system

Abstract

The system according to this embodiment aims to efficiently manage the growth and harvesting times of agricultural crops. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a management unit, and a prediction unit. The data collection unit collects data necessary for crop cultivation. The analysis unit analyzes the data collected by the data collection unit. The management unit manages crop cultivation based on the analysis results obtained by the analysis unit. The prediction unit predicts the harvest time of the crops managed by the management unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the cultivation of agricultural crops and the management of the harvest time depend on the experience of farmers, and there is a problem that efficient management is difficult.

[0005] The system according to the embodiment aims to efficiently manage the growth and harvest time of agricultural crops.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a management unit, and a prediction unit. The collection unit collects data necessary for the cultivation of agricultural crops. The analysis unit analyzes the data collected by the collection unit. The management unit manages the cultivation of agricultural crops based on the analysis result obtained by the analysis unit. The prediction unit predicts the harvest time of the agricultural crops managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the growth and harvesting times of crops. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 crop cultivation management system according to an embodiment of the present invention is a system that aims to stabilize market prices by managing the cultivation and harvesting of crops. This crop cultivation management system manages the "shape," "size," "taste (sweetness, astringency)," and "harvest time" of crops, which are expected to be harvested, by utilizing "farmer's experience data," "past weather forecasts," "recent weather forecast data," "soil preparation," "moisture content," "sunshine hours," and "sunlight amount data." This optimizes crop cultivation and ensures quality at harvest time. For example, it collects data necessary for crop cultivation. Specifically, it collects data on farmers' experience, past weather forecasts, recent weather forecast data, soil preparation, moisture content, sunshine hours, and sunlight amount data. This data is analyzed by AI to identify the optimal conditions for crop cultivation. Next, based on the data analyzed by the AI, it manages crop cultivation. For example, it optimizes crop cultivation by managing soil preparation, temperature control, and sunlight amount. This ensures crop quality and stabilizes market prices at harvest time. Furthermore, to ensure the quality of crops at harvest time, AI predicts the harvest time and proposes the optimal harvest time. This helps maintain crop quality and stabilize market prices. This system allows for efficient management of crop cultivation and harvesting, enabling a stable supply of crops. In addition, cost reductions can be achieved through the effective use of fuel costs. For example, when using greenhouse equipment, crops can be managed through complete automation. In this way, an AI-powered crop cultivation management system can efficiently manage crop cultivation and harvesting, thereby stabilizing market prices.

[0029] The crop cultivation management system according to this embodiment comprises a data collection unit, an analysis unit, a management unit, and a prediction unit. The data collection unit collects data necessary for crop cultivation. For example, the data collection unit collects data on farmers' experience, past weather forecasts, recent weather forecast data, soil preparation, moisture content, sunshine duration, and sunlight amount data. The data collection unit can collect data in real time, for example, using sensors. The data collection unit can also manually input data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the data and identify the optimal conditions for crop cultivation. For example, the analysis unit can use deep learning or neural networks to analyze the data. The management unit manages crop cultivation based on the analysis results obtained by the analysis unit. For example, the management unit optimizes crop cultivation by managing soil preparation, temperature control, and sunlight amount. For example, the management unit can control the irrigation system to maintain an appropriate moisture level. The management unit can also manage the application of fertilizers to optimize the nutritional status of crops. The prediction unit predicts the harvest time of crops managed by the management unit. The prediction unit can predict the harvest time using, for example, a growth model. The prediction unit can also predict the harvest time by referring to, for example, past data. As a result, the crop cultivation management system according to the embodiment can efficiently manage crop cultivation and harvesting and stabilize market prices.

[0030] The data collection unit collects data necessary for crop cultivation. For example, it collects data on farmers' experience, past weather forecasts, recent weather forecasts, soil preparation, moisture content, sunshine duration, and sunlight intensity. Specifically, farmer experience data is compiled by digitizing past cultivation history and success / failure cases and storing them in a database. Past weather forecasts and recent weather forecasts are automatically retrieved from a weather database and updated in real time. Regarding soil preparation, soil sensors are used to measure soil pH, nutrient concentration, temperature, etc., and collect data. Moisture content is measured in real time using soil moisture sensors and used as basic data for appropriate irrigation. Sunshine duration and sunlight intensity data are measured using light sensors and used to optimize the photosynthetic efficiency of crops. The data collection unit can collect data in real time using these sensors. Furthermore, the data collection unit can also manually input data. For example, farmers can manually input observed crop growth status and pest / disease occurrences and add them to the database. This allows the data collection unit to gather a wide range of data from diverse data sources and comprehensively understand the information necessary for crop cultivation. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and management units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and identify the optimal conditions for crop growth. Specifically, it can analyze data using deep learning or neural networks. Based on the collected data, the AI ​​learns the correlation between crop growth patterns and environmental conditions, and derives the optimal growing conditions. For example, using deep learning, it analyzes data such as soil pH, nutrient concentration, temperature, moisture content, and sunshine duration to identify the optimal conditions for crop growth. It also uses neural networks to analyze past weather data and weather forecast data to formulate crop growth plans in response to future weather fluctuations. Furthermore, the AI ​​can analyze farmers' experience data and learn from past successes and failures to provide more accurate growing conditions. Based on these analysis results, the analysis unit identifies the optimal conditions for crop growth and provides them to the management unit. This allows the analysis unit to quickly and accurately analyze the collected data and provide the optimal conditions for crop growth. Additionally, the analysis unit can utilize past data and statistical information to conduct long-term growth planning and trend analysis. For example, based on past cultivation data, the system can predict crop growth patterns in specific seasons and regions and formulate future cultivation plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term cultivation management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The management department manages crop growth based on the analysis results obtained by the analysis department. For example, the management department optimizes crop growth by managing soil preparation, temperature control, and sunlight levels. Specifically, in soil preparation, it selects appropriate fertilizers and amendments based on the analysis results and optimizes the soil's pH value and nutrient concentration. In temperature control, it monitors the temperature in real time using temperature sensors and provides heating or cooling as needed. In sunlight level control, it measures the duration and amount of sunlight using light sensors and provides shading or supplemental lighting as needed. For example, the management department can control the irrigation system to maintain appropriate moisture levels. The irrigation system automatically adjusts the moisture level based on data from soil moisture sensors to prevent excessive or insufficient irrigation. Furthermore, the management department can manage fertilizer application to optimize the nutritional status of crops. Fertilizer application is determined based on the analysis results, determining the appropriate timing and amount, and is performed automatically. This allows the management department to optimize crop growth and improve yield and quality. In addition, the management department can monitor the growth status of crops in real time and modify the cultivation plan as needed. For example, in the event of an abnormal situation such as a sudden change in weather or an outbreak of pests or diseases, the management department can respond quickly and revise the cultivation plan. Furthermore, the management department can maintain close communication with farmers and implement cultivation management that reflects on-site conditions and requests. This allows the management department to efficiently and effectively manage crop cultivation and improve the overall performance of the system.

[0033] The forecasting unit predicts the harvest time of crops managed by the management unit. For example, the forecasting unit can predict harvest time using a growth model. Specifically, a growth model is an algorithm that predicts harvest time based on crop growth patterns and environmental conditions. The growth model can predict harvest time by referring to historical data. For example, it can learn the growth patterns of a specific crop based on past cultivation and weather data and predict future growth. Furthermore, the growth model can continuously modify its prediction results based on real-time updated data to respond to the latest situations. For example, it can recalculate the harvest time in response to sudden changes in weather or environmental conditions, providing the optimal harvest timing. This allows the forecasting unit to predict crop harvest times with high accuracy and formulate appropriate harvest plans. In addition, the forecasting unit can predict not only harvest time but also yield and quality. For example, it can use the growth model to predict yield and quality under specific environmental conditions and reflect this in harvest plans and market strategies. The forecasting unit can also predict market prices at harvest time based on historical market data. This allows the forecasting unit to provide information to stabilize crop harvests and market prices, contributing to increased profits for farmers. Furthermore, the forecasting unit also contributes to planning post-harvest storage and distribution. For example, based on forecasts of harvest time and yield, it can plan appropriate storage methods and distribution routes, enabling efficient supply to the market while maintaining crop quality. In this way, the forecasting unit can optimize the entire process from crop harvesting to market supply, improving the overall performance of the system.

[0034] The data collection unit can collect data on farmers' experience, past weather forecasts, recent weather forecasts, soil preparation, moisture content, sunshine duration, and sunlight intensity. For example, the unit can collect data on farmers' experience, such as records of past harvest yields and cultivation methods. It can also collect data on past weather forecasts, such as using historical weather databases. Furthermore, it can collect recent weather forecasts, such as obtaining data from weather forecasting services. The unit can also collect data related to soil preparation, such as the use of soil conditioners and cultivation methods. Additionally, it can collect moisture content data, such as using soil moisture sensors. It can also collect sunshine duration data, such as using sunshine meters. Finally, it can collect sunlight intensity data, such as using light sensors. This allows the data collection unit to comprehensively collect the data necessary for crop cultivation. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input farmer experience data into a generating AI and have the generating AI perform data collection.

[0035] The analysis unit can analyze the data collected by the collection unit using AI to identify conditions suitable for crop cultivation. For example, the analysis unit can analyze the data collected by the collection unit using deep learning. For example, it can analyze the data using a deep learning model to identify the optimal conditions for crop cultivation. The analysis unit can also analyze the data using a neural network. For example, it can analyze the data using a neural network model to identify conditions suitable for crop cultivation. Furthermore, the analysis unit can analyze the data using statistical analysis. For example, it can analyze the data using statistical analysis methods to identify the optimal conditions for crop cultivation. In this way, the analysis unit can identify the optimal conditions for crop cultivation by using AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collected by the collection unit into a generating AI and have the generating AI perform the data analysis.

[0036] The management unit can optimize crop cultivation by performing soil preparation, temperature control, and sunlight control based on the analysis results obtained by the analysis unit. For example, the management unit can perform soil preparation based on the analysis results obtained by the analysis unit. For example, it can optimize the nutrient status of the soil by using soil conditioners. The management unit can also perform temperature control. For example, it can control the temperature using a greenhouse to maintain the optimal temperature for crop cultivation. Furthermore, the management unit can also manage the amount of sunlight. For example, it can adjust the amount of sunlight using a shading net to ensure the optimal amount of sunlight for crop cultivation. In this way, the management unit can optimize crop cultivation. Some or all of the above processes in the management unit may be performed using AI, for example, or without using AI. For example, the management unit can input the analysis results obtained by the analysis unit into a generating AI and have the generating AI perform soil preparation, temperature control, and sunlight control.

[0037] The prediction unit can predict the harvest time of crops managed by the management unit and propose an appropriate harvest time. For example, the prediction unit can predict the harvest time of crops managed by the management unit using a growth model. For example, it can simulate the growth of crops using a growth model and predict the optimal harvest time. The prediction unit can also predict the harvest time by referring to past data. For example, it can predict the harvest time based on past harvest data and propose an optimal harvest time. Furthermore, the prediction unit can also predict the harvest time by referring to weather data. For example, it can predict the harvest time based on the latest weather forecast data and propose an appropriate harvest time. In this way, the prediction unit can optimally predict and propose the harvest time of crops. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input data on crops managed by the management unit into a generating AI and have the generating AI perform the harvest time prediction.

[0038] The management department can manage crops through automation when using greenhouse equipment. For example, the management department can manage crop growth using greenhouse equipment. For example, it can control the growing environment of crops using glass greenhouses or plastic greenhouses. The management department can also manage the moisture content of crops using an automated irrigation system. For example, it can maintain the appropriate moisture content using an automatic irrigation system. Furthermore, the management department can manage the temperature of crops using an automated temperature control system. For example, it can maintain the optimal temperature for crop growth using an automatic temperature control system. In this way, the management department can fully automate crop management by using greenhouse equipment. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input control data for greenhouse equipment into a generating AI and have the generating AI perform automated management.

[0039] The data collection unit can analyze past harvest data from farmers and select an appropriate data collection method. For example, the data collection unit can identify data collection methods used during periods of high yields from past harvest data and apply similar methods. For example, the data collection unit can analyze past harvest data and improve data collection methods used during periods of low yields. For example, the data collection unit can identify factors influencing yield fluctuations based on past harvest data and select a data collection method accordingly. In this way, the data collection unit can select the optimal data collection method by analyzing past harvest data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past harvest data into a generating AI and have the generating AI select a data collection method.

[0040] The data collection unit can adjust the content of the data it collects based on the growth stage of the crop. For example, in the early growth stage, the collection unit can focus on collecting data related to soil nutrient status and moisture content. In the mid-growth stage, for example, the collection unit can focus on collecting data related to sunshine hours and temperature. In the late growth stage, for example, the collection unit can focus on collecting data necessary for predicting the harvest time. This allows the collection unit to efficiently collect data by adjusting the type of data it collects according to the growth stage of the crop. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input crop growth stage data into a generating AI and have the generating AI adjust the content of the data to be collected.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the farmland during data collection. For example, the data collection unit can prioritize the collection of temperature and precipitation data based on the elevation and topography of the farmland. For example, the data collection unit can prioritize the collection of surrounding weather data based on the location information of the farmland. For example, the data collection unit can prioritize the collection of soil nutrient status and moisture content data according to the geographical conditions of the farmland. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the farmland. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the farmland into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze farmers' social media activities and collect relevant data during data collection. For example, the data collection unit can collect data on harvest time and yield based on information shared by farmers on social media. For example, the data collection unit can collect data on crop growth status from farmers' social media activities. For example, the data collection unit can collect weather data based on weather information shared by farmers on social media. In this way, the data collection unit can collect relevant data by analyzing farmers' social media activities. 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 farmers' social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to provide highly accurate results. For example, the analysis unit can perform a simplified analysis on less important data to provide results quickly. The analysis unit can, for example, optimally allocate analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of 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 importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, the analysis unit can apply a weather forecasting algorithm to meteorological data. For example, the analysis unit can apply a soil analysis algorithm to soil data. For example, the analysis unit can apply a growth forecasting algorithm to crop growth data. In this way, the analysis unit can provide highly accurate analysis results by applying an appropriate analysis algorithm according to the type of data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type of data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. For example, the analysis unit can analyze long-term trends based on historical data. For example, the analysis unit can optimally allocate analysis resources according to the data collection timing. As a result, the analysis unit can provide real-time information by determining the priority of analysis based on the data collection timing. 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 data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. For example, the analysis unit can postpone the analysis of less relevant data to perform analysis efficiently. For example, the analysis unit can optimally allocate analysis resources according to the relevance of the data. As a result, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The management department can analyze the growth stage of crops and select appropriate management methods during management. For example, during the early growth stage, the management department can focus on managing soil nutrient levels and moisture content. During the mid-growth stage, the management department can focus on managing sunlight hours and temperature. During the late growth stage, the management department can manage based on predictions of the harvest time. This enables efficient management by allowing the management department to select the optimal management method according to the growth stage of the crops. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input crop growth stage data into a generating AI and have the generating AI select appropriate management methods.

[0048] The management unit can customize management methods based on the type of crop during management. For example, the management unit can perform optimal soil preparation and moisture management for different crops. For example, the management unit can adjust the management of sunlight hours and temperature according to the type of crop. For example, the management unit can predict the harvest time based on the type of crop and optimize management. In this way, the management unit can perform efficient management by customizing management methods according to the type of crop. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input crop type data into a generating AI and have the generating AI perform the customization of management methods.

[0049] The management unit can select an appropriate management method based on the geographical location information of the farmland during management. For example, the management unit can manage temperature and precipitation based on the elevation and topography of the farmland. For example, the management unit can perform management in accordance with the surrounding weather conditions based on the location information of the farmland. For example, the management unit can manage the nutrient status and moisture content of the soil according to the geographical conditions of the farmland. In this way, the management unit can select the optimal management method by considering the geographical location information of the farmland. Some or all of the above processes in the management unit may be performed using AI, for example, or without using AI. For example, the management unit can input the geographical location information of the farmland into a generating AI and have the generating AI perform the selection of an appropriate management method.

[0050] The management department can analyze farmers' social media activities during management and propose management measures. For example, the management department can propose management measures regarding harvest time and yield based on information shared by farmers on social media. For example, the management department can propose management measures according to the growth status of crops based on farmers' social media activities. For example, the management department can propose management measures according to the weather based on weather information shared by farmers on social media. In this way, the management department can propose appropriate management measures by analyzing farmers' social media activities. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input farmers' social media data into a generating AI and have the generating AI execute proposals for management measures.

[0051] The prediction unit can predict the appropriate harvest time by referring to past harvest data during the prediction process. For example, the prediction unit can identify periods with high yields based on past harvest data and predict the harvest time under similar conditions. For example, the prediction unit can analyze past harvest data to identify factors that caused periods with low yields and predict the harvest time to avoid those factors. For example, the prediction unit can identify factors that cause fluctuations in harvest time based on past harvest data and make predictions accordingly. In this way, the prediction unit can predict the optimal harvest time by referring to past harvest 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 past harvest data into a generating AI and have the generating AI perform the harvest time prediction.

[0052] The prediction unit can adjust the harvest time based on the growth stage of the crop during prediction. For example, the prediction unit does not predict the harvest time in the early growth stage, but starts predicting from the mid-growth stage onward. For example, the prediction unit can predict the harvest time in the mid-growth stage and improve the accuracy of the prediction towards the late growth stage. For example, the prediction unit can predict the harvest time with the highest accuracy in the late growth stage and propose the optimal harvest time. In this way, the prediction unit can propose the optimal harvest time by customizing the harvest time according to the growth stage of the crop. 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 crop growth stage data into a generating AI and have the generating AI perform the adjustment of the harvest time.

[0053] The prediction unit can predict the appropriate harvest time based on the geographical location information of the farmland during the prediction process. For example, the prediction unit can predict the harvest time considering the effects of temperature and precipitation based on the elevation and topography of the farmland. For example, the prediction unit can predict the harvest time according to the surrounding weather conditions based on the location information of the farmland. For example, the prediction unit can optimize the harvest time prediction according to the geographical conditions of the farmland. In this way, the prediction unit can predict the optimal harvest time by considering the geographical location information of the farmland. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the geographical location information of the farmland into a generating AI and have the generating AI perform the harvest time prediction.

[0054] The prediction unit can analyze farmers' social media activity during prediction to suggest harvest times. For example, the prediction unit can predict harvest times based on information shared by farmers on social media. For example, the prediction unit can suggest harvest times that correspond to the growth status of crops based on farmers' social media activity. For example, the prediction unit can suggest harvest times that correspond to the weather based on weather information shared by farmers on social media. In this way, the prediction unit can suggest appropriate harvest times by analyzing farmers' social media activity. 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 farmers' social media data into a generating AI and have the generating AI perform the harvest time suggestion.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can customize its data collection methods based on the type of crop when collecting data necessary for crop cultivation. For example, in fruit cultivation, it can focus on collecting data related to the sugar content and acidity of the fruit. In vegetable cultivation, it can focus on collecting data related to leaf color and growth rate. Furthermore, in grain cultivation, it can focus on collecting data related to ear length and grain size. This allows the data collection unit to efficiently collect data by selecting the optimal data collection method according to the type of crop. 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 crop type data into a generating AI and have the generating AI perform the customization of the data collection method.

[0057] The analysis unit can adjust the analysis method based on the data source when analyzing data collected by the collection unit. For example, for farmer experience data, the analysis can be performed by referring to past success and failure cases. For weather data, the analysis can be performed using a weather forecasting model. Furthermore, for soil data, the analysis can be performed using a soil analysis model. In this way, the analysis unit can provide highly accurate analysis results by selecting the optimal analysis method according to the data source. 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 data source information into a generating AI and have the generating AI perform the adjustment of the analysis method.

[0058] The management unit can adjust its management methods according to the growth stage of crops when managing crop cultivation, based on the analysis results obtained by the analysis unit. For example, in the early growth stage, it can focus on managing soil nutrient levels and moisture content. In the middle growth stage, it can focus on managing sunlight hours and temperature. Furthermore, in the late growth stage, it can manage crops based on predictions of the harvest time. This allows the management unit to efficiently manage crops by selecting the optimal management method according to their growth stage. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input crop growth stage data into a generating AI and have the generating AI perform adjustments to the management methods.

[0059] The prediction unit can customize its prediction method based on the type of crop when predicting the harvest time of crops managed by the management unit. For example, when predicting the harvest time of fruit, it can consider changes in the sugar content and acidity of the fruit. When predicting the harvest time of vegetables, it can consider changes in leaf color and growth rate. Furthermore, when predicting the harvest time of grains, it can consider changes in ear length and grain size. As a result, the prediction unit can select the optimal prediction method according to the type of crop, enabling highly accurate harvest time predictions. 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 crop type data into a generating AI and have the generating AI perform the customization of the prediction method.

[0060] The data collection unit can adjust the content of the data it collects based on the growth stage of the crop. For example, in the early growth stage, it can focus on collecting data related to soil nutrient status and moisture content. In the middle growth stage, it can focus on collecting data related to sunshine hours and temperature. Furthermore, in the late growth stage, it can focus on collecting data necessary for predicting the harvest time. This allows the data collection unit to efficiently collect data by adjusting the type of data collected according to the growth stage of the crop. 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 crop growth stage data into a generating AI and have the generating AI adjust the content of the data to be collected.

[0061] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, weather data can be analyzed using a weather forecasting algorithm. Soil data can be analyzed using a soil analysis algorithm. Furthermore, crop growth data can be analyzed using a growth forecasting algorithm. In this way, the analysis unit can provide highly accurate analysis results by applying the appropriate analysis algorithm according to the type of data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type of data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects data necessary for crop cultivation. For example, the data collection unit collects data on farmers' experience, past weather forecasts, recent weather forecasts, soil preparation, moisture content, sunshine duration, and sunlight intensity. The data collection unit can collect data in real time using sensors, for example. The data collection unit can also manually input data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze the data and identify the optimal conditions for crop cultivation. The analysis unit can, for example, use deep learning or neural networks to analyze the data. Step 3: The management department manages crop growth based on the analysis results obtained by the analysis department. For example, the management department optimizes crop growth by managing soil preparation, temperature control, and sunlight levels. For example, the management department can control the irrigation system to maintain appropriate moisture levels. The management department can also manage fertilizer application to optimize the nutritional status of crops. Step 4: The prediction unit predicts the harvest time of crops managed by the management unit. The prediction unit can predict the harvest time using, for example, a growth model. The prediction unit can predict the harvest time by, for example, referring to past data.

[0064] (Example of form 2) The crop cultivation management system according to an embodiment of the present invention is a system that aims to stabilize market prices by managing the cultivation and harvesting of crops. This crop cultivation management system manages the "shape," "size," "taste (sweetness, astringency)," and "harvest time" of crops, which are expected to be harvested, by utilizing "farmer's experience data," "past weather forecasts," "recent weather forecast data," "soil preparation," "moisture content," "sunshine hours," and "sunlight amount data." This optimizes crop cultivation and ensures quality at harvest time. For example, it collects data necessary for crop cultivation. Specifically, it collects data on farmers' experience, past weather forecasts, recent weather forecast data, soil preparation, moisture content, sunshine hours, and sunlight amount data. This data is analyzed by AI to identify the optimal conditions for crop cultivation. Next, based on the data analyzed by the AI, it manages crop cultivation. For example, it optimizes crop cultivation by managing soil preparation, temperature control, and sunlight amount. This ensures crop quality and stabilizes market prices at harvest time. Furthermore, to ensure the quality of crops at harvest time, AI predicts the harvest time and proposes the optimal harvest time. This helps maintain crop quality and stabilize market prices. This system allows for efficient management of crop cultivation and harvesting, enabling a stable supply of crops. In addition, cost reductions can be achieved through the effective use of fuel costs. For example, when using greenhouse equipment, crops can be managed through complete automation. In this way, an AI-powered crop cultivation management system can efficiently manage crop cultivation and harvesting, thereby stabilizing market prices.

[0065] The crop cultivation management system according to this embodiment comprises a data collection unit, an analysis unit, a management unit, and a prediction unit. The data collection unit collects data necessary for crop cultivation. For example, the data collection unit collects data on farmers' experience, past weather forecasts, recent weather forecast data, soil preparation, moisture content, sunshine duration, and sunlight amount data. The data collection unit can collect data in real time, for example, using sensors. The data collection unit can also manually input data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the data and identify the optimal conditions for crop cultivation. For example, the analysis unit can use deep learning or neural networks to analyze the data. The management unit manages crop cultivation based on the analysis results obtained by the analysis unit. For example, the management unit optimizes crop cultivation by managing soil preparation, temperature control, and sunlight amount. For example, the management unit can control the irrigation system to maintain an appropriate moisture level. The management unit can also manage the application of fertilizers to optimize the nutritional status of crops. The prediction unit predicts the harvest time of crops managed by the management unit. The prediction unit can predict the harvest time using, for example, a growth model. The prediction unit can also predict the harvest time by referring to, for example, past data. As a result, the crop cultivation management system according to the embodiment can efficiently manage crop cultivation and harvesting and stabilize market prices.

[0066] The data collection unit collects data necessary for crop cultivation. For example, it collects data on farmers' experience, past weather forecasts, recent weather forecasts, soil preparation, moisture content, sunshine duration, and sunlight intensity. Specifically, farmer experience data is compiled by digitizing past cultivation history and success / failure cases and storing them in a database. Past weather forecasts and recent weather forecasts are automatically retrieved from a weather database and updated in real time. Regarding soil preparation, soil sensors are used to measure soil pH, nutrient concentration, temperature, etc., and collect data. Moisture content is measured in real time using soil moisture sensors and used as basic data for appropriate irrigation. Sunshine duration and sunlight intensity data are measured using light sensors and used to optimize the photosynthetic efficiency of crops. The data collection unit can collect data in real time using these sensors. Furthermore, the data collection unit can also manually input data. For example, farmers can manually input observed crop growth status and pest / disease occurrences and add them to the database. This allows the data collection unit to gather a wide range of data from diverse data sources and comprehensively understand the information necessary for crop cultivation. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and management units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0067] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and identify the optimal conditions for crop growth. Specifically, it can analyze data using deep learning or neural networks. Based on the collected data, the AI ​​learns the correlation between crop growth patterns and environmental conditions, and derives the optimal growing conditions. For example, using deep learning, it analyzes data such as soil pH, nutrient concentration, temperature, moisture content, and sunshine duration to identify the optimal conditions for crop growth. It also uses neural networks to analyze past weather data and weather forecast data to formulate crop growth plans in response to future weather fluctuations. Furthermore, the AI ​​can analyze farmers' experience data and learn from past successes and failures to provide more accurate growing conditions. Based on these analysis results, the analysis unit identifies the optimal conditions for crop growth and provides them to the management unit. This allows the analysis unit to quickly and accurately analyze the collected data and provide the optimal conditions for crop growth. Additionally, the analysis unit can utilize past data and statistical information to conduct long-term growth planning and trend analysis. For example, based on past cultivation data, the system can predict crop growth patterns in specific seasons and regions and formulate future cultivation plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term cultivation management and anomaly detection, improving the overall reliability and safety of the system.

[0068] The management department manages crop growth based on the analysis results obtained by the analysis department. For example, the management department optimizes crop growth by managing soil preparation, temperature control, and sunlight levels. Specifically, in soil preparation, it selects appropriate fertilizers and amendments based on the analysis results and optimizes the soil's pH value and nutrient concentration. In temperature control, it monitors the temperature in real time using temperature sensors and provides heating or cooling as needed. In sunlight level control, it measures the duration and amount of sunlight using light sensors and provides shading or supplemental lighting as needed. For example, the management department can control the irrigation system to maintain appropriate moisture levels. The irrigation system automatically adjusts the moisture level based on data from soil moisture sensors to prevent excessive or insufficient irrigation. Furthermore, the management department can manage fertilizer application to optimize the nutritional status of crops. Fertilizer application is determined based on the analysis results, determining the appropriate timing and amount, and is performed automatically. This allows the management department to optimize crop growth and improve yield and quality. In addition, the management department can monitor the growth status of crops in real time and modify the cultivation plan as needed. For example, in the event of an abnormal situation such as a sudden change in weather or an outbreak of pests or diseases, the management department can respond quickly and revise the cultivation plan. Furthermore, the management department can maintain close communication with farmers and implement cultivation management that reflects on-site conditions and requests. This allows the management department to efficiently and effectively manage crop cultivation and improve the overall performance of the system.

[0069] The forecasting unit predicts the harvest time of crops managed by the management unit. For example, the forecasting unit can predict harvest time using a growth model. Specifically, a growth model is an algorithm that predicts harvest time based on crop growth patterns and environmental conditions. The growth model can predict harvest time by referring to historical data. For example, it can learn the growth patterns of a specific crop based on past cultivation and weather data and predict future growth. Furthermore, the growth model can continuously modify its prediction results based on real-time updated data to respond to the latest situations. For example, it can recalculate the harvest time in response to sudden changes in weather or environmental conditions, providing the optimal harvest timing. This allows the forecasting unit to predict crop harvest times with high accuracy and formulate appropriate harvest plans. In addition, the forecasting unit can predict not only harvest time but also yield and quality. For example, it can use the growth model to predict yield and quality under specific environmental conditions and reflect this in harvest plans and market strategies. The forecasting unit can also predict market prices at harvest time based on historical market data. This allows the forecasting unit to provide information to stabilize crop harvests and market prices, contributing to increased profits for farmers. Furthermore, the forecasting unit also contributes to planning post-harvest storage and distribution. For example, based on forecasts of harvest time and yield, it can plan appropriate storage methods and distribution routes, enabling efficient supply to the market while maintaining crop quality. In this way, the forecasting unit can optimize the entire process from crop harvesting to market supply, improving the overall performance of the system.

[0070] The data collection unit can collect data on farmers' experience, past weather forecasts, recent weather forecasts, soil preparation, moisture content, sunshine duration, and sunlight intensity. For example, the unit can collect data on farmers' experience, such as records of past harvest yields and cultivation methods. It can also collect data on past weather forecasts, such as using historical weather databases. Furthermore, it can collect recent weather forecasts, such as obtaining data from weather forecasting services. The unit can also collect data related to soil preparation, such as the use of soil conditioners and cultivation methods. Additionally, it can collect moisture content data, such as using soil moisture sensors. It can also collect sunshine duration data, such as using sunshine meters. Finally, it can collect sunlight intensity data, such as using light sensors. This allows the data collection unit to comprehensively collect the data necessary for crop cultivation. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input farmer experience data into a generating AI and have the generating AI perform data collection.

[0071] The analysis unit can analyze the data collected by the collection unit using AI to identify conditions suitable for crop cultivation. For example, the analysis unit can analyze the data collected by the collection unit using deep learning. For example, it can analyze the data using a deep learning model to identify the optimal conditions for crop cultivation. The analysis unit can also analyze the data using a neural network. For example, it can analyze the data using a neural network model to identify conditions suitable for crop cultivation. Furthermore, the analysis unit can analyze the data using statistical analysis. For example, it can analyze the data using statistical analysis methods to identify the optimal conditions for crop cultivation. In this way, the analysis unit can identify the optimal conditions for crop cultivation by using AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collected by the collection unit into a generating AI and have the generating AI perform the data analysis.

[0072] The management unit can optimize crop cultivation by performing soil preparation, temperature control, and sunlight control based on the analysis results obtained by the analysis unit. For example, the management unit can perform soil preparation based on the analysis results obtained by the analysis unit. For example, it can optimize the nutrient status of the soil by using soil conditioners. The management unit can also perform temperature control. For example, it can control the temperature using a greenhouse to maintain the optimal temperature for crop cultivation. Furthermore, the management unit can also manage the amount of sunlight. For example, it can adjust the amount of sunlight using a shading net to ensure the optimal amount of sunlight for crop cultivation. In this way, the management unit can optimize crop cultivation. Some or all of the above processes in the management unit may be performed using AI, for example, or without using AI. For example, the management unit can input the analysis results obtained by the analysis unit into a generating AI and have the generating AI perform soil preparation, temperature control, and sunlight control.

[0073] The prediction unit can predict the harvest time of crops managed by the management unit and propose an appropriate harvest time. For example, the prediction unit can predict the harvest time of crops managed by the management unit using a growth model. For example, it can simulate the growth of crops using a growth model and predict the optimal harvest time. The prediction unit can also predict the harvest time by referring to past data. For example, it can predict the harvest time based on past harvest data and propose an optimal harvest time. Furthermore, the prediction unit can also predict the harvest time by referring to weather data. For example, it can predict the harvest time based on the latest weather forecast data and propose an appropriate harvest time. In this way, the prediction unit can optimally predict and propose the harvest time of crops. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input data on crops managed by the management unit into a generating AI and have the generating AI perform the harvest time prediction.

[0074] The management department can manage crops through automation when using greenhouse equipment. For example, the management department can manage crop growth using greenhouse equipment. For example, it can control the growing environment of crops using glass greenhouses or plastic greenhouses. The management department can also manage the moisture content of crops using an automated irrigation system. For example, it can maintain the appropriate moisture content using an automatic irrigation system. Furthermore, the management department can manage the temperature of crops using an automated temperature control system. For example, it can maintain the optimal temperature for crop growth using an automatic temperature control system. In this way, the management department can fully automate crop management by using greenhouse equipment. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input control data for greenhouse equipment into a generating AI and have the generating AI perform automated management.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can collect more detailed data to obtain more information. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data and process it quickly. In this way, the data collection unit can reduce the user's burden by adjusting the timing of data collection 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 user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0076] The data collection unit can analyze past harvest data from farmers and select an appropriate data collection method. For example, the data collection unit can identify data collection methods used during periods of high yields from past harvest data and apply similar methods. For example, the data collection unit can analyze past harvest data and improve data collection methods used during periods of low yields. For example, the data collection unit can identify factors influencing yield fluctuations based on past harvest data and select a data collection method accordingly. In this way, the data collection unit can select the optimal data collection method by analyzing past harvest data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past harvest data into a generating AI and have the generating AI select a data collection method.

[0077] The data collection unit can adjust the content of the data it collects based on the growth stage of the crop. For example, in the early growth stage, the collection unit can focus on collecting data related to soil nutrient status and moisture content. In the mid-growth stage, for example, the collection unit can focus on collecting data related to sunshine hours and temperature. In the late growth stage, for example, the collection unit can focus on collecting data necessary for predicting the harvest time. This allows the collection unit to efficiently collect data by adjusting the type of data it collects according to the growth stage of the crop. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input crop growth stage data into a generating AI and have the generating AI adjust the content of the data to be collected.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data to reduce the user's burden. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data to obtain more information. 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, the data collection unit can reduce the user's burden by determining the priority of data to collect 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 and have the generative AI determine the priority of data to collect.

[0079] The data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the farmland during data collection. For example, the data collection unit can prioritize the collection of temperature and precipitation data based on the elevation and topography of the farmland. For example, the data collection unit can prioritize the collection of surrounding weather data based on the location information of the farmland. For example, the data collection unit can prioritize the collection of soil nutrient status and moisture content data according to the geographical conditions of the farmland. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the farmland. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the farmland into a generating AI and have the generating AI perform the collection of highly relevant data.

[0080] The data collection unit can analyze farmers' social media activities and collect relevant data during data collection. For example, the data collection unit can collect data on harvest time and yield based on information shared by farmers on social media. For example, the data collection unit can collect data on crop growth status from farmers' social media activities. For example, the data collection unit can collect weather data based on weather information shared by farmers on social media. In this way, the data collection unit can collect relevant data by analyzing farmers' social media activities. 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 farmers' social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. 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 user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to provide highly accurate results. For example, the analysis unit can perform a simplified analysis on less important data to provide results quickly. The analysis unit can, for example, optimally allocate analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of 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 importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, the analysis unit can apply a weather forecasting algorithm to meteorological data. For example, the analysis unit can apply a soil analysis algorithm to soil data. For example, the analysis unit can apply a growth forecasting algorithm to crop growth data. In this way, the analysis unit can provide highly accurate analysis results by applying an appropriate analysis algorithm according to the type of data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type of data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated 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. In this way, the analysis unit can provide an appropriate analysis result for the user by adjusting the length of the analysis according to the user's emotions. 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 the generative AI and have the generative AI adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. For example, the analysis unit can analyze long-term trends based on historical data. For example, the analysis unit can optimally allocate analysis resources according to the data collection timing. As a result, the analysis unit can provide real-time information by determining the priority of analysis based on the data collection timing. 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 data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. For example, the analysis unit can postpone the analysis of less relevant data to perform analysis efficiently. For example, the analysis unit can optimally allocate analysis resources according to the relevance of the data. As a result, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0087] The management unit can estimate the user's emotions and adjust the management method based on the estimated emotions. For example, if the user is tense, the management unit can provide a simple and easy-to-understand management method. For example, if the user is relaxed, the management unit can provide a detailed management method. For example, if the user is in a hurry, the management unit can provide a concise management method. In this way, the management unit can provide an appropriate management method for the user by adjusting the management method 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 management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the management method.

[0088] The management department can analyze the growth stage of crops and select appropriate management methods during management. For example, during the early growth stage, the management department can focus on managing soil nutrient levels and moisture content. During the mid-growth stage, the management department can focus on managing sunlight hours and temperature. During the late growth stage, the management department can manage based on predictions of the harvest time. This enables efficient management by allowing the management department to select the optimal management method according to the growth stage of the crops. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input crop growth stage data into a generating AI and have the generating AI select appropriate management methods.

[0089] The management unit can customize management methods based on the type of crop during management. For example, the management unit can perform optimal soil preparation and moisture management for different crops. For example, the management unit can adjust the management of sunlight hours and temperature according to the type of crop. For example, the management unit can predict the harvest time based on the type of crop and optimize management. In this way, the management unit can perform efficient management by customizing management methods according to the type of crop. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input crop type data into a generating AI and have the generating AI perform the customization of management methods.

[0090] The management unit can estimate the user's emotions and determine management priorities based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize only important management items. For example, if the user is relaxed, the management unit will prioritize detailed management. For example, if the user is in a hurry, the management unit will prioritize management items that can be done quickly. In this way, the management unit can reduce the user's burden by determining management priorities 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 management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine management priorities.

[0091] The management unit can select an appropriate management method based on the geographical location information of the farmland during management. For example, the management unit can manage temperature and precipitation based on the elevation and topography of the farmland. For example, the management unit can perform management in accordance with the surrounding weather conditions based on the location information of the farmland. For example, the management unit can manage the nutrient status and moisture content of the soil according to the geographical conditions of the farmland. In this way, the management unit can select the optimal management method by considering the geographical location information of the farmland. Some or all of the above processes in the management unit may be performed using AI, for example, or without using AI. For example, the management unit can input the geographical location information of the farmland into a generating AI and have the generating AI perform the selection of an appropriate management method.

[0092] The management department can analyze farmers' social media activities during management and propose management measures. For example, the management department can propose management measures regarding harvest time and yield based on information shared by farmers on social media. For example, the management department can propose management measures according to the growth status of crops based on farmers' social media activities. For example, the management department can propose management measures according to the weather based on weather information shared by farmers on social media. In this way, the management department can propose appropriate management measures by analyzing farmers' social media activities. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input farmers' social media data into a generating AI and have the generating AI execute proposals for management measures.

[0093] The prediction unit can estimate the user's emotions and adjust the harvest time prediction method based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple and easy-to-understand prediction method. For example, if the user is relaxed, the prediction unit can provide a detailed prediction method. For example, if the user is in a hurry, the prediction unit can provide a concise prediction method. In this way, the prediction unit can provide prediction results that are easy for the user to understand by adjusting the harvest time prediction method 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 prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the harvest time prediction method.

[0094] The prediction unit can predict the appropriate harvest time by referring to past harvest data during the prediction process. For example, the prediction unit can identify periods with high yields based on past harvest data and predict the harvest time under similar conditions. For example, the prediction unit can analyze past harvest data to identify factors that caused periods with low yields and predict the harvest time to avoid those factors. For example, the prediction unit can identify factors that cause fluctuations in harvest time based on past harvest data and make predictions accordingly. In this way, the prediction unit can predict the optimal harvest time by referring to past harvest 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 past harvest data into a generating AI and have the generating AI perform the harvest time prediction.

[0095] The prediction unit can adjust the harvest time based on the growth stage of the crop during prediction. For example, the prediction unit does not predict the harvest time in the early growth stage, but starts predicting from the mid-growth stage onward. For example, the prediction unit can predict the harvest time in the mid-growth stage and improve the accuracy of the prediction towards the late growth stage. For example, the prediction unit can predict the harvest time with the highest accuracy in the late growth stage and propose the optimal harvest time. In this way, the prediction unit can propose the optimal harvest time by customizing the harvest time according to the growth stage of the crop. 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 crop growth stage data into a generating AI and have the generating AI perform the adjustment of the harvest time.

[0096] The prediction unit can estimate the user's emotions and determine the priority of harvest times based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize predicting only important harvest times. For example, if the user is relaxed, the prediction unit can prioritize predicting detailed harvest times. For example, if the user is in a hurry, the prediction unit can prioritize predicting harvest times that can be predicted quickly. In this way, the prediction unit can reduce the burden on the user by determining the priority of harvest times 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 prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the determination of harvest time priorities.

[0097] The prediction unit can predict the appropriate harvest time based on the geographical location information of the farmland during the prediction process. For example, the prediction unit can predict the harvest time considering the effects of temperature and precipitation based on the elevation and topography of the farmland. For example, the prediction unit can predict the harvest time according to the surrounding weather conditions based on the location information of the farmland. For example, the prediction unit can optimize the harvest time prediction according to the geographical conditions of the farmland. In this way, the prediction unit can predict the optimal harvest time by considering the geographical location information of the farmland. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the geographical location information of the farmland into a generating AI and have the generating AI perform the harvest time prediction.

[0098] The prediction unit can analyze farmers' social media activity during prediction to suggest harvest times. For example, the prediction unit can predict harvest times based on information shared by farmers on social media. For example, the prediction unit can suggest harvest times that correspond to the growth status of crops based on farmers' social media activity. For example, the prediction unit can suggest harvest times that correspond to the weather based on weather information shared by farmers on social media. In this way, the prediction unit can suggest appropriate harvest times by analyzing farmers' social media activity. 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 farmers' social media data into a generating AI and have the generating AI perform the harvest time suggestion.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can customize its data collection methods based on the type of crop when collecting data necessary for crop cultivation. For example, in fruit cultivation, it can focus on collecting data related to the sugar content and acidity of the fruit. In vegetable cultivation, it can focus on collecting data related to leaf color and growth rate. Furthermore, in grain cultivation, it can focus on collecting data related to ear length and grain size. This allows the data collection unit to efficiently collect data by selecting the optimal data collection method according to the type of crop. 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 crop type data into a generating AI and have the generating AI perform the customization of the data collection method.

[0101] The analysis unit can adjust the analysis method based on the data source when analyzing data collected by the collection unit. For example, for farmer experience data, the analysis can be performed by referring to past success and failure cases. For weather data, the analysis can be performed using a weather forecasting model. Furthermore, for soil data, the analysis can be performed using a soil analysis model. In this way, the analysis unit can provide highly accurate analysis results by selecting the optimal analysis method according to the data source. 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 data source information into a generating AI and have the generating AI perform the adjustment of the analysis method.

[0102] The management unit can adjust its management methods according to the growth stage of crops when managing crop cultivation, based on the analysis results obtained by the analysis unit. For example, in the early growth stage, it can focus on managing soil nutrient levels and moisture content. In the middle growth stage, it can focus on managing sunlight hours and temperature. Furthermore, in the late growth stage, it can manage crops based on predictions of the harvest time. This allows the management unit to efficiently manage crops by selecting the optimal management method according to their growth stage. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input crop growth stage data into a generating AI and have the generating AI perform adjustments to the management methods.

[0103] The prediction unit can customize its prediction method based on the type of crop when predicting the harvest time of crops managed by the management unit. For example, when predicting the harvest time of fruit, it can consider changes in the sugar content and acidity of the fruit. When predicting the harvest time of vegetables, it can consider changes in leaf color and growth rate. Furthermore, when predicting the harvest time of grains, it can consider changes in ear length and grain size. As a result, the prediction unit can select the optimal prediction method according to the type of crop, enabling highly accurate harvest time predictions. 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 crop type data into a generating AI and have the generating AI perform the customization of the prediction method.

[0104] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to obtain more information. Furthermore, if the user is in a hurry, only important data can be prioritized and processed quickly. In this way, the data collection unit can reduce the user's burden by adjusting the frequency of data collection 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, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the frequency of data collection.

[0105] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide simple and easy-to-read analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the display method according to the user's emotions. 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 the generative AI and have the generative AI adjust the display method of the analysis results.

[0106] The management department can estimate the user's emotions and determine management priorities based on those estimated emotions. For example, if the user is stressed, only important management items can be prioritized. If the user is relaxed, detailed management can be prioritized. Furthermore, if the user is in a hurry, management items that can be handled quickly can be prioritized. In this way, the management department can reduce the user's burden by determining management priorities 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 management department may be performed using AI, or not using AI. For example, the management department can input user emotion data into a generative AI and have the generative AI determine management priorities.

[0107] The prediction unit can estimate the user's emotions and adjust the harvest time prediction method based on the estimated user emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand prediction method. If the user is relaxed, it can provide a detailed prediction method. Furthermore, if the user is in a hurry, it can provide a concise prediction method. In this way, the prediction unit can provide prediction results that are easy for the user to understand by adjusting the harvest time prediction method 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 prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the harvest time prediction method.

[0108] The data collection unit can adjust the content of the data it collects based on the growth stage of the crop. For example, in the early growth stage, it can focus on collecting data related to soil nutrient status and moisture content. In the middle growth stage, it can focus on collecting data related to sunshine hours and temperature. Furthermore, in the late growth stage, it can focus on collecting data necessary for predicting the harvest time. This allows the data collection unit to efficiently collect data by adjusting the type of data collected according to the growth stage of the crop. 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 crop growth stage data into a generating AI and have the generating AI adjust the content of the data to be collected.

[0109] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, weather data can be analyzed using a weather forecasting algorithm. Soil data can be analyzed using a soil analysis algorithm. Furthermore, crop growth data can be analyzed using a growth forecasting algorithm. In this way, the analysis unit can provide highly accurate analysis results by applying the appropriate analysis algorithm according to the type of data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type of data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects data necessary for crop cultivation. For example, the data collection unit collects data on farmers' experience, past weather forecasts, recent weather forecasts, soil preparation, moisture content, sunshine duration, and sunlight intensity. The data collection unit can collect data in real time using sensors, for example. The data collection unit can also manually input data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze the data and identify the optimal conditions for crop cultivation. The analysis unit can, for example, use deep learning or neural networks to analyze the data. Step 3: The management department manages crop growth based on the analysis results obtained by the analysis department. For example, the management department optimizes crop growth by managing soil preparation, temperature control, and sunlight levels. For example, the management department can control the irrigation system to maintain appropriate moisture levels. The management department can also manage fertilizer application to optimize the nutritional status of crops. Step 4: The prediction unit predicts the harvest time of crops managed by the management unit. The prediction unit can predict the harvest time using, for example, a growth model. The prediction unit can predict the harvest time by, for example, referring to past data.

[0112] 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.

[0113] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] 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.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, management unit, and prediction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect data using the sensors or manual input device of the smart device 14. The analysis unit analyzes the data using AI, for example, by the identification processing unit 290 of the data processing unit 12, to identify the optimal conditions for crop growth. The management unit optimizes crop growth by managing soil preparation, temperature control, and sunlight intensity, for example, by the control unit 46A of the smart device 14. The prediction unit can predict the harvest time using a growth model, for example, by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, management unit, and prediction 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 can collect data using the sensors or manual input device of the smart glasses 214. The analysis unit, for example, uses AI to analyze the data using the identification processing unit 290 of the data processing unit 12 to identify the optimal conditions for crop growth. The management unit, for example, uses the control unit 46A of the smart glasses 214 to optimize crop growth by managing soil preparation, temperature, and sunlight. The prediction unit, for example, uses the identification processing unit 290 of the data processing unit 12 to predict the harvest time using a growth model. The correspondence between each unit and the devices or control units is not limited to the examples described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, management unit, and prediction unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit can collect data using the sensors or manual input device of the headset terminal 314. The analysis unit, for example, uses AI to analyze the data by the identification processing unit 290 of the data processing unit 12 to identify the optimal conditions for crop growth. The management unit, for example, uses the control unit 46A of the headset terminal 314 to manage soil preparation, temperature control, and sunlight amount to optimize crop growth. The prediction unit, for example, uses the identification processing unit 290 of the data processing unit 12 to predict the harvest time using a growth model. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, management unit, and prediction unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit can collect data using the sensors or manual input device of the robot 414. The analysis unit, for example, uses AI to analyze the data by the identification processing unit 290 of the data processing unit 12 to identify the optimal conditions for crop growth. The management unit optimizes crop growth by, for example, using the control unit 46A of the robot 414 to manage soil preparation, temperature, and sunlight. The prediction unit can predict the harvest time using a growth model by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The data collection unit collects data necessary for growing crops, An analysis unit analyzes the data collected by the aforementioned collection unit, A management unit manages the cultivation of crops based on the analysis results obtained by the aforementioned analysis unit, The system includes a prediction unit that predicts the harvest time of crops managed by the aforementioned management unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on farmers' experience, past weather forecasts, recent weather forecasts, soil preparation, moisture content, sunshine hours, and sunlight intensity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The data collected by the aforementioned collection unit is analyzed by AI to identify conditions suitable for growing crops. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Based on the analysis results obtained by the aforementioned analysis unit, soil preparation, temperature control, and sunlight control are performed to improve the efficiency of crop cultivation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, The aforementioned management department predicts the harvest time of crops managed by the department and proposes an appropriate harvest time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, When using greenhouse facilities, crops are managed through automation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze farmers' past harvest data and select appropriate data collection methods. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, adjust the content of the data collected based on the growth stage of the crops. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the geographical location information of agricultural land. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze farmers' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, It estimates user sentiment and adjusts management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, analyze the growth stage of the crops and select the appropriate management method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, During management, adjust management methods based on the type of crop. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, the appropriate management method is selected based on the geographical location information of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, During management, we analyze farmers' social media activity and propose management strategies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, We estimate the user's emotions and adjust the harvest time prediction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, When making predictions, historical harvest data is used to predict the appropriate harvest time. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, adjust the harvest time based on the growth stage of the crop. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, The system estimates user sentiment and determines harvest timing priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, When making predictions, the appropriate harvest time is predicted based on the geographical location information of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, During the forecasting process, we analyze farmers' social media activity to suggest optimal harvest times. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection unit collects data necessary for growing crops, An analysis unit analyzes the data collected by the aforementioned collection unit, A management unit manages the cultivation of crops based on the analysis results obtained by the aforementioned analysis unit, The system includes a prediction unit that predicts the harvest time of crops managed by the aforementioned management unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on farmers' experience, past weather forecasts, recent weather forecasts, soil preparation, moisture content, sunshine hours, and sunlight intensity. The system according to feature 1.

3. The aforementioned analysis unit, The data collected by the aforementioned collection unit is analyzed by AI to identify conditions suitable for growing crops. The system according to feature 1.

4. The aforementioned management department, Based on the analysis results obtained by the aforementioned analysis unit, soil preparation, temperature control, and sunlight control are performed to improve the efficiency of crop cultivation. The system according to feature 1.

5. The prediction unit, The aforementioned management department predicts the harvest time of crops managed by the department and proposes an appropriate harvest time. The system according to feature 1.

6. The aforementioned management department, When using greenhouse facilities, crops are managed through automation. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze farmers' past harvest data and select appropriate data collection methods. The system according to feature 1.

9. The aforementioned collection unit is During data collection, adjust the content of the data collected based on the growth stage of the crops. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

Images