system

Abstract

The system according to this embodiment aims to efficiently manage crop growth, predict pests and diseases, optimize harvest timing, and match sales channels in agriculture. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a prediction unit, an optimization unit, and a matching unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. The prediction unit predicts pests and diseases. The optimization unit optimizes the harvest time. The matching unit performs sales channel matching.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, growth management, pest prediction, optimization of harvest time, and sales channel matching in agriculture are not efficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently perform growth management, pest prediction, optimization of harvest time, and sales channel matching in agriculture.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a prediction unit, an optimization unit, and a matching unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes optimal suggestions based on the analysis results obtained by the analysis unit. The prediction unit predicts pests and diseases. The optimization unit optimizes the harvest time. The matching unit performs sales channel matching. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently perform crop growth management, pest and disease prediction, harvest timing optimization, and sales channel matching in agriculture. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The agricultural production optimization system according to an embodiment of the present invention is a system for solving problems arising from backgrounds such as the aging of agricultural workers, a shortage of successors, the impact of climate change, and the need for food safety. This agricultural production optimization system monitors the growth status of crops using drones and sensors, and an AI agent analyzes the data. Next, using a pest and disease prediction system, the AI ​​agent predicts the occurrence of pests and diseases and proposes appropriate countermeasures. Furthermore, it optimizes the harvest time and proposes the optimal harvest time. In addition, it performs sales channel matching and the AI ​​agent proposes the optimal sales channel. Finally, it digitizes agricultural know-how and the AI ​​agent supports the transfer of agricultural technology. This mechanism aims to improve productivity, reduce weather risks, alleviate labor shortages, support technology transfer, and improve profitability. For example, the agricultural production optimization system monitors the growth status of crops using drones and sensors. In this case, the drone flies over the farmland, and the sensors collect data such as soil moisture and temperature, and crop growth status. For example, an AI agent can analyze image data captured by drones flying over farmland and soil data collected by sensors to understand the health and growth status of crops. Next, the AI ​​agent predicts the occurrence of pests and diseases using a pest and disease prediction system. For example, based on past weather data and crop growth data, the AI ​​agent analyzes the risk of pest and disease outbreaks and proposes appropriate countermeasures. This helps to prevent damage from pests and diseases. Furthermore, the AI ​​agent optimizes harvest timing and proposes the optimal harvest time. For example, based on crop growth data and weather data, the AI ​​agent analyzes the timing of harvest and proposes the optimal harvest time. This helps to maximize yield and improve quality. In addition, the AI ​​agent performs sales channel matching and proposes the optimal sales channels. For example, based on the quality of harvested crops and market demand data, the AI ​​agent proposes the optimal sales destination. This is expected to improve profitability. Finally, the AI ​​agent supports the transfer of agricultural know-how by digitizing agricultural know-how. For example, the know-how of veteran farmers is digitized, and the AI ​​agent provides appropriate advice to new farmers.This will alleviate the difficulties of technology transfer and enhance the sustainability of agriculture. The agricultural production optimization system will improve productivity, reduce weather risks, address labor shortages, support technology transfer, and increase profitability. For example, following the optimal cultivation methods and harvest times suggested by the AI ​​agent will improve crop quality and increase profitability. Furthermore, utilizing a pest and disease prediction system will prevent pest and disease damage and ensure a sufficient harvest. In addition, sales channel matching will ensure that harvested crops are delivered to the most suitable buyers, further improving profitability. Moreover, the digitalization of agricultural know-how will alleviate the difficulties of technology transfer and support new farmers. This is expected to lead to the realization of sustainable and profitable next-generation agriculture. The agricultural production optimization system will achieve improved productivity, reduced weather risks, address labor shortages, support technology transfer, and increased profitability.

[0029] The agricultural production optimization system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a prediction unit, an optimization unit, and a matching unit. The data collection unit collects data. The data collection unit monitors the growth status of crops using, for example, drones or sensors. The data collection unit can collect, for example, image data taken by a drone while flying over farmland, or soil data collected by sensors. The data collection unit can also collect, for example, data such as soil humidity, temperature, and crop growth status. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, AI. The analysis unit can, for example, understand the health and growth status of crops based on the collected data. The analysis unit can also, for example, analyze the risk of pest and disease outbreaks based on past weather data and crop growth data. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose the optimal cultivation method and harvest time based on the analysis results. The proposal unit can also, for example, propose the optimal sales destination based on the quality of harvested crops and market demand data. The prediction unit predicts pests and diseases. For example, the prediction unit predicts the risk of pest and disease outbreaks based on past weather data and crop growth data. The prediction unit can also analyze the risk of pest and disease outbreaks and propose appropriate countermeasures. The optimization unit optimizes the harvest time. For example, the optimization unit analyzes the timing of harvest based on crop growth data and weather data. The optimization unit can also propose the optimal harvest time. The matching unit performs sales channel matching. For example, the matching unit proposes the optimal sales destination based on the quality of harvested crops and market demand data. The matching unit can also propose sales channels that are expected to improve profitability. As a result, the agricultural production optimization system according to this embodiment can efficiently perform data collection, analysis, proposal, prediction, optimization, and matching.

[0030] The data collection unit collects data. For example, the data collection unit monitors the growth of crops using drones and sensors. Specifically, drones fly over farmland and capture high-resolution image data, allowing for detailed observation of the color and shape of crop leaves, as well as signs of pests and diseases. Drones are equipped with multispectral cameras and infrared cameras, which can be used to visualize the health and stress levels of crops. Sensors collect data such as soil moisture, temperature, pH value, and nutrient concentration in real time. This allows for a detailed understanding of the environmental conditions of the entire farmland. The data collection unit centrally manages this data and transmits it to a cloud server, making it accessible to other departments. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific crops and environmental conditions. For example, data can be collected frequently in the early stages of growth and reduced once growth has stabilized. This allows the data collection unit to 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 collected data. Specifically, the AI ​​uses image recognition technology to analyze image data captured by drones and detect the health of crops and signs of pests and diseases. For example, it can detect changes in leaf color and abnormal shapes, which helps in the early detection of pests and diseases. It also analyzes data obtained from soil sensors to evaluate the nutrient status and moisture content of the soil. This makes it possible to understand the optimal environmental conditions for crop growth. Furthermore, the analysis unit analyzes the risk of pest and disease outbreaks based on past weather data and crop growth data. For example, it can analyze how specific weather and soil conditions affect the occurrence of pests and diseases, and identify high-risk periods and areas. As a result, the analysis unit can quickly and accurately analyze the collected data and understand the health and growth status of crops in real time. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The proposal department makes optimal suggestions based on the analysis results obtained by the analysis department. For example, the proposal department proposes optimal cultivation methods and harvest times based on the analysis results. Specifically, it proposes appropriate types of fertilizers, timing of fertilization, and irrigation methods based on the health and growth status of the crops. Regarding harvest times, it proposes the optimal harvest time considering crop growth data and market demand data. This maximizes crop quality and improves profitability. Furthermore, the proposal department can also propose optimal sales destinations based on the quality of harvested crops and market demand data. For example, for crops with high demand in a particular market, it proposes shipping to that market to improve profitability. In addition, the proposal department can utilize the experience and knowledge of agricultural workers to propose optimal cultivation methods for individual farms and crops. In this way, the proposal department can support the efficiency and profitability of agricultural production.

[0033] The prediction unit predicts pest and disease outbreaks. For example, it predicts the risk of pest and disease outbreaks based on past weather data and crop growth data. Specifically, it uses AI to analyze past data and model how specific weather and soil conditions affect pest and disease outbreaks. This allows it to predict the risk of pest and disease outbreaks based on future weather and soil conditions. For example, it predicts that the risk of certain pests and diseases will increase during periods of high temperature and humidity, allowing appropriate countermeasures to be taken during those times. The prediction unit can also analyze the risk of pest and disease outbreaks and propose appropriate countermeasures. For example, if there is a high risk of a particular pest or disease outbreak, it will propose control methods and pesticide usage methods for that pest or disease. This allows the prediction unit to prevent pest and disease outbreaks and maintain the health of crops. Furthermore, the prediction unit updates prediction results in real time, enabling it to respond to the latest situation. This allows the prediction unit to always provide highly accurate predictions based on the latest information, supporting quick and appropriate responses.

[0034] The optimization unit optimizes the harvest time. For example, the optimization unit analyzes the timing of harvest based on crop growth data and weather data. Specifically, it identifies the optimal harvest time by considering the crop's growth stage and weather conditions. For example, harvesting when crop growth is at its peak and quality is at its highest can maximize profits. By considering weather conditions, it can also identify the time when harvesting can be performed most efficiently. In this way, the optimization unit can simultaneously achieve increased efficiency in harvesting and improved crop quality. Furthermore, the optimization unit can not only optimize the harvest time but also propose methods for post-harvest processing and storage. For example, it can propose methods for properly storing harvested crops and maintaining their quality. In this way, the optimization unit can optimize the entire process from harvesting to sales, supporting increased efficiency and profitability in agricultural production.

[0035] The matching department performs sales channel matching. For example, the matching department proposes the most suitable sales destination based on the quality of harvested crops and market demand data. Specifically, it analyzes crop quality and market demand to identify the most profitable sales destinations. For example, for high-quality crops, it proposes sales to markets and buyers that trade at high prices. Also, for crops with high demand in specific markets, it proposes shipments to those markets to improve profitability. Furthermore, the matching department can also support negotiations and contracts with sales destinations. For example, it provides advice on sales conditions and price negotiations, helping farmers to conduct transactions under favorable conditions. In this way, the matching department can efficiently match agricultural producers with sales destinations and support improved profitability. In addition, the matching department can collect feedback from sales destinations and reflect it in future sales strategies. In this way, the matching department can continuously improve sales strategies and increase the profitability of agricultural producers.

[0036] The agricultural production optimization system includes a digitization unit that digitizes know-how. The digitization unit digitizes, for example, the know-how of veteran farmers. The digitization unit can digitize know-how using methods such as text conversion, video conversion, and database creation. The digitization unit can also digitize know-how using AI. The digitization unit can provide the digitized know-how to new farmers and offer appropriate advice, thereby supporting the transfer of agricultural technology. Some or all of the above-described processes in the digitization unit may be performed using AI, or not. For example, the digitization unit can digitize the know-how of veteran farmers, input that text data into a generating AI, and improve the accuracy of the digitization using the generating AI.

[0037] The data collection unit collects data using drones and sensors. For example, the data collection unit collects image data taken by a drone while flying over farmland. The data collection unit can also collect data such as soil moisture and temperature, and crop growth status using sensors. For example, the data collection unit can monitor the health of crops using a drone equipped with a multispectral camera. The data collection unit can also measure soil nutrient status and moisture content using soil sensors. This allows for efficient monitoring of crop growth. 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 image data taken by a drone into a generating AI, and the generating AI can analyze the image data.

[0038] The analysis unit analyzes the collected data using AI. The analysis unit analyzes the collected data using, for example, machine learning algorithms. The analysis unit can analyze the health and growth status of crops using, for example, deep learning technology. The analysis unit can also analyze crop growth predictions and the risk of pest and disease outbreaks based on the collected data. This improves the accuracy of data analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generating AI, and the generating AI can perform data analysis.

[0039] The proposal unit makes optimal suggestions based on the analysis results. For example, the proposal unit proposes the optimal cultivation method aimed at maximizing yields or reducing costs. The proposal unit can also propose the optimal sales destination based on the quality of harvested crops and market demand data. The proposal unit can make optimal suggestions based on the analysis results using AI, for example. This makes optimal suggestions possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the analysis results into a generating AI, and the generating AI can make optimal suggestions.

[0040] The prediction unit predicts the occurrence of pests and diseases. The prediction unit predicts the risk of pest and disease outbreaks based, for example, on the analysis of past data and consideration of weather conditions. The prediction unit can, for example, use AI to analyze the risk of pest and disease outbreaks and propose appropriate countermeasures. The prediction unit can also, for example, propose the timing and amount of pesticide use based on the risk of pest and disease outbreaks. This makes it possible to prevent damage from pests and diseases. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input past weather data and crop growth data into a generating AI, and the generating AI can predict the risk of pest and disease outbreaks.

[0041] The optimization unit optimizes the harvest time. The optimization unit analyzes the timing of harvest based on factors such as the crop's growth stage and weather conditions. The optimization unit can use AI to optimize the harvest time and propose the optimal harvest time. The optimization unit can also propose a harvest time aimed at maximizing yield or improving quality. This helps to maximize yield and improve quality. Some or all of the above-described processes in the optimization unit may be performed using AI, or not. For example, the optimization unit can input crop growth data and weather data into a generating AI, which can then propose the optimal harvest time.

[0042] The matching unit performs sales channel matching. The matching unit proposes the most suitable sales destination based, for example, on the quality of harvested crops and market demand data. The matching unit can, for example, use AI to perform sales channel matching and propose sales channels that are expected to improve profitability. The matching unit can also propose the most suitable sales destination based, for example, on the quality of harvested crops and market demand data. This is expected to improve profitability. Some or all of the above processing in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input the quality of harvested crops and market demand data into a generating AI, and the generating AI can propose the most suitable sales destination.

[0043] The data collection unit analyzes past collected data and selects the optimal collection method. For example, the data collection unit analyzes past data collection history to identify the most efficient collection method. For example, the data collection unit can improve the collection method and increase accuracy based on past data collection results. The data collection unit can also analyze past successful and unsuccessful data collection cases to select the optimal collection method. By selecting the optimal collection method, the efficiency of data collection is improved. Some or all of the above 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 collected data into a generating AI, and the generating AI can select the optimal collection method.

[0044] The data collection unit filters data based on the geographical and meteorological conditions of the farmland during data collection. For example, the data collection unit considers the geographical conditions of the farmland and places appropriate sensors. For example, the data collection unit can adjust the timing of data collection based on meteorological conditions. For example, the data collection unit can combine geographical and meteorological conditions to create an optimal data collection plan. This improves the accuracy of data collection based on geographical and meteorological conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the geographical and meteorological conditions of the farmland into a generating AI, and the generating AI can perform the filtering of data collection.

[0045] The data collection unit prioritizes the collection of highly relevant data, taking into account the geographical location information of the farmland during data collection. For example, the data collection unit prioritizes the collection of data from a specific area based on the geographical location information of the farmland. For example, the data collection unit can select the types of data to collect, taking into account the geographical location information. For example, the data collection unit can adjust the frequency of data collection based on the geographical location information. This allows for the priority collection of highly relevant data based on the geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the farmland into a generating AI, which can then prioritize the collection of highly relevant data.

[0046] The data collection unit analyzes the social media activities of farmers during data collection and collects relevant data. For example, the data collection unit analyzes farmers' social media posts to identify data to be collected. For example, the data collection unit can determine the priority of data collection based on the information obtained from social media activities. The data collection unit can also analyze social media trends and collect relevant data. This allows for the collection of relevant data by analyzing 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 post data into a generating AI, and the generating AI can collect relevant data.

[0047] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data to improve accuracy. For example, the analysis unit can perform a simplified analysis on less important data. The analysis unit can also optimally allocate analysis resources according to the importance of the data. This allows the level of detail of the analysis to be adjusted 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 the generating AI can adjust the level of detail of the analysis.

[0048] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a growth analysis algorithm to crop growth data. For example, it can apply a weather analysis algorithm to weather data. For example, it can apply a pest and disease analysis algorithm to pest and disease data. This allows the optimal analysis algorithm to be applied according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply the optimal analysis algorithm.

[0049] The analysis unit determines the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit prioritizes the analysis of the latest data to provide real-time information. For example, the analysis unit can analyze current data while referring to past data. For example, the analysis unit can also optimally allocate analysis resources according to the data collection timing. This allows the analysis priority to be determined 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 the generating AI can determine the analysis priority.

[0050] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data to improve accuracy. For example, the analysis unit can perform analysis efficiently by postponing the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This allows the order of analysis to be adjusted 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 the generating AI can adjust the order of analysis.

[0051] The proposal unit adjusts the level of detail of the proposal based on the importance of the analysis results. For example, the proposal unit provides detailed proposals for important analysis results. For example, it can provide simplified proposals for less important analysis results. The proposal unit can also optimally allocate resources for the proposal according to the importance of the analysis results. This allows the level of detail of the proposal to be adjusted according to the importance of the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the analysis results into a generating AI, and the generating AI can adjust the level of detail of the proposal.

[0052] The proposal unit applies different proposal algorithms depending on the category of the analysis results during the proposal process. For example, the proposal unit may apply a growth proposal algorithm to crop growth analysis results. For example, the proposal unit may apply a weather proposal algorithm to weather analysis results. For example, the proposal unit may apply a pest and disease proposal algorithm to pest and disease analysis results. This allows the application of the most suitable proposal algorithm according to the category of the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit may input the category of the analysis results into a generating AI, and the generating AI may apply the most suitable proposal algorithm.

[0053] The proposal unit determines the priority of proposals based on the timing of analysis result collection when making a proposal. For example, the proposal unit prioritizes the latest analysis results and provides real-time information. For example, the proposal unit can make current proposals while referring to past analysis results. For example, the proposal unit can also optimally allocate resources for proposals according to the timing of analysis result collection. This allows the proposal unit to determine the priority of proposals based on the timing of analysis result collection. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of analysis result collection into a generating AI, and the generating AI can determine the priority of proposals.

[0054] The proposal unit adjusts the order of proposals based on the relevance of the analysis results when making proposals. For example, the proposal unit prioritizes proposing highly relevant analysis results to improve accuracy. For example, the proposal unit can efficiently make proposals by postponing less relevant analysis results. The proposal unit can also optimally adjust the order of proposals according to the relevance of the analysis results. This allows the order of proposals to be adjusted based on the relevance of the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the analysis results into a generating AI, and the generating AI can adjust the order of proposals.

[0055] The prediction unit optimizes the current prediction by referring to past prediction data during the prediction process. For example, the prediction unit improves the accuracy of the current prediction based on past prediction data. For example, the prediction unit can make the current prediction while referring to past prediction results. For example, the prediction unit can also analyze past prediction data and apply the optimal prediction algorithm. This improves the accuracy of the current prediction by referring to past prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past prediction data into a generating AI, and the generating AI can optimize the current prediction.

[0056] The prediction unit applies different prediction algorithms to each data category during prediction. For example, the prediction unit can apply a growth prediction algorithm to crop growth data. For example, it can apply a weather prediction algorithm to weather data. For example, it can apply a pest and disease prediction algorithm to pest and disease data. This allows the optimal prediction algorithm to be applied according to the data category. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the data category into a generating AI, and the generating AI can apply the optimal prediction algorithm.

[0057] The prediction unit analyzes changes in the prediction based on the data collection timing during the prediction process. The prediction unit can, for example, analyze changes in the prediction in real time based on the latest data. The prediction unit can, for example, analyze changes in the current prediction by referring to past data. The prediction unit can also, for example, optimally analyze changes in the prediction according to the data collection timing. This allows for analysis of changes in the prediction based on the data collection timing. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the data collection timing into a generating AI, and the generating AI can analyze changes in the prediction.

[0058] The forecasting unit analyzes the forecast by referring to relevant market data during the forecasting process. The forecasting unit improves the accuracy of the forecast based on relevant market data, for example. The forecasting unit can make the current forecast by referring to market data, for example. The forecasting unit can also analyze relevant market data and apply the optimal forecasting algorithm, for example. This improves the accuracy of the forecast by referring to relevant market data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input relevant market data into a generating AI and have the generating AI analyze the forecast.

[0059] The optimization unit optimizes the optimization algorithm by referring to past optimization data during the optimization process. The optimization unit improves the accuracy of the current optimization based on past optimization data, for example. The optimization unit can perform the current optimization while referring to past optimization results, for example. The optimization unit can also analyze past optimization data and apply the optimal optimization algorithm, for example. This improves the accuracy of the current optimization by referring to past optimization data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past optimization data into a generating AI and optimize the optimization algorithm using the generating AI.

[0060] The optimization unit applies different optimization methods to each data category during optimization. For example, the optimization unit can apply a growth optimization method to crop growth data. For example, it can apply a weather optimization method to weather data. For example, it can apply a pest and disease optimization method to pest and disease data. This allows the optimal optimization method to be applied according to the data category. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the data category into a generating AI, and the generating AI can apply the optimal optimization method.

[0061] The optimization unit weights the optimization based on the data collection timing during optimization. For example, the optimization unit weights the optimization based on the latest data. For example, the optimization unit can weight the current optimization by referring to past data. For example, the optimization unit can optimally adjust the optimization weights according to the data collection timing. This allows the optimization weights to be weighted based on the data collection timing. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the data collection timing into a generating AI, and the generating AI can perform the optimization weighting.

[0062] The optimization unit proposes optimization methods by referring to relevant market data during optimization. For example, the optimization unit proposes optimization methods based on relevant market data. For example, the optimization unit can perform current optimization while referring to market data. For example, the optimization unit can analyze relevant market data and propose the optimal optimization method. In this way, the optimal optimization method can be proposed by referring to relevant market data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input relevant market data into a generating AI, and the generating AI can propose optimization methods.

[0063] The matching unit optimizes the matching algorithm by referring to past matching data during the matching process. The matching unit improves the accuracy of the current matching based on past matching data, for example. The matching unit can perform the current matching while referring to past matching results, for example. The matching unit can also analyze past matching data and apply the optimal matching algorithm, for example. This improves the accuracy of the current matching by referring to past matching data. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input past matching data into a generating AI, and the generating AI can optimize the matching algorithm.

[0064] The matching unit applies different matching methods to each data category during the matching process. For example, the matching unit can apply a growth matching method to crop growth data. For example, it can apply a weather matching method to weather data. For example, it can apply a pest and disease matching method to pest and disease data. This allows the optimal matching method to be applied according to the data category. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the data category into a generating AI, and the generating AI can apply the optimal matching method.

[0065] The matching unit weights the matches based on the data collection timing during the matching process. For example, the matching unit weights the matches based on the latest data. For example, the matching unit can weight the current matches by referring to past data. For example, the matching unit can also optimally adjust the matching weights according to the data collection timing. This allows the matching weights to be determined based on the data collection timing. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the data collection timing into a generating AI, and the generating AI can perform the matching weighting.

[0066] The matching unit proposes matching methods by referring to relevant market data during the matching process. For example, the matching unit proposes matching methods based on relevant market data. For example, the matching unit can perform current matching while referring to market data. The matching unit can also, for example, analyze relevant market data and propose the optimal matching method. This allows the optimal matching method to be proposed by referring to relevant market data. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input relevant market data into a generating AI, and the generating AI can propose matching methods.

[0067] The digitization unit optimizes the digitization algorithm by referring to past digitization data during the digitization process. For example, the digitization unit improves the accuracy of the current digitization based on past digitization data. For example, the digitization unit can perform the current digitization while referring to past digitization results. For example, the digitization unit can also analyze past digitization data and apply the optimal digitization algorithm. This improves the accuracy of the current digitization by referring to past digitization data. Some or all of the above processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input past digitization data into a generating AI and optimize the digitization algorithm using the generating AI.

[0068] The digitization unit weights the digitization process based on the data collection timing. For example, the digitization unit weights the digitization process based on the latest data. For example, the digitization unit can weight the current digitization process while referring to past data. For example, the digitization unit can also optimally adjust the digitization weighting according to the data collection timing. This allows for digitization weighting based on the data collection timing. Some or all of the above-described processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the data collection timing into a generating AI, and the generating AI can perform the digitization weighting.

[0069] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0070] The data collection unit can utilize satellite imagery in addition to drones and sensors when monitoring crop growth. For example, using satellite imagery allows for a comprehensive overview of crop growth across a wide area of ​​farmland. This enables efficient data collection for vast areas of farmland that cannot be covered by drones or sensors alone. Furthermore, analyzing satellite imagery allows for the identification of overall farmland health and any abnormalities. By comparing satellite imagery with historical data, long-term growth trends can be identified. This allows for more accurate monitoring of crop growth.

[0071] The analysis unit can also utilize cloud computing when analyzing collected data. For example, analyzing data on the cloud allows for rapid processing of large amounts of data. This improves the speed of analysis and enables real-time data analysis. Furthermore, analysis on the cloud allows for the integration and analysis of multiple data sources. In addition, cloud computing makes the analysis results accessible from multiple devices. This makes it easier to share analysis results, allowing agricultural workers to check the results from anywhere.

[0072] The proposal department can also consider the user's past behavior history when making optimal suggestions based on analysis results. For example, it can make suggestions tailored to the user's preferences based on data on cultivation methods and harvest times the user has used in the past. This makes it possible to make suggestions that are more practical and acceptable to the user. Furthermore, by analyzing past behavior history, it is possible to understand the user's tendencies and patterns and make more accurate suggestions. In addition, by referring to the user's past successes and failures, it is possible to make suggestions that minimize risk. This can improve user satisfaction.

[0073] The prediction unit can also refer to local pest and disease outbreak information when predicting the occurrence of pests and diseases. For example, it can make more accurate predictions based on pest and disease outbreak information provided by local agricultural cooperatives or government agencies. This allows for an understanding of the overall pest and disease situation in the region and the implementation of appropriate countermeasures. Furthermore, obtaining local pest and disease outbreak information in real time enables a rapid response. In addition, by comparing local pest and disease outbreak information with past data, it is possible to understand trends in pest and disease outbreaks and implement preventive measures. This makes it possible to prevent damage from pests and diseases before it occurs.

[0074] The optimization unit can also apply different optimization algorithms to each crop variety when optimizing harvest timing. For example, by using optimization algorithms tailored to the growth characteristics of different crops such as tomatoes and cucumbers, it becomes possible to propose more accurate harvest timings. This allows for the determination of the optimal harvest time for each crop. Furthermore, by applying variety-specific optimization algorithms, it is possible to maximize yields and improve quality. In addition, by applying variety-specific optimization algorithms, the efficiency of harvesting work can be improved, thereby reducing the burden of harvesting.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The collection unit collects data. The collection unit monitors the growth status of crops using, for example, drones or sensors. The collection unit can collect, for example, image data taken by a drone while flying over farmland, or soil data collected by sensors. The collection unit can also collect data such as soil moisture and temperature, and crop growth status. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, AI. The analysis unit can, for example, understand the health and growth status of crops based on the collected data. The analysis unit can also, for example, analyze the risk of pest and disease outbreaks based on past weather data and crop growth data. Step 3: The proposal unit makes optimal suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit may suggest the optimal cultivation method and harvest time based on the analysis results. The proposal unit may also suggest the optimal sales destination based on the quality of the harvested crops and market demand data. Step 4: The prediction unit predicts pest and disease outbreaks. For example, the prediction unit predicts the risk of pest and disease outbreaks based on past weather data and crop growth data. The prediction unit can also analyze the risk of pest and disease outbreaks and propose appropriate countermeasures. Step 5: The optimization unit optimizes the harvest time. The optimization unit analyzes the timing of harvest based on, for example, crop growth data and weather data. The optimization unit can also, for example, suggest the optimal harvest time. Step 6: The matching department performs sales channel matching. The matching department proposes the most suitable sales channels based on data such as the quality of harvested crops and market demand. The matching department can also propose sales channels that are expected to improve profitability.

[0077] (Example of form 2) The agricultural production optimization system according to an embodiment of the present invention is a system for solving problems arising from backgrounds such as the aging of agricultural workers, a shortage of successors, the impact of climate change, and the need for food safety. This agricultural production optimization system monitors the growth status of crops using drones and sensors, and an AI agent analyzes the data. Next, using a pest and disease prediction system, the AI ​​agent predicts the occurrence of pests and diseases and proposes appropriate countermeasures. Furthermore, it optimizes the harvest time and proposes the optimal harvest time. In addition, it performs sales channel matching and the AI ​​agent proposes the optimal sales channel. Finally, it digitizes agricultural know-how and the AI ​​agent supports the transfer of agricultural technology. This mechanism aims to improve productivity, reduce weather risks, alleviate labor shortages, support technology transfer, and improve profitability. For example, the agricultural production optimization system monitors the growth status of crops using drones and sensors. In this case, the drone flies over the farmland, and the sensors collect data such as soil moisture and temperature, and crop growth status. For example, an AI agent can analyze image data captured by drones flying over farmland and soil data collected by sensors to understand the health and growth status of crops. Next, the AI ​​agent predicts the occurrence of pests and diseases using a pest and disease prediction system. For example, based on past weather data and crop growth data, the AI ​​agent analyzes the risk of pest and disease outbreaks and proposes appropriate countermeasures. This helps to prevent damage from pests and diseases. Furthermore, the AI ​​agent optimizes harvest timing and proposes the optimal harvest time. For example, based on crop growth data and weather data, the AI ​​agent analyzes the timing of harvest and proposes the optimal harvest time. This helps to maximize yield and improve quality. In addition, the AI ​​agent performs sales channel matching and proposes the optimal sales channels. For example, based on the quality of harvested crops and market demand data, the AI ​​agent proposes the optimal sales destination. This is expected to improve profitability. Finally, the AI ​​agent supports the transfer of agricultural know-how by digitizing agricultural know-how. For example, the know-how of veteran farmers is digitized, and the AI ​​agent provides appropriate advice to new farmers.This will alleviate the difficulties of technology transfer and enhance the sustainability of agriculture. The agricultural production optimization system will improve productivity, reduce weather risks, address labor shortages, support technology transfer, and increase profitability. For example, following the optimal cultivation methods and harvest times suggested by the AI ​​agent will improve crop quality and increase profitability. Furthermore, utilizing a pest and disease prediction system will prevent pest and disease damage and ensure a sufficient harvest. In addition, sales channel matching will ensure that harvested crops are delivered to the most suitable buyers, further improving profitability. Moreover, the digitalization of agricultural know-how will alleviate the difficulties of technology transfer and support new farmers. This is expected to lead to the realization of sustainable and profitable next-generation agriculture. The agricultural production optimization system will achieve improved productivity, reduced weather risks, address labor shortages, support technology transfer, and increased profitability.

[0078] The agricultural production optimization system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a prediction unit, an optimization unit, and a matching unit. The data collection unit collects data. The data collection unit monitors the growth status of crops using, for example, drones or sensors. The data collection unit can collect, for example, image data taken by a drone while flying over farmland, or soil data collected by sensors. The data collection unit can also collect, for example, data such as soil humidity, temperature, and crop growth status. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, AI. The analysis unit can, for example, understand the health and growth status of crops based on the collected data. The analysis unit can also, for example, analyze the risk of pest and disease outbreaks based on past weather data and crop growth data. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose the optimal cultivation method and harvest time based on the analysis results. The proposal unit can also, for example, propose the optimal sales destination based on the quality of harvested crops and market demand data. The prediction unit predicts pests and diseases. For example, the prediction unit predicts the risk of pest and disease outbreaks based on past weather data and crop growth data. The prediction unit can also analyze the risk of pest and disease outbreaks and propose appropriate countermeasures. The optimization unit optimizes the harvest time. For example, the optimization unit analyzes the timing of harvest based on crop growth data and weather data. The optimization unit can also propose the optimal harvest time. The matching unit performs sales channel matching. For example, the matching unit proposes the optimal sales destination based on the quality of harvested crops and market demand data. The matching unit can also propose sales channels that are expected to improve profitability. As a result, the agricultural production optimization system according to this embodiment can efficiently perform data collection, analysis, proposal, prediction, optimization, and matching.

[0079] The data collection unit collects data. For example, the data collection unit monitors the growth of crops using drones and sensors. Specifically, drones fly over farmland and capture high-resolution image data, allowing for detailed observation of the color and shape of crop leaves, as well as signs of pests and diseases. Drones are equipped with multispectral cameras and infrared cameras, which can be used to visualize the health and stress levels of crops. Sensors collect data such as soil moisture, temperature, pH value, and nutrient concentration in real time. This allows for a detailed understanding of the environmental conditions of the entire farmland. The data collection unit centrally manages this data and transmits it to a cloud server, making it accessible to other departments. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific crops and environmental conditions. For example, data can be collected frequently in the early stages of growth and reduced once growth has stabilized. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0080] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data. Specifically, the AI ​​uses image recognition technology to analyze image data captured by drones and detect the health of crops and signs of pests and diseases. For example, it can detect changes in leaf color and abnormal shapes, which helps in the early detection of pests and diseases. It also analyzes data obtained from soil sensors to evaluate the nutrient status and moisture content of the soil. This makes it possible to understand the optimal environmental conditions for crop growth. Furthermore, the analysis unit analyzes the risk of pest and disease outbreaks based on past weather data and crop growth data. For example, it can analyze how specific weather and soil conditions affect the occurrence of pests and diseases, and identify high-risk periods and areas. As a result, the analysis unit can quickly and accurately analyze the collected data and understand the health and growth status of crops in real time. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0081] The proposal department makes optimal suggestions based on the analysis results obtained by the analysis department. For example, the proposal department proposes optimal cultivation methods and harvest times based on the analysis results. Specifically, it proposes appropriate types of fertilizers, timing of fertilization, and irrigation methods based on the health and growth status of the crops. Regarding harvest times, it proposes the optimal harvest time considering crop growth data and market demand data. This maximizes crop quality and improves profitability. Furthermore, the proposal department can also propose optimal sales destinations based on the quality of harvested crops and market demand data. For example, for crops with high demand in a particular market, it proposes shipping to that market to improve profitability. In addition, the proposal department can utilize the experience and knowledge of agricultural workers to propose optimal cultivation methods for individual farms and crops. In this way, the proposal department can support the efficiency and profitability of agricultural production.

[0082] The prediction unit predicts pest and disease outbreaks. For example, it predicts the risk of pest and disease outbreaks based on past weather data and crop growth data. Specifically, it uses AI to analyze past data and model how specific weather and soil conditions affect pest and disease outbreaks. This allows it to predict the risk of pest and disease outbreaks based on future weather and soil conditions. For example, it predicts that the risk of certain pests and diseases will increase during periods of high temperature and humidity, allowing appropriate countermeasures to be taken during those times. The prediction unit can also analyze the risk of pest and disease outbreaks and propose appropriate countermeasures. For example, if there is a high risk of a particular pest or disease outbreak, it will propose control methods and pesticide usage methods for that pest or disease. This allows the prediction unit to prevent pest and disease outbreaks and maintain the health of crops. Furthermore, the prediction unit updates prediction results in real time, enabling it to respond to the latest situation. This allows the prediction unit to always provide highly accurate predictions based on the latest information, supporting quick and appropriate responses.

[0083] The optimization unit optimizes the harvest time. For example, the optimization unit analyzes the timing of harvest based on crop growth data and weather data. Specifically, it identifies the optimal harvest time by considering the crop's growth stage and weather conditions. For example, harvesting when crop growth is at its peak and quality is at its highest can maximize profits. By considering weather conditions, it can also identify the time when harvesting can be performed most efficiently. In this way, the optimization unit can simultaneously achieve increased efficiency in harvesting and improved crop quality. Furthermore, the optimization unit can not only optimize the harvest time but also propose methods for post-harvest processing and storage. For example, it can propose methods for properly storing harvested crops and maintaining their quality. In this way, the optimization unit can optimize the entire process from harvesting to sales, supporting increased efficiency and profitability in agricultural production.

[0084] The matching department performs sales channel matching. For example, the matching department proposes the most suitable sales destination based on the quality of harvested crops and market demand data. Specifically, it analyzes crop quality and market demand to identify the most profitable sales destinations. For example, for high-quality crops, it proposes sales to markets and buyers that trade at high prices. Also, for crops with high demand in specific markets, it proposes shipments to those markets to improve profitability. Furthermore, the matching department can also support negotiations and contracts with sales destinations. For example, it provides advice on sales conditions and price negotiations, helping farmers to conduct transactions under favorable conditions. In this way, the matching department can efficiently match agricultural producers with sales destinations and support improved profitability. In addition, the matching department can collect feedback from sales destinations and reflect it in future sales strategies. In this way, the matching department can continuously improve sales strategies and increase the profitability of agricultural producers.

[0085] The agricultural production optimization system includes a digitization unit that digitizes know-how. The digitization unit digitizes, for example, the know-how of veteran farmers. The digitization unit can digitize know-how using methods such as text conversion, video conversion, and database creation. The digitization unit can also digitize know-how using AI. The digitization unit can provide the digitized know-how to new farmers and offer appropriate advice, thereby supporting the transfer of agricultural technology. Some or all of the above-described processes in the digitization unit may be performed using AI, or not. For example, the digitization unit can digitize the know-how of veteran farmers, input that text data into a generating AI, and improve the accuracy of the digitization using the generating AI.

[0086] The data collection unit collects data using drones and sensors. For example, the data collection unit collects image data taken by a drone while flying over farmland. The data collection unit can also collect data such as soil moisture and temperature, and crop growth status using sensors. For example, the data collection unit can monitor the health of crops using a drone equipped with a multispectral camera. The data collection unit can also measure soil nutrient status and moisture content using soil sensors. This allows for efficient monitoring of crop growth. 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 image data taken by a drone into a generating AI, and the generating AI can analyze the image data.

[0087] The analysis unit analyzes the collected data using AI. The analysis unit analyzes the collected data using, for example, machine learning algorithms. The analysis unit can analyze the health and growth status of crops using, for example, deep learning technology. The analysis unit can also analyze crop growth predictions and the risk of pest and disease outbreaks based on the collected data. This improves the accuracy of data analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generating AI, and the generating AI can perform data analysis.

[0088] The proposal unit makes optimal suggestions based on the analysis results. For example, the proposal unit proposes the optimal cultivation method aimed at maximizing yields or reducing costs. The proposal unit can also propose the optimal sales destination based on the quality of harvested crops and market demand data. The proposal unit can make optimal suggestions based on the analysis results using AI, for example. This makes optimal suggestions possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the analysis results into a generating AI, and the generating AI can make optimal suggestions.

[0089] The prediction unit predicts the occurrence of pests and diseases. The prediction unit predicts the risk of pest and disease outbreaks based, for example, on the analysis of past data and consideration of weather conditions. The prediction unit can, for example, use AI to analyze the risk of pest and disease outbreaks and propose appropriate countermeasures. The prediction unit can also, for example, propose the timing and amount of pesticide use based on the risk of pest and disease outbreaks. This makes it possible to prevent damage from pests and diseases. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input past weather data and crop growth data into a generating AI, and the generating AI can predict the risk of pest and disease outbreaks.

[0090] The optimization unit optimizes the harvest time. The optimization unit analyzes the timing of harvest based on factors such as the crop's growth stage and weather conditions. The optimization unit can use AI to optimize the harvest time and propose the optimal harvest time. The optimization unit can also propose a harvest time aimed at maximizing yield or improving quality. This helps to maximize yield and improve quality. Some or all of the above-described processes in the optimization unit may be performed using AI, or not. For example, the optimization unit can input crop growth data and weather data into a generating AI, which can then propose the optimal harvest time.

[0091] The matching unit performs sales channel matching. The matching unit proposes the most suitable sales destination based, for example, on the quality of harvested crops and market demand data. The matching unit can, for example, use AI to perform sales channel matching and propose sales channels that are expected to improve profitability. The matching unit can also propose the most suitable sales destination based, for example, on the quality of harvested crops and market demand data. This is expected to improve profitability. Some or all of the above processing in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input the quality of harvested crops and market demand data into a generating AI, and the generating AI can propose the most suitable sales destination.

[0092] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces 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. This allows the timing of data collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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. For example, the data collection unit can input user facial expression data into a generative AI, which can then estimate the user's emotions and adjust the timing of data collection.

[0093] The data collection unit analyzes past collected data and selects the optimal collection method. For example, the data collection unit analyzes past data collection history to identify the most efficient collection method. For example, the data collection unit can improve the collection method and increase accuracy based on past data collection results. The data collection unit can also analyze past successful and unsuccessful data collection cases to select the optimal collection method. By selecting the optimal collection method, the efficiency of data collection is improved. Some or all of the above 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 collected data into a generating AI, and the generating AI can select the optimal collection method.

[0094] The data collection unit filters data based on the geographical and meteorological conditions of the farmland during data collection. For example, the data collection unit considers the geographical conditions of the farmland and places appropriate sensors. For example, the data collection unit can adjust the timing of data collection based on meteorological conditions. For example, the data collection unit can combine geographical and meteorological conditions to create an optimal data collection plan. This improves the accuracy of data collection based on geographical and meteorological conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the geographical and meteorological conditions of the farmland into a generating AI, and the generating AI can perform the filtering of data collection.

[0095] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting only important data. For example, if the user is relaxed, the data collection unit can collect detailed data to improve the accuracy of the analysis. For example, if the user is in a hurry, the data collection unit can also prioritize data that can be collected quickly. This allows the data to be prioritized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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. For example, the data collection unit can input user facial expression data into a generative AI, which can then estimate the user's emotions and determine the priority of data to collect.

[0096] The data collection unit prioritizes the collection of highly relevant data, taking into account the geographical location information of the farmland during data collection. For example, the data collection unit prioritizes the collection of data from a specific area based on the geographical location information of the farmland. For example, the data collection unit can select the types of data to collect, taking into account the geographical location information. For example, the data collection unit can adjust the frequency of data collection based on the geographical location information. This allows for the priority collection of highly relevant data based on the geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the farmland into a generating AI, which can then prioritize the collection of highly relevant data.

[0097] The data collection unit analyzes the social media activities of farmers during data collection and collects relevant data. For example, the data collection unit analyzes farmers' social media posts to identify data to be collected. For example, the data collection unit can determine the priority of data collection based on the information obtained from social media activities. The data collection unit can also analyze social media trends and collect relevant data. This allows for the collection of relevant data by analyzing 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 post data into a generating AI, and the generating AI can collect relevant data.

[0098] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generative AI, have the generative AI estimate the user's emotions, and adjust the presentation of the analysis.

[0099] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data to improve accuracy. For example, the analysis unit can perform a simplified analysis on less important data. The analysis unit can also optimally allocate analysis resources according to the importance of the data. This allows the level of detail of the analysis to be adjusted 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 the generating AI can adjust the level of detail of the analysis.

[0100] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a growth analysis algorithm to crop growth data. For example, it can apply a weather analysis algorithm to weather data. For example, it can apply a pest and disease analysis algorithm to pest and disease data. This allows the optimal analysis algorithm to be applied according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply the optimal analysis algorithm.

[0101] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides 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. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generative AI, which can then estimate the user's emotions and adjust the length of the analysis.

[0102] The analysis unit determines the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit prioritizes the analysis of the latest data to provide real-time information. For example, the analysis unit can analyze current data while referring to past data. For example, the analysis unit can also optimally allocate analysis resources according to the data collection timing. This allows the analysis priority to be determined 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 the generating AI can determine the analysis priority.

[0103] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data to improve accuracy. For example, the analysis unit can perform analysis efficiently by postponing the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This allows the order of analysis to be adjusted 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 the generating AI can adjust the order of analysis.

[0104] The suggestion unit estimates the user's emotions and adjusts the way the suggestion is presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit will make a simple and easily understandable suggestion. If the user is relaxed, the suggestion unit can make a detailed suggestion. If the user is in a hurry, the suggestion unit can also make a concise suggestion. This allows the presentation of the suggestion to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which will estimate the user's emotions and adjust the presentation of the suggestion.

[0105] The proposal unit adjusts the level of detail of the proposal based on the importance of the analysis results. For example, the proposal unit provides detailed proposals for important analysis results. For example, it can provide simplified proposals for less important analysis results. The proposal unit can also optimally allocate resources for the proposal according to the importance of the analysis results. This allows the level of detail of the proposal to be adjusted according to the importance of the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the analysis results into a generating AI, and the generating AI can adjust the level of detail of the proposal.

[0106] The proposal unit applies different proposal algorithms depending on the category of the analysis results during the proposal process. For example, the proposal unit may apply a growth proposal algorithm to crop growth analysis results. For example, the proposal unit may apply a weather proposal algorithm to weather analysis results. For example, the proposal unit may apply a pest and disease proposal algorithm to pest and disease analysis results. This allows the application of the most suitable proposal algorithm according to the category of the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit may input the category of the analysis results into a generating AI, and the generating AI may apply the most suitable proposal algorithm.

[0107] The suggestion unit estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a detailed suggestion. If the user is excited, the suggestion unit can also provide a visually stimulating suggestion. This allows the length of the suggestion to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can then estimate the user's emotions and adjust the length of the suggestion.

[0108] The proposal unit determines the priority of proposals based on the timing of analysis result collection when making a proposal. For example, the proposal unit prioritizes the latest analysis results and provides real-time information. For example, the proposal unit can make current proposals while referring to past analysis results. For example, the proposal unit can also optimally allocate resources for proposals according to the timing of analysis result collection. This allows the proposal unit to determine the priority of proposals based on the timing of analysis result collection. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of analysis result collection into a generating AI, and the generating AI can determine the priority of proposals.

[0109] The proposal unit adjusts the order of proposals based on the relevance of the analysis results when making proposals. For example, the proposal unit prioritizes proposing highly relevant analysis results to improve accuracy. For example, the proposal unit can efficiently make proposals by postponing less relevant analysis results. The proposal unit can also optimally adjust the order of proposals according to the relevance of the analysis results. This allows the order of proposals to be adjusted based on the relevance of the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the analysis results into a generating AI, and the generating AI can adjust the order of proposals.

[0110] The prediction unit estimates the user's emotions and adjusts the display method of the prediction based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. For example, if the user is relaxed, the prediction unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit can also provide a display method that gets straight to the point. This allows the display method of the prediction to be adjusted 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 without AI. For example, the prediction unit can input user facial expression data into the generative AI, the generative AI can estimate the user's emotions, and the display method of the prediction can be adjusted.

[0111] The prediction unit optimizes the current prediction by referring to past prediction data during the prediction process. For example, the prediction unit improves the accuracy of the current prediction based on past prediction data. For example, the prediction unit can make the current prediction while referring to past prediction results. For example, the prediction unit can also analyze past prediction data and apply the optimal prediction algorithm. This improves the accuracy of the current prediction by referring to past prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past prediction data into a generating AI, and the generating AI can optimize the current prediction.

[0112] The prediction unit applies different prediction algorithms to each data category during prediction. For example, the prediction unit can apply a growth prediction algorithm to crop growth data. For example, it can apply a weather prediction algorithm to weather data. For example, it can apply a pest and disease prediction algorithm to pest and disease data. This allows the optimal prediction algorithm to be applied according to the data category. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the data category into a generating AI, and the generating AI can apply the optimal prediction algorithm.

[0113] The prediction unit estimates the user's emotions and adjusts the importance of predictions based on the estimated emotions. For example, if the user is nervous, the prediction unit prioritizes displaying only important predictions. If the user is relaxed, the prediction unit can provide detailed prediction information. If the user is in a hurry, the prediction unit can also provide concise prediction information. This allows the importance of predictions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or not. For example, the prediction unit can input user facial expression data into a generative AI, which can then estimate the user's emotions and adjust the importance of predictions.

[0114] The prediction unit analyzes changes in the prediction based on the data collection timing during the prediction process. The prediction unit can, for example, analyze changes in the prediction in real time based on the latest data. The prediction unit can, for example, analyze changes in the current prediction by referring to past data. The prediction unit can also, for example, optimally analyze changes in the prediction according to the data collection timing. This allows for analysis of changes in the prediction based on the data collection timing. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the data collection timing into a generating AI, and the generating AI can analyze changes in the prediction.

[0115] The forecasting unit analyzes the forecast by referring to relevant market data during the forecasting process. The forecasting unit improves the accuracy of the forecast based on relevant market data, for example. The forecasting unit can make the current forecast by referring to market data, for example. The forecasting unit can also analyze relevant market data and apply the optimal forecasting algorithm, for example. This improves the accuracy of the forecast by referring to relevant market data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input relevant market data into a generating AI and have the generating AI analyze the forecast.

[0116] The optimization unit estimates the user's emotions and adjusts the optimization method based on the estimated emotions. For example, if the user is tense, the optimization unit can provide a simple and easily understandable optimization method. For example, if the user is relaxed, the optimization unit can provide a detailed optimization method. For example, if the user is in a hurry, the optimization unit can provide a concise optimization method. This allows the optimization method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input user facial expression data into a generative AI, have the generative AI estimate the user's emotions, and adjust the optimization method.

[0117] The optimization unit optimizes the optimization algorithm by referring to past optimization data during the optimization process. The optimization unit improves the accuracy of the current optimization based on past optimization data, for example. The optimization unit can perform the current optimization while referring to past optimization results, for example. The optimization unit can also analyze past optimization data and apply the optimal optimization algorithm, for example. This improves the accuracy of the current optimization by referring to past optimization data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past optimization data into a generating AI and optimize the optimization algorithm using the generating AI.

[0118] The optimization unit applies different optimization methods to each data category during optimization. For example, the optimization unit can apply a growth optimization method to crop growth data. For example, it can apply a weather optimization method to weather data. For example, it can apply a pest and disease optimization method to pest and disease data. This allows the optimal optimization method to be applied according to the data category. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the data category into a generating AI, and the generating AI can apply the optimal optimization method.

[0119] The optimization unit estimates the user's emotions and determines optimization priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit prioritizes only the most important optimizations. If the user is relaxed, the optimization unit can perform detailed optimizations. If the user is in a hurry, the optimization unit can also perform concise optimizations. This allows the optimization priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Optimization can be performed.

[0120] The optimization unit weights the optimization based on the data collection timing during optimization. For example, the optimization unit weights the optimization based on the latest data. For example, the optimization unit can weight the current optimization by referring to past data. For example, the optimization unit can optimally adjust the optimization weights according to the data collection timing. This allows the optimization weights to be weighted based on the data collection timing. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the data collection timing into a generating AI, and the generating AI can perform the optimization weighting.

[0121] The optimization unit proposes optimization methods by referring to relevant market data during optimization. For example, the optimization unit proposes optimization methods based on relevant market data. For example, the optimization unit can perform current optimization while referring to market data. For example, the optimization unit can analyze relevant market data and propose the optimal optimization method. In this way, the optimal optimization method can be proposed by referring to relevant market data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input relevant market data into a generating AI, and the generating AI can propose optimization methods.

[0122] The matching unit estimates the user's emotions and adjusts the matching method based on the estimated emotions. For example, if the user is nervous, the matching unit can provide a simple and highly visible matching method. For example, if the user is relaxed, the matching unit can provide a detailed matching method. For example, if the user is in a hurry, the matching unit can provide a concise matching method. This allows the matching method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 matching unit may be performed using AI or not using AI. For example, the matching unit can input user facial expression data into a generative AI, which can then estimate the user's emotions and adjust the matching method.

[0123] The matching unit optimizes the matching algorithm by referring to past matching data during the matching process. The matching unit improves the accuracy of the current matching based on past matching data, for example. The matching unit can perform the current matching while referring to past matching results, for example. The matching unit can also analyze past matching data and apply the optimal matching algorithm, for example. This improves the accuracy of the current matching by referring to past matching data. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input past matching data into a generating AI, and the generating AI can optimize the matching algorithm.

[0124] The matching unit applies different matching methods to each data category during the matching process. For example, the matching unit can apply a growth matching method to crop growth data. For example, it can apply a weather matching method to weather data. For example, it can apply a pest and disease matching method to pest and disease data. This allows the optimal matching method to be applied according to the data category. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the data category into a generating AI, and the generating AI can apply the optimal matching method.

[0125] The matching unit estimates the user's emotions and determines matching priorities based on the estimated emotions. For example, if the user is nervous, the matching unit prioritizes only important matches. If the user is relaxed, the matching unit can perform detailed matches. If the user is in a hurry, the matching unit can also perform concise matches. This allows the matching priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 matching unit may be performed using AI or not. For example, the matching unit can input user facial expression data into a generative AI, which can then estimate the user's emotions and determine matching priorities.

[0126] The matching unit weights the matches based on the data collection timing during the matching process. For example, the matching unit weights the matches based on the latest data. For example, the matching unit can weight the current matches by referring to past data. For example, the matching unit can also optimally adjust the matching weights according to the data collection timing. This allows the matching weights to be determined based on the data collection timing. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the data collection timing into a generating AI, and the generating AI can perform the matching weighting.

[0127] The matching unit proposes matching methods by referring to relevant market data during the matching process. For example, the matching unit proposes matching methods based on relevant market data. For example, the matching unit can perform current matching while referring to market data. The matching unit can also, for example, analyze relevant market data and propose the optimal matching method. This allows the optimal matching method to be proposed by referring to relevant market data. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input relevant market data into a generating AI, and the generating AI can propose matching methods.

[0128] The digitization unit estimates the user's emotions and adjusts the digitization method based on the estimated emotions. For example, if the user is tense, the digitization unit can provide a simple and highly visible digitization method. For example, if the user is relaxed, the digitization unit can provide a detailed digitization method. For example, if the user is in a hurry, the digitization unit can provide a concise digitization method. This allows the digitization method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input user facial expression data into the generative AI, the generative AI can estimate the user's emotions, and the digitization method can be adjusted.

[0129] The digitization unit optimizes the digitization algorithm by referring to past digitization data during the digitization process. For example, the digitization unit improves the accuracy of the current digitization based on past digitization data. For example, the digitization unit can perform the current digitization while referring to past digitization results. For example, the digitization unit can also analyze past digitization data and apply the optimal digitization algorithm. This improves the accuracy of the current digitization by referring to past digitization data. Some or all of the above processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input past digitization data into a generating AI and optimize the digitization algorithm using the generating AI.

[0130] The digitization unit estimates the user's emotions and determines the priority of digitization based on the estimated emotions. For example, if the user is tense, the digitization unit will prioritize only important digitization. For example, if the user is relaxed, the digitization unit can perform detailed digitization. For example, if the user is in a hurry, the digitization unit can also perform concise digitization. This allows the priority of digitization to be determined 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 digitization unit may be performed using AI, for example, or not using AI. For example, the digitization unit can input user facial expression data into a generative AI, which will estimate the user's emotions and determine the priority of digitization.

[0131] The digitization unit weights the digitization process based on the data collection timing. For example, the digitization unit weights the digitization process based on the latest data. For example, the digitization unit can weight the current digitization process while referring to past data. For example, the digitization unit can also optimally adjust the digitization weighting according to the data collection timing. This allows for digitization weighting based on the data collection timing. Some or all of the above-described processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the data collection timing into a generating AI, and the generating AI can perform the digitization weighting.

[0132] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0133] The data collection unit can utilize satellite imagery in addition to drones and sensors when monitoring crop growth. For example, using satellite imagery allows for a comprehensive overview of crop growth across a wide area of ​​farmland. This enables efficient data collection for vast areas of farmland that cannot be covered by drones or sensors alone. Furthermore, analyzing satellite imagery allows for the identification of overall farmland health and any abnormalities. By comparing satellite imagery with historical data, long-term growth trends can be identified. This allows for more accurate monitoring of crop growth.

[0134] The analysis unit can also utilize cloud computing when analyzing collected data. For example, analyzing data on the cloud allows for rapid processing of large amounts of data. This improves the speed of analysis and enables real-time data analysis. Furthermore, analysis on the cloud allows for the integration and analysis of multiple data sources. In addition, cloud computing makes the analysis results accessible from multiple devices. This makes it easier to share analysis results, allowing agricultural workers to check the results from anywhere.

[0135] The proposal department can also consider the user's past behavior history when making optimal suggestions based on analysis results. For example, it can make suggestions tailored to the user's preferences based on data on cultivation methods and harvest times the user has used in the past. This makes it possible to make suggestions that are more practical and acceptable to the user. Furthermore, by analyzing past behavior history, it is possible to understand the user's tendencies and patterns and make more accurate suggestions. In addition, by referring to the user's past successes and failures, it is possible to make suggestions that minimize risk. This can improve user satisfaction.

[0136] The prediction unit can also refer to local pest and disease outbreak information when predicting the occurrence of pests and diseases. For example, it can make more accurate predictions based on pest and disease outbreak information provided by local agricultural cooperatives or government agencies. This allows for an understanding of the overall pest and disease situation in the region and the implementation of appropriate countermeasures. Furthermore, obtaining local pest and disease outbreak information in real time enables a rapid response. In addition, by comparing local pest and disease outbreak information with past data, it is possible to understand trends in pest and disease outbreaks and implement preventive measures. This makes it possible to prevent damage from pests and diseases before it occurs.

[0137] The optimization unit can also apply different optimization algorithms to each crop variety when optimizing harvest timing. For example, by using optimization algorithms tailored to the growth characteristics of different crops such as tomatoes and cucumbers, it becomes possible to propose more accurate harvest timings. This allows for the determination of the optimal harvest time for each crop. Furthermore, by applying variety-specific optimization algorithms, it is possible to maximize yields and improve quality. In addition, by applying variety-specific optimization algorithms, the efficiency of harvesting work can be improved, thereby reducing the burden of harvesting.

[0138] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. This reduces user stress and improves work efficiency. Conversely, if the user is relaxed, more detailed data can be collected to obtain more information. This improves the accuracy of the data. Furthermore, if the user is in a hurry, only important data can be collected as a priority. This allows the user to make better use of their time.

[0139] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and highly visual analysis results, making them easier for the user to understand. If the user is relaxed, it can provide detailed analysis results, allowing the user to understand them more deeply. Furthermore, if the user is in a hurry, it can provide concise analysis results, allowing the user to quickly obtain the information they need.

[0140] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is stressed, it can present a simple and highly visual proposal, making it easier for the user to understand. If the user is relaxed, it can present a more detailed proposal, allowing the user to understand it more deeply. Furthermore, if the user is in a hurry, it can present a concise proposal, allowing the user to quickly obtain the information they need.

[0141] The prediction unit can estimate the user's emotions and adjust the display method of the prediction based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided, making it easier for the user to understand the prediction results. If the user is relaxed, a display method including detailed information can be provided, allowing the user to understand the prediction results more deeply. Furthermore, if the user is in a hurry, a concise display method can be provided, allowing the user to quickly obtain the information they need.

[0142] The optimization unit can estimate the user's emotions and adjust the optimization method based on those emotions. For example, if the user is stressed, it can provide a simple and highly visual optimization method, making it easier for the user to understand. If the user is relaxed, it can provide a more detailed optimization method, allowing the user to understand it more deeply. Furthermore, if the user is in a hurry, it can provide a concise optimization method, allowing the user to quickly obtain the information they need.

[0143] The following briefly describes the processing flow for example form 2.

[0144] Step 1: The collection unit collects data. The collection unit monitors the growth status of crops using, for example, drones or sensors. The collection unit can collect, for example, image data taken by a drone while flying over farmland, or soil data collected by sensors. The collection unit can also collect data such as soil moisture and temperature, and crop growth status. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, AI. The analysis unit can, for example, understand the health and growth status of crops based on the collected data. The analysis unit can also, for example, analyze the risk of pest and disease outbreaks based on past weather data and crop growth data. Step 3: The proposal unit makes optimal suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit may suggest the optimal cultivation method and harvest time based on the analysis results. The proposal unit may also suggest the optimal sales destination based on the quality of the harvested crops and market demand data. Step 4: The prediction unit predicts pest and disease outbreaks. For example, the prediction unit predicts the risk of pest and disease outbreaks based on past weather data and crop growth data. The prediction unit can also analyze the risk of pest and disease outbreaks and propose appropriate countermeasures. Step 5: The optimization unit optimizes the harvest time. The optimization unit analyzes the timing of harvest based on, for example, crop growth data and weather data. The optimization unit can also, for example, suggest the optimal harvest time. Step 6: The matching department performs sales channel matching. The matching department proposes the most suitable sales channels based on data such as the quality of harvested crops and market demand. The matching department can also propose sales channels that are expected to improve profitability.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, prediction unit, optimization unit, and matching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit monitors the growth status of crops using the drone and sensors of the smart device 14. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal cultivation method and harvest time based on the analysis results. The prediction unit predicts the risk of pest and disease outbreaks and proposes appropriate countermeasures. The optimization unit optimizes the harvest time and proposes the optimal harvest timing. The matching unit proposes the optimal sales destination based on the quality of the harvested crops and market demand data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[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 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.

[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 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.

[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 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, prediction unit, optimization unit, and matching unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit monitors the growth status of crops using the camera and sensors of the smart glasses 214. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal cultivation method and harvest time based on the analysis results. The prediction unit predicts the risk of pest and disease outbreaks and proposes appropriate countermeasures. The optimization unit optimizes the harvest time and proposes the optimal harvest timing. The matching unit proposes the optimal sales destination based on the quality of the harvested crops and market demand data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, prediction unit, optimization unit, and matching unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the data collection unit monitors the growth status of crops using the camera and sensors of the headset terminal 314. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal cultivation method and harvest time based on the analysis results. The prediction unit predicts the risk of pest and disease outbreaks and proposes appropriate countermeasures. The optimization unit optimizes the harvest time and proposes the optimal harvest timing. The matching unit proposes the optimal sales destination based on the quality of the harvested crops and market demand data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0181] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, prediction unit, optimization unit, and matching unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit monitors the growth status of crops using the camera and sensors of the robot 414. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal cultivation method and harvest time based on the analysis results. The prediction unit predicts the risk of pest and disease outbreaks and proposes appropriate countermeasures. The optimization unit optimizes the harvest time and proposes the optimal harvest timing. The matching unit proposes the optimal sales destination based on the quality of the harvested crops and market demand data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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."

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes an optimal proposal based on the analysis results obtained by the analysis unit, A prediction unit that predicts pests and diseases, An optimization unit for optimizing the harvest time, It includes a matching unit that performs sales channel matching. A system characterized by the following features. (Note 2) The company has a digitalization department that digitizes know-how. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect data using drones and sensors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected data is analyzed using AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We will make the best proposal based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The prediction unit, Predicting the occurrence of pests and diseases The system described in Appendix 1, characterized by the features described herein. (Note 7) The optimization unit, Optimizing harvest time The system described in Appendix 1, characterized by the features described herein. (Note 8) The matching unit is We conduct sales channel matching. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, filtering is performed based on the geographical and meteorological conditions of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the geographical location information of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, analyze the social media activity of agricultural workers and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) 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 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) 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 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the analysis results were collected. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, It estimates the user's emotions and adjusts how predictions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, When making a prediction, the current prediction is optimized by referring to past prediction data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, When making predictions, different prediction algorithms are applied for each data category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, It estimates the user's emotions and adjusts the importance of the prediction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The prediction unit, When making predictions, analyze how the predictions change based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 32) The prediction unit, When making predictions, we analyze the forecast by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, During optimization, the optimization algorithm is optimized by referring to past optimization data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The optimization unit, During optimization, different optimization methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 36) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The optimization unit, During optimization, weights are applied to the optimization based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 38) The optimization unit, During optimization, we propose optimization methods by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The matching unit is It estimates the user's emotions and adjusts the matching method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The matching unit is During the matching process, the matching algorithm is optimized by referring to past matching data. The system described in Appendix 1, characterized by the features described herein. (Note 41) The matching unit is When matching, different matching methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 42) The matching unit is The system estimates the user's emotions and determines matching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The matching unit is When performing matching, perform weighting of the matching based on the data collection time. The system according to Addendum 1, characterized by this. (Addendum 44) The matching unit When performing matching, propose a means of matching by referring to relevant market data of the data. The system according to Addendum 1, characterized by this. (Addendum 45) The digitization unit Estimate the user's emotion and adjust the digitization method based on the estimated user's emotion. The system according to Addendum 2, characterized by this. (Addendum 46) The digitization unit When digitizing, optimize the digitization algorithm by referring to past digitized data. The system according to Addendum 2, characterized by this. (Addendum 47) The digitization unit Estimate the user's emotion and determine the digitization priority based on the estimated user's emotion. The system according to Addendum 2, characterized by this. (Addendum 48) The digitization unit When digitizing, perform weighting of the digitization based on the data collection time The system according to Addendum 2, characterized by this

Explanation of Signs

[0217] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes an optimal proposal based on the analysis results obtained by the analysis unit, A prediction unit that predicts pests and diseases, An optimization unit for optimizing the harvest time, It includes a matching unit that performs sales channel matching. A system characterized by the following features.

2. The company has a digitalization department that digitizes know-how. The system according to feature 1.

3. The aforementioned collection unit is Collect data using drones and sensors. The system according to feature 1.

4. The aforementioned analysis unit, The collected data is analyzed using AI. The system according to feature 1.

5. The aforementioned proposal section is, We will make the best proposal based on the analysis results. The system according to feature 1.

6. The prediction unit, Predicting the occurrence of pests and diseases The system according to feature 1.

7. The optimization unit, Optimizing harvest time The system according to feature 1.

8. The matching unit is We conduct sales channel matching. The system according to feature 1.

9. 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.

Images