System

Abstract

An object of a system according to an embodiment is to optimally control an environment in a warm room and guarantee quality and yield.SOLUTION: A system includes an environmental data collection unit, an analysis unit, a setting unit, and an adjustment unit. The environmental data collection part collects environmental data in the warm room. The analysis unit analyzes the environmental data collected by the environmental data collection unit. The setting unit sets an optimum environmental condition on the basis of the result analyzed by the analysis unit. The adjusting part adjusts the environment in the warm room based on the environmental condition set by the setting part.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] With conventional technology, it is difficult to optimally control the environment inside the greenhouse, and there is room for improvement in ensuring quality and yield.

[0005] The system according to the embodiment aims to optimally control the environment in the greenhouse and guarantee quality and yield. [Means for solving the problem]

[0006] The system according to the embodiment includes an environmental data collection unit, an analysis unit, a setting unit, and an adjustment unit. The environmental data collection unit collects environmental data within the greenhouse. The analysis unit analyzes the environmental data collected by the environmental data collection unit. The setting unit sets optimal environmental conditions based on the results of the analysis by the analysis unit. The adjustment unit adjusts the environment within the greenhouse based on the environmental conditions set by the setting unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimally control the environment in the greenhouse and ensure quality and yield. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The environmental control system according to an embodiment of the present invention uses AI 4.0 to optimally control the greenhouse environment and produce high-quality, high-yield crops while keeping costs down. This allows the environmental control system to optimally control the greenhouse environment and ensure the quality and yield of crops.

[0029] The environmental control system according to the embodiment includes an environmental data collection unit, an analysis unit, a setting unit, and an adjustment unit. The environmental data collection unit collects environmental data from within the greenhouse. For example, a temperature sensor measures the temperature within the greenhouse and transmits the data to the generation AI 4.0. A humidity sensor can also measure humidity and transmit the data to the generation AI 4.0. A carbon dioxide sensor can also measure carbon dioxide concentration and transmit the data to the generation AI 4.0. The analysis unit analyzes the environmental data collected by the environmental data collection unit. For example, the generation AI 4.0 analyzes data such as the temperature, humidity, carbon dioxide concentration, and light intensity within the greenhouse in real time to identify optimal environmental conditions for crop growth. The generation AI 4.0 performs analysis based on prompts containing the environmental data from within the greenhouse. The setting unit sets optimal environmental conditions based on the results of the analysis by the analysis unit. For example, the generation AI 4.0 activates a cooling device if the temperature is too high and a humidifier if the humidity is too low. It also turns on lights if there is insufficient light and activates a carbon dioxide supply device if the carbon dioxide concentration is low. The adjustment unit adjusts the environment in the greenhouse based on the environmental conditions set by the setting unit. For example, the generation AI 4.0 optimizes the operating time of cooling devices and humidifiers to reduce energy consumption. It also reduces power consumption by minimizing the lighting time. As a result, the environmental control system according to the embodiment can optimally control the environment in the greenhouse and ensure the quality and yield of crops.

[0030] The environmental data collection unit can automatically optimize sensor placement within the greenhouse to improve data accuracy. For example, Generative AI 4.0 analyzes sensor placement within the greenhouse in real time and automatically determines the optimal placement. For example, it adjusts the position of temperature and humidity sensors to improve data accuracy. Automatically optimizing sensor placement also expands the data collection range. For example, placing sensors in different areas of the greenhouse prevents bias in environmental data. Furthermore, Generative AI 4.0 dynamically changes sensor placement to respond to environmental changes. For example, it adjusts sensor position according to changes in the season or weather to maintain data accuracy. This optimizes sensor placement and improves data accuracy.

[0031] The analysis unit compares past environmental data with current data and detects anomalies, allowing preventative environmental adjustments to be made. For example, the analysis unit's Generator AI 4.0 compares past environmental data with current data to detect anomalies. For example, it detects sudden changes in temperature or humidity and makes preventative adjustments. It also uses anomaly detection algorithms to analyze anomalies in environmental data in real time. For example, it detects an abnormal increase in carbon dioxide concentration and responds immediately. Furthermore, Generator AI 4.0 learns from past data and identifies patterns of anomalies. For example, it predicts anomalies that will occur during specific seasons or times of day and takes measures in advance. This makes it possible to make preventative environmental adjustments through anomaly detection.

[0032] The adjustment unit can optimize the operating schedule of the environmental adjustment device to maximize energy efficiency. For example, the adjustment unit uses generation AI 4.0 to analyze the operating schedule of the environmental adjustment device and set the optimal schedule to maximize energy efficiency. For example, it optimizes the operating times of cooling devices and humidifiers. Furthermore, generation AI 4.0 builds a system that dynamically changes the operating schedule of the environmental adjustment device based on energy consumption data. For example, it adjusts operating times to reduce energy consumption during peak hours. Furthermore, generation AI 4.0 learns past energy consumption data and automatically generates an operating schedule to maximize energy efficiency. For example, it changes the operating schedule according to the season and weather. This maximizes energy efficiency and reduces costs.

[0033] The adjustment unit utilizes renewable energy to adjust the environment inside the greenhouse, enabling sustainable agriculture. The adjustment unit, for example, constructs a system in which generation AI 4.0 utilizes renewable energy to adjust the environment inside the greenhouse. For example, energy is supplied using solar power or wind power. Furthermore, generation AI 4.0 sets an optimal energy supply schedule based on renewable energy usage data. For example, it operates environmental adjustment devices according to the amount of power generated by solar power. Furthermore, generation AI 4.0 performs optimization to maximize the use of renewable energy. For example, it adjusts the operating hours of environmental adjustment devices to reduce energy consumption. In this way, sustainable agriculture can be achieved by utilizing renewable energy.

[0034] The adjustment unit can use music or sound waves to adjust the environment in the greenhouse to promote crop growth. For example, the adjustment unit constructs a system in which the generation AI 4.0 uses music or sound waves to promote crop growth. For example, sound waves of a specific frequency are used to promote growth. The generation AI 4.0 also analyzes the effects of music or sound waves and selects the optimal music or sound waves. For example, it plays music that is effective in promoting growth. Furthermore, the generation AI 4.0 learns data on the use of music and sound waves and sets the optimal conditions to maximize crop growth. For example, it plays music at a specific time of day. This makes it possible to promote crop growth by using music and sound waves.

[0035] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0036] The environmental data collection unit can use drones to collect environmental data within the greenhouse. For example, sensors mounted on the drones collect data such as temperature, humidity, and carbon dioxide concentration, and transmit it to the generated AI 4.0 in real time. The drones can also patrol different areas of the greenhouse to prevent data bias. Furthermore, the drones can periodically change the position of the sensors to improve data accuracy. This allows for efficient collection of environmental data using drones.

[0037] The environmental data collection unit can monitor the microbial environment in the greenhouse and evaluate the health of the crops. For example, it measures the types and numbers of microorganisms in the soil and sends the results to Generation AI 4.0. Based on the microbial environment data, Generation AI 4.0 can also evaluate the health of the crops and suggest necessary measures. Furthermore, a system will be built that monitors changes in the microbial environment in real time and immediately notifies the user if an abnormality is detected. This makes it possible to maintain the health of crops by monitoring the microbial environment.

[0038] The analysis unit can predict crop growth based on environmental data from within the greenhouse. For example, it analyzes data such as temperature, humidity, and carbon dioxide concentration to predict crop growth rates and harvest times. Generative AI 4.0 can also propose optimal cultivation plans based on growth prediction data. Furthermore, a system will be built that updates growth prediction data in real time and corrects predictions in response to environmental changes. This makes it possible to predict crop growth and create optimal cultivation plans.

[0039] The adjustment unit can monitor the physiological responses of plants to provide optimal environmental conditions when adjusting the environment in the greenhouse. For example, sensors can detect changes in the color and shape of plant leaves and send them to the generation AI 4.0. The generation AI 4.0 can also set optimal environmental conditions based on the plant's physiological response data. Furthermore, a system can be constructed that monitors the plant's physiological responses in real time and makes adjustments according to changes in the environment. This makes it possible to provide optimal environmental conditions by monitoring the plant's physiological responses.

[0040] The analysis unit can predict pest outbreaks based on environmental data within the greenhouse. For example, it analyzes data such as temperature, humidity, and carbon dioxide concentration to predict the risk of pest outbreaks. Furthermore, based on the outbreak prediction data, Generative AI 4.0 can propose optimal control plans. Furthermore, a system will be built that updates outbreak prediction data in real time and corrects predictions in response to environmental changes. This makes it possible to predict pest outbreaks and create effective control plans.

[0041] The adjustment unit can analyze the acoustic responses of plants to provide optimal environmental conditions when adjusting the environment in the greenhouse. For example, a sensor can detect the faint sounds made by plants and send them to the generation AI 4.0. The generation AI 4.0 can also set optimal environmental conditions based on the plant's acoustic response data. Furthermore, a system can be built that monitors the plant's acoustic responses in real time and makes adjustments according to changes in the environment. This makes it possible to provide optimal environmental conditions by analyzing the plant's acoustic responses.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The environmental data collection unit collects environmental data in the greenhouse. For example, a temperature sensor measures the temperature in the greenhouse and sends the data to the generated AI 4.0. A humidity sensor can also measure humidity and send the data to the generated AI 4.0. A carbon dioxide sensor can also measure the carbon dioxide concentration and send the data to the generated AI 4.0. Step 2: The analysis unit analyzes the environmental data collected by the environmental data collection unit. For example, the generation AI 4.0 analyzes data such as the temperature, humidity, carbon dioxide concentration, and light intensity inside the greenhouse in real time to identify the optimal environmental conditions for growing crops. The generation AI 4.0 performs analysis based on prompts containing the greenhouse's environmental data. Step 3: The setting unit sets the optimal environmental conditions based on the results of the analysis by the analysis unit. For example, the generation AI 4.0 activates the cooling device if the temperature is too high, activates the humidifier if the humidity is too low, turns on the lights if there is insufficient light, and activates the carbon dioxide supply device if the carbon dioxide concentration is low. Step 4: The adjustment unit adjusts the environment in the greenhouse based on the environmental conditions set by the setting unit. For example, the generation AI 4.0 optimizes the operating times of cooling devices and humidifiers to reduce energy consumption. It also reduces power consumption by minimizing the lighting time. As a result, the environmental control system according to the embodiment can optimally control the environment in the greenhouse and ensure the quality and yield of crops.

[0044] (Example 2) The environmental control system according to an embodiment of the present invention uses AI 4.0 to optimally control the greenhouse environment and produce high-quality, high-yield crops while keeping costs down. This allows the environmental control system to optimally control the greenhouse environment and ensure the quality and yield of crops.

[0045] The environmental control system according to the embodiment includes an environmental data collection unit, an analysis unit, a setting unit, and an adjustment unit. The environmental data collection unit collects environmental data from within the greenhouse. For example, a temperature sensor measures the temperature within the greenhouse and transmits the data to the generation AI 4.0. A humidity sensor can also measure humidity and transmit the data to the generation AI 4.0. A carbon dioxide sensor can also measure carbon dioxide concentration and transmit the data to the generation AI 4.0. The analysis unit analyzes the environmental data collected by the environmental data collection unit. For example, the generation AI 4.0 analyzes data such as the temperature, humidity, carbon dioxide concentration, and light intensity within the greenhouse in real time to identify optimal environmental conditions for crop growth. The generation AI 4.0 performs analysis based on prompts containing the environmental data from within the greenhouse. The setting unit sets optimal environmental conditions based on the results of the analysis by the analysis unit. For example, the generation AI 4.0 activates a cooling device if the temperature is too high and a humidifier if the humidity is too low. It also turns on lights if there is insufficient light and activates a carbon dioxide supply device if the carbon dioxide concentration is low. The adjustment unit adjusts the environment in the greenhouse based on the environmental conditions set by the setting unit. For example, the generation AI 4.0 optimizes the operating time of cooling devices and humidifiers to reduce energy consumption. It also reduces power consumption by minimizing the lighting time. As a result, the environmental control system according to the embodiment can optimally control the environment in the greenhouse and ensure the quality and yield of crops.

[0046] The environmental data collection unit can automatically optimize sensor placement within the greenhouse to improve data accuracy. For example, Generative AI 4.0 analyzes sensor placement within the greenhouse in real time and automatically determines the optimal placement. For example, it adjusts the position of temperature and humidity sensors to improve data accuracy. Automatically optimizing sensor placement also expands the data collection range. For example, placing sensors in different areas of the greenhouse prevents bias in environmental data. Furthermore, Generative AI 4.0 dynamically changes sensor placement to respond to environmental changes. For example, it adjusts sensor position according to changes in the season or weather to maintain data accuracy. This optimizes sensor placement and improves data accuracy.

[0047] The analysis unit compares past environmental data with current data and detects anomalies, allowing preventative environmental adjustments to be made. For example, the analysis unit's Generator AI 4.0 compares past environmental data with current data to detect anomalies. For example, it detects sudden changes in temperature or humidity and makes preventative adjustments. It also uses anomaly detection algorithms to analyze anomalies in environmental data in real time. For example, it detects an abnormal increase in carbon dioxide concentration and responds immediately. Furthermore, Generator AI 4.0 learns from past data and identifies patterns of anomalies. For example, it predicts anomalies that will occur during specific seasons or times of day and takes measures in advance. This makes it possible to make preventative environmental adjustments through anomaly detection.

[0048] The adjustment unit can optimize the operating schedule of the environmental adjustment device to maximize energy efficiency. For example, the adjustment unit uses generation AI 4.0 to analyze the operating schedule of the environmental adjustment device and set the optimal schedule to maximize energy efficiency. For example, it optimizes the operating times of cooling devices and humidifiers. Furthermore, generation AI 4.0 builds a system that dynamically changes the operating schedule of the environmental adjustment device based on energy consumption data. For example, it adjusts operating times to reduce energy consumption during peak hours. Furthermore, generation AI 4.0 learns past energy consumption data and automatically generates an operating schedule to maximize energy efficiency. For example, it changes the operating schedule according to the season and weather. This maximizes energy efficiency and reduces costs.

[0049] The adjustment unit utilizes renewable energy to adjust the environment inside the greenhouse, enabling sustainable agriculture. The adjustment unit, for example, constructs a system in which generation AI 4.0 utilizes renewable energy to adjust the environment inside the greenhouse. For example, energy is supplied using solar power or wind power. Furthermore, generation AI 4.0 sets an optimal energy supply schedule based on renewable energy usage data. For example, it operates environmental adjustment devices according to the amount of power generated by solar power. Furthermore, generation AI 4.0 performs optimization to maximize the use of renewable energy. For example, it adjusts the operating hours of environmental adjustment devices to reduce energy consumption. In this way, sustainable agriculture can be achieved by utilizing renewable energy.

[0050] The adjustment unit can use music or sound waves to adjust the environment in the greenhouse to promote crop growth. For example, the adjustment unit constructs a system in which the generation AI 4.0 uses music or sound waves to promote crop growth. For example, sound waves of a specific frequency are used to promote growth. The generation AI 4.0 also analyzes the effects of music or sound waves and selects the optimal music or sound waves. For example, it plays music that is effective in promoting growth. Furthermore, the generation AI 4.0 learns data on the use of music and sound waves and sets the optimal conditions to maximize crop growth. For example, it plays music at a specific time of day. This makes it possible to promote crop growth by using music and sound waves.

[0051] The adjustment unit can use the emotion estimation function to adjust the frequency or timing of environmental adjustment based on the emotions of the farmer. For example, the adjustment unit uses the emotion estimation function to analyze the emotions of the farmer and adjust the frequency and timing of environmental adjustment. For example, if positive emotions are strong, the frequency of environmental adjustment is reduced. In addition, a system is constructed in which Generative AI 4.0 dynamically changes the timing of environmental adjustment based on the farmer's emotion data. For example, if negative emotions are strong, the environmental adjustment is increased. Furthermore, emotion estimation data is collected in real time, and environmental adjustment is performed based on the farmer's emotions. For example, the timing of environmental adjustment is adjusted according to changes in emotions. This makes it possible to adjust the frequency and timing of environmental adjustment based on the farmer's emotions.

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

[0053] The analysis unit can use the emotion estimation function to estimate the stress level of agricultural workers and adjust the environment to reduce stress. For example, if a farmer is highly stressed, the temperature and humidity in the greenhouse can be adjusted to a comfortable range. Relaxing music can also be played to reduce stress. Furthermore, it is possible to further automate tasks to reduce the burden of work depending on the stress level. This can reduce stress for farmers and improve work efficiency.

[0054] The environmental data collection unit can use drones to collect environmental data within the greenhouse. For example, sensors mounted on the drones collect data such as temperature, humidity, and carbon dioxide concentration, and transmit it to the generated AI 4.0 in real time. The drones can also patrol different areas of the greenhouse to prevent data bias. Furthermore, the drones can periodically change the position of the sensors to improve data accuracy. This allows for efficient collection of environmental data using drones.

[0055] The adjustment unit can use the emotion estimation function to adjust the environment to increase the motivation of farmers. For example, if a farmer's motivation is low, the lighting in the greenhouse can be brightened to improve the working environment. It can also display encouraging messages to increase motivation. Furthermore, based on the farmer's emotional data, Generative AI 4.0 sets the optimal environmental conditions to increase motivation. This can increase the motivation of farmers and improve work efficiency.

[0056] The analysis unit uses the emotion estimation function to estimate the fatigue level of agricultural workers and suggest when to take a break. For example, if a farmer's fatigue level is high, the analysis unit will notify them to take a break. It can also optimize break times according to the level of fatigue. Furthermore, based on the fatigue level data, Generative AI 4.0 will adjust the work schedule and suggest the optimal work plan to reduce fatigue. This will reduce the fatigue of agricultural workers and improve work efficiency.

[0057] The adjustment unit can use the emotion estimation function to adjust the color and brightness of the lighting in the greenhouse based on the farmer's emotions. For example, if the farmer wants to relax, warm-colored lighting can be used. On the other hand, if the farmer wants to improve their concentration, bright white lighting can be used. Furthermore, based on the emotion data, Generative AI 4.0 sets the optimal lighting conditions and provides an environment that corresponds to the farmer's emotions. This makes it possible to provide an optimal lighting environment based on the farmer's emotions.

[0058] The environmental data collection unit can monitor the microbial environment in the greenhouse and evaluate the health of the crops. For example, it measures the types and numbers of microorganisms in the soil and sends the results to Generation AI 4.0. Based on the microbial environment data, Generation AI 4.0 can also evaluate the health of the crops and suggest necessary measures. Furthermore, a system will be built that monitors changes in the microbial environment in real time and immediately notifies the user if an abnormality is detected. This makes it possible to maintain the health of crops by monitoring the microbial environment.

[0059] The analysis unit can predict crop growth based on environmental data from within the greenhouse. For example, it analyzes data such as temperature, humidity, and carbon dioxide concentration to predict crop growth rates and harvest times. Generative AI 4.0 can also propose optimal cultivation plans based on growth prediction data. Furthermore, a system will be built that updates growth prediction data in real time and corrects predictions in response to environmental changes. This makes it possible to predict crop growth and create optimal cultivation plans.

[0060] The adjustment unit can monitor the physiological responses of plants to provide optimal environmental conditions when adjusting the environment in the greenhouse. For example, sensors can detect changes in the color and shape of plant leaves and send them to the generation AI 4.0. The generation AI 4.0 can also set optimal environmental conditions based on the plant's physiological response data. Furthermore, a system can be constructed that monitors the plant's physiological responses in real time and makes adjustments according to changes in the environment. This makes it possible to provide optimal environmental conditions by monitoring the plant's physiological responses.

[0061] The analysis unit can predict pest outbreaks based on environmental data within the greenhouse. For example, it analyzes data such as temperature, humidity, and carbon dioxide concentration to predict the risk of pest outbreaks. Furthermore, based on the outbreak prediction data, Generative AI 4.0 can propose optimal control plans. Furthermore, a system will be built that updates outbreak prediction data in real time and corrects predictions in response to environmental changes. This makes it possible to predict pest outbreaks and create effective control plans.

[0062] The adjustment unit can analyze the acoustic responses of plants to provide optimal environmental conditions when adjusting the environment in the greenhouse. For example, a sensor can detect the faint sounds made by plants and send them to the generation AI 4.0. The generation AI 4.0 can also set optimal environmental conditions based on the plant's acoustic response data. Furthermore, a system can be built that monitors the plant's acoustic responses in real time and makes adjustments according to changes in the environment. This makes it possible to provide optimal environmental conditions by analyzing the plant's acoustic responses.

[0063] The processing flow of the second embodiment will be briefly explained below.

[0064] Step 1: The environmental data collection unit collects environmental data in the greenhouse. For example, a temperature sensor measures the temperature in the greenhouse and sends the data to the generated AI 4.0. A humidity sensor can also measure humidity and send the data to the generated AI 4.0. A carbon dioxide sensor can also measure the carbon dioxide concentration and send the data to the generated AI 4.0. Step 2: The analysis unit analyzes the environmental data collected by the environmental data collection unit. For example, the generation AI 4.0 analyzes data such as the temperature, humidity, carbon dioxide concentration, and light intensity inside the greenhouse in real time to identify the optimal environmental conditions for growing crops. The generation AI 4.0 performs analysis based on prompts containing the greenhouse's environmental data. Step 3: The setting unit sets the optimal environmental conditions based on the results of the analysis by the analysis unit. For example, the generation AI 4.0 activates the cooling device if the temperature is too high, activates the humidifier if the humidity is too low, turns on the lights if there is insufficient light, and activates the carbon dioxide supply device if the carbon dioxide concentration is low. Step 4: The adjustment unit adjusts the environment in the greenhouse based on the environmental conditions set by the setting unit. For example, the generation AI 4.0 optimizes the operating times of cooling devices and humidifiers to reduce energy consumption. It also reduces power consumption by minimizing the lighting time. As a result, the environmental control system according to the embodiment can optimally control the environment in the greenhouse and ensure the quality and yield of crops.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0069] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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).

[0074] 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.

[0075] 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.

[0076] 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.

[0077] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0078] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0079] 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.

[0080] 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.

[0081] 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 AI 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.

[0082] 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.

[0083] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0094] 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.

[0095] 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.

[0096] 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 AI 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.

[0097] 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.

[0098] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0099] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] 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 AI 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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."

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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]

[0132] 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. an environmental data collection unit that collects environmental data in the greenhouse; an analysis unit that analyzes the environmental data collected by the environmental data collection unit; a setting unit that sets optimal environmental conditions based on the results of the analysis by the analysis unit; an adjusting unit that adjusts the environment in the greenhouse based on the environmental conditions set by the setting unit; A system characterized by:

2. The environmental data collection unit Automatically optimizes sensor placement in greenhouses to improve data accuracy 2. The system of claim 1.

3. The analysis unit Compare past and current environmental data to detect anomalies and make preventative environmental adjustments.

2. The system of claim 1.

4. The adjustment unit Optimize the operating schedule of environmental control equipment to maximize energy efficiency 2. The system of claim 1.

5. The adjustment unit Renewable energy will be used to regulate the environment within the greenhouse, achieving sustainable agriculture.

2. The system of claim 1.

Images