System

Abstract

An object of the system according to the embodiment is to streamline an agricultural process and provide accurate advice to a farmer by utilizing growth data of crops.SOLUTION: A system according to an embodiment includes a collection unit, a transmission unit, an analysis unit, and a notification unit. The collection unit collects data of humidity, temperature, insolation, and rainfall of soil. The transmission unit transmits the data collected by the collection unit to the cloud. The analysis unit analyzes the data transmitted by the transmission unit and provides a cultivation method, fertilization, and timing of irrigation based on the growth state of the crop and the weather condition. The notification unit notifies the farmer of the advice provided by the analysis unit.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] Existing technologies do not fully streamline agricultural processes or utilize crop growth data, leaving room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of agricultural processes and provide farmers with accurate advice by utilizing crop growth data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a transmission unit, an analysis unit, and a notification unit. The collection unit collects data on soil humidity, temperature, amount of sunlight, and amount of rainfall. The transmission unit transmits the data collected by the collection unit to the cloud. The analysis unit analyzes the data transmitted by the transmission unit and provides advice on cultivation methods and timing of fertilization and irrigation based on the growth status of the crops and weather conditions. The notification unit notifies the farmer of the advice provided by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of agricultural processes and provide farmers with accurate advice by utilizing crop growth data. [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) An agricultural efficiency system according to an embodiment of the present invention utilizes IoT devices and sensors to streamline agricultural processes. This system collects data such as soil humidity, temperature, sunlight, and rainfall, transmits it to the cloud, and AI analyzes it to provide farmers with advice on optimal cultivation methods, fertilization, and irrigation timing. For example, if soil humidity drops, the AI ​​notifies them of irrigation timing and specifies the appropriate amount of water. Furthermore, based on weather information, the system predicts the optimal harvest timing and notifies farmers. This improves yields and reduces costs. For example, sensors can be placed at regular intervals, with soil sensors and weather sensors in the center of the farmland. Data is collected in real time and transmitted to the cloud. Next, the AI ​​uses machine learning to build a predictive model based on past data and combines it with real-time data to provide optimal advice. Furthermore, farmers can receive advice via notifications on a smartphone app or detailed advice via email. This allows the agricultural efficiency system to improve yields and reduce costs. For example, farmers can learn optimal cultivation methods, fertilization, and irrigation timing in real time, enabling efficient agricultural management. In addition, AI analysis allows farmers to receive appropriate advice based on weather conditions and crop growth status, thereby optimizing the agricultural process.

[0029] An agricultural efficiency system according to an embodiment includes a collection unit, a transmission unit, an analysis unit, and a notification unit. The collection unit collects data on soil humidity, temperature, solar radiation, and rainfall. For example, the collection unit can measure soil humidity using a soil sensor. The collection unit can also measure soil temperature using a temperature sensor. The collection unit can also measure solar radiation using a pyranometer. For example, the collection unit measures soil humidity in real time using a humidity sensor and collects data. The collection unit can also measure soil temperature in real time using a temperature sensor and collect data. The collection unit can also measure solar radiation in real time using a pyranometer and collect data. The transmission unit transmits the data collected by the collection unit to a cloud. For example, the transmission unit can transmit the collected data to the cloud in real time. The transmission unit has a redundancy function to ensure communication stability during data transmission. For example, the transmission unit can transmit data using multiple communication paths. The analysis unit analyzes the data transmitted by the transmission unit and provides optimal cultivation methods and timing of fertilization and irrigation based on the crop growth status and weather conditions. For example, the analysis unit can use machine learning to build a predictive model based on past data and combine it with real-time data to provide optimal advice. The analysis unit also has a function for predicting abnormal weather and pest outbreaks and taking early countermeasures. For example, the analysis unit can predict the occurrence of abnormal weather based on weather data and propose countermeasures. The notification unit notifies the farmer of the advice provided by the analysis unit. For example, the notification unit can receive notifications using a smartphone app. The notification unit can also receive detailed advice via email. Furthermore, the notification unit can estimate the user's emotions and adjust the content and timing of notifications based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can provide concise and to-the-point notifications. As a result, the agricultural efficiency system according to the embodiment can improve the efficiency of agricultural processes, improve yields, and reduce costs.

[0030] The collection unit may arrange the soil sensors at regular intervals and the weather sensor at the center of the farmland. The regular intervals may include, but are not limited to, intervals in meters. For example, the collection unit may arrange the soil sensors at 10-meter intervals. The collection unit may also arrange the soil sensors at 5-meter intervals. The collection unit may also arrange the soil sensors at 20-meter intervals. The center of the farmland may include, but is not limited to, the central position relative to the area of ​​the farmland. For example, the collection unit may arrange the weather sensor at the center of the farmland. The collection unit may also arrange multiple weather sensors at the center of the farmland. This may optimize the sensor arrangement and improve the accuracy of data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input sensor arrangement data to a generation AI and cause the generation AI to execute an optimal arrangement pattern.

[0031] The transmitting unit can transmit the collected data to the cloud in real time. Real time includes, but is not limited to, transmitting data in units of seconds, for example. The transmitting unit can transmit the collected data to the cloud every second, for example. The transmitting unit can also transmit the collected data to the cloud every five seconds. Furthermore, the transmitting unit can also transmit the collected data to the cloud every ten seconds. This enables rapid analysis and advice provision through real-time transmission of data. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the collected data to a generating AI and have the generating AI determine the timing of data transmission.

[0032] The analysis unit can use machine learning to build a predictive model based on past data and combine it with real-time data to provide optimal advice. Examples of machine learning include, but are not limited to, algorithms such as regression analysis and neural networks. For example, the analysis unit can build a predictive model based on past data using regression analysis. The analysis unit can also build a predictive model based on past data using neural networks. Furthermore, the analysis unit can build a predictive model based on past data using support vector machines. Examples of predictive models include, but are not limited to, the type of training data and the evaluation method. For example, the analysis unit can use past weather data as training data and build a predictive model based on accuracy as an evaluation method. Furthermore, the analysis unit can use past crop growth data as training data and build a predictive model based on recall as an evaluation method. Furthermore, the analysis unit can use past harvest data as training data and build a predictive model based on F-value as an evaluation method. This allows for more accurate advice to be provided by using machine learning. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past data and real-time data into the generation AI and have the generation AI execute optimal advice.

[0033] The notification unit can receive notifications using a smartphone app or detailed advice via email. Examples of smartphone apps include, but are not limited to, iOS apps and Android apps. The notification unit can receive notifications using, for example, an iOS app. The notification unit can also receive notifications using an Android app. The notification unit can also receive notifications using a cross-platform app. Examples of email include, but are not limited to, email transmission using the SMTP protocol. The notification unit can receive detailed advice via email using the SMTP protocol. The notification unit can also receive detailed advice via email using the IMAP protocol. The notification unit can also receive detailed advice via email using the POP3 protocol. This allows the user to receive advice via various means. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the content of the smartphone app or email notification into the generation AI and have the generation AI execute the optimal notification method.

[0034] The collection unit can analyze soil components and detect deficiencies of specific nutrients. For example, the collection unit can use a soil sensor to measure the concentrations of nitrogen, phosphorus, and potassium and notify the user if a specific nutrient is lacking. The collection unit can also use a soil sensor to measure the concentrations of trace elements (such as iron, zinc, and manganese) and issue an alert if a specific nutrient is lacking. Furthermore, the collection unit can use the soil sensor to measure the organic matter content and suggest adding organic fertilizer as needed. This allows the soil component analysis to detect deficiencies of specific nutrients and take appropriate measures. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input soil component data into a generation AI and have the generation AI perform an analysis to detect deficiencies of specific nutrients.

[0035] The collection unit can dynamically change the type and frequency of data collection depending on the growth stage of the crop. For example, when the crop is in the germination stage, the collection unit can focus on collecting soil humidity and temperature. Also, when the crop is in the growing stage, the collection unit can frequently collect data on the amount of sunlight and rainfall. Furthermore, when the crop is approaching the harvest period, the collection unit can focus on collecting weather information and soil nutrient data. This enables more appropriate cultivation management by collecting data according to the crop growth stage. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input crop growth stage data into the generation AI and have the generation AI execute the type and frequency of data collection.

[0036] The collection unit can add a self-diagnosis function to the sensor and detect and notify of failures or abnormalities. For example, the collection unit can cause the sensor to periodically perform self-diagnosis and notify if an abnormality is detected. The collection unit can also monitor the remaining battery level of the sensor and issue an alert if the battery level drops. Furthermore, the collection unit can monitor the communication status of the sensor and notify if there is a problem with data transmission. This makes it possible to detect sensor failures or abnormalities early and take appropriate measures. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the sensor's self-diagnosis data to the generation AI and have the generation AI detect failures and abnormalities.

[0037] The collection unit can use a drone to collect wide-area data and combine it with ground sensors to improve accuracy. For example, the collection unit can use a drone to take aerial photographs of the entire farmland and combine the data with ground sensor data for analysis. The collection unit can also use a drone to collect detailed data of a specific area and integrate it with ground sensor data. Furthermore, the collection unit can use a drone to collect weather data and combine it with ground sensor data to analyze weather conditions. This makes it possible to collect wide-area data and improve accuracy by using a drone. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data collected by a drone into a generation AI and have the generation AI perform an analysis combining the data with ground sensor data.

[0038] The collection unit can customize the placement and type of sensors according to different crops and farmland conditions. For example, the collection unit can propose optimal sensor placement according to different crops. The collection unit can also select an appropriate sensor type according to different soil conditions. Furthermore, the collection unit can adjust the sensor placement according to different weather conditions. This enables optimal data collection by customizing the placement and type of sensors according to different conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on crops and farmland conditions into the generation AI and have the generation AI execute the optimal sensor placement and type.

[0039] The collection unit can link the sensor data with other agriculture-related systems to achieve comprehensive agricultural management. For example, the collection unit can link the sensor data with an irrigation system to automatically set optimal irrigation timing. The collection unit can also link the sensor data with a fertilization system to automatically set optimal fertilization timing. Furthermore, the collection unit can link the sensor data with a harvest management system to automatically set optimal harvest timing. This enables comprehensive agricultural management by linking with other agriculture-related systems. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input sensor data into a generation AI and cause the generation AI to link with other agriculture-related systems.

[0040] The transmitting unit can add a redundancy function to ensure communication stability when transmitting data. For example, the transmitting unit can achieve redundancy by using multiple communication paths when transmitting data. The transmitting unit can also achieve data redundancy by using a backup server when transmitting data. Furthermore, the transmitting unit can also ensure communication stability by adding an error detection and retransmission function when transmitting data. This improves the reliability of data transmission by using the redundancy function to ensure communication stability. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input redundancy settings for data transmission to the generating AI and have the generating AI execute the optimal redundancy method.

[0041] The transmitting unit can compress and encrypt data when transmitting data, thereby improving communication costs and security. For example, the transmitting unit can compress data when transmitting data to reduce communication costs. The transmitting unit can also encrypt data when transmitting data to improve security. Furthermore, the transmitting unit can combine compression and encryption when transmitting data to achieve both communication costs and security. This reduces communication costs and improves security through data compression and encryption. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input data compression and encryption settings to the generating AI and have the generating AI execute the optimal compression and encryption method.

[0042] The transmitting unit can be added with a function of detecting transmission errors or delays during data transmission and automatically retransmitting the data. For example, the transmitting unit can automatically retransmit data if a transmission error occurs during data transmission. The transmitting unit can also retransmit data if a delay occurs during data transmission. Furthermore, the transmitting unit can detect errors or delays during data transmission and retransmit data at the optimal timing. This improves the reliability of data transmission by detecting transmission errors or delays and automatically retransmitting data. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input data on transmission errors or delays into a generating AI and have the generating AI determine the timing of retransmission.

[0043] The transmitting unit can transmit data to multiple cloud services using different communication protocols when transmitting data. For example, the transmitting unit can transmit data to multiple cloud services using HTTP and MQTT when transmitting data. The transmitting unit can also transmit data to multiple cloud services using WebSocket and CoAP when transmitting data. Furthermore, the transmitting unit can combine different communication protocols when transmitting data to achieve data redundancy. By using different communication protocols, the redundancy and reliability of data transmission are improved. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input settings for different communication protocols into a generating AI and have the generating AI execute an optimal protocol combination.

[0044] The transmitting unit temporarily stores data in local storage when transmitting data, thereby protecting the data even when communication is unstable. For example, the transmitting unit can temporarily store data in local storage when transmitting data and transmit the data when communication is restored. The transmitting unit can also create a backup in local storage when transmitting data and use it when communication is unstable. Furthermore, the transmitting unit can periodically transmit the data stored in local storage to the cloud when transmitting data. In this way, by temporarily storing the data in local storage, it is possible to protect the data even when communication is unstable. Some or all of the above-mentioned processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input data from local storage to a generating AI and have the generating AI execute the optimal transmission timing.

[0045] The transmission unit can cooperate with other agriculture-related systems when transmitting data to share and integrate data. For example, the transmission unit can cooperate with an irrigation system when transmitting data to share data. The transmission unit can also cooperate with a fertilization system when transmitting data to share data. Furthermore, the transmission unit can also cooperate with a harvest management system when transmitting data to share data. This enables comprehensive data management by coordinating with other agriculture-related systems. Some or all of the above-mentioned processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input cooperation data with other agriculture-related systems into the generation AI and have the generation AI share and integrate data.

[0046] During analysis, the analysis unit can predict abnormal weather and the occurrence of pests and diseases and take early countermeasures. The analysis unit can, for example, predict the occurrence of abnormal weather based on weather data and propose countermeasures. The analysis unit can also predict the occurrence of pests and diseases based on crop growth data and take early countermeasures. Furthermore, the analysis unit can analyze abnormal weather and pest occurrence patterns based on past data and build a predictive model. This makes it possible to predict abnormal weather and the occurrence of pests and diseases and take early countermeasures, thereby minimizing damage to crops. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on abnormal weather and pests and diseases into the generation AI and have the generation AI build a predictive model.

[0047] During analysis, the analysis unit can apply different analysis algorithms depending on the crop variety and cultivation method. For example, the analysis unit can apply an optimal analysis algorithm depending on different crop varieties. The analysis unit can also customize the analysis algorithm depending on different cultivation methods. Furthermore, the analysis unit can dynamically change the analysis algorithm depending on the growth stage of the crop. This enables more accurate analysis by applying an analysis algorithm depending on the crop variety and cultivation method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the crop variety and cultivation method into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0048] During analysis, the analysis unit can combine past data and real-time data to make more accurate predictions. The analysis unit can, for example, combine past weather data and real-time weather data to make more accurate predictions. The analysis unit can also combine past crop growth data and real-time growth data to propose optimal cultivation methods. Furthermore, the analysis unit can combine past harvest data and real-time data to predict harvest yields. This combination of past data and real-time data enables more accurate predictions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data and real-time data into a generation AI and have the generation AI execute a prediction.

[0049] During analysis, the analysis unit can integrate other agriculture-related data and provide comprehensive advice. The analysis unit can, for example, suggest optimal harvest timing based on market price data. The analysis unit can also optimize cultivation plans based on demand forecast data. Furthermore, the analysis unit can integrate other agriculture-related data and provide comprehensive cultivation advice. This makes it possible to provide comprehensive advice by integrating other agriculture-related data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input other agriculture-related data into the generation AI and have the generation AI execute comprehensive advice.

[0050] During analysis, the analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit can integrate weather data, soil data, and crop growth data to improve the accuracy of the analysis. The analysis unit can also integrate data from different sensors to perform a comprehensive analysis. Furthermore, the analysis unit can also integrate external data sources (satellite data, drone data, etc.) to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by integrating information from different data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information from different data sources into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0051] The analysis unit can customize the analysis algorithm according to different crops and farmland conditions during analysis. For example, the analysis unit can customize the optimal analysis algorithm according to different crops. The analysis unit can also adjust the analysis algorithm according to different farmland conditions. Furthermore, the analysis unit can dynamically change the analysis algorithm according to the growth stage of the crop. This enables more accurate analysis by customizing the analysis algorithm according to different conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on crops and farmland conditions into the generation AI and have the generation AI customize the optimal analysis algorithm.

[0052] The notification unit can select different notification methods depending on the level of urgency when making a notification. For example, if the level of urgency is high, the notification unit can notify by phone. If the level of urgency is medium, the notification unit can also notify by SMS. If the level of urgency is low, the notification unit can also notify by push notification. This allows important information to be conveyed quickly by selecting a notification method depending on the level of urgency. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input urgency data into the generation AI and have the generation AI execute the optimal notification method.

[0053] The notification unit can provide optimal advice by referring to the user's past behavioral history when making a notification. The notification unit can provide optimal advice based on, for example, the user's past behavioral history. The notification unit can also determine the optimal notification timing based on the user's past behavioral history. Furthermore, the notification unit can analyze the user's past behavioral history and provide optimal notification content. This makes it possible to provide more appropriate advice by referring to the user's past behavioral history. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's past behavioral history data into a generation AI and cause the generation AI to execute optimal advice.

[0054] The notification unit can improve the notification content by reflecting user feedback at the time of notification. The notification unit can improve the notification content based on, for example, user feedback. The notification unit can also analyze the user feedback and propose an optimal notification method. Furthermore, the notification unit can also adjust the timing of the notification by reflecting user feedback. This makes it possible to improve the notification content by reflecting user feedback. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input user feedback data into a generation AI and have the generation AI improve the notification content.

[0055] The notification unit can provide notification methods compatible with different devices when providing notifications. For example, the notification unit can provide push notifications to smartphones. The notification unit can also provide in-app notifications to tablets. The notification unit can also provide email notifications to PCs. This allows compatibility with different devices, allowing users to receive notifications on a variety of devices. Some or all of the above-described processing in the notification unit may be performed using, or without, AI, for example. For example, the notification unit can input device type data to the generation AI and have the generation AI execute the optimal notification method.

[0056] When issuing a notification, the notification unit can cooperate with other agriculture-related systems to provide a comprehensive notification. For example, the notification unit can cooperate with an irrigation system to provide notification of optimal irrigation timing. The notification unit can also cooperate with a fertilization system to provide notification of optimal fertilization timing. Furthermore, the notification unit can cooperate with a harvest management system to provide notification of optimal harvest timing. This enables comprehensive notification by coordinating with other agriculture-related systems. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input coordination data with other agriculture-related systems into the generation AI and cause the generation AI to execute a comprehensive notification.

[0057] The notification unit can provide multilingual notifications according to the user's language settings when issuing a notification. The notification unit can automatically set the notification language based on, for example, the language setting of the user's device. The notification unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the notification unit can provide notifications in that language. This enables notifications that are easy for the user to understand by providing multilingual notifications according to the user's language settings. Some or all of the above-described processing in the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input the user's language setting data into a generation AI and have the generation AI execute a notification in the optimal language.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can use a drone to collect wide-area data and combine it with ground sensors to improve accuracy. For example, a drone can take aerial photographs of the entire farmland and combine the data with ground sensor data for analysis. A drone can also collect detailed data of a specific area and integrate it with ground sensor data. Furthermore, a drone can collect weather data and combine it with ground sensor data to analyze weather conditions. This makes it possible to collect wide-area data and improve accuracy by using a drone. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data collected by a drone into a generation AI and have the generation AI perform an analysis combining the data with ground sensor data.

[0060] The transmitting unit can add a redundancy function to ensure communication stability when transmitting data. For example, redundancy can be achieved by using multiple communication paths when transmitting data. Also, data redundancy can be achieved by using a backup server when transmitting data. Furthermore, error detection and retransmission functions can be added when transmitting data to ensure communication stability. This improves the reliability of data transmission by using a redundancy function to ensure communication stability. Some or all of the above-mentioned processing in the transmitting unit can be performed using AI, for example, or without AI. For example, the transmitting unit can input redundancy settings for data transmission to the generating AI and have the generating AI execute the optimal redundancy method.

[0061] During analysis, the analysis unit can predict abnormal weather and the occurrence of pests and diseases and take early countermeasures. For example, it can predict the occurrence of abnormal weather based on weather data and propose countermeasures. It can also predict the occurrence of pests and diseases based on crop growth data and take early countermeasures. Furthermore, it can analyze abnormal weather and pest occurrence patterns based on past data and build a predictive model. This makes it possible to predict abnormal weather and the occurrence of pests and diseases and take early countermeasures, thereby minimizing damage to crops. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on abnormal weather and pests and diseases into the generation AI and have the generation AI build a predictive model.

[0062] The collection unit can customize the placement and type of sensors according to different crops and farmland conditions. For example, it can propose optimal sensor placement according to different crops. It can also select appropriate sensor types according to different soil conditions. It can also adjust sensor placement according to different weather conditions. This enables optimal data collection by customizing the placement and type of sensors according to different conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on crops and farmland conditions into the generation AI and have the generation AI execute the optimal sensor placement and type.

[0063] During analysis, the analysis unit can integrate other agriculture-related data and provide comprehensive advice. For example, it can suggest optimal harvest timing based on market price data. It can also optimize cultivation plans based on demand forecast data. It can also integrate other agriculture-related data and provide comprehensive cultivation advice. This makes it possible to provide comprehensive advice by integrating other agriculture-related data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input other agriculture-related data into the generation AI and have the generation AI execute comprehensive advice.

[0064] The notification unit can select different notification methods depending on the level of urgency when making a notification. For example, if the level of urgency is high, the notification can be made by phone. If the level of urgency is medium, the notification can be made by SMS. Furthermore, if the level of urgency is low, the notification can be made by push notification. This allows important information to be conveyed quickly by selecting a notification method depending on the level of urgency. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input urgency data into the generation AI and have the generation AI execute the optimal notification method.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects data on soil humidity, temperature, solar radiation, and rainfall. For example, the collection unit can measure soil humidity using a soil sensor, soil temperature using a temperature sensor, and solar radiation using a pyranometer. These data are collected in real time. Step 2: The transmitter transmits the data collected by the collector to the cloud. For example, the transmitter transmits the collected data to the cloud in real time and has a redundancy function to ensure communication stability. Data can be transmitted using multiple communication paths. Step 3: The analysis unit analyzes the data sent by the transmission unit and provides optimal cultivation methods and fertilization and irrigation timing based on the crop's growth status and weather conditions. For example, the analysis unit uses machine learning to build a predictive model based on past data, and combines it with real-time data to provide optimal advice. The system also has the ability to predict abnormal weather and the occurrence of pests and diseases, allowing for early countermeasures. Step 4: The notification unit notifies the farmer of the advice provided by the analysis unit. For example, the notification unit can receive notifications via a smartphone app or email. The notification unit can also estimate the user's emotions and adjust the content and timing of notifications based on the estimated emotions.

[0067] (Example 2) An agricultural efficiency system according to an embodiment of the present invention utilizes IoT devices and sensors to streamline agricultural processes. This system collects data such as soil humidity, temperature, sunlight, and rainfall, transmits it to the cloud, and AI analyzes it to provide farmers with advice on optimal cultivation methods, fertilization, and irrigation timing. For example, if soil humidity drops, the AI ​​notifies them of irrigation timing and specifies the appropriate amount of water. Furthermore, based on weather information, the system predicts the optimal harvest timing and notifies farmers. This improves yields and reduces costs. For example, sensors can be placed at regular intervals, with soil sensors and weather sensors in the center of the farmland. Data is collected in real time and transmitted to the cloud. Next, the AI ​​uses machine learning to build a predictive model based on past data and combines it with real-time data to provide optimal advice. Furthermore, farmers can receive advice via notifications on a smartphone app or detailed advice via email. This allows the agricultural efficiency system to improve yields and reduce costs. For example, farmers can learn optimal cultivation methods, fertilization, and irrigation timing in real time, enabling efficient agricultural management. In addition, AI analysis allows farmers to receive appropriate advice based on weather conditions and crop growth status, thereby optimizing the agricultural process.

[0068] An agricultural efficiency system according to an embodiment includes a collection unit, a transmission unit, an analysis unit, and a notification unit. The collection unit collects data on soil humidity, temperature, solar radiation, and rainfall. For example, the collection unit can measure soil humidity using a soil sensor. The collection unit can also measure soil temperature using a temperature sensor. The collection unit can also measure solar radiation using a pyranometer. For example, the collection unit measures soil humidity in real time using a humidity sensor and collects data. The collection unit can also measure soil temperature in real time using a temperature sensor and collect data. The collection unit can also measure solar radiation in real time using a pyranometer and collect data. The transmission unit transmits the data collected by the collection unit to a cloud. For example, the transmission unit can transmit the collected data to the cloud in real time. The transmission unit has a redundancy function to ensure communication stability during data transmission. For example, the transmission unit can transmit data using multiple communication paths. The analysis unit analyzes the data transmitted by the transmission unit and provides optimal cultivation methods and timing of fertilization and irrigation based on the crop growth status and weather conditions. For example, the analysis unit can use machine learning to build a predictive model based on past data and combine it with real-time data to provide optimal advice. The analysis unit also has a function for predicting abnormal weather and pest outbreaks and taking early countermeasures. For example, the analysis unit can predict the occurrence of abnormal weather based on weather data and propose countermeasures. The notification unit notifies the farmer of the advice provided by the analysis unit. For example, the notification unit can receive notifications using a smartphone app. The notification unit can also receive detailed advice via email. Furthermore, the notification unit can estimate the user's emotions and adjust the content and timing of notifications based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can provide concise and to-the-point notifications. As a result, the agricultural efficiency system according to the embodiment can improve the efficiency of agricultural processes, improve yields, and reduce costs.

[0069] The collection unit may arrange the soil sensors at regular intervals and the weather sensor at the center of the farmland. The regular intervals may include, but are not limited to, intervals in meters. For example, the collection unit may arrange the soil sensors at 10-meter intervals. The collection unit may also arrange the soil sensors at 5-meter intervals. The collection unit may also arrange the soil sensors at 20-meter intervals. The center of the farmland may include, but is not limited to, the central position relative to the area of ​​the farmland. For example, the collection unit may arrange the weather sensor at the center of the farmland. The collection unit may also arrange multiple weather sensors at the center of the farmland. This may optimize the sensor arrangement and improve the accuracy of data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input sensor arrangement data to a generation AI and cause the generation AI to execute an optimal arrangement pattern.

[0070] The transmitting unit can transmit the collected data to the cloud in real time. Real time includes, but is not limited to, transmitting data in units of seconds, for example. The transmitting unit can transmit the collected data to the cloud every second, for example. The transmitting unit can also transmit the collected data to the cloud every five seconds. Furthermore, the transmitting unit can also transmit the collected data to the cloud every ten seconds. This enables rapid analysis and advice provision through real-time transmission of data. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the collected data to a generating AI and have the generating AI determine the timing of data transmission.

[0071] The analysis unit can use machine learning to build a predictive model based on past data and combine it with real-time data to provide optimal advice. Examples of machine learning include, but are not limited to, algorithms such as regression analysis and neural networks. For example, the analysis unit can build a predictive model based on past data using regression analysis. The analysis unit can also build a predictive model based on past data using neural networks. Furthermore, the analysis unit can build a predictive model based on past data using support vector machines. Examples of predictive models include, but are not limited to, the type of training data and the evaluation method. For example, the analysis unit can use past weather data as training data and build a predictive model based on accuracy as an evaluation method. Furthermore, the analysis unit can use past crop growth data as training data and build a predictive model based on recall as an evaluation method. Furthermore, the analysis unit can use past harvest data as training data and build a predictive model based on F-value as an evaluation method. This allows for more accurate advice to be provided by using machine learning. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past data and real-time data into the generation AI and have the generation AI execute optimal advice.

[0072] The notification unit can receive notifications using a smartphone app or detailed advice via email. Examples of smartphone apps include, but are not limited to, iOS apps and Android apps. The notification unit can receive notifications using, for example, an iOS app. The notification unit can also receive notifications using an Android app. The notification unit can also receive notifications using a cross-platform app. Examples of email include, but are not limited to, email transmission using the SMTP protocol. The notification unit can receive detailed advice via email using the SMTP protocol. The notification unit can also receive detailed advice via email using the IMAP protocol. The notification unit can also receive detailed advice via email using the POP3 protocol. This allows the user to receive advice via various means. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the content of the smartphone app or email notification into the generation AI and have the generation AI execute the optimal notification method.

[0073] The collection unit can estimate the user's emotions and adjust the sensor placement and data collection timing based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can simplify the sensor placement and reduce the frequency of data collection. Furthermore, if the user is relaxed, the collection unit can increase the sensor placement and data collection frequency to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data and optimize the sensor placement. This reduces the burden on the user by adjusting the sensor placement and data collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI execute the sensor placement and data collection timing.

[0074] The collection unit can analyze soil components and detect deficiencies of specific nutrients. For example, the collection unit can use a soil sensor to measure the concentrations of nitrogen, phosphorus, and potassium and notify the user if a specific nutrient is lacking. The collection unit can also use a soil sensor to measure the concentrations of trace elements (such as iron, zinc, and manganese) and issue an alert if a specific nutrient is lacking. Furthermore, the collection unit can use the soil sensor to measure the organic matter content and suggest adding organic fertilizer as needed. This allows the soil component analysis to detect deficiencies of specific nutrients and take appropriate measures. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input soil component data into a generation AI and have the generation AI perform an analysis to detect deficiencies of specific nutrients.

[0075] The collection unit can dynamically change the type and frequency of data collection depending on the growth stage of the crop. For example, when the crop is in the germination stage, the collection unit can focus on collecting soil humidity and temperature. Also, when the crop is in the growing stage, the collection unit can frequently collect data on the amount of sunlight and rainfall. Furthermore, when the crop is approaching the harvest period, the collection unit can focus on collecting weather information and soil nutrient data. This enables more appropriate cultivation management by collecting data according to the crop growth stage. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input crop growth stage data into the generation AI and have the generation AI execute the type and frequency of data collection.

[0076] The collection unit can add a self-diagnosis function to the sensor and detect and notify of failures or abnormalities. For example, the collection unit can cause the sensor to periodically perform self-diagnosis and notify if an abnormality is detected. The collection unit can also monitor the remaining battery level of the sensor and issue an alert if the battery level drops. Furthermore, the collection unit can monitor the communication status of the sensor and notify if there is a problem with data transmission. This makes it possible to detect sensor failures or abnormalities early and take appropriate measures. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the sensor's self-diagnosis data to the generation AI and have the generation AI detect failures and abnormalities.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit can prioritize collecting only important data. Furthermore, when the user is relaxed, the collection unit can also collect detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize data that can be collected quickly. This enables efficient data collection by determining the priority of data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI prioritize the data to be collected.

[0078] The collection unit can use a drone to collect wide-area data and combine it with ground sensors to improve accuracy. For example, the collection unit can use a drone to take aerial photographs of the entire farmland and combine the data with ground sensor data for analysis. The collection unit can also use a drone to collect detailed data of a specific area and integrate it with ground sensor data. Furthermore, the collection unit can use a drone to collect weather data and combine it with ground sensor data to analyze weather conditions. This makes it possible to collect wide-area data and improve accuracy by using a drone. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data collected by a drone into a generation AI and have the generation AI perform an analysis combining the data with ground sensor data.

[0079] The collection unit can customize the placement and type of sensors according to different crops and farmland conditions. For example, the collection unit can propose optimal sensor placement according to different crops. The collection unit can also select an appropriate sensor type according to different soil conditions. Furthermore, the collection unit can adjust the sensor placement according to different weather conditions. This enables optimal data collection by customizing the placement and type of sensors according to different conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on crops and farmland conditions into the generation AI and have the generation AI execute the optimal sensor placement and type.

[0080] The collection unit can link the sensor data with other agriculture-related systems to achieve comprehensive agricultural management. For example, the collection unit can link the sensor data with an irrigation system to automatically set optimal irrigation timing. The collection unit can also link the sensor data with a fertilization system to automatically set optimal fertilization timing. Furthermore, the collection unit can link the sensor data with a harvest management system to automatically set optimal harvest timing. This enables comprehensive agricultural management by linking with other agriculture-related systems. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input sensor data into a generation AI and cause the generation AI to link with other agriculture-related systems.

[0081] The transmission unit can estimate the user's emotions and adjust the frequency and timing of data transmission based on the estimated user emotions. For example, if the user is feeling stressed, the transmission unit can reduce the frequency of data transmission and transmit only important data. Furthermore, if the user is relaxed, the transmission unit can frequently transmit detailed data. Furthermore, if the user is in a hurry, the transmission unit can prioritize data that can be transmitted quickly. This enables efficient data transmission by adjusting the frequency and timing of data transmission according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit can be performed using, for example, an AI, or without an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI control the frequency and timing of data transmission.

[0082] The transmitting unit can add a redundancy function to ensure communication stability when transmitting data. For example, the transmitting unit can achieve redundancy by using multiple communication paths when transmitting data. The transmitting unit can also achieve data redundancy by using a backup server when transmitting data. Furthermore, the transmitting unit can also ensure communication stability by adding an error detection and retransmission function when transmitting data. This improves the reliability of data transmission by using the redundancy function to ensure communication stability. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input redundancy settings for data transmission to the generating AI and have the generating AI execute the optimal redundancy method.

[0083] The transmitting unit can compress and encrypt data when transmitting data, thereby improving communication costs and security. For example, the transmitting unit can compress data when transmitting data to reduce communication costs. The transmitting unit can also encrypt data when transmitting data to improve security. Furthermore, the transmitting unit can combine compression and encryption when transmitting data to achieve both communication costs and security. This reduces communication costs and improves security through data compression and encryption. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input data compression and encryption settings to the generating AI and have the generating AI execute the optimal compression and encryption method.

[0084] The transmitting unit can be added with a function of detecting transmission errors or delays during data transmission and automatically retransmitting the data. For example, the transmitting unit can automatically retransmit data if a transmission error occurs during data transmission. The transmitting unit can also retransmit data if a delay occurs during data transmission. Furthermore, the transmitting unit can detect errors or delays during data transmission and retransmit data at the optimal timing. This improves the reliability of data transmission by detecting transmission errors or delays and automatically retransmitting data. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input data on transmission errors or delays into a generating AI and have the generating AI determine the timing of retransmission.

[0085] The transmission unit can estimate the user's emotions and determine the priority of data to be transmitted based on the estimated user emotions. For example, when the user is stressed, the transmission unit can prioritize transmitting only important data. Furthermore, when the user is relaxed, the transmission unit can also transmit detailed data. Furthermore, when the user is in a hurry, the transmission unit can prioritize data that can be transmitted quickly. This enables efficient data transmission by determining the priority of data to be transmitted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data to be transmitted.

[0086] The transmitting unit can transmit data to multiple cloud services using different communication protocols when transmitting data. For example, the transmitting unit can transmit data to multiple cloud services using HTTP and MQTT when transmitting data. The transmitting unit can also transmit data to multiple cloud services using WebSocket and CoAP when transmitting data. Furthermore, the transmitting unit can combine different communication protocols when transmitting data to achieve data redundancy. By using different communication protocols, the redundancy and reliability of data transmission are improved. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input settings for different communication protocols into a generating AI and have the generating AI execute an optimal protocol combination.

[0087] The transmitting unit temporarily stores data in local storage when transmitting data, thereby protecting the data even when communication is unstable. For example, the transmitting unit can temporarily store data in local storage when transmitting data and transmit the data when communication is restored. The transmitting unit can also create a backup in local storage when transmitting data and use it when communication is unstable. Furthermore, the transmitting unit can periodically transmit the data stored in local storage to the cloud when transmitting data. In this way, by temporarily storing the data in local storage, it is possible to protect the data even when communication is unstable. Some or all of the above-mentioned processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input data from local storage to a generating AI and have the generating AI execute the optimal transmission timing.

[0088] The transmission unit can cooperate with other agriculture-related systems when transmitting data to share and integrate data. For example, the transmission unit can cooperate with an irrigation system when transmitting data to share data. The transmission unit can also cooperate with a fertilization system when transmitting data to share data. Furthermore, the transmission unit can also cooperate with a harvest management system when transmitting data to share data. This enables comprehensive data management by coordinating with other agriculture-related systems. Some or all of the above-mentioned processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input cooperation data with other agriculture-related systems into the generation AI and have the generation AI share and integrate data.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it easier for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI execute the display method of the analysis results.

[0090] During analysis, the analysis unit can predict abnormal weather and the occurrence of pests and diseases and take early countermeasures. The analysis unit can, for example, predict the occurrence of abnormal weather based on weather data and propose countermeasures. The analysis unit can also predict the occurrence of pests and diseases based on crop growth data and take early countermeasures. Furthermore, the analysis unit can analyze abnormal weather and pest occurrence patterns based on past data and build a predictive model. This makes it possible to predict abnormal weather and the occurrence of pests and diseases and take early countermeasures, thereby minimizing damage to crops. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on abnormal weather and pests and diseases into the generation AI and have the generation AI build a predictive model.

[0091] During analysis, the analysis unit can apply different analysis algorithms depending on the crop variety and cultivation method. For example, the analysis unit can apply an optimal analysis algorithm depending on different crop varieties. The analysis unit can also customize the analysis algorithm depending on different cultivation methods. Furthermore, the analysis unit can dynamically change the analysis algorithm depending on the growth stage of the crop. This enables more accurate analysis by applying an analysis algorithm depending on the crop variety and cultivation method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the crop variety and cultivation method into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0092] During analysis, the analysis unit can combine past data and real-time data to make more accurate predictions. The analysis unit can, for example, combine past weather data and real-time weather data to make more accurate predictions. The analysis unit can also combine past crop growth data and real-time growth data to propose optimal cultivation methods. Furthermore, the analysis unit can combine past harvest data and real-time data to predict harvest yields. This combination of past data and real-time data enables more accurate predictions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data and real-time data into a generation AI and have the generation AI execute a prediction.

[0093] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying only important analysis results. Furthermore, if the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis results that can be quickly viewed. Thus, by prioritizing the analysis results according to the user's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI prioritize the analysis results.

[0094] During analysis, the analysis unit can integrate other agriculture-related data and provide comprehensive advice. The analysis unit can, for example, suggest optimal harvest timing based on market price data. The analysis unit can also optimize cultivation plans based on demand forecast data. Furthermore, the analysis unit can integrate other agriculture-related data and provide comprehensive cultivation advice. This makes it possible to provide comprehensive advice by integrating other agriculture-related data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input other agriculture-related data into the generation AI and have the generation AI execute comprehensive advice.

[0095] During analysis, the analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit can integrate weather data, soil data, and crop growth data to improve the accuracy of the analysis. The analysis unit can also integrate data from different sensors to perform a comprehensive analysis. Furthermore, the analysis unit can also integrate external data sources (satellite data, drone data, etc.) to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by integrating information from different data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information from different data sources into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0096] The analysis unit can customize the analysis algorithm according to different crops and farmland conditions during analysis. For example, the analysis unit can customize the optimal analysis algorithm according to different crops. The analysis unit can also adjust the analysis algorithm according to different farmland conditions. Furthermore, the analysis unit can dynamically change the analysis algorithm according to the growth stage of the crop. This enables more accurate analysis by customizing the analysis algorithm according to different conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on crops and farmland conditions into the generation AI and have the generation AI customize the optimal analysis algorithm.

[0097] The notification unit can estimate the user's emotions and adjust the content and timing of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can provide a concise and to-the-point notification. Furthermore, if the user is relaxed, the notification unit can provide a notification with detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a notification that can be quickly confirmed. This allows for more appropriate notifications by adjusting the content and timing of the notification according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI execute the content and timing of the notification.

[0098] The notification unit can select different notification methods depending on the level of urgency when making a notification. For example, if the level of urgency is high, the notification unit can notify by phone. If the level of urgency is medium, the notification unit can also notify by SMS. If the level of urgency is low, the notification unit can also notify by push notification. This allows important information to be conveyed quickly by selecting a notification method depending on the level of urgency. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input urgency data into the generation AI and have the generation AI execute the optimal notification method.

[0099] The notification unit can provide optimal advice by referring to the user's past behavioral history when making a notification. The notification unit can provide optimal advice based on, for example, the user's past behavioral history. The notification unit can also determine the optimal notification timing based on the user's past behavioral history. Furthermore, the notification unit can analyze the user's past behavioral history and provide optimal notification content. This makes it possible to provide more appropriate advice by referring to the user's past behavioral history. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's past behavioral history data into a generation AI and cause the generation AI to execute optimal advice.

[0100] The notification unit can improve the notification content by reflecting user feedback at the time of notification. The notification unit can improve the notification content based on, for example, user feedback. The notification unit can also analyze the user feedback and propose an optimal notification method. Furthermore, the notification unit can also adjust the timing of the notification by reflecting user feedback. This makes it possible to improve the notification content by reflecting user feedback. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input user feedback data into a generation AI and have the generation AI improve the notification content.

[0101] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can prioritize only important notifications. Furthermore, if the user is relaxed, the notification unit can also provide detailed notifications. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications that can be quickly confirmed. Thus, by determining the priority of notifications according to the user's emotions, important information can be conveyed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of notifications.

[0102] The notification unit can provide notification methods compatible with different devices when providing notifications. For example, the notification unit can provide push notifications to smartphones. The notification unit can also provide in-app notifications to tablets. The notification unit can also provide email notifications to PCs. This allows compatibility with different devices, allowing users to receive notifications on a variety of devices. Some or all of the above-described processing in the notification unit may be performed using, or without, AI, for example. For example, the notification unit can input device type data to the generation AI and have the generation AI execute the optimal notification method.

[0103] When issuing a notification, the notification unit can cooperate with other agriculture-related systems to provide a comprehensive notification. For example, the notification unit can cooperate with an irrigation system to provide notification of optimal irrigation timing. The notification unit can also cooperate with a fertilization system to provide notification of optimal fertilization timing. Furthermore, the notification unit can cooperate with a harvest management system to provide notification of optimal harvest timing. This enables comprehensive notification by coordinating with other agriculture-related systems. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input coordination data with other agriculture-related systems into the generation AI and cause the generation AI to execute a comprehensive notification.

[0104] The notification unit can provide multilingual notifications according to the user's language settings when issuing a notification. The notification unit can automatically set the notification language based on, for example, the language setting of the user's device. The notification unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the notification unit can provide notifications in that language. This enables notifications that are easy for the user to understand by providing multilingual notifications according to the user's language settings. Some or all of the above-described processing in the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input the user's language setting data into a generation AI and have the generation AI execute a notification in the optimal language. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, transmission unit, analysis unit, and notification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses sensors in the smart device 14 to measure soil humidity, temperature, amount of sunlight, and rainfall and collect data. The transmission unit has a function of transmitting the collected data to the cloud in real time and is realized via the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and uses machine learning to provide optimal cultivation methods and timing of fertilization and irrigation. The notification unit uses the control unit 46A of the smart device 14 to notify farmers of advice via a smartphone app or email. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, transmission unit, analysis unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses sensors in the smart glasses 214 to measure soil humidity, temperature, amount of sunlight, and rainfall and collect data. The transmission unit has a function of transmitting the collected data to the cloud in real time and is realized via the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and uses machine learning to provide optimal cultivation methods and timing of fertilization and irrigation. The notification unit uses the control unit 46A of the smart glasses 214 to notify farmers of advice via a smartphone app or email. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, transmission unit, analysis unit, and notification unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit uses sensors in the headset terminal 314 to measure soil humidity, temperature, amount of sunlight, and rainfall and collect data. The transmission unit has a function of transmitting the collected data to the cloud in real time and is realized via the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and uses machine learning to provide optimal cultivation methods and timing of fertilization and irrigation. The notification unit uses the control unit 46A of the headset terminal 314 to notify farmers of advice via a smartphone app or email. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, transmission unit, analysis unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses sensors in the robot 414 to measure soil humidity, temperature, amount of sunlight, and amount of rainfall and collect data. The transmission unit has a function of transmitting the collected data to the cloud in real time and is realized via the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and uses machine learning to provide optimal cultivation methods and timing of fertilization and irrigation. The notification unit uses the control unit 46A of the robot 414 to notify farmers of advice via a smartphone app or email.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. By adjusting the display method of the analysis results according to the user's emotions, a display that is easy for the user to view can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI execute the display method of the analysis results.

[0107] The collection unit can use a drone to collect wide-area data and combine it with ground sensors to improve accuracy. For example, a drone can take aerial photographs of the entire farmland and combine the data with ground sensor data for analysis. A drone can also collect detailed data of a specific area and integrate it with ground sensor data. Furthermore, a drone can collect weather data and combine it with ground sensor data to analyze weather conditions. This makes it possible to collect wide-area data and improve accuracy by using a drone. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data collected by a drone into a generation AI and have the generation AI perform an analysis combining the data with ground sensor data.

[0108] The transmitting unit can add a redundancy function to ensure communication stability when transmitting data. For example, redundancy can be achieved by using multiple communication paths when transmitting data. Also, data redundancy can be achieved by using a backup server when transmitting data. Furthermore, error detection and retransmission functions can be added when transmitting data to ensure communication stability. This improves the reliability of data transmission by using a redundancy function to ensure communication stability. Some or all of the above-mentioned processing in the transmitting unit can be performed using AI, for example, or without AI. For example, the transmitting unit can input redundancy settings for data transmission to the generating AI and have the generating AI execute the optimal redundancy method.

[0109] During analysis, the analysis unit can predict abnormal weather and the occurrence of pests and diseases and take early countermeasures. For example, it can predict the occurrence of abnormal weather based on weather data and propose countermeasures. It can also predict the occurrence of pests and diseases based on crop growth data and take early countermeasures. Furthermore, it can analyze abnormal weather and pest occurrence patterns based on past data and build a predictive model. This makes it possible to predict abnormal weather and the occurrence of pests and diseases and take early countermeasures, thereby minimizing damage to crops. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on abnormal weather and pests and diseases into the generation AI and have the generation AI build a predictive model.

[0110] The notification unit can estimate the user's emotions and adjust the content and timing of notifications based on the estimated user emotions. For example, if the user is stressed, a concise and to-the-point notification can be provided. If the user is relaxed, a notification containing detailed information can be provided. Furthermore, if the user is in a hurry, a notification that can be quickly confirmed can be provided. This allows for more appropriate notifications by adjusting the content and timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or without an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI execute the content and timing of the notification.

[0111] The collection unit can customize the placement and type of sensors according to different crops and farmland conditions. For example, it can propose optimal sensor placement according to different crops. It can also select appropriate sensor types according to different soil conditions. It can also adjust sensor placement according to different weather conditions. This enables optimal data collection by customizing the placement and type of sensors according to different conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on crops and farmland conditions into the generation AI and have the generation AI execute the optimal sensor placement and type.

[0112] The transmission unit can estimate the user's emotions and adjust the frequency and timing of data transmission based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data transmission can be reduced and only important data can be transmitted. Also, if the user is relaxed, detailed data can be transmitted more frequently. Furthermore, if the user is in a hurry, data that can be transmitted quickly can be prioritized. This enables efficient data transmission by adjusting the frequency and timing of data transmission according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit can be performed using, for example, an AI, or without an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI control the frequency and timing of data transmission.

[0113] During analysis, the analysis unit can integrate other agriculture-related data and provide comprehensive advice. For example, it can suggest optimal harvest timing based on market price data. It can also optimize cultivation plans based on demand forecast data. It can also integrate other agriculture-related data and provide comprehensive cultivation advice. This makes it possible to provide comprehensive advice by integrating other agriculture-related data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input other agriculture-related data into the generation AI and have the generation AI execute comprehensive advice.

[0114] The notification unit can select different notification methods depending on the level of urgency when making a notification. For example, if the level of urgency is high, the notification can be made by phone. If the level of urgency is medium, the notification can be made by SMS. Furthermore, if the level of urgency is low, the notification can be made by push notification. This allows important information to be conveyed quickly by selecting a notification method depending on the level of urgency. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input urgency data into the generation AI and have the generation AI execute the optimal notification method.

[0115] The analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated user emotions. For example, if the user is feeling stressed, only important analysis results can be displayed preferentially. Also, if the user is relaxed, detailed analysis results can be displayed. Furthermore, if the user is in a hurry, analysis results that can be quickly viewed can be prioritized. By prioritizing analysis results according to the user's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI prioritize the analysis results.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects data on soil humidity, temperature, solar radiation, and rainfall. For example, the collection unit can measure soil humidity using a soil sensor, soil temperature using a temperature sensor, and solar radiation using a pyranometer. These data are collected in real time. Step 2: The transmitter transmits the data collected by the collector to the cloud. For example, the transmitter transmits the collected data to the cloud in real time and has a redundancy function to ensure communication stability. Data can be transmitted using multiple communication paths. Step 3: The analysis unit analyzes the data sent by the transmission unit and provides optimal cultivation methods and fertilization and irrigation timing based on the crop's growth status and weather conditions. For example, the analysis unit uses machine learning to build a predictive model based on past data, and combines it with real-time data to provide optimal advice. The system also has the ability to predict abnormal weather and the occurrence of pests and diseases, allowing for early countermeasures. Step 4: The notification unit notifies the farmer of the advice provided by the analysis unit. For example, the notification unit can receive notifications via a smartphone app or email. The notification unit can also estimate the user's emotions and adjust the content and timing of notifications based on the estimated emotions.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0146] 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.

[0147] 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.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 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.

[0152] 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.

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

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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, to avoid confusion and 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 collection unit that collects data on soil humidity, temperature, amount of sunlight, and amount of rainfall; a transmitting unit that transmits the data collected by the collecting unit to a cloud; an analysis unit that analyzes the data transmitted by the transmission unit and provides cultivation methods and timings for fertilization and irrigation based on the growth status of the crops and weather conditions; a notification unit that notifies the farmer of the advice provided by the analysis unit. A system characterized by:

2. The collecting unit Soil sensors are placed at regular intervals, and weather sensors are placed in the center of the field.

2. The system of claim 1.

3. The transmission unit Send collected data to the cloud in real time 2. The system of claim 1.

4. The analysis unit Using machine learning to build predictive models based on past data, and combining them with real-time data to provide optimal advice 2. The system of claim 1.

5. The system according to claim 1 , wherein the notification unit can receive notifications using a smartphone app or detailed advice by email.

6. The collecting unit Estimate the user's emotions and adjust the placement of sensors and the timing of data collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Add functionality to analyze soil components and detect deficiencies of specific nutrients 2. The system of claim 1.

8. The collecting unit Dynamically change the type and frequency of data collected depending on the stage of crop growth 2. The system of claim 1.

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