growth prediction unit. The crop growth prediction unit predicts crop growth based on past climate data and soil data. For example, the crop growth prediction unit analyzes past data such as temperature, precipitation, and sunshine hours to predict crop growth patterns. The crop growth prediction unit can also predict crop growth taking into account the nutritional state and moisture content of the soil. This allows the automatic pesticide spraying system to create an optimal spraying plan based on the crop growth prediction.

[0076] The automatic pesticide spraying system can further include a pest detection unit. The pest detection unit detects whether pests have infested crops. For example, the pest detection unit photographs crops with a camera mounted on a drone and detects the presence of pests using image analysis technology. The pest detection unit can also identify the type and number of pests and create an optimal pesticide spraying plan based on that information. This allows the automatic pesticide spraying system to spray the appropriate pesticide depending on the pest infestation situation.

[0077] The automatic pesticide spraying system can further include a crop health assessment unit. The crop health assessment unit assesses the health of the crop and adjusts the pesticide spraying plan based on the results. For example, the crop health assessment unit may use a sensor mounted on the drone to measure the color and shape of the crop's leaves and assess their health. The crop health assessment unit may also comprehensively assess the health of the crop, taking into account the nutrient status and moisture content of the soil. This allows the automatic pesticide spraying system to create an optimal pesticide spraying plan based on the health of the crop.

[0078] The automatic pesticide spraying system can further include a harvest prediction unit. The harvest prediction unit predicts the crop harvest time and adjusts the pesticide spraying plan based on that information. For example, the harvest prediction unit analyzes past weather data and crop growth data to predict the harvest time. The harvest prediction unit can also more accurately predict the harvest time by taking into account the health and growth rate of the crop. This allows the automatic pesticide spraying system to create an optimal pesticide spraying plan according to the harvest time.

[0079] The automatic pesticide spraying system can further include a crop quality evaluation unit. The crop quality evaluation unit evaluates the quality of harvested crops and improves the pesticide spraying plan based on the results. For example, the crop quality evaluation unit measures the size, color, and shape of harvested crops and evaluates their quality. The crop quality evaluation unit can also comprehensively evaluate the quality, taking into account the storage conditions after harvest and market evaluation. This allows the automatic pesticide spraying system to continuously improve the pesticide spraying plan in order to improve crop quality.

[0080] The automatic pesticide spraying system may further include an emotion estimation unit that estimates the user's emotion and adjusts the spraying plan based on the estimated emotion. For example, if the user is feeling stressed, the emotion estimation unit simplifies the spraying plan to reduce the user's burden. Alternatively, if the user is satisfied, the emotion estimation unit maintains the current spraying plan. This allows the automatic pesticide spraying system to provide an optimal spraying plan according to the user's emotion.

[0081] The automatic pesticide spraying system can further estimate the user's emotions and adjust the spraying timing based on the estimated emotions. For example, if the user feels anxious, the emotion estimation unit changes the spraying timing and sprays at a timing that makes the user feel at ease. On the other hand, if the user feels relaxed, the emotion estimation unit maintains the current spraying timing. This allows the automatic pesticide spraying system to provide the optimal spraying timing according to the user's emotions.

[0082] The automatic pesticide spraying system can further estimate the user's emotions and adjust the spraying amount based on the estimated emotions. For example, if the user feels anxious, the emotion estimation unit reduces the spraying amount to alleviate the user's anxiety. On the other hand, if the user feels satisfied, the emotion estimation unit maintains the current spraying amount. This allows the automatic pesticide spraying system to provide the optimal spraying amount according to the user's emotions.

[0083] The automatic pesticide spraying system can further estimate the user's emotions and adjust the spraying range based on the estimated emotions. For example, if the user is feeling stressed, the emotion estimation unit reduces the spraying range to reduce the user's burden. On the other hand, if the user is satisfied, the emotion estimation unit maintains the current spraying range. This allows the automatic pesticide spraying system to provide an optimal spraying range according to the user's emotions.

[0084] The automatic pesticide spraying system can further estimate the user's emotions and adjust the spraying method based on the estimated emotions. For example, if the user feels anxious, the emotion estimation unit changes the spraying method and sprays in a way that makes the user feel at ease. On the other hand, if the user feels satisfied, the emotion estimation unit maintains the current spraying method. This allows the automatic pesticide spraying system to provide the optimal spraying method according to the user's emotions.

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

[0086] Step 1: The terrain data collection unit collects terrain data. For example, the terrain data collection unit can collect terrain data from a drone or a satellite. The terrain data collection unit can also collect terrain gradient and elevation data. Step 2: The climate data collection unit collects climate data. For example, the climate data collection unit can collect climate data such as temperature, humidity, and precipitation. The climate data collection unit can also obtain weather forecast data in real time. Step 3: The analysis unit analyzes the data collected by the topographical data collection unit and the climatic data collection unit. For example, the analysis unit performs statistical analysis of the topographical data and climatic data to generate basic data for developing an optimal spraying plan. The analysis unit can also analyze the data using machine learning algorithms. Step 4: The spraying plan generation unit generates an optimal spraying plan based on the data analyzed by the analysis unit. For example, the spraying plan generation unit calculates the timing, amount, and range of spraying. The spraying plan generation unit can also generate a spraying plan using generation AI. Step 5: The drone control unit controls the drone based on the spraying plan generated by the spraying plan generation unit. For example, the drone control unit sets the drone's flight route and adjusts the flight altitude. The drone control unit can also operate the spraying device.

[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 a 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, 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.

[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 topographical data collection unit that collects topographical data; a climate data collection unit that collects climate data; an analysis unit that analyzes the data collected by the topographical data collection unit and the climatic data collection unit; a spraying plan generation unit that generates an optimal spraying plan based on the data analyzed by the analysis unit; a drone control unit that controls the drone based on the spraying plan generated by the spraying plan generation unit. A system characterized by:

2. The topographical data collection unit collecting said terrain data in real time from a drone or satellite; The climate data collection unit Get real-time weather forecast data 2. The system of claim 1.

3. The analysis unit In addition to the topographical data, soil nutrient status and moisture content are also analyzed.

2. The system of claim 1.

4. The spraying plan generation unit The topographical data and the climate data are also used to predict disasters and develop disaster prevention plans.

2. The system of claim 1.

5. The drone control unit Optimize the drone's flight route to minimize battery consumption 2. The system of claim 1.

6. The drone control unit Adjust the design of the rice field art to one that will impress tourists the most.

2. The system of claim 1.

7. The drone control unit Analyzing farmers' feelings about wide-area spraying work and proposing work plans with less stress 2. The system of claim 1.

8. The drone control unit Using emotion estimation function, we suggest spraying patterns that will satisfy farmers the most.

2. The system of claim 1.

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