System

Abstract

An object of the system according to the embodiment is to optimize the growth of crops by analyzing environmental data, growth records, and work histories.SOLUTION: A system includes a data collection unit, an analysis unit, a simulation generation unit, and a communication unit. The data collection unit collects environmental data, growth records, and work histories. The analyzer analyzes the environmental data, the growth record, and the work history collected by the data collector. The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. The communication unit provides the simulation result generated by the simulation generation unit to the agriculture worker.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately utilized environmental data, growth records, and work history to optimize crop growth, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize crop growth by analyzing environmental data, growth records, and work history. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a simulation generation unit, and a communication unit. The data collection unit collects environmental data, growth records, and work history. The analysis unit analyzes the environmental data, growth records, and work history collected by the data collection unit. The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. The communication unit provides the farmer with the simulation results generated by the simulation generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze environmental data, growth records, and work history to optimize crop growth. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​system according to the embodiment of the present invention collects data such as environmental data, growth records, and work history, analyzes the data, generates crop simulations based on the results, and communicates with farmers. This allows farmers to accurately grasp the growth status of their crops and implement optimal cultivation methods.

[0029] The AI ​​system according to the embodiment includes a data collection unit, an analysis unit, a simulation generation unit, and a communication unit. The data collection unit collects environmental data, growth records, and work histories. For example, the data collection unit uses sensors to collect environmental data such as temperature, humidity, and precipitation. The data collection unit can also collect crop growth stages and health conditions as growth records by manual input or automatic sensors. The data collection unit also records work data such as fertilization, irrigation, and harvesting as work histories. The analysis unit analyzes the environmental data, growth records, and work histories collected by the data collection unit. For example, the analysis unit can analyze data using data mining technology to understand the growth status of crops. The analysis unit can also analyze data trends using statistical analysis. The analysis unit can also analyze data using machine learning algorithms to predict crop growth. The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. For example, the simulation generation unit can simulate the crop growth process using a growth prediction model. The simulation generation unit can also predict harvest and simulate the harvest yield. The simulation generation unit also performs disease risk assessment and simulates the risk of disease occurrence. The communication unit provides the simulation results generated by the simulation generation unit to the farmer. For example, the communication unit displays the simulation results in a graph and provides them visually to the farmer. The communication unit can also generate a report of the simulation results and provide it to the farmer. The communication unit also performs real-time notifications, immediately notifying the farmer of the simulation results. As a result, the AI ​​system according to the embodiment allows the farmer to accurately grasp the growth status of crops and practice optimal cultivation methods.

[0030] The data collection unit can use drones to collect environmental data from the air in real time. For example, the data collection unit uses sensors mounted on the drone to collect environmental data such as temperature, humidity, and precipitation from the air in real time. The collected data is sent to the generation AI for analysis. The data collection unit also flies drones periodically to collect environmental data for the entire farmland. For example, the drone is flown at a set time each day to record detailed changes in weather conditions. The data collection unit also uses drones to intensively collect environmental data for specific crop areas. For example, areas with a high risk of pest infestation are monitored intensively, and the generation AI analyzes the data. This allows environmental data to be collected from the air in real time.

[0031] The data collection unit can use soil sensors to measure the nutrient and moisture content of the soil in detail. For example, the data collection unit installs soil sensors in farmland to measure the nutrient and moisture content of the soil in real time. The collected data is sent to the generation AI for analysis. The data collection unit also moves the soil sensor around to collect soil data from different areas. For example, it takes measurements at multiple points to identify factors that affect crop growth. The data collection unit also uses the soil sensor data to allow the generation AI to analyze the soil condition and suggest optimal fertilization and irrigation timing. For example, it recommends irrigation if the soil moisture content drops. This allows for detailed measurement of the nutrient and moisture content of the soil.

[0032] The data collection unit can collect agricultural data from different regions and analyze the optimal cultivation method for each region. The data collection unit, for example, collects weather and soil data from different regions, and the generation AI analyzes the optimal cultivation method for each region. For example, it selects crops according to the climatic conditions of each region. The data collection unit also collects feedback from farmers in different regions, and the generation AI analyzes that data. For example, it proposes the optimal cultivation method based on success stories and failure stories from each region. The data collection unit also allows the generation AI to create a cultivation calendar for each region based on the agricultural data from different regions. For example, it proposes the timing of sowing and harvesting according to the weather conditions of each region. This makes it possible to analyze the optimal cultivation method for each region.

[0033] The data collection unit collects animal behavior data and can use it to optimize pest control and pollination. For example, the data collection unit installs sensors and cameras to collect animal behavior data, and the generation AI analyzes the data. For example, it identifies pest infestation patterns and proposes countermeasures. The data collection unit also collects behavioral data of bees and other pollinators to promote pollination, and the generation AI analyzes the data. For example, it proposes the optimal timing for pollination. The data collection unit also uses the animal behavior data to allow the generation AI to propose farmland management methods. For example, it identifies areas where pests are infested and takes countermeasures in those areas. In this way, the collection of animal behavior data can be used to optimize pest control and pollination.

[0034] The simulation generation unit can simulate the risk of crop disease under different weather conditions and propose preventive measures. In the simulation generation unit, for example, the generation AI simulates the risk of crop disease under different weather conditions and proposes preventive measures. For example, it simulates the risk of disease under high temperature and humidity conditions and proposes appropriate control methods. In addition, the simulation generation unit predicts the risk of disease based on past weather data and proposes preventive measures. For example, it analyzes past data and identifies disease occurrence patterns under specific weather conditions. In addition, the simulation generation unit simulates the risk of disease based on real-time weather data and proposes preventive measures. For example, it proposes control methods according to the current weather conditions. In this way, it is possible to simulate the risk of crop disease under different weather conditions and propose preventive measures.

[0035] The simulation generation unit can analyze the genetic information of crops and simulate the optimal breeding method. In the simulation generation unit, for example, the generation AI analyzes the genetic information of crops and simulates the optimal breeding method. For example, the growth characteristics of crops with specific genes are simulated and the optimal breeding method is proposed. In addition, the simulation generation unit simulates crops with different gene combinations and proposes the optimal breeding method. For example, the generation AI simulates gene combinations for improving disease resistance and yield. In addition, the simulation generation unit simulates crop growth based on the genetic information and proposes the optimal breeding method. For example, the growth rate and yield of crops with specific genes are simulated. In this way, the genetic information of crops can be analyzed and the optimal breeding method can be simulated.

[0036] The simulation generation unit can simulate the interactions between different crops and optimize intercropping. In the simulation generation unit, for example, the generation AI simulates the interactions between different crops and optimizes intercropping. For example, it simulates how specific crop combinations affect each other. In addition, the simulation generation unit uses the generation AI to simulate the effects of intercropping and propose optimal crop combinations. For example, it simulates crop combinations that take into account pest control and soil nutrient balance. In addition, the simulation generation unit uses the generation AI to simulate the optimization of intercropping based on the interactions between different crops. For example, it proposes crop combinations that improve yield and quality. In this way, it is possible to simulate the interactions between different crops and optimize intercropping.

[0037] The simulation generation unit can analyze agricultural machinery operation data and simulate the optimal way to use the machinery. In the simulation generation unit, for example, the generation AI analyzes agricultural machinery operation data and simulates the optimal way to use the machinery. For example, it proposes efficient usage methods based on operation data of tractors and combine harvesters. In addition, the simulation generation unit can analyze agricultural machinery maintenance data and simulate the optimal maintenance schedule. For example, it proposes maintenance timing to reduce the risk of machinery breakdown. In addition, the simulation generation unit can perform simulations to improve work efficiency based on agricultural machinery operation data. For example, it makes proposals to optimize the machinery's operating hours and fuel consumption. In this way, it is possible to analyze agricultural machinery operation data and simulate the optimal way to use the machinery.

[0038] The communication unit uses voice recognition technology to converse with farmers and provide advice in real time. In the communication unit, for example, the generation AI uses voice recognition technology to converse with farmers and provide advice in real time. For example, the farmer inputs a question by voice, and the generation AI immediately returns an answer. In addition, in the communication unit, the generation AI uses voice recognition technology to provide advice in real time while the farmer is working. For example, it receives instructions by voice while working and suggests appropriate work methods. In addition, in the communication unit, the generation AI uses voice recognition technology to provide detailed information in response to questions from farmers. For example, it provides voice explanations about how to cultivate a particular crop or how to prevent disease. In this way, it is possible to converse with farmers and provide advice in real time using voice recognition technology.

[0039] The communication unit can analyze the farmer's past question history and provide personalized advice. For example, the generation AI in the communication unit analyzes the farmer's past question history and provides personalized advice. For example, it provides related information based on the content of questions asked in the past. The communication unit also uses the generation AI to propose individual cultivation methods and work schedules based on the farmer's question history. For example, it proposes the optimal timing for fertilization and irrigation based on the content of past questions. The communication unit also uses the generation AI to analyze the farmer's question history and provide solutions to specific problems. For example, it proposes the latest pest control methods based on information on disease control that has been asked in the past. In this way, the farmer's past question history can be analyzed and personalized advice can be provided.

[0040] The communication department can communicate with farmers through video calls and provide visual information. For example, the generation AI can communicate with farmers through video calls and provide visual information. For example, it can check the condition of crops with a camera and provide advice. The generation AI can also provide visual demonstrations to farmers through video calls. For example, it can explain fertilization and irrigation methods by actually showing them. The generation AI can also share visual materials in response to farmers' questions through video calls. For example, it can explain by sharing photos of diseases or cultivation manuals on the screen. This allows it to communicate with farmers through video calls and provide visual information.

[0041] The communication section can form a community of farmers and promote information sharing and collaboration. For example, the generative AI in the communication section can form a community of farmers and promote information sharing and collaboration. For example, it can create online forums and chat groups to exchange information. The generative AI in the communication section can also analyze questions and answers within the farmer community and propose common issues and solutions. For example, it can connect farmers who have the same problem. The generative AI in the communication section can also support collaboration within the farmer community. For example, it can manage the progress of joint projects and assign tasks. This can form a community of farmers and promote information sharing and collaboration.

[0042] The analysis unit can analyze the collected feedback data and automatically improve the simulation algorithm. In the analysis unit, for example, the generation AI analyzes the collected feedback data and automatically improves the simulation algorithm. For example, it analyzes the difference between the actual harvest yield and the simulation results and adjusts the algorithm. In addition, in the analysis unit, the generation AI improves the simulation algorithm based on feedback from farmers. For example, it develops an algorithm that reflects the opinions and experience of farmers. In addition, in the analysis unit, the generation AI improves the accuracy of the simulation algorithm based on the feedback data. For example, it analyzes past data and makes improvements to increase prediction accuracy. In this way, the collected feedback data can be analyzed and the simulation algorithm can be automatically improved.

[0043] The analysis unit can propose new cultivation methods based on the feedback data and simulate their effects. In the analysis unit, for example, the generation AI proposes new cultivation methods based on the feedback data and simulates their effects. For example, it proposes a new fertilization method based on feedback from farmers. In addition, the analysis unit has the generation AI analyze the feedback data and propose the optimal cultivation method. For example, it simulates a new cultivation method to improve yield and quality. In addition, the analysis unit has the generation AI simulate a new cultivation method based on the feedback data and evaluate its effects. For example, it simulates and proposes the effects of a new irrigation method. This makes it possible to propose a new cultivation method based on the feedback data and simulate its effects.

[0044] The analysis unit can integrate feedback from different farmers and identify common issues. In the analysis unit, for example, the generation AI integrates feedback from different farmers and identifies common issues. For example, it analyzes common problems faced by multiple farmers and proposes solutions. In addition, the analysis unit uses the generation AI to identify common issues based on feedback data and simulate solutions. For example, it proposes control methods for the same disease. In addition, the analysis unit builds a system in which the generation AI analyzes feedback from different farmers and identifies common issues. For example, it clusters the feedback data and extracts common issues. This makes it possible to integrate feedback from different farmers and identify common issues.

[0045] The analysis unit can support the development of new agricultural technologies based on the feedback data. For example, the generation AI in the analysis unit supports the development of new agricultural technologies based on the feedback data. For example, it develops new fertilization techniques based on feedback from farmers. The analysis unit also has the generation AI analyze the feedback data and use it to develop new agricultural technologies. For example, it proposes new technologies to improve harvest yields and quality. The analysis unit also has the generation AI simulate the development of new agricultural technologies based on the feedback data and evaluate their effectiveness. For example, it simulates and proposes the effects of new irrigation technologies. This makes it possible to support the development of new agricultural technologies based on the feedback data.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit can collect health data of agricultural workers and provide advice to improve work efficiency. For example, the data collection unit can use a wearable device to monitor the heart rate and body temperature of agricultural workers and provide advice to reduce the risk of overwork and heatstroke. The data collection unit can also collect sleep data of agricultural workers and provide advice to ensure adequate rest. Furthermore, the data collection unit can collect dietary data of agricultural workers and recommend nutritionally balanced meals. This allows the agricultural workers' health status to be understood and advice to improve work efficiency to be provided.

[0048] The analysis unit can optimize the work schedules of agricultural workers based on the collected data. For example, the analysis unit can suggest optimal work timings based on weather data and crop growth data. The analysis unit can also analyze the past work history of agricultural workers to create efficient work schedules. Furthermore, the analysis unit can take into account the health data of agricultural workers and suggest work schedules that avoid overwork. This allows the work schedules of agricultural workers to be optimized and agricultural work to be achieved efficiently.

[0049] The data collection unit can collect data on agricultural workers' working environments and make suggestions for improving them. For example, it can collect data on temperature, humidity, illuminance, etc. and suggest the optimal working environment. It can also collect data on noise levels and air quality to provide a health-conscious working environment. Furthermore, based on the working environment data, it can also suggest layout changes and the introduction of new equipment to improve work efficiency. This can improve the working environment for agricultural workers and achieve more efficient work.

[0050] The analysis unit can evaluate the work performance of agricultural workers based on the collected data and suggest areas for improvement. For example, it can analyze work time and work content and suggest efficient work methods. It can also suggest training programs to improve agricultural workers' skills based on work performance data. It can also suggest incentive systems to improve agricultural workers' motivation based on work performance data. This makes it possible to evaluate agricultural workers' work performance and achieve efficient work.

[0051] The simulation generation unit can simulate the risk of crop disease under different weather conditions and propose preventive measures. For example, the generation AI can simulate the risk of crop disease under different weather conditions and propose preventive measures. For example, it can simulate the risk of disease under hot and humid conditions and propose appropriate control methods. The generation AI can also predict the risk of disease based on past weather data and propose preventive measures. For example, it can analyze past data and identify disease occurrence patterns under specific weather conditions. The generation AI can also simulate the risk of disease based on real-time weather data and propose preventive measures. For example, it can propose control methods according to the current weather conditions. This makes it possible to simulate the risk of crop disease under different weather conditions and propose preventive measures.

[0052] The simulation generation unit can analyze the genetic information of crops and simulate the optimal breeding method. For example, the generation AI analyzes the genetic information of crops and simulates the optimal breeding method. For example, it simulates the growth characteristics of crops with specific genes and proposes the optimal breeding method. The generation AI also simulates crops with different gene combinations and proposes the optimal breeding method. For example, it simulates gene combinations to improve disease resistance and yield. The generation AI also simulates crop growth based on the genetic information and proposes the optimal breeding method. For example, it simulates the growth rate and yield of crops with specific genes. This makes it possible to analyze the genetic information of crops and simulate the optimal breeding method.

[0053] The communications department can analyze farmers' past question history and provide personalized advice. For example, the generation AI can analyze farmers' past question history and provide personalized advice. For example, it can provide related information based on the content of questions asked in the past. The generation AI can also suggest individual cultivation methods and work schedules based on farmers' question history. For example, it can suggest the optimal timing for fertilization and irrigation based on the content of past questions. The generation AI can also analyze farmers' question history and provide solutions to specific problems. For example, it can suggest the latest pest control methods based on information about disease control that has been asked in the past. In this way, the generation AI can analyze farmers' past question history and provide personalized advice.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data collection unit collects environmental data, growth records, and work history. For example, the data collection unit uses sensors to collect environmental data such as temperature, humidity, and precipitation. Additionally, the data collection unit can collect the growth stage and health status of crops as growth records by manual input or through automatic sensors. Furthermore, work data such as fertilization, irrigation, and harvesting are recorded as work history. Step 2: The analysis unit analyzes the environmental data, growth records, and work history collected by the data collection unit. For example, it uses data mining technology to analyze the data and understand the crop growth status. It also uses statistical analysis to analyze data trends and machine learning algorithms to predict crop growth. Step 3: The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. For example, it uses a growth prediction model to simulate the crop growth process, predicts the harvest, and simulates the yield. It also evaluates the risk of disease and simulates the risk of disease occurrence. Step 4: The communication unit provides the farmer with the simulation results generated by the simulation generation unit. For example, the simulation results may be displayed in a graph and provided visually to the farmer. The simulation results may also be generated as a report and provided to the farmer. Furthermore, real-time notifications may be provided to the farmer to immediately notify the farmer of the simulation results.

[0056] (Example 2) The AI ​​system according to the embodiment of the present invention collects data such as environmental data, growth records, and work history, analyzes the data, generates crop simulations based on the results, and communicates with farmers. This allows farmers to accurately grasp the growth status of their crops and implement optimal cultivation methods.

[0057] The AI ​​system according to the embodiment includes a data collection unit, an analysis unit, a simulation generation unit, and a communication unit. The data collection unit collects environmental data, growth records, and work histories. For example, the data collection unit uses sensors to collect environmental data such as temperature, humidity, and precipitation. The data collection unit can also collect crop growth stages and health conditions as growth records by manual input or automatic sensors. The data collection unit also records work data such as fertilization, irrigation, and harvesting as work histories. The analysis unit analyzes the environmental data, growth records, and work histories collected by the data collection unit. For example, the analysis unit can analyze data using data mining technology to understand the growth status of crops. The analysis unit can also analyze data trends using statistical analysis. The analysis unit can also analyze data using machine learning algorithms to predict crop growth. The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. For example, the simulation generation unit can simulate the crop growth process using a growth prediction model. The simulation generation unit can also predict harvest and simulate the harvest yield. The simulation generation unit also performs disease risk assessment and simulates the risk of disease occurrence. The communication unit provides the simulation results generated by the simulation generation unit to the farmer. For example, the communication unit displays the simulation results in a graph and provides them visually to the farmer. The communication unit can also generate a report of the simulation results and provide it to the farmer. The communication unit also performs real-time notifications, immediately notifying the farmer of the simulation results. As a result, the AI ​​system according to the embodiment allows the farmer to accurately grasp the growth status of crops and practice optimal cultivation methods.

[0058] The data collection unit can use drones to collect environmental data from the air in real time. For example, the data collection unit uses sensors mounted on the drone to collect environmental data such as temperature, humidity, and precipitation from the air in real time. The collected data is sent to the generation AI for analysis. The data collection unit also flies drones periodically to collect environmental data for the entire farmland. For example, the drone is flown at a set time each day to record detailed changes in weather conditions. The data collection unit also uses drones to intensively collect environmental data for specific crop areas. For example, areas with a high risk of pest infestation are monitored intensively, and the generation AI analyzes the data. This allows environmental data to be collected from the air in real time.

[0059] The data collection unit can use soil sensors to measure the nutrient and moisture content of the soil in detail. For example, the data collection unit installs soil sensors in farmland to measure the nutrient and moisture content of the soil in real time. The collected data is sent to the generation AI for analysis. The data collection unit also moves the soil sensor around to collect soil data from different areas. For example, it takes measurements at multiple points to identify factors that affect crop growth. The data collection unit also uses the soil sensor data to allow the generation AI to analyze the soil condition and suggest optimal fertilization and irrigation timing. For example, it recommends irrigation if the soil moisture content drops. This allows for detailed measurement of the nutrient and moisture content of the soil.

[0060] The data collection unit can use the emotion estimation function to analyze the emotional state of the farmer and provide work advice according to the stress level. For example, the data collection unit uses the emotion estimation function to analyze the farmer's facial expressions and voice to grasp the farmer's emotional state in real time. For example, if the stress level is high, the data collection unit provides advice to relax. The data collection unit also provides advice to improve the farmer's work efficiency based on the emotion estimation data. For example, if fatigue is accumulating, the data collection unit recommends taking a break. The data collection unit also uses the emotion estimation function to select a communication style according to the farmer's emotional state. For example, if the farmer is in a positive emotional state, the data collection unit sends an encouraging message. This makes it possible to provide work advice according to the farmer's emotional state.

[0061] The data collection unit can collect agricultural data from different regions and analyze the optimal cultivation method for each region. The data collection unit, for example, collects weather and soil data from different regions, and the generation AI analyzes the optimal cultivation method for each region. For example, it selects crops according to the climatic conditions of each region. The data collection unit also collects feedback from farmers in different regions, and the generation AI analyzes that data. For example, it proposes the optimal cultivation method based on success stories and failure stories from each region. The data collection unit also allows the generation AI to create a cultivation calendar for each region based on the agricultural data from different regions. For example, it proposes the timing of sowing and harvesting according to the weather conditions of each region. This makes it possible to analyze the optimal cultivation method for each region.

[0062] The data collection unit collects animal behavior data and can use it to optimize pest control and pollination. For example, the data collection unit installs sensors and cameras to collect animal behavior data, and the generation AI analyzes the data. For example, it identifies pest infestation patterns and proposes countermeasures. The data collection unit also collects behavioral data of bees and other pollinators to promote pollination, and the generation AI analyzes the data. For example, it proposes the optimal timing for pollination. The data collection unit also uses the animal behavior data to allow the generation AI to propose farmland management methods. For example, it identifies areas where pests are infested and takes countermeasures in those areas. In this way, the collection of animal behavior data can be used to optimize pest control and pollination.

[0063] The data collection unit can use the emotion estimation function to evaluate the reliability of data entered by farmers and improve the accuracy of the data. The data collection unit, for example, uses the emotion estimation function to evaluate the reliability of data entered by farmers. For example, if the emotional state is stable, it determines that the reliability of the data is high. The data collection unit also provides advice to improve the accuracy of the data entered by farmers based on the emotion estimation data. For example, it recommends rechecking the data if the emotional state is unstable. The data collection unit also uses the emotion estimation function to provide a data input interface that corresponds to the emotional state of the farmer. For example, it displays a message encouraging input if the emotional state is positive. This makes it possible to evaluate the reliability of data entered by farmers and improve the accuracy of the data.

[0064] The simulation generation unit can simulate the risk of crop disease under different weather conditions and propose preventive measures. In the simulation generation unit, for example, the generation AI simulates the risk of crop disease under different weather conditions and proposes preventive measures. For example, it simulates the risk of disease under high temperature and humidity conditions and proposes appropriate control methods. In addition, the simulation generation unit predicts the risk of disease based on past weather data and proposes preventive measures. For example, it analyzes past data and identifies disease occurrence patterns under specific weather conditions. In addition, the simulation generation unit simulates the risk of disease based on real-time weather data and proposes preventive measures. For example, it proposes control methods according to the current weather conditions. In this way, it is possible to simulate the risk of crop disease under different weather conditions and propose preventive measures.

[0065] The simulation generation unit can analyze the genetic information of crops and simulate the optimal breeding method. In the simulation generation unit, for example, the generation AI analyzes the genetic information of crops and simulates the optimal breeding method. For example, the growth characteristics of crops with specific genes are simulated and the optimal breeding method is proposed. In addition, the simulation generation unit simulates crops with different gene combinations and proposes the optimal breeding method. For example, the generation AI simulates gene combinations for improving disease resistance and yield. In addition, the simulation generation unit simulates crop growth based on the genetic information and proposes the optimal breeding method. For example, the growth rate and yield of crops with specific genes are simulated. In this way, the genetic information of crops can be analyzed and the optimal breeding method can be simulated.

[0066] The simulation generation unit uses the emotion estimation function to provide simulation results based on the emotions of the farmer, thereby increasing motivation. The simulation generation unit, for example, uses the emotion estimation function to provide simulation results based on the emotional state of the farmer. For example, when the emotional state is positive, simulation results that highlight success stories are provided. The simulation generation unit also provides simulation results to increase the motivation of the farmer based on the emotion estimation data. For example, when the emotional state is low, simulation results that include encouraging messages are provided. The simulation generation unit also uses the emotion estimation function to customize the simulation results according to the emotional state of the farmer. For example, when the emotional state is positive, simulation results that include challenging goals are provided. In this way, simulation results based on the emotions of the farmer can be provided, thereby increasing motivation.

[0067] The simulation generation unit can simulate the interactions between different crops and optimize intercropping. In the simulation generation unit, for example, the generation AI simulates the interactions between different crops and optimizes intercropping. For example, it simulates how specific crop combinations affect each other. In addition, the simulation generation unit uses the generation AI to simulate the effects of intercropping and propose optimal crop combinations. For example, it simulates crop combinations that take into account pest control and soil nutrient balance. In addition, the simulation generation unit uses the generation AI to simulate the optimization of intercropping based on the interactions between different crops. For example, it proposes crop combinations that improve yield and quality. In this way, it is possible to simulate the interactions between different crops and optimize intercropping.

[0068] The simulation generation unit can analyze agricultural machinery operation data and simulate the optimal way to use the machinery. In the simulation generation unit, for example, the generation AI analyzes agricultural machinery operation data and simulates the optimal way to use the machinery. For example, it proposes efficient usage methods based on operation data of tractors and combine harvesters. In addition, the simulation generation unit can analyze agricultural machinery maintenance data and simulate the optimal maintenance schedule. For example, it proposes maintenance timing to reduce the risk of machinery breakdown. In addition, the simulation generation unit can perform simulations to improve work efficiency based on agricultural machinery operation data. For example, it makes proposals to optimize the machinery's operating hours and fuel consumption. In this way, it is possible to analyze agricultural machinery operation data and simulate the optimal way to use the machinery.

[0069] The simulation generation unit can use the emotion estimation function to preferentially provide simulation results that are of greatest interest to farmers. The simulation generation unit, for example, uses the emotion estimation function to preferentially provide simulation results that are of greatest interest to farmers. For example, it displays simulation results related to themes with high emotion scores. The simulation generation unit also customizes simulation results according to the interests of farmers based on the emotion estimation data. For example, it provides simulation results related to crops or cultivation methods that are of great interest to farmers. The simulation generation unit also uses the emotion estimation function to provide simulation results based on the interests of farmers in real time. For example, it displays the latest simulation results related to themes that have attracted increased interest. This makes it possible to preferentially provide simulation results that are of greatest interest to farmers.

[0070] The communication unit uses voice recognition technology to converse with farmers and provide advice in real time. In the communication unit, for example, the generation AI uses voice recognition technology to converse with farmers and provide advice in real time. For example, the farmer inputs a question by voice, and the generation AI immediately returns an answer. In addition, in the communication unit, the generation AI uses voice recognition technology to provide advice in real time while the farmer is working. For example, it receives instructions by voice while working and suggests appropriate work methods. In addition, in the communication unit, the generation AI uses voice recognition technology to provide detailed information in response to questions from farmers. For example, it provides voice explanations about how to cultivate a particular crop or how to prevent disease. In this way, it is possible to converse with farmers and provide advice in real time using voice recognition technology.

[0071] The communication unit can analyze the farmer's past question history and provide personalized advice. For example, the generation AI in the communication unit analyzes the farmer's past question history and provides personalized advice. For example, it provides related information based on the content of questions asked in the past. The communication unit also uses the generation AI to propose individual cultivation methods and work schedules based on the farmer's question history. For example, it proposes the optimal timing for fertilization and irrigation based on the content of past questions. The communication unit also uses the generation AI to analyze the farmer's question history and provide solutions to specific problems. For example, it proposes the latest pest control methods based on information on disease control that has been asked in the past. In this way, the farmer's past question history can be analyzed and personalized advice can be provided.

[0072] The communication unit can use the emotion estimation function to select a communication style according to the emotional state of the farmer. For example, the communication unit uses the emotion estimation function to select a communication style according to the emotional state of the farmer. For example, if the emotional state is positive, it sends an encouraging message. The communication unit also provides advice according to the emotional state of the farmer based on the emotion estimation data. For example, if the emotional state is unstable, it provides advice to relax. The communication unit also uses the emotion estimation function to customize a communication method according to the emotional state of the farmer. For example, if the emotional state is negative, it provides advice in a gentle tone. This makes it possible to select a communication style according to the emotional state of the farmer.

[0073] The communication department can communicate with farmers through video calls and provide visual information. For example, the generation AI can communicate with farmers through video calls and provide visual information. For example, it can check the condition of crops with a camera and provide advice. The generation AI can also provide visual demonstrations to farmers through video calls. For example, it can explain fertilization and irrigation methods by actually showing them. The generation AI can also share visual materials in response to farmers' questions through video calls. For example, it can explain by sharing photos of diseases or cultivation manuals on the screen. This allows it to communicate with farmers through video calls and provide visual information.

[0074] The communication section can form a community of farmers and promote information sharing and collaboration. For example, the generative AI in the communication section can form a community of farmers and promote information sharing and collaboration. For example, it can create online forums and chat groups to exchange information. The generative AI in the communication section can also analyze questions and answers within the farmer community and propose common issues and solutions. For example, it can connect farmers who have the same problem. The generative AI in the communication section can also support collaboration within the farmer community. For example, it can manage the progress of joint projects and assign tasks. This can form a community of farmers and promote information sharing and collaboration.

[0075] The communication unit can use the emotion estimation function to collect feedback based on the emotions of farmers and improve the quality of communication. For example, the communication unit uses the emotion estimation function to collect feedback based on the emotions of farmers and improve the quality of communication. For example, if the emotional state is positive, the feedback is emphasized. The communication unit also provides feedback according to the emotional state of the farmers based on the emotion estimation data. For example, if the emotional state is negative, the communication unit gently points out areas for improvement. The communication unit also uses the emotion estimation function to collect feedback based on the emotional state of farmers in real time and improve the quality of communication. For example, if the emotional state changes, the communication unit immediately responds. In this way, it is possible to collect feedback based on the emotions of farmers and improve the quality of communication.

[0076] The analysis unit can analyze the collected feedback data and automatically improve the simulation algorithm. In the analysis unit, for example, the generation AI analyzes the collected feedback data and automatically improves the simulation algorithm. For example, it analyzes the difference between the actual harvest yield and the simulation results and adjusts the algorithm. In addition, in the analysis unit, the generation AI improves the simulation algorithm based on feedback from farmers. For example, it develops an algorithm that reflects the opinions and experience of farmers. In addition, in the analysis unit, the generation AI improves the accuracy of the simulation algorithm based on the feedback data. For example, it analyzes past data and makes improvements to increase prediction accuracy. In this way, the collected feedback data can be analyzed and the simulation algorithm can be automatically improved.

[0077] The analysis unit can propose new cultivation methods based on the feedback data and simulate their effects. In the analysis unit, for example, the generation AI proposes new cultivation methods based on the feedback data and simulates their effects. For example, it proposes a new fertilization method based on feedback from farmers. In addition, the analysis unit has the generation AI analyze the feedback data and propose the optimal cultivation method. For example, it simulates a new cultivation method to improve yield and quality. In addition, the analysis unit has the generation AI simulate a new cultivation method based on the feedback data and evaluate its effects. For example, it simulates and proposes the effects of a new irrigation method. This makes it possible to propose a new cultivation method based on the feedback data and simulate its effects.

[0078] The analysis unit can use the emotion estimation function to analyze the emotional aspects of the farmer's feedback and reflect them in improving the simulation. For example, the analysis unit uses the emotion estimation function to analyze the emotional aspects of the farmer's feedback and reflect them in improving the simulation. For example, the analysis unit adjusts the simulation results based on positive feedback. The analysis unit also analyzes feedback according to the farmer's emotional state based on the emotion estimation data and uses it to improve the simulation. For example, it identifies areas for improvement based on negative feedback. The analysis unit also uses the emotion estimation function to collect feedback based on the farmer's emotional state and improve the accuracy of the simulation. For example, it reevaluates the simulation results if the emotional state changes. In this way, the emotional aspects of the farmer's feedback can be analyzed and reflected in improving the simulation.

[0079] The analysis unit can integrate feedback from different farmers and identify common issues. In the analysis unit, for example, the generation AI integrates feedback from different farmers and identifies common issues. For example, it analyzes common problems faced by multiple farmers and proposes solutions. In addition, the analysis unit uses the generation AI to identify common issues based on feedback data and simulate solutions. For example, it proposes control methods for the same disease. In addition, the analysis unit builds a system in which the generation AI analyzes feedback from different farmers and identifies common issues. For example, it clusters the feedback data and extracts common issues. This makes it possible to integrate feedback from different farmers and identify common issues.

[0080] The analysis unit can support the development of new agricultural technologies based on the feedback data. For example, the generation AI in the analysis unit supports the development of new agricultural technologies based on the feedback data. For example, it develops new fertilization techniques based on feedback from farmers. The analysis unit also has the generation AI analyze the feedback data and use it to develop new agricultural technologies. For example, it proposes new technologies to improve harvest yields and quality. The analysis unit also has the generation AI simulate the development of new agricultural technologies based on the feedback data and evaluate their effectiveness. For example, it simulates and proposes the effects of new irrigation technologies. This makes it possible to support the development of new agricultural technologies based on the feedback data.

[0081] The analysis unit uses the emotion estimation function to analyze the emotional trends of the feedback and improve the satisfaction of farmers. The analysis unit, for example, uses the emotion estimation function to analyze the emotional trends of the feedback and improve the satisfaction of farmers. For example, it emphasizes trends with a lot of positive feedback. The analysis unit also analyzes the emotional trends of the feedback based on the emotion estimation data and identifies areas for improvement to improve satisfaction. For example, it improves areas with a lot of negative feedback. The analysis unit also uses the emotion estimation function to analyze the emotional trends of the feedback in real time and improve the satisfaction of farmers. For example, it responds immediately if the emotional state changes. In this way, it is possible to analyze the emotional trends of the feedback and improve the satisfaction of farmers.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The data collection unit can collect health data of agricultural workers and provide advice to improve work efficiency. For example, the data collection unit can use a wearable device to monitor the heart rate and body temperature of agricultural workers and provide advice to reduce the risk of overwork and heatstroke. The data collection unit can also collect sleep data of agricultural workers and provide advice to ensure adequate rest. Furthermore, the data collection unit can collect dietary data of agricultural workers and recommend nutritionally balanced meals. This allows the agricultural workers' health status to be understood and advice to improve work efficiency to be provided.

[0084] The analysis unit can optimize the work schedules of agricultural workers based on the collected data. For example, the analysis unit can suggest optimal work timings based on weather data and crop growth data. The analysis unit can also analyze the past work history of agricultural workers to create efficient work schedules. Furthermore, the analysis unit can take into account the health data of agricultural workers and suggest work schedules that avoid overwork. This allows the work schedules of agricultural workers to be optimized and agricultural work to be achieved efficiently.

[0085] The simulation generation unit can customize the crop growth simulation based on the emotional state of the farmer. For example, by using the emotion estimation function, if the farmer is feeling stressed, the simulation results can be displayed concisely to make them easier to understand. In addition, if the farmer is in a positive emotional state, detailed simulation results can be provided to attract the farmer's interest. Furthermore, encouraging messages can be added to the simulation results depending on the farmer's emotional state. This makes it possible to provide simulation results based on the farmer's emotional state and increase motivation.

[0086] The communication department can select an appropriate communication method based on the farmer's emotional state. For example, by using the emotion estimation function, if the farmer is feeling stressed, it can provide advice in a gentle tone. If the farmer is in a positive emotional state, it can send an encouraging message. Furthermore, it can adjust the frequency and content of communication depending on the farmer's emotional state. This makes it possible to select a communication method that suits the farmer's emotional state and provide effective support.

[0087] The data collection unit can analyze the emotional state of farmers and provide work advice according to their stress level. For example, the emotion estimation function can be used to analyze the facial expressions and voice of farmers to grasp their emotional state in real time. For example, if the stress level is high, advice to relax can be provided. Furthermore, based on the emotion estimation data, advice to improve the farmer's work efficiency can be provided. For example, if fatigue is accumulating, a break can be recommended. The emotion estimation function can also be used to select a communication style according to the farmer's emotional state. For example, an encouraging message can be sent when the farmer is in a positive emotional state. This makes it possible to provide work advice according to the farmer's emotional state.

[0088] The data collection unit can collect data on agricultural workers' working environments and make suggestions for improving them. For example, it can collect data on temperature, humidity, illuminance, etc. and suggest the optimal working environment. It can also collect data on noise levels and air quality to provide a health-conscious working environment. Furthermore, based on the working environment data, it can also suggest layout changes and the introduction of new equipment to improve work efficiency. This can improve the working environment for agricultural workers and achieve more efficient work.

[0089] The analysis unit can evaluate the work performance of agricultural workers based on the collected data and suggest areas for improvement. For example, it can analyze work time and work content and suggest efficient work methods. It can also suggest training programs to improve agricultural workers' skills based on work performance data. It can also suggest incentive systems to improve agricultural workers' motivation based on work performance data. This makes it possible to evaluate agricultural workers' work performance and achieve efficient work.

[0090] The simulation generation unit can simulate the risk of crop disease under different weather conditions and propose preventive measures. For example, the generation AI can simulate the risk of crop disease under different weather conditions and propose preventive measures. For example, it can simulate the risk of disease under hot and humid conditions and propose appropriate control methods. The generation AI can also predict the risk of disease based on past weather data and propose preventive measures. For example, it can analyze past data and identify disease occurrence patterns under specific weather conditions. The generation AI can also simulate the risk of disease based on real-time weather data and propose preventive measures. For example, it can propose control methods according to the current weather conditions. This makes it possible to simulate the risk of crop disease under different weather conditions and propose preventive measures.

[0091] The simulation generation unit can analyze the genetic information of crops and simulate the optimal breeding method. For example, the generation AI analyzes the genetic information of crops and simulates the optimal breeding method. For example, it simulates the growth characteristics of crops with specific genes and proposes the optimal breeding method. The generation AI also simulates crops with different gene combinations and proposes the optimal breeding method. For example, it simulates gene combinations to improve disease resistance and yield. The generation AI also simulates crop growth based on the genetic information and proposes the optimal breeding method. For example, it simulates the growth rate and yield of crops with specific genes. This makes it possible to analyze the genetic information of crops and simulate the optimal breeding method.

[0092] The communications department can analyze farmers' past question history and provide personalized advice. For example, the generation AI can analyze farmers' past question history and provide personalized advice. For example, it can provide related information based on the content of questions asked in the past. The generation AI can also suggest individual cultivation methods and work schedules based on farmers' question history. For example, it can suggest the optimal timing for fertilization and irrigation based on the content of past questions. The generation AI can also analyze farmers' question history and provide solutions to specific problems. For example, it can suggest the latest pest control methods based on information about disease control that has been asked in the past. In this way, the generation AI can analyze farmers' past question history and provide personalized advice.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The data collection unit collects environmental data, growth records, and work history. For example, the data collection unit uses sensors to collect environmental data such as temperature, humidity, and precipitation. Additionally, the data collection unit can collect the growth stage and health status of crops as growth records by manual input or through automatic sensors. Furthermore, work data such as fertilization, irrigation, and harvesting are recorded as work history. Step 2: The analysis unit analyzes the environmental data, growth records, and work history collected by the data collection unit. For example, it uses data mining technology to analyze the data and understand the crop growth status. It also uses statistical analysis to analyze data trends and machine learning algorithms to predict crop growth. Step 3: The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. For example, it uses a growth prediction model to simulate the crop growth process, predicts the harvest, and simulates the yield. It also evaluates the risk of disease and simulates the risk of disease occurrence. Step 4: The communication unit provides the farmer with the simulation results generated by the simulation generation unit. For example, the simulation results may be displayed in a graph and provided visually to the farmer. The simulation results may also be generated as a report and provided to the farmer. Furthermore, real-time notifications may be provided to the farmer to immediately notify the farmer of the simulation results.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] 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.

[0154] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects environmental data, growth records, and work histories; an analysis unit that analyzes the environmental data, the growth record, and the work history collected by the data collection unit; a simulation generation unit that generates a crop simulation based on the data analyzed by the analysis unit; a communication unit that provides the farmer with the simulation results generated by the simulation generation unit. A system characterized by:

2. The data collection unit Collecting the environmental data in real time from the air using a drone 2. The system of claim 1.

3. The data collection unit Using soil sensors to measure soil nutrients and moisture in detail 2. The system of claim 1.

4. The data collection unit Analyzing the emotional state of the farmer and providing work advice according to the stress level 2. The system of claim 1.

5. The data collection unit Collect agricultural data from the different regions and analyze the optimal cultivation method for each region.

2. The system of claim 1.

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