system

Abstract

The system according to this embodiment aims to make it easier for inexperienced people to enter agriculture by analyzing environmental data and predicting the timing and methods of agricultural work. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a prediction unit, and a monitoring unit. The data collection unit collects environmental data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts the timing and methods of agricultural work based on the data analyzed by the analysis unit. The monitoring unit monitors the growth status of crops based on the information predicted by the prediction unit.

Description

Technical Field

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

Background Art

[0007] The system according to this embodiment analyzes environmental data and predicts the timing and methods of agricultural work, making it easier for inexperienced individuals to enter the agricultural field. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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. 1 shows an example of the configuration of a 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 transforms the "intuition and experience" of agriculture into "automation" and "prediction." This agricultural support system aims to improve the efficiency and quality of agriculture by collecting information from various devices using IoT technology and analyzing and predicting that information using generative AI. For example, the agricultural support system collects environmental data of farmland (temperature, humidity, soil nutrient status, etc.) using IoT devices. Next, the collected data is input into the generative AI, which analyzes the data. The generative AI predicts the optimal timing and method of agricultural work by comparing it with past data. For example, the generative AI can suggest the appropriate timing of irrigation and the amount of fertilizer. Furthermore, the generative AI monitors the growth status of crops in real time based on the collected data and issues an alert if an abnormality occurs. This allows agricultural workers to respond quickly and maintain the quality of crops. In addition, the generative AI can predict future harvest yields based on the collected data, which can be used as a reference when creating sales plans. This mechanism realizes automation and prediction in agriculture, making it easier for inexperienced people to enter the field. For example, even those with no prior agricultural experience can efficiently perform farm work by utilizing IoT devices and generative AI, thereby improving agricultural productivity. Furthermore, by performing farm work at the appropriate time based on the predictive information provided by generative AI, the quality of crops can be improved. In this way, by utilizing IoT and generative AI, the "intuition and experience" of agriculture can be transformed into "automation" and "prediction," solving challenges such as an aging workforce and a shortage of successors. As a result, agricultural support systems will realize automation and prediction in agriculture, making it easier for even inexperienced individuals to enter the field.

[0029] The agricultural support system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, and a monitoring unit. The collection unit collects environmental data. Environmental data includes, but is not limited to, temperature, humidity, and soil pH values. The collection unit collects environmental data using IoT devices such as temperature sensors, humidity sensors, and soil sensors. For example, a temperature sensor measures the temperature of farmland in real time and collects data. For example, a humidity sensor measures the humidity of farmland and collects data. For example, a soil sensor measures the pH value and nutrient status of the soil and collects data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data and predicts the timing and method of agricultural work by comparing it with past data. For example, the analysis unit analyzes the data using generative AI and predicts the optimal timing and method of agricultural work. For example, the generative AI predicts the optimal timing of irrigation and the amount of fertilizer based on past harvest data and weather data. The prediction unit predicts the timing and method of agricultural work based on the data analyzed by the analysis unit. The prediction unit, for example, uses a generative AI to predict future harvest yields based on collected data, and uses this as a reference when formulating sales plans. The generative AI predicts future harvest yields based on past harvest data and weather data, for example. The monitoring unit monitors the growth status of crops based on the information predicted by the prediction unit. The monitoring unit, for example, uses a generative AI to monitor the growth status of crops in real time and issues an alert if an abnormality occurs. The generative AI monitors the growth status of crops and issues an alert if an abnormality occurs. As a result, the agricultural support system according to this embodiment achieves automation and prediction in agriculture through the collection, analysis, prediction, and monitoring of environmental data, making it easier for inexperienced people to enter the field.

[0030] The data collection unit can collect environmental data using IoT devices such as temperature sensors, humidity sensors, and soil sensors. For example, the data collection unit can measure the temperature of farmland in real time using a temperature sensor and collect the data. The temperature sensor can measure the temperature of farmland and collect the data. The data collection unit can also measure the humidity of farmland using a humidity sensor and collect the data. The humidity sensor can measure the humidity of farmland and collect the data. The data collection unit can also measure the pH value and nutrient status of soil using a soil sensor and collect the data. The soil sensor can measure the pH value and nutrient status of soil and collect the data. This makes the collection of environmental data more efficient by using IoT devices. 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 data from the temperature sensor, humidity sensor, and soil sensor into a generating AI, and the generating AI can optimize the data collection.

[0031] The analysis unit can analyze the collected data and predict the timing and methods of farm work by comparing it with past data. For example, the analysis unit can analyze the collected data and predict the timing and methods of farm work by comparing it with past data. For example, the analysis unit can use generative AI to analyze the data and predict the optimal timing and methods of farm work. For example, the generative AI predicts the optimal timing of irrigation and the amount of fertilizer based on past harvest data and weather data. The analysis unit can also use generative AI to predict future harvest yields based on the collected data and use this as a reference when creating sales plans. For example, the generative AI predicts future harvest yields based on past harvest data and weather data. This allows for the prediction of the optimal timing and methods of farm work by comparing it with past data. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the collected data into the generative AI, and the generative AI can perform the data analysis.

[0032] The monitoring unit can monitor the growth status of crops and issue alerts if abnormalities occur. For example, the monitoring unit can monitor the growth status of crops in real time and issue alerts if abnormalities occur. For example, the monitoring unit can use a generation AI to monitor the growth status of crops and issue alerts if abnormalities occur. For example, the generation AI can monitor the growth status of crops and issue alerts if abnormalities occur. This allows for a quick response when abnormalities occur through real-time monitoring. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the growth status of crops into a generation AI, which can then detect abnormalities and issue alerts.

[0033] The forecasting unit predicts harvest yields based on collected data, which can be used as a reference when formulating sales plans. For example, the forecasting unit predicts future harvest yields based on collected data, which can be used as a reference when formulating sales plans. For example, the forecasting unit uses a generative AI to predict future harvest yields based on collected data, which can be used as a reference when formulating sales plans. The generative AI predicts future harvest yields based on past harvest data and weather data, for example. This allows for predictions of future harvest yields, which can be used as a reference when formulating sales plans. Some or all of the above-described processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input collected data into a generative AI, which can then use to predict harvest yields.

[0034] The data collection unit can analyze past collected data and determine the placement of sensors. For example, the data collection unit can determine the optimal placement of temperature sensors based on past data. For example, the data collection unit can analyze past humidity data and optimize the placement of humidity sensors. For example, the data collection unit can determine the optimal placement of soil sensors by referring to past data. This improves the efficiency of data collection by determining the optimal sensor placement based on past 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 past collected data into a generating AI, and the generating AI can determine the optimal sensor placement.

[0035] The data collection unit can adjust the collection frequency of environmental data based on weather conditions and seasons. For example, the collection unit can increase the collection frequency of humidity data during the rainy season. For example, the collection unit can increase the collection frequency of soil moisture data during the dry season. For example, the collection unit can increase the collection frequency of temperature data during winter. By adjusting the collection frequency according to weather conditions and seasons, more accurate data can be collected. 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 collection frequency based on weather conditions and seasons into a generating AI, and the generating AI can adjust the collection frequency.

[0036] The data collection unit can prioritize the collection of highly relevant data based on geographical location information when collecting environmental data. For example, the data collection unit can prioritize the collection of temperature data in a specific area of ​​farmland. For example, the data collection unit can prioritize the collection of humidity data in a specific area of ​​farmland. For example, the data collection unit can prioritize the collection of soil data in a specific area of ​​farmland. This allows for the priority collection of highly relevant data by considering geographical location information. 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 geographical location information into a generating AI, and the generating AI can prioritize the collection of highly relevant data.

[0037] The data collection unit can analyze social media activity and collect relevant data when collecting environmental data. For example, the data collection unit can analyze agriculture-related posts on social media and collect relevant environmental data. For example, the data collection unit can analyze weather information on social media and collect relevant environmental data. For example, the data collection unit can analyze the growth status of crops on social media and collect relevant environmental data. This allows for the efficient collection of relevant data by analyzing social media activity. 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 social media activity into a generating AI and have the generating AI collect relevant data.

[0038] The analysis unit can adjust the level of detail of the analysis, taking into account the importance of the data. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a standard analysis on general data. For example, the analysis unit can perform a simplified analysis on less important data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. 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 importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms based on the data category during analysis. For example, the analysis unit may apply a specific algorithm to temperature data. For example, it may apply a different algorithm to humidity data. For example, it may apply yet another algorithm to soil data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm 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 the data category into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0040] The analysis unit can determine the priority of analysis by considering the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can analyze the most recent data while referring to past data. For example, the analysis unit can prioritize the analysis of data from a specific period. In this way, by determining the priority of analysis based on the data collection period, the most recent data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collection period into a generating AI, and the generating AI can determine the priority of analysis.

[0041] The analysis unit can adjust the order of analysis by considering the relationships between the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can optimize the order of analysis based on the relationships between the data. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI, and the generating AI can adjust the order of analysis.

[0042] The prediction unit can improve the accuracy of its predictions based on the interrelationships between data. For example, the prediction unit can make predictions considering the interrelationship between temperature and humidity. For example, the prediction unit can make predictions considering the interrelationship between soil nutrient status and crop growth status. For example, the prediction unit can make predictions considering the interrelationship between weather conditions and crop growth status. This improves the accuracy of predictions by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI, and the generating AI can improve the accuracy of the predictions.

[0043] The prediction unit can make predictions based on the attribute information of the data submitter. For example, if the submitter is an experienced farmer, the prediction unit will take their knowledge into consideration when making predictions. For example, if the submitter is inexperienced, the prediction unit can provide a basic prediction. The prediction unit can also make predictions considering the submitter's past data submission history. This allows for more appropriate predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the attribute information of the data submitter into a generating AI, and the generating AI can perform the prediction.

[0044] The prediction unit can make predictions based on the geographical distribution of the data. For example, the prediction unit can make predictions based on a specific area of ​​farmland. For example, the prediction unit can make predictions by comparing data from geographically different areas. For example, the prediction unit can provide optimal predictions based on geographical distribution. This makes it possible to make appropriate predictions for each region by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of the data into a generating AI and have the generating AI make predictions.

[0045] The prediction unit can improve the accuracy of its predictions based on relevant literature during the prediction process. For example, the prediction unit can make predictions by referring to the latest agricultural research literature. For example, the prediction unit can make predictions by referring to literature based on past agricultural data. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant academic papers. Thus, the accuracy of the predictions is improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature into a generating AI, and the generating AI can improve the accuracy of the predictions.

[0046] The monitoring unit can optimize the current monitoring method based on past monitoring data during monitoring. For example, the monitoring unit optimizes the current monitoring method based on past monitoring data. For example, the monitoring unit can adjust the current monitoring method by referring to past abnormal data. For example, the monitoring unit can analyze past monitoring history and propose the optimal monitoring method. This allows the current monitoring method to be optimized by referring to past monitoring data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input past monitoring data into a generating AI, and the generating AI can optimize the current monitoring method.

[0047] The monitoring unit can apply different monitoring methods based on the data category during monitoring. For example, the monitoring unit may apply a specific monitoring method to temperature data. For example, it may apply a different monitoring method to humidity data. For example, it may apply yet another monitoring method to soil data. This improves the accuracy of monitoring by applying the appropriate monitoring method according to the data category. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the data category into a generating AI, and the generating AI can apply an appropriate monitoring method.

[0048] The monitoring unit can analyze changes in monitoring while considering the data collection timing. For example, the monitoring unit can analyze changes in monitoring based on the latest data. For example, the monitoring unit can analyze current monitoring data while referring to past data. For example, the monitoring unit can analyze changes in monitoring based on data from a specific period. This enables more accurate monitoring by analyzing changes in monitoring based on the data collection timing. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the data collection timing into a generating AI, and the generating AI can analyze changes in monitoring.

[0049] The monitoring unit can analyze monitoring data by referring to relevant market data during monitoring. For example, the monitoring unit can analyze monitoring data based on market demand data. For example, the monitoring unit can analyze monitoring data while referring to market supply data. For example, the monitoring unit can analyze monitoring data based on market price data. This improves the accuracy of monitoring by referring to relevant market data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant market data into a generating AI, and the generating AI can analyze the monitoring.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can collect not only environmental data from farmland but also health data from agricultural workers. For example, it can measure the heart rate and body temperature of agricultural workers and predict the risk of overwork and heatstroke. It can also monitor the activity levels of agricultural workers and suggest appropriate rest times. Furthermore, it can measure the stress levels of agricultural workers and suggest relaxation methods if stress levels are high. This strengthens the health management of agricultural workers and improves work efficiency.

[0052] The analysis unit can analyze not only environmental data for farmland but also regional socioeconomic data. For example, it can analyze regional demographic data to forecast demand for agricultural products. It can also analyze regional economic growth data to predict the optimal timing for agricultural investment. Furthermore, it can analyze regional traffic data to optimize the efficiency of transporting harvested crops. This supports strategic decision-making in agricultural management.

[0053] The forecasting unit can make predictions using weather forecast data in addition to environmental data for farmland. For example, based on long-term weather forecasts, the forecasting unit can suggest the optimal planting time for crop growth. It can also predict the timing of irrigation and windbreak measures based on short-term weather forecasts. Furthermore, based on weather forecast data, the forecasting unit can optimize the harvest time. This makes it easier to plan farm work according to weather conditions.

[0054] The monitoring unit can analyze not only environmental data from farmland but also image data of crops. For example, it can use drones to take aerial photographs of crop growth and perform image analysis. Furthermore, based on the image data, the monitoring unit can detect pests and diseases early and propose appropriate countermeasures. In addition, based on the image data, the monitoring unit can predict the optimal harvest time and improve the efficiency of harvesting operations. This strengthens the quality control of crops.

[0055] The data collection unit can collect not only environmental data from farmland but also microbial data from the soil. For example, it can measure the types and quantities of beneficial microorganisms in the soil and assess the soil's health. It can also detect the presence of pathogens in the soil and predict the risk of disease. Furthermore, based on the microbial data from the soil, the unit can suggest optimal methods for using fertilizers and soil conditioners. This strengthens soil health management and promotes crop growth.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects environmental data. This environmental data includes temperature, humidity, and soil pH. The collection unit uses IoT devices such as temperature sensors, humidity sensors, and soil sensors to collect environmental data. The temperature sensor measures the temperature of the farmland in real time, the humidity sensor measures the humidity of the farmland, and the soil sensor measures the pH and nutrient status of the soil. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data and predicts the timing and methods of farm work by comparing it with past data. Using generative AI, the data is analyzed to predict the optimal timing and methods of farm work. Based on past harvest data and weather data, the generative AI predicts the optimal timing for irrigation and the amount of fertilizer. Step 3: The prediction unit predicts the timing and methods of farm work based on the data analyzed by the analysis unit. The prediction unit uses generative AI to predict future harvest yields based on the collected data and uses this as a reference when creating sales plans. The generative AI predicts future harvest yields based on past harvest data and weather data. Step 4: The monitoring unit monitors the growth status of crops based on the information predicted by the prediction unit. The monitoring unit uses generation AI to monitor the growth status of crops in real time and issues an alert if an abnormality occurs.

[0058] (Example of form 2) The agricultural support system according to an embodiment of the present invention is a system that transforms the "intuition and experience" of agriculture into "automation" and "prediction." This agricultural support system aims to improve the efficiency and quality of agriculture by collecting information from various devices using IoT technology and analyzing and predicting that information using generative AI. For example, the agricultural support system collects environmental data of farmland (temperature, humidity, soil nutrient status, etc.) using IoT devices. Next, the collected data is input into the generative AI, which analyzes the data. The generative AI predicts the optimal timing and method of agricultural work by comparing it with past data. For example, the generative AI can suggest the appropriate timing of irrigation and the amount of fertilizer. Furthermore, the generative AI monitors the growth status of crops in real time based on the collected data and issues an alert if an abnormality occurs. This allows agricultural workers to respond quickly and maintain the quality of crops. In addition, the generative AI can predict future harvest yields based on the collected data, which can be used as a reference when creating sales plans. This mechanism realizes automation and prediction in agriculture, making it easier for inexperienced people to enter the field. For example, even those with no prior agricultural experience can efficiently perform farm work by utilizing IoT devices and generative AI, thereby improving agricultural productivity. Furthermore, by performing farm work at the appropriate time based on the predictive information provided by generative AI, the quality of crops can be improved. In this way, by utilizing IoT and generative AI, the "intuition and experience" of agriculture can be transformed into "automation" and "prediction," solving challenges such as an aging workforce and a shortage of successors. As a result, agricultural support systems will realize automation and prediction in agriculture, making it easier for even inexperienced individuals to enter the field.

[0059] The agricultural support system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, and a monitoring unit. The collection unit collects environmental data. Environmental data includes, but is not limited to, temperature, humidity, and soil pH values. The collection unit collects environmental data using IoT devices such as temperature sensors, humidity sensors, and soil sensors. For example, a temperature sensor measures the temperature of farmland in real time and collects data. For example, a humidity sensor measures the humidity of farmland and collects data. For example, a soil sensor measures the pH value and nutrient status of the soil and collects data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data and predicts the timing and method of agricultural work by comparing it with past data. For example, the analysis unit analyzes the data using generative AI and predicts the optimal timing and method of agricultural work. For example, the generative AI predicts the optimal timing of irrigation and the amount of fertilizer based on past harvest data and weather data. The prediction unit predicts the timing and method of agricultural work based on the data analyzed by the analysis unit. The prediction unit, for example, uses a generative AI to predict future harvest yields based on collected data, and uses this as a reference when formulating sales plans. The generative AI predicts future harvest yields based on past harvest data and weather data, for example. The monitoring unit monitors the growth status of crops based on the information predicted by the prediction unit. The monitoring unit, for example, uses a generative AI to monitor the growth status of crops in real time and issues an alert if an abnormality occurs. The generative AI monitors the growth status of crops and issues an alert if an abnormality occurs. As a result, the agricultural support system according to this embodiment achieves automation and prediction in agriculture through the collection, analysis, prediction, and monitoring of environmental data, making it easier for inexperienced people to enter the field.

[0060] The data collection unit can collect environmental data using IoT devices such as temperature sensors, humidity sensors, and soil sensors. For example, the data collection unit can measure the temperature of farmland in real time using a temperature sensor and collect the data. The temperature sensor can measure the temperature of farmland and collect the data. The data collection unit can also measure the humidity of farmland using a humidity sensor and collect the data. The humidity sensor can measure the humidity of farmland and collect the data. The data collection unit can also measure the pH value and nutrient status of soil using a soil sensor and collect the data. The soil sensor can measure the pH value and nutrient status of soil and collect the data. This makes the collection of environmental data more efficient by using IoT devices. 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 data from the temperature sensor, humidity sensor, and soil sensor into a generating AI, and the generating AI can optimize the data collection.

[0061] The analysis unit can analyze the collected data and predict the timing and methods of farm work by comparing it with past data. For example, the analysis unit can analyze the collected data and predict the timing and methods of farm work by comparing it with past data. For example, the analysis unit can use generative AI to analyze the data and predict the optimal timing and methods of farm work. For example, the generative AI predicts the optimal timing of irrigation and the amount of fertilizer based on past harvest data and weather data. The analysis unit can also use generative AI to predict future harvest yields based on the collected data and use this as a reference when creating sales plans. For example, the generative AI predicts future harvest yields based on past harvest data and weather data. This allows for the prediction of the optimal timing and methods of farm work by comparing it with past data. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the collected data into the generative AI, and the generative AI can perform the data analysis.

[0062] The monitoring unit can monitor the growth status of crops and issue alerts if abnormalities occur. For example, the monitoring unit can monitor the growth status of crops in real time and issue alerts if abnormalities occur. For example, the monitoring unit can use a generation AI to monitor the growth status of crops and issue alerts if abnormalities occur. For example, the generation AI can monitor the growth status of crops and issue alerts if abnormalities occur. This allows for a quick response when abnormalities occur through real-time monitoring. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the growth status of crops into a generation AI, which can then detect abnormalities and issue alerts.

[0063] The forecasting unit predicts harvest yields based on collected data, which can be used as a reference when formulating sales plans. For example, the forecasting unit predicts future harvest yields based on collected data, which can be used as a reference when formulating sales plans. For example, the forecasting unit uses a generative AI to predict future harvest yields based on collected data, which can be used as a reference when formulating sales plans. The generative AI predicts future harvest yields based on past harvest data and weather data, for example. This allows for predictions of future harvest yields, which can be used as a reference when formulating sales plans. Some or all of the above-described processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input collected data into a generative AI, which can then use to predict harvest yields.

[0064] The data collection unit can analyze the user's emotions and adjust the timing of environmental data collection based on the analyzed emotions. For example, if the user is stressed, the data collection unit can automatically adjust the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can set the collection timing to be more frequent to collect detailed data. For example, if the user is in a hurry, the data collection unit can optimize the collection timing to quickly collect the necessary data. This reduces the user's burden 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. 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 user's emotion data into a generative AI, which can then estimate the emotions and adjust the collection timing.

[0065] The data collection unit can analyze past collected data and determine the placement of sensors. For example, the data collection unit can determine the optimal placement of temperature sensors based on past data. For example, the data collection unit can analyze past humidity data and optimize the placement of humidity sensors. For example, the data collection unit can determine the optimal placement of soil sensors by referring to past data. This improves the efficiency of data collection by determining the optimal sensor placement based on past 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 past collected data into a generating AI, and the generating AI can determine the optimal sensor placement.

[0066] The data collection unit can adjust the collection frequency of environmental data based on weather conditions and seasons. For example, the collection unit can increase the collection frequency of humidity data during the rainy season. For example, the collection unit can increase the collection frequency of soil moisture data during the dry season. For example, the collection unit can increase the collection frequency of temperature data during winter. By adjusting the collection frequency according to weather conditions and seasons, more accurate data can be collected. 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 collection frequency based on weather conditions and seasons into a generating AI, and the generating AI can adjust the collection frequency.

[0067] The data collection unit can analyze the user's emotions and determine the priority of data to collect based on the analyzed emotions. For example, if the user is stressed, the data collection unit can prioritize collecting important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, important data can be collected preferentially by determining the priority of 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can then estimate the emotions and determine the priority of the data.

[0068] The data collection unit can prioritize the collection of highly relevant data based on geographical location information when collecting environmental data. For example, the data collection unit can prioritize the collection of temperature data in a specific area of ​​farmland. For example, the data collection unit can prioritize the collection of humidity data in a specific area of ​​farmland. For example, the data collection unit can prioritize the collection of soil data in a specific area of ​​farmland. This allows for the priority collection of highly relevant data by considering geographical location information. 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 geographical location information into a generating AI, and the generating AI can prioritize the collection of highly relevant data.

[0069] The data collection unit can analyze social media activity and collect relevant data when collecting environmental data. For example, the data collection unit can analyze agriculture-related posts on social media and collect relevant environmental data. For example, the data collection unit can analyze weather information on social media and collect relevant environmental data. For example, the data collection unit can analyze the growth status of crops on social media and collect relevant environmental data. This allows for the efficient collection of relevant data by analyzing social media activity. 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 social media activity into a generating AI and have the generating AI collect relevant data.

[0070] The analysis unit can analyze the user's emotions and adjust the presentation of the analysis based on the analyzed emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis result can be provided in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user's emotion data into a generative AI, and the generative AI can perform emotion estimation and adjust the presentation of the analysis.

[0071] The analysis unit can adjust the level of detail of the analysis, taking into account the importance of the data. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a standard analysis on general data. For example, the analysis unit can perform a simplified analysis on less important data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. 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 importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms based on the data category during analysis. For example, the analysis unit may apply a specific algorithm to temperature data. For example, it may apply a different algorithm to humidity data. For example, it may apply yet another algorithm to soil data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm 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 the data category into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0073] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 user's emotion data into a generative AI, which can then perform emotion estimation and adjust the length of the analysis.

[0074] The analysis unit can determine the priority of analysis by considering the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can analyze the most recent data while referring to past data. For example, the analysis unit can prioritize the analysis of data from a specific period. In this way, by determining the priority of analysis based on the data collection period, the most recent data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collection period into a generating AI, and the generating AI can determine the priority of analysis.

[0075] The analysis unit can adjust the order of analysis by considering the relationships between the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can optimize the order of analysis based on the relationships between the data. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI, and the generating AI can adjust the order of analysis.

[0076] The prediction unit can analyze the user's emotions and adjust the prediction criteria based on the analyzed emotions. For example, if the user is relaxed, the prediction unit can provide a detailed prediction. For example, if the user is in a hurry, the prediction unit can provide a concise prediction. For example, if the user is excited, the prediction unit can provide a visually stimulating prediction. By adjusting the prediction criteria according to the user's emotions, the prediction unit can provide the user with an appropriate prediction result. 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. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into a generative AI, which can then perform emotion estimation and adjust the prediction criteria.

[0077] The prediction unit can improve the accuracy of its predictions based on the interrelationships between data. For example, the prediction unit can make predictions considering the interrelationship between temperature and humidity. For example, the prediction unit can make predictions considering the interrelationship between soil nutrient status and crop growth status. For example, the prediction unit can make predictions considering the interrelationship between weather conditions and crop growth status. This improves the accuracy of predictions by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI, and the generating AI can improve the accuracy of the predictions.

[0078] The prediction unit can make predictions based on the attribute information of the data submitter. For example, if the submitter is an experienced farmer, the prediction unit will take their knowledge into consideration when making predictions. For example, if the submitter is inexperienced, the prediction unit can provide a basic prediction. The prediction unit can also make predictions considering the submitter's past data submission history. This allows for more appropriate predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the attribute information of the data submitter into a generating AI, and the generating AI can perform the prediction.

[0079] The prediction unit can analyze the user's emotions and adjust the order in which prediction results are displayed based on the analyzed emotions. For example, if the user is nervous, the prediction unit can display important results first. If the user is relaxed, the prediction unit can display detailed results sequentially. If the user is in a hurry, the prediction unit can display concise results first. By adjusting the display order of prediction results according to the user's emotions, the system can provide results 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into a generative AI, which can then use the generative AI to estimate emotions and adjust the display order of prediction results.

[0080] The prediction unit can make predictions based on the geographical distribution of the data. For example, the prediction unit can make predictions based on a specific area of ​​farmland. For example, the prediction unit can make predictions by comparing data from geographically different areas. For example, the prediction unit can provide optimal predictions based on geographical distribution. This makes it possible to make appropriate predictions for each region by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of the data into a generating AI and have the generating AI make predictions.

[0081] The prediction unit can improve the accuracy of its predictions based on relevant literature during the prediction process. For example, the prediction unit can make predictions by referring to the latest agricultural research literature. For example, the prediction unit can make predictions by referring to literature based on past agricultural data. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant academic papers. Thus, the accuracy of the predictions is improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature into a generating AI, and the generating AI can improve the accuracy of the predictions.

[0082] The monitoring unit can analyze the user's emotions and adjust the display method of the monitoring based on the analyzed emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. For example, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the monitoring unit can provide a display method that gets straight to the point. By adjusting the display method of the monitoring 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 a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation and adjust the display method of the monitoring.

[0083] The monitoring unit can optimize the current monitoring method based on past monitoring data during monitoring. For example, the monitoring unit optimizes the current monitoring method based on past monitoring data. For example, the monitoring unit can adjust the current monitoring method by referring to past abnormal data. For example, the monitoring unit can analyze past monitoring history and propose the optimal monitoring method. This allows the current monitoring method to be optimized by referring to past monitoring data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input past monitoring data into a generating AI, and the generating AI can optimize the current monitoring method.

[0084] The monitoring unit can apply different monitoring methods based on the data category during monitoring. For example, the monitoring unit may apply a specific monitoring method to temperature data. For example, it may apply a different monitoring method to humidity data. For example, it may apply yet another monitoring method to soil data. This improves the accuracy of monitoring by applying the appropriate monitoring method according to the data category. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the data category into a generating AI, and the generating AI can apply an appropriate monitoring method.

[0085] The monitoring unit can analyze the user's emotions and adjust the importance of monitoring based on the analyzed emotions. For example, if the user is tense, the monitoring unit can prioritize displaying important monitoring items. For example, if the user is relaxed, the monitoring unit can display detailed monitoring items. For example, if the user is in a hurry, the monitoring unit can display concise monitoring items. This allows for prioritizing the display of important monitoring items by adjusting the importance of monitoring 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. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI, which can then perform emotion estimation and adjust the importance of monitoring.

[0086] The monitoring unit can analyze changes in monitoring while considering the data collection timing. For example, the monitoring unit can analyze changes in monitoring based on the latest data. For example, the monitoring unit can analyze current monitoring data while referring to past data. For example, the monitoring unit can analyze changes in monitoring based on data from a specific period. This enables more accurate monitoring by analyzing changes in monitoring based on the data collection timing. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the data collection timing into a generating AI, and the generating AI can analyze changes in monitoring.

[0087] The monitoring unit can analyze monitoring data by referring to relevant market data during monitoring. For example, the monitoring unit can analyze monitoring data based on market demand data. For example, the monitoring unit can analyze monitoring data while referring to market supply data. For example, the monitoring unit can analyze monitoring data based on market price data. This improves the accuracy of monitoring by referring to relevant market data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant market data into a generating AI, and the generating AI can analyze the monitoring. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and monitoring unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects environmental data using the temperature sensor, humidity sensor, and soil sensor of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and predicts the timing and method of agricultural work based on the analyzed data. The monitoring unit is implemented, for example, by the control unit 46A of the smart device 14, and monitors the growth status of crops in real time and issues an alert if an abnormality occurs. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects environmental data using the temperature sensor, humidity sensor, and soil sensor of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The prediction unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and predicts the timing and method of agricultural work based on the analyzed data. The monitoring unit is implemented, for example, in the control unit 46A of the smart glasses 214, and monitors the growth status of crops in real time and issues an alert if an abnormality occurs. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects environmental data using the temperature sensor, humidity sensor, and soil sensor of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts the timing and method of agricultural work based on the analyzed data. The monitoring unit is implemented in the control unit 46A of the headset terminal 314 and monitors the growth status of crops in real time and issues an alert if an abnormality occurs. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and monitoring unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects environmental data using the temperature sensor, humidity sensor, and soil sensor of the robot 414. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The prediction unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and predicts the timing and method of agricultural work based on the analyzed data. The monitoring unit is implemented, for example, in the control unit 46A of the robot 414, and monitors the growth status of crops in real time and issues an alert if an abnormality occurs.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can collect not only environmental data from farmland but also health data from agricultural workers. For example, it can measure the heart rate and body temperature of agricultural workers and predict the risk of overwork and heatstroke. It can also monitor the activity levels of agricultural workers and suggest appropriate rest times. Furthermore, it can measure the stress levels of agricultural workers and suggest relaxation methods if stress levels are high. This strengthens the health management of agricultural workers and improves work efficiency.

[0090] The analysis unit can analyze not only environmental data for farmland but also regional socioeconomic data. For example, it can analyze regional demographic data to forecast demand for agricultural products. It can also analyze regional economic growth data to predict the optimal timing for agricultural investment. Furthermore, it can analyze regional traffic data to optimize the efficiency of transporting harvested crops. This supports strategic decision-making in agricultural management.

[0091] The forecasting unit can make predictions using weather forecast data in addition to environmental data for farmland. For example, based on long-term weather forecasts, the forecasting unit can suggest the optimal planting time for crop growth. It can also predict the timing of irrigation and windbreak measures based on short-term weather forecasts. Furthermore, based on weather forecast data, the forecasting unit can optimize the harvest time. This makes it easier to plan farm work according to weather conditions.

[0092] The monitoring unit can analyze not only environmental data from farmland but also image data of crops. For example, it can use drones to take aerial photographs of crop growth and perform image analysis. Furthermore, based on the image data, the monitoring unit can detect pests and diseases early and propose appropriate countermeasures. In addition, based on the image data, the monitoring unit can predict the optimal harvest time and improve the efficiency of harvesting operations. This strengthens the quality control of crops.

[0093] The data collection unit can collect not only environmental data from farmland but also microbial data from the soil. For example, it can measure the types and quantities of beneficial microorganisms in the soil and assess the soil's health. It can also detect the presence of pathogens in the soil and predict the risk of disease. Furthermore, based on the microbial data from the soil, the unit can suggest optimal methods for using fertilizers and soil conditioners. This strengthens soil health management and promotes crop growth.

[0094] The analysis unit can analyze the user's emotions and adjust the priority of the analysis based on those emotions. For example, if the user is stressed, it will prioritize displaying important analysis results. If the user is relaxed, the analysis unit can sequentially display detailed analysis results. Furthermore, if the user is in a hurry, it can display concise analysis results first. In this way, by adjusting the priority of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0095] The prediction unit can analyze the user's emotions and adjust the level of detail in the predictions based on the analyzed emotions. For example, if the user is relaxed, it can provide detailed prediction information. If the user is in a hurry, the prediction unit can provide concise prediction information. Furthermore, if the user is excited, it can provide visually stimulating prediction information. In this way, by adjusting the level of detail in the predictions according to the user's emotions, it is possible to provide the user with appropriate prediction information.

[0096] The monitoring unit can analyze the user's emotions and adjust the frequency of monitoring alerts based on the analyzed emotions. For example, if the user is stressed, only important alerts will be issued. If the user is relaxed, the monitoring unit can issue detailed alerts frequently. Furthermore, if the user is in a hurry, it can issue concise alerts. In this way, by adjusting the alert frequency according to the user's emotions, it becomes possible to provide the user with appropriate information.

[0097] The data collection unit can analyze the user's emotions and adjust the type of data collected based on those emotions. For example, if the user is stressed, it will collect only essential data. If the user is relaxed, the unit can collect detailed data. Furthermore, if the user is in a hurry, it can prioritize collecting data that can be retrieved quickly. This allows for efficient data collection by adjusting the type of data according to the user's emotions.

[0098] The monitoring unit can analyze the user's emotions and adjust the display method based on the analyzed emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The collection unit collects environmental data. This environmental data includes temperature, humidity, and soil pH. The collection unit uses IoT devices such as temperature sensors, humidity sensors, and soil sensors to collect environmental data. The temperature sensor measures the temperature of the farmland in real time, the humidity sensor measures the humidity of the farmland, and the soil sensor measures the pH and nutrient status of the soil. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data and predicts the timing and methods of farm work by comparing it with past data. Using generative AI, the data is analyzed to predict the optimal timing and methods of farm work. Based on past harvest data and weather data, the generative AI predicts the optimal timing for irrigation and the amount of fertilizer. Step 3: The prediction unit predicts the timing and methods of farm work based on the data analyzed by the analysis unit. The prediction unit uses generative AI to predict future harvest yields based on the collected data and uses this as a reference when creating sales plans. The generative AI predicts future harvest yields based on past harvest data and weather data. Step 4: The monitoring unit monitors the growth status of crops based on the information predicted by the prediction unit. The monitoring unit uses generation AI to monitor the growth status of crops in real time and issues an alert if an abnormality occurs.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of Symbols]

[0173] 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. A collection unit that collects environmental data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit predicts the timing and method of agricultural work based on the data analyzed by the aforementioned analysis unit, A monitoring unit monitors the growth status of crops based on the information predicted by the prediction unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect environmental data using IoT devices such as temperature sensors, humidity sensors, and soil sensors. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing the collected data and comparing it with past data, we can predict the timing and methods of agricultural work. The system according to feature 1.

4. The aforementioned monitoring unit, It monitors the growth status of crops and issues alerts if any abnormalities occur. The system according to feature 1.

5. The prediction unit, The collected data will be used to predict harvest yields and as a reference when developing sales plans. The system according to feature 1.

6. The aforementioned collection unit is We analyze user emotions and adjust the timing of environmental data collection based on the analyzed user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past collected data to determine sensor placement. The system according to feature 1.

8. The aforementioned collection unit is When collecting environmental data, adjust the collection frequency based on weather conditions and season. The system according to feature 1.

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