system

Abstract

The system according to this embodiment aims to efficiently collect and analyze environmental data from agricultural land and propose appropriate agricultural management methods. [Solution] The system according to the embodiment comprises a collection unit, a transmission unit, an analysis unit, and a proposal unit. The collection unit collects environmental data of farmland. The transmission unit transmits the data collected by the collection unit through a 5G network. The analysis unit analyzes the data transmitted by the transmission unit. The proposal unit proposes an agricultural management method based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, environmental data of farmland has not been sufficiently collected and analyzed efficiently, and appropriate agricultural management methods have not been proposed sufficiently, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and analyze environmental data of farmland and propose appropriate agricultural management methods.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a transmission unit, an analysis unit, and a proposal unit. The collection unit collects environmental data of farmland. The transmission unit transmits the data collected by the collection unit through a 5G network. The analysis unit analyzes the data transmitted by the transmission unit. The proposal unit proposes agricultural management methods based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze environmental data from farmland and propose appropriate agricultural management methods. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The agricultural support system according to an embodiment of the present invention is a system that utilizes AI, IoT, and 5G technologies to support farmers in improving productivity, promoting sustainable agriculture, and stabilizing food supply. This agricultural support system collects environmental data from farmland, transmits it via a 5G network, and has AI analyze it to propose optimal agricultural management methods. For example, IoT sensors (temperature sensors, humidity sensors, soil sensors, etc.) are used to collect environmental data from farmland (temperature, humidity, soil nutrient status, etc.) in real time. Next, the collected data is transmitted to the cloud via a 5G network, and AI analyzes it using machine learning algorithms. Based on the analysis results, the AI ​​proposes optimal agricultural management methods. For example, it proposes appropriate irrigation schedules and fertilizer usage amounts. This allows farmers to perform agricultural work efficiently and improves productivity. Furthermore, to realize sustainable agriculture, the AI ​​proposes ways to minimize environmental impact. For example, it proposes methods for saving water resources and conserving soil. In addition, to support the stabilization of food supply, the AI ​​performs harvest forecasts and optimizes the supply chain. This ensures a stable food supply and allows consumers to receive fresh food. This enables agricultural support systems to improve farmers' productivity, promote sustainable agriculture, and stabilize food supply.

[0029] The agricultural support system according to this embodiment comprises a collection unit, a transmission unit, an analysis unit, and a proposal unit. The collection unit collects environmental data of farmland. Environmental data of farmland includes, but is not limited to, temperature, humidity, and soil pH values. The collection unit collects data using, for example, a temperature sensor, a humidity sensor, and a soil sensor. The temperature sensor, for example, measures the temperature of farmland in real time and collects data. The humidity sensor, for example, measures the humidity of farmland in real time and collects data. The soil sensor, for example, measures the pH value and nutrient status of the soil and collects data. The transmission unit transmits the data collected by the collection unit through a 5G network. The 5G network enables high-speed and stable data transmission. The transmission unit compresses the data and transmits it to the cloud through the 5G network. The analysis unit analyzes the data transmitted by the transmission unit. The analysis unit analyzes the data using, for example, a machine learning algorithm. Machine learning algorithms include deep learning and support vector machines. The analysis unit proposes agricultural management methods based on the data analysis results. The proposal unit proposes optimal agricultural management methods based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes appropriate irrigation schedules and fertilizer usage amounts. The proposal unit can also propose methods for conserving water resources and soil conservation. As a result, the agricultural support system according to this embodiment can improve farmers' productivity, promote sustainable agriculture, and stabilize food supply.

[0030] The data collection unit may include temperature sensors, humidity sensors, and soil sensors. For example, the temperature sensor measures the temperature of farmland in real time and collects data. Examples of temperature sensors include thermistors and infrared thermometers. The humidity sensor measures the humidity of farmland in real time and collects data. Examples of humidity sensors include capacitance humidity sensors and resistance humidity sensors. The soil sensor measures the pH value and nutrient status of the soil and collects data. Examples of soil sensors include electrochemical sensors and optical sensors. This allows for more accurate agricultural management by collecting diverse environmental data.

[0031] The transmitting unit can utilize a 5G network. A 5G network enables high-speed and stable data transmission. 5G network specifications include, for example, the communication protocol and communication speed used. The transmitting unit compresses data and transmits it to the cloud via the 5G network. Data compression methods include, for example, ZIP compression and GZIP compression. This enables high-speed and stable data transmission.

[0032] The analysis unit can utilize machine learning algorithms. These include, for example, deep learning and support vector machines. Deep learning is a method that analyzes data using multilayer neural networks and possesses advanced pattern recognition capabilities. Support vector machines are used for data classification and regression analysis and have high accuracy. The analysis unit uses these machine learning algorithms to analyze collected data and propose agricultural management methods. This improves the accuracy of data analysis and allows for the proposal of more appropriate agricultural management methods.

[0033] The proposal department can propose an irrigation schedule. This schedule includes, for example, the frequency, amount, and timing of irrigation. Based on collected environmental data, the proposal department will suggest the optimal irrigation schedule. For example, if the temperature is high, the frequency of irrigation can be increased, and if the humidity is low, the amount of irrigation can be increased. This enables the efficient use of water resources.

[0034] The proposal department can suggest the amount of fertilizer to use. This includes, for example, the fertilizer's composition and the timing of its application. Based on the collected environmental data, the proposal department will suggest the optimal amount of fertilizer to use. For example, if the soil's nutrient status is poor, the amount of fertilizer can be increased, and if the nutrient status is good, the amount can be decreased. This allows for the optimization of crop growth through the appropriate use of fertilizer.

[0035] The proposal department can propose methods for utilizing water resources. These methods include, for example, water conservation technologies and reuse methods. Based on collected environmental data, the proposal department will propose optimal water resource utilization methods. For example, water resources can be saved by adjusting the frequency and amount of irrigation. They can also propose methods for reusing rainwater. This will enable water conservation to achieve sustainable agriculture.

[0036] The proposal department can propose soil management methods. These methods include, for example, soil improvement methods and conservation techniques. Based on collected environmental data, the proposal department will propose optimal soil management methods. For example, they can propose methods for adjusting soil pH levels or improving nutrient levels. This helps maintain soil health and supports sustainable agriculture.

[0037] The proposed system can predict harvest times. This prediction includes methods such as observing growth stages and utilizing weather data. Based on collected environmental data, the system predicts the optimal harvest time. For example, it can predict harvest times based on changes in temperature and humidity. This allows for the optimization of the supply chain by predicting harvest times.

[0038] The data collection unit can analyze historical environmental data and select the optimal sensor placement. For example, it can analyze historical temperature data to determine the optimal placement of temperature sensors. It can also analyze historical humidity data to determine the optimal placement of humidity sensors. Furthermore, it can analyze historical soil data to determine the optimal placement of soil sensors. This allows for the optimization of sensor placement by utilizing historical data.

[0039] The data collection unit can filter environmental data based on the growth stage of the crop. For example, if the crop is in the germination stage, the data collection unit can prioritize collecting temperature data. If the crop is in the growth stage, the data collection unit can prioritize collecting humidity data. Furthermore, if the crop is in the harvest stage, the data collection unit can prioritize collecting soil data. This makes crop management more efficient by collecting data according to the growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the growth stage of the crop into a generating AI and have the generating AI perform data filtering based on the growth stage.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the farmland when collecting environmental data. For example, if the farmland is located at high altitude, the data collection unit can prioritize the collection of temperature data. If the farmland is located at low altitude, the data collection unit can prioritize the collection of humidity data. Furthermore, if the farmland is located along the coast, the data collection unit can prioritize the collection of salinity data. In this way, more relevant data can be collected by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of the farmland into a generating AI and have the generating AI prioritize highly relevant data.

[0041] The data collection unit can improve the accuracy of environmental data collection by referring to historical data of farmland. For example, the data collection unit can improve the accuracy of collection by referring to past harvest data of farmland. Furthermore, the data collection unit can improve the accuracy of collection by referring to past weather data of farmland. In addition, the data collection unit can improve the accuracy of collection by referring to past soil data of farmland. Thus, the accuracy of collection is improved by referring to historical data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical data of farmland into a generating AI and have the generating AI perform the task of improving the accuracy of collection.

[0042] The transmission unit can determine the transmission priority based on the importance of the data at the time of transmission. For example, the transmission unit can prioritize the transmission of important data and perform analysis quickly. It can also prioritize the transmission of important data and postpone the transmission of general data. Furthermore, the transmission unit can dynamically adjust the transmission priority according to the importance of the data. This enables efficient data transmission by determining the transmission priority according to the importance of the data. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the importance of the data into a generating AI and have the generating AI execute the transmission priority.

[0043] The transmission unit can apply different transmission protocols depending on the type of data during transmission. For example, the transmission unit can apply a high-speed transmission protocol to temperature data for rapid transmission. It can also apply a highly reliable transmission protocol to humidity data for accurate transmission. Furthermore, it can apply a high-capacity transmission protocol to soil data for detailed data transmission. This allows for efficient data transmission by applying the appropriate transmission protocol for each data type. Some or all of the above-described processes in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the data type into a generating AI and have the generating AI apply the transmission protocol.

[0044] The transmitting unit can optimize the transmission route based on the data destination during transmission. For example, if the data destination is nearby, the transmitting unit will select the shortest route for transmission. If the data destination is far away, the transmitting unit can transmit via the optimal relay point. Furthermore, if there are multiple data destinations, the transmitting unit can dynamically select the optimal route for transmission. This enables efficient data transmission by optimizing the transmission route based on the destination. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the data destinations into a generating AI and have the generating AI perform the optimization of the transmission route.

[0045] The transmitting unit can minimize network load by adjusting the data transmission frequency during transmission. For example, if the network is congested, the transmitting unit can reduce the load by lowering the transmission frequency. Conversely, if the network is not congested, the transmitting unit can increase the transmission frequency to transmit data quickly. Furthermore, the transmitting unit can dynamically adjust the transmission frequency according to the network conditions. This minimizes network load, enabling efficient data transmission. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the network conditions into a generating AI and have the generating AI adjust the transmission frequency.

[0046] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the current analysis by referring to the analysis results of past temperature data. Furthermore, the analysis unit can improve the accuracy of the current analysis by referring to the analysis results of past humidity data. In addition, the analysis unit can improve the accuracy of the current analysis by referring to the analysis results of past soil data. Thus, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis results into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0047] The analysis unit can apply different analysis methods to each data category during analysis. For example, the analysis unit can apply statistical analysis methods to temperature data to provide highly accurate results. It can also apply machine learning algorithms to humidity data to provide highly accurate results. Furthermore, it can apply data mining methods to soil data to provide highly accurate results. This improves the accuracy of the analysis by applying the most appropriate analysis method according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI execute the application of analysis methods.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data and provide results quickly. Alternatively, the analysis unit can prioritize the analysis of the latest data and postpone the analysis of past data. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the data collection timing. This allows for the rapid analysis of the latest data by determining the analysis priority based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI execute the analysis priority.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. Alternatively, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of highly relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI execute the order of analysis.

[0050] The proposal unit can adjust the level of detail of its proposals based on the crop's growth stage. For example, if the crop is in the germination stage, the proposal unit will provide basic suggestions. If the crop is in the growth stage, it can provide detailed suggestions. Furthermore, if the crop is in the harvest stage, the proposal unit can provide suggestions related to harvesting. This enables appropriate agricultural management by providing suggestions according to the crop's growth stage. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the crop's growth stage into a generating AI and have the generating AI determine the level of detail of the suggestions.

[0051] The proposal unit can apply different proposal algorithms depending on the environmental data of the farmland when making a proposal. For example, if the temperature is high, the proposal unit can make a proposal regarding irrigation. If the humidity is low, the proposal unit can make a proposal regarding humidity control. Furthermore, if the soil's nutrient status is poor, the proposal unit can make a proposal regarding fertilizer use. By applying a proposal algorithm tailored to the environmental data of the farmland, appropriate agricultural management becomes possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the environmental data of the farmland into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0052] The proposal unit can determine the priority of proposals based on the harvest time of crops. For example, the proposal unit will prioritize proposals for crops that are close to harvest. It can also postpone proposals for crops that are far from harvest. Furthermore, the proposal unit can dynamically adjust the priority of proposals according to the harvest time. This enables efficient agricultural management by determining the priority of proposals based on the harvest time of crops. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the harvest time of crops into a generating AI and have the generating AI execute the proposal priority.

[0053] The proposal unit can adjust the order of proposals based on relevant data for farmland when making proposals. For example, the proposal unit can prioritize proposals based on highly relevant data. It can also postpone proposals based on less relevant data. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of the data. This allows for efficient agricultural management by adjusting the order of proposals based on relevant data for farmland. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input relevant data for farmland into a generating AI and have the generating AI execute the order of proposals.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can collect not only environmental data for farmland but also data to monitor the health of crops. For example, it can collect information on the color and shape of crop leaves, as well as the occurrence of pests and diseases, using cameras and image analysis technology. Furthermore, it can collect data useful for predicting crop growth rates and yields. This allows for real-time monitoring of crop health and the implementation of appropriate countermeasures.

[0056] The transmitting unit can adjust the transmission timing when sending data, taking into account the load status of the destination server. For example, if the destination server is under heavy load, the transmitting unit can temporarily delay data transmission. Conversely, if the destination server is under light load, the transmitting unit can transmit data quickly. Furthermore, the transmitting unit can also adjust the data compression ratio according to the load status of the destination server. This optimizes the load on the destination server and enables efficient data transmission.

[0057] The analysis unit can detect abnormal data by applying an anomaly detection algorithm during data analysis. For example, if there are abnormal fluctuations in temperature data, the analysis unit can use the anomaly detection algorithm to identify the cause. Furthermore, if there are abnormalities in humidity data, the analysis unit can use the anomaly detection algorithm to quickly propose countermeasures. In addition, if there are abnormalities in soil data, the analysis unit can use the anomaly detection algorithm to take appropriate measures. This allows for the rapid detection of abnormal data and the implementation of appropriate responses.

[0058] The proposal department can suggest harvest timing and sales strategies, taking into account market price data for agricultural products. For example, if current market prices are high, the department can suggest harvesting earlier. Conversely, if market prices are low, it can suggest harvesting later. Furthermore, if there is high demand in a particular market, the department can suggest sales strategies tailored to that market. This allows for the proposal of optimal harvest timing and sales strategies to maximize agricultural profits.

[0059] The proposal department can propose the next cultivation plan based on crop cultivation history data. For example, it can analyze past cultivation history data and propose the optimal crop selection and cultivation schedule. Furthermore, it can propose soil improvement methods and fertilizer usage based on past cultivation history data. In addition, it can propose pest and disease prevention measures based on past cultivation history data. This allows for the optimization of the next cultivation plan by utilizing past cultivation history data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects environmental data from the farmland. This environmental data includes temperature, humidity, and soil pH. The data collection unit uses temperature sensors, humidity sensors, and soil sensors to collect the data. The temperature sensor measures the temperature of the farmland in real time, the humidity sensor measures the humidity of the farmland in real time, and the soil sensor measures the soil pH and nutrient status. Step 2: The transmitting unit transmits the data collected by the collecting unit via the 5G network. The transmitting unit compresses the data and sends it to the cloud via the 5G network. Step 3: The analysis unit analyzes the data transmitted by the transmission unit. The analysis unit uses machine learning algorithms to analyze the data, employing methods such as deep learning and support vector machines. Step 4: The proposal team proposes optimal agricultural management methods based on the analysis results obtained by the analysis team. The proposal team proposes appropriate irrigation schedules, fertilizer usage, water resource conservation methods, and soil conservation methods.

[0062] (Example of form 2) The agricultural support system according to an embodiment of the present invention is a system that utilizes AI, IoT, and 5G technologies to support farmers in improving productivity, promoting sustainable agriculture, and stabilizing food supply. This agricultural support system collects environmental data from farmland, transmits it via a 5G network, and has AI analyze it to propose optimal agricultural management methods. For example, IoT sensors (temperature sensors, humidity sensors, soil sensors, etc.) are used to collect environmental data from farmland (temperature, humidity, soil nutrient status, etc.) in real time. Next, the collected data is transmitted to the cloud via a 5G network, and AI analyzes it using machine learning algorithms. Based on the analysis results, the AI ​​proposes optimal agricultural management methods. For example, it proposes appropriate irrigation schedules and fertilizer usage amounts. This allows farmers to perform agricultural work efficiently and improves productivity. Furthermore, to realize sustainable agriculture, the AI ​​proposes ways to minimize environmental impact. For example, it proposes methods for saving water resources and conserving soil. In addition, to support the stabilization of food supply, the AI ​​performs harvest forecasts and optimizes the supply chain. This ensures a stable food supply and allows consumers to receive fresh food. This enables agricultural support systems to improve farmers' productivity, promote sustainable agriculture, and stabilize food supply.

[0063] The agricultural support system according to this embodiment comprises a collection unit, a transmission unit, an analysis unit, and a proposal unit. The collection unit collects environmental data of farmland. Environmental data of farmland includes, but is not limited to, temperature, humidity, and soil pH values. The collection unit collects data using, for example, a temperature sensor, a humidity sensor, and a soil sensor. The temperature sensor, for example, measures the temperature of farmland in real time and collects data. The humidity sensor, for example, measures the humidity of farmland in real time and collects data. The soil sensor, for example, measures the pH value and nutrient status of the soil and collects data. The transmission unit transmits the data collected by the collection unit through a 5G network. The 5G network enables high-speed and stable data transmission. The transmission unit compresses the data and transmits it to the cloud through the 5G network. The analysis unit analyzes the data transmitted by the transmission unit. The analysis unit analyzes the data using, for example, a machine learning algorithm. Machine learning algorithms include deep learning and support vector machines. The analysis unit proposes agricultural management methods based on the data analysis results. The proposal unit proposes optimal agricultural management methods based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes appropriate irrigation schedules and fertilizer usage amounts. The proposal unit can also propose methods for conserving water resources and soil conservation. As a result, the agricultural support system according to this embodiment can improve farmers' productivity, promote sustainable agriculture, and stabilize food supply.

[0064] The data collection unit may include temperature sensors, humidity sensors, and soil sensors. For example, the temperature sensor measures the temperature of farmland in real time and collects data. Examples of temperature sensors include thermistors and infrared thermometers. The humidity sensor measures the humidity of farmland in real time and collects data. Examples of humidity sensors include capacitance humidity sensors and resistance humidity sensors. The soil sensor measures the pH value and nutrient status of the soil and collects data. Examples of soil sensors include electrochemical sensors and optical sensors. This allows for more accurate agricultural management by collecting diverse environmental data.

[0065] The transmitting unit can utilize a 5G network. A 5G network enables high-speed and stable data transmission. 5G network specifications include, for example, the communication protocol and communication speed used. The transmitting unit compresses data and transmits it to the cloud via the 5G network. Data compression methods include, for example, ZIP compression and GZIP compression. This enables high-speed and stable data transmission.

[0066] The analysis unit can utilize machine learning algorithms. These include, for example, deep learning and support vector machines. Deep learning is a method that analyzes data using multilayer neural networks and possesses advanced pattern recognition capabilities. Support vector machines are used for data classification and regression analysis and have high accuracy. The analysis unit uses these machine learning algorithms to analyze collected data and propose agricultural management methods. This improves the accuracy of data analysis and allows for the proposal of more appropriate agricultural management methods.

[0067] The proposal department can propose an irrigation schedule. This schedule includes, for example, the frequency, amount, and timing of irrigation. Based on collected environmental data, the proposal department will suggest the optimal irrigation schedule. For example, if the temperature is high, the frequency of irrigation can be increased, and if the humidity is low, the amount of irrigation can be increased. This enables the efficient use of water resources.

[0068] The proposal department can suggest the amount of fertilizer to use. This includes, for example, the fertilizer's composition and the timing of its application. Based on the collected environmental data, the proposal department will suggest the optimal amount of fertilizer to use. For example, if the soil's nutrient status is poor, the amount of fertilizer can be increased, and if the nutrient status is good, the amount can be decreased. This allows for the optimization of crop growth through the appropriate use of fertilizer.

[0069] The proposal department can propose methods for utilizing water resources. These methods include, for example, water conservation technologies and reuse methods. Based on collected environmental data, the proposal department will propose optimal water resource utilization methods. For example, water resources can be saved by adjusting the frequency and amount of irrigation. They can also propose methods for reusing rainwater. This will enable water conservation to achieve sustainable agriculture.

[0070] The proposal department can propose soil management methods. These methods include, for example, soil improvement methods and conservation techniques. Based on collected environmental data, the proposal department will propose optimal soil management methods. For example, they can propose methods for adjusting soil pH levels or improving nutrient levels. This helps maintain soil health and supports sustainable agriculture.

[0071] The proposed system can predict harvest times. This prediction includes methods such as observing growth stages and utilizing weather data. Based on collected environmental data, the system predicts the optimal harvest time. For example, it can predict harvest times based on changes in temperature and humidity. This allows for the optimization of the supply chain by predicting harvest times.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of environmental data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can automatically adjust the collection timing to reduce the user's burden. If the user is relaxed, the data collection unit can set the collection timing to be more frequent to collect detailed data. Furthermore, if the user is in a hurry, the data collection unit can optimize the collection timing to quickly collect the necessary data. In this way, the user's burden can be reduced by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The data collection unit can analyze historical environmental data and select the optimal sensor placement. For example, it can analyze historical temperature data to determine the optimal placement of temperature sensors. It can also analyze historical humidity data to determine the optimal placement of humidity sensors. Furthermore, it can analyze historical soil data to determine the optimal placement of soil sensors. This allows for the optimization of sensor placement by utilizing historical data.

[0074] The data collection unit can filter environmental data based on the growth stage of the crop. For example, if the crop is in the germination stage, the data collection unit can prioritize collecting temperature data. If the crop is in the growth stage, the data collection unit can prioritize collecting humidity data. Furthermore, if the crop is in the harvest stage, the data collection unit can prioritize collecting soil data. This makes crop management more efficient by collecting data according to the growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the growth stage of the crop into a generating AI and have the generating AI perform data filtering based on the growth stage.

[0075] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the farmland when collecting environmental data. For example, if the farmland is located at high altitude, the data collection unit can prioritize the collection of temperature data. If the farmland is located at low altitude, the data collection unit can prioritize the collection of humidity data. Furthermore, if the farmland is located along the coast, the data collection unit can prioritize the collection of salinity data. In this way, more relevant data can be collected by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of the farmland into a generating AI and have the generating AI prioritize highly relevant data.

[0077] The data collection unit can improve the accuracy of environmental data collection by referring to historical data of farmland. For example, the data collection unit can improve the accuracy of collection by referring to past harvest data of farmland. Furthermore, the data collection unit can improve the accuracy of collection by referring to past weather data of farmland. In addition, the data collection unit can improve the accuracy of collection by referring to past soil data of farmland. Thus, the accuracy of collection is improved by referring to historical data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical data of farmland into a generating AI and have the generating AI perform the task of improving the accuracy of collection.

[0078] The transmission unit can estimate the user's emotions and adjust the timing of data transmission based on the estimated emotions. For example, if the user is stressed, the transmission unit can automatically adjust the timing of data transmission to reduce the user's burden. If the user is relaxed, the transmission unit can set the timing of data transmission to be more frequent and send more detailed data. Furthermore, if the user is in a hurry, the transmission unit can optimize the timing of data transmission to quickly send the necessary data. In this way, the user's burden can be reduced by adjusting the transmission timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The transmission unit can determine the transmission priority based on the importance of the data at the time of transmission. For example, the transmission unit can prioritize the transmission of important data and perform analysis quickly. It can also prioritize the transmission of important data and postpone the transmission of general data. Furthermore, the transmission unit can dynamically adjust the transmission priority according to the importance of the data. This enables efficient data transmission by determining the transmission priority according to the importance of the data. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the importance of the data into a generating AI and have the generating AI execute the transmission priority.

[0080] The transmission unit can apply different transmission protocols depending on the type of data during transmission. For example, the transmission unit can apply a high-speed transmission protocol to temperature data for rapid transmission. It can also apply a highly reliable transmission protocol to humidity data for accurate transmission. Furthermore, it can apply a high-capacity transmission protocol to soil data for detailed data transmission. This allows for efficient data transmission by applying the appropriate transmission protocol for each data type. Some or all of the above-described processes in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the data type into a generating AI and have the generating AI apply the transmission protocol.

[0081] The transmission unit can estimate the user's emotions and adjust the data compression method based on the estimated emotions. For example, if the user is stressed, the transmission unit can increase the data compression rate to shorten the transmission time. Conversely, if the user is relaxed, the transmission unit can lower the data compression rate to transmit more detailed data. Furthermore, if the user is in a hurry, the transmission unit can optimize the data compression rate for faster transmission. This allows for efficient data transmission by adjusting the data compression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The transmitting unit can optimize the transmission route based on the data destination during transmission. For example, if the data destination is nearby, the transmitting unit will select the shortest route for transmission. If the data destination is far away, the transmitting unit can transmit via the optimal relay point. Furthermore, if there are multiple data destinations, the transmitting unit can dynamically select the optimal route for transmission. This enables efficient data transmission by optimizing the transmission route based on the destination. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the data destinations into a generating AI and have the generating AI perform the optimization of the transmission route.

[0083] The transmitting unit can minimize network load by adjusting the data transmission frequency during transmission. For example, if the network is congested, the transmitting unit can reduce the load by lowering the transmission frequency. Conversely, if the network is not congested, the transmitting unit can increase the transmission frequency to transmit data quickly. Furthermore, the transmitting unit can dynamically adjust the transmission frequency according to the network conditions. This minimizes network load, enabling efficient data transmission. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the network conditions into a generating AI and have the generating AI adjust the transmission frequency.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple analysis algorithm to provide results quickly. If the user is relaxed, the analysis unit can use a detailed analysis algorithm to provide highly accurate results. Furthermore, if the user is in a hurry, the analysis unit can use an optimized analysis algorithm to provide results quickly. This allows for efficient data analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the current analysis by referring to the analysis results of past temperature data. Furthermore, the analysis unit can improve the accuracy of the current analysis by referring to the analysis results of past humidity data. In addition, the analysis unit can improve the accuracy of the current analysis by referring to the analysis results of past soil data. Thus, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis results into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0086] The analysis unit can apply different analysis methods to each data category during analysis. For example, the analysis unit can apply statistical analysis methods to temperature data to provide highly accurate results. It can also apply machine learning algorithms to humidity data to provide highly accurate results. Furthermore, it can apply data mining methods to soil data to provide highly accurate results. This improves the accuracy of the analysis by applying the most appropriate analysis method according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI execute the application of analysis methods.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data and provide results quickly. Alternatively, the analysis unit can prioritize the analysis of the latest data and postpone the analysis of past data. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the data collection timing. This allows for the rapid analysis of the latest data by determining the analysis priority based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI execute the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. Alternatively, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of highly relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI execute the order of analysis.

[0090] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and highly visible suggestions. If the user is relaxed, it can present suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can present suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The proposal unit can adjust the level of detail of its proposals based on the crop's growth stage. For example, if the crop is in the germination stage, the proposal unit will provide basic suggestions. If the crop is in the growth stage, it can provide detailed suggestions. Furthermore, if the crop is in the harvest stage, the proposal unit can provide suggestions related to harvesting. This enables appropriate agricultural management by providing suggestions according to the crop's growth stage. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the crop's growth stage into a generating AI and have the generating AI determine the level of detail of the suggestions.

[0092] The proposal unit can apply different proposal algorithms depending on the environmental data of the farmland when making a proposal. For example, if the temperature is high, the proposal unit can make a proposal regarding irrigation. If the humidity is low, the proposal unit can make a proposal regarding humidity control. Furthermore, if the soil's nutrient status is poor, the proposal unit can make a proposal regarding fertilizer use. By applying a proposal algorithm tailored to the environmental data of the farmland, appropriate agricultural management becomes possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the environmental data of the farmland into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0093] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion function will provide a short, concise suggestion. If the user is relaxed, the suggestion function can provide a longer suggestion with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion function can provide a short, easily understandable suggestion. By adjusting the length of the suggestion according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The proposal unit can determine the priority of proposals based on the harvest time of crops. For example, the proposal unit will prioritize proposals for crops that are close to harvest. It can also postpone proposals for crops that are far from harvest. Furthermore, the proposal unit can dynamically adjust the priority of proposals according to the harvest time. This enables efficient agricultural management by determining the priority of proposals based on the harvest time of crops. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the harvest time of crops into a generating AI and have the generating AI execute the proposal priority.

[0095] The proposal unit can adjust the order of proposals based on relevant data for farmland when making proposals. For example, the proposal unit can prioritize proposals based on highly relevant data. It can also postpone proposals based on less relevant data. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of the data. This allows for efficient agricultural management by adjusting the order of proposals based on relevant data for farmland. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input relevant data for farmland into a generating AI and have the generating AI execute the order of proposals. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, transmission unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects environmental data of farmland in real time using the temperature sensor, humidity sensor, and soil sensor of the smart device 14. The transmission unit transmits the collected data to the cloud via a 5G network. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using a machine learning algorithm. The proposal unit proposes an optimal agricultural management method based on the analysis results and provides it to the user through the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, transmission unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects environmental data of farmland in real time using the temperature sensor, humidity sensor, and soil sensor of the smart glasses 214. The transmission unit transmits the collected data to the cloud via a 5G network. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using a machine learning algorithm. The proposal unit proposes an optimal agricultural management method based on the analysis results and provides it to the user through the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, transmission unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects environmental data of farmland in real time using the temperature sensor, humidity sensor, and soil sensor of the headset terminal 314. The transmission unit transmits the collected data to the cloud via a 5G network. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using a machine learning algorithm. The proposal unit proposes an optimal agricultural management method based on the analysis results and provides it to the user through the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, transmission unit, analysis unit, and proposal unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects environmental data of farmland in real time using the temperature sensor, humidity sensor, and soil sensor of the robot 414. The transmission unit transmits the collected data to the cloud via a 5G network. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using a machine learning algorithm. The proposal unit proposes an optimal agricultural management method based on the analysis results and provides it to the user through the control unit 46A of the robot 414.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can collect not only environmental data for farmland but also data to monitor the health of crops. For example, it can collect information on the color and shape of crop leaves, as well as the occurrence of pests and diseases, using cameras and image analysis technology. Furthermore, it can collect data useful for predicting crop growth rates and yields. This allows for real-time monitoring of crop health and the implementation of appropriate countermeasures.

[0098] The transmitting unit can adjust the transmission timing when sending data, taking into account the load status of the destination server. For example, if the destination server is under heavy load, the transmitting unit can temporarily delay data transmission. Conversely, if the destination server is under light load, the transmitting unit can transmit data quickly. Furthermore, the transmitting unit can also adjust the data compression ratio according to the load status of the destination server. This optimizes the load on the destination server and enables efficient data transmission.

[0099] The analysis unit can detect abnormal data by applying an anomaly detection algorithm during data analysis. For example, if there are abnormal fluctuations in temperature data, the analysis unit can use the anomaly detection algorithm to identify the cause. Furthermore, if there are abnormalities in humidity data, the analysis unit can use the anomaly detection algorithm to quickly propose countermeasures. In addition, if there are abnormalities in soil data, the analysis unit can use the anomaly detection algorithm to take appropriate measures. This allows for the rapid detection of abnormal data and the implementation of appropriate responses.

[0100] The proposal department can suggest harvest timing and sales strategies, taking into account market price data for agricultural products. For example, if current market prices are high, the department can suggest harvesting earlier. Conversely, if market prices are low, it can suggest harvesting later. Furthermore, if there is high demand in a particular market, the department can suggest sales strategies tailored to that market. This allows for the proposal of optimal harvest timing and sales strategies to maximize agricultural profits.

[0101] The proposal department can propose the next cultivation plan based on crop cultivation history data. For example, it can analyze past cultivation history data and propose the optimal crop selection and cultivation schedule. Furthermore, it can propose soil improvement methods and fertilizer usage based on past cultivation history data. In addition, it can propose pest and disease prevention measures based on past cultivation history data. This allows for the optimization of the next cultivation plan by utilizing past cultivation history data.

[0102] The data collection unit can estimate the user's emotions and adjust the types of data collected based on those emotions. For example, if the user is stressed, the unit will collect only essential data, reducing the user's burden. If the user is relaxed, the unit will collect detailed data, enabling more accurate analysis. Furthermore, if the user is in a hurry, the unit will prioritize collecting data that can be retrieved quickly. This allows for efficient data collection by adjusting the types of data collected according to the user's emotions.

[0103] The transmission unit can estimate the user's emotions and adjust the frequency of data transmission based on those emotions. For example, if the user is stressed, the transmission unit will reduce the frequency of data transmission to lessen the user's burden. Conversely, if the user is relaxed, the transmission unit will increase the frequency of data transmission and send more detailed data. Furthermore, if the user is in a hurry, the transmission unit will optimize the frequency of data transmission to quickly send the necessary data. This allows for efficient data transmission by adjusting the frequency of data transmission according to the user's emotions.

[0104] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible notification method. If the user is relaxed, the analysis unit can provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a notification method that gets straight to the point. In this way, by adjusting the notification method of the analysis results according to the user's emotions, it becomes possible to provide notifications that are easy for the user to understand.

[0105] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion function will delay the suggestion to reduce the user's burden. Conversely, if the user is relaxed, the suggestion function will speed up the suggestion and provide more detailed suggestions. Furthermore, if the user is in a hurry, the suggestion function will optimize the timing of suggestions to quickly provide the necessary advice. This allows for more efficient suggestions by adjusting the timing of suggestions according to the user's emotions.

[0106] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is stressed, the suggestion function will provide simple and highly visual suggestions. If the user is relaxed, the suggestion function can provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion function can provide suggestions that get straight to the point. By adjusting the content of suggestions according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects environmental data from the farmland. This environmental data includes temperature, humidity, and soil pH. The data collection unit uses temperature sensors, humidity sensors, and soil sensors to collect the data. The temperature sensor measures the temperature of the farmland in real time, the humidity sensor measures the humidity of the farmland in real time, and the soil sensor measures the soil pH and nutrient status. Step 2: The transmitting unit transmits the data collected by the collecting unit via the 5G network. The transmitting unit compresses the data and sends it to the cloud via the 5G network. Step 3: The analysis unit analyzes the data transmitted by the transmission unit. The analysis unit uses machine learning algorithms to analyze the data, employing methods such as deep learning and support vector machines. Step 4: The proposal team proposes optimal agricultural management methods based on the analysis results obtained by the analysis team. The proposal team proposes appropriate irrigation schedules, fertilizer usage, water resource conservation methods, and soil conservation methods.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects environmental data for agricultural land, A transmission unit that transmits the data collected by the aforementioned collection unit through a 5G network, An analysis unit that analyzes the data transmitted by the transmission unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an agricultural management method, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Includes temperature sensor, humidity sensor, and soil sensor. The system according to feature 1.

3. The aforementioned transmitting unit Using a 5G network The system according to feature 1.

4. The aforementioned analysis unit, Use machine learning algorithms The system according to feature 1.

5. The aforementioned proposal section is, Propose an irrigation schedule The system according to feature 1.

6. The aforementioned proposal section is, Suggestion for fertilizer usage The system according to feature 1.

7. The aforementioned proposal section is, Proposing methods for utilizing water resources. The system according to feature 1.

8. The aforementioned proposal section is, Proposing soil management methods The system according to feature 1.

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