System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting environmental information; means for transmitting the collected environmental information to a central database; a generative AI model for analyzing the information stored in the central database; means for generating crop management advice based on the analysis; and means for providing the generated advice to a farmer.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] In agriculture, climate change and fluctuations in environmental conditions have a significant impact on crop yields. The aging of the agricultural workforce and the issue of knowledge transfer are also serious problems, making agriculture a high barrier for new entrants. Given this background, there is a demand for optimal crop management in real time, but conventional methods are difficult to adequately address. Furthermore, solutions that can be easily used even by farmers without advanced skills are needed. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes a means for collecting environmental data, a means for transmitting the collected environmental data to a central database, a generative AI model that analyzes the data stored in the central database, a means for generating crop management advice based on the analysis results, and a means for providing the generated advice to farmers. Specifically, environmental data such as temperature, humidity, sunshine hours, and soil moisture are collected in real time using IoT devices and transmitted to the central database. The generative AI model analyzes past and real-time data to provide optimal advice for crop management. The system also supports farmers in efficiently carrying out their farm work by providing easy-to-understand advice via an audio output device or smartphone app and integrating weather data to provide long-term forecasts and disease warnings.

[0006] "Environmental data" refers to information related to the growing environment of agricultural crops, such as temperature, humidity, hours of sunlight, and soil moisture.

[0007] "Central database" refers to a database that has the function of centrally managing, analyzing, and storing multiple data.

[0008] A "generative AI model" refers to an artificial intelligence algorithm that uses historical and real-time data to analyze and provide optimal advice.

[0009] "Crop management advice" refers to recommendations that include specific instructions on optimal watering, fertilization, disease prevention, and other aspects of growing crops.

[0010] The "audio output device" refers to a device that converts the generated advice into audio and notifies the farmer.

[0011] "Smartphone app" refers to application software that allows farmers to receive crop management advice using their mobile devices.

[0012] "Weather data" refers to information relating to weather, such as wind speed, precipitation, temperature, and humidity.

[0013] "Disease warning" refers to a notice to warn of a disease that may occur under certain environmental conditions. [Brief explanation of the drawings]

[0014] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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, a 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), and an APU (Accelerated Processing Unit).

[0018] 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.

[0019] 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.

[0020] 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), Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0026] 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.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention relates to an agricultural support system that uses IoT devices and generative AI models to collect and analyze environmental data to provide optimal crop management advice.

[0036] System Configuration

[0037] The system consists of the following main components:

[0038] 1. Terminal (IoT device)

[0039] 2. Server

[0040] 3. Users (farmers)

[0041] 4. Audio output devices and smartphone apps

[0042] Program processing

[0043] Data collection

[0044] Terminals (IoT devices) are installed on farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is obtained through sensors. For example, at 8:00 a.m., the collected data is 25°C temperature, 65% humidity, 5 hours of sunlight, and 40% soil moisture. The terminals assemble this data into data packets and send them to a server.

[0045] Data Aggregation and Storage

[0046] The server stores the data packets received from each device in a temporary storage area. It then checks the integrity of the data and stores only valid data in the central database. When checking the integrity, it checks the timestamp and checks for abnormal values.

[0047] Data analysis and advice generation

[0048] The server inputs the latest environmental data stored in the database into the generative AI model. The generative AI model analyzes trends and anomalies based on past and real-time data. As a result, optimal crop management advice is generated. The advice might be, for example, "Watering at 3 p.m. is effective" or "No additional fertilizer is required for the next three days." Weather data is also integrated to generate long-term forecasts and disease warnings. Warnings include, "Beware of certain disease-causing fungi due to prolonged high humidity."

[0049] Providing advice

[0050] The server sends the generated advice to each terminal (audio output device or smartphone app). The terminal (audio output device) converts the received text data into voice and notifies the farmer. Voice advice such as "Water now" is provided to the farmer. Meanwhile, the user can also check the advice in text format using the smartphone app. When the app is opened, advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0051] Specific examples

[0052] For example, suppose that at 8:00 AM, a device collects data showing a temperature of 25°C, humidity of 65%, sunshine for 5 hours, and soil moisture content of 40%. This data is sent to a server, where it is checked for consistency and then stored in a central database. The server analyzes the data and generates advice such as, "It is effective to water plants at 3:00 PM." The generated advice is announced via voice output by the device, and the user can also check the advice on a smartphone app.

[0053] Through the above process, the present invention is a system that supports efficient farming for farmers and helps achieve sustainable agriculture.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, sunshine hours, and soil moisture content through sensors installed in farmland. For example, at 8 a.m., the data obtained is: temperature 25°C, humidity 65%, sunshine hours 5 hours, soil moisture content 40%.

[0057] Step 2:

[0058] The device assembles the collected environmental data into a data packet, which includes the sensor ID, a timestamp, and the acquired data.

[0059] Step 3:

[0060] The terminal sends data packets to the server using a communication protocol (e.g., MQTT, HTTP).

[0061] Step 4:

[0062] The server stores the data packets received from each terminal in a temporary storage area.

[0063] Step 5:

[0064] The server verifies the integrity of the data it receives by checking the timestamps in the data packets and for invalid or missing values.

[0065] Step 6:

[0066] The server then stores the data that has been verified as consistent in a central database, where it is managed together with other past data.

[0067] Step 7:

[0068] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model.

[0069] Step 8:

[0070] The server runs a generative AI model and performs analysis based on the acquired data, comparing historical data with real-time data to detect trends and anomalies.

[0071] Step 9:

[0072] The server generates optimal crop management advice based on the analysis results of the generative AI model. The advice includes information on watering timing, adding fertilizer, and disease prevention measures. Examples include "Watering at 3 p.m. is effective" and "There is no need to add fertilizer for the next three days."

[0073] Step 10:

[0074] The server also integrates meteorological data to generate weather forecasts and disease warnings, such as "Beware of certain disease pathogens as high humidity continues."

[0075] Step 11:

[0076] The server transmits the generated advice to each device (audio output device or smartphone app), including text data of the audio advice.

[0077] Step 12:

[0078] The terminal (audio output device) converts the received text data into audio and notifies the farmer, providing advice such as "Please water your plants now."

[0079] Step 13:

[0080] Users open the smartphone app to view text-based crop management advice. The app's notifications allow users to receive advice in real time. For example, the advice might say, "You don't need to add fertilizer for the next three days."

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] Previous agricultural support systems had issues with the collection, analysis, and provision of environmental data, such as insufficient data consistency verification, elimination of time lags, and insufficient convenience through multiple notification methods. Additionally, the quality of crop management advice based on analysis results was low, and accurate information for farmers to carry out their farm work efficiently was not provided.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes means for collecting environmental data, means for assembling the collected environmental data into data packets and transmitting them to a central database, means for verifying the consistency of the data stored in the central database, a generative AI model for analyzing the data whose consistency has been verified, means for generating crop management advice based on the analysis results of the generative AI model, and means for providing the generated advice to farmers. This enables the collection and analysis of accurate environmental data in real time, and makes it possible to provide appropriate and timely crop management advice to farmers.

[0086] "Environmental data" refers to data such as temperature, humidity, hours of sunlight, and soil moisture collected at a specific location such as farmland.

[0087] "Collection means" refers to the sensor-equipped equipment or devices used to collect environmental data.

[0088] A "data packet" is a unit of data that is converted into a certain format in order to transmit collected environmental data collectively.

[0089] The "central database" is a database system for centrally storing and managing collected environmental data.

[0090] "Integrity checks" means the functions or processes used to check data for accuracy and integrity before it is stored in the central database.

[0091] A "generative AI model" is an artificial intelligence model that analyzes collected environmental data and generates crop management advice.

[0092] "Means for generating advice" refers to the process or algorithm for creating crop management advice based on the analysis results of the generative AI model.

[0093] An "agricultural worker" is someone who works in agriculture, that is, someone who is responsible for the work of growing crops.

[0094] The "audio output device" refers to a device that transmits the generated crop management advice as audio.

[0095] A "portable information terminal application" is application software that runs on a portable device such as a smartphone or tablet and provides crop management advice.

[0096] MODE FOR CARRYING OUT THE INVENTION

[0097] This invention is a system for agricultural support using IoT devices and generative AI models. The system aims to provide farmers with optimal crop management advice by collecting environmental data and analyzing the collected data.

[0098] System Configuration

[0099] The system consists of the following main components:

[0100] 1. Terminal: An IoT device that collects environmental data, such as temperature, humidity, hours of sunlight, and soil moisture.

[0101] 2. Server: Refers to the computer system that receives the collected data, verifies the data integrity, and analyzes the data using generative AI models.

[0102] 3. User: Refers to the farmer who uses the system.

[0103] 4. Audio output devices and handheld information terminal applications: Devices for providing crop management advice.

[0104] Examples of data collection

[0105] The device acquires environmental data from sensors installed in the farmland. For example, data on temperature, humidity, hours of sunlight, and soil moisture is collected periodically. At 8:00 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5, and soil moisture 40%. This data is measured in real time by the sensors and temporarily stored in the device's internal memory.

[0106] Specific examples of data transmission

[0107] The device collects data and assembles it into data packets. Then, it sends the resulting data packets to a server via an internet connection. HTTP or MQTT is typically used as the communication protocol. For example, data collected at 8:00 AM can be sent immediately to the server.

[0108] Data Aggregation and Storage

[0109] The server receives data packets from each device. The received data is stored in a temporary storage area (cache, queue, etc.). The server then checks the integrity of the data, verifying timestamps, checking for outliers, and inspecting for missing data, and stores only normal data in the central database. For example, at 8 a.m., data such as a temperature of 25 degrees and humidity of 65% is confirmed to be normal and stored in the database.

[0110] Data analysis and advice generation

[0111] The server inputs the latest environmental data stored in a central database into the generative AI model. The generative AI model analyzes trends and anomalies based on past and real-time data to generate optimal crop management advice. For example, the generative AI model analyzes humidity and temperature trends and generates specific advice such as "Watering at 3 p.m." It also predicts the risk of specific diseases and generates warning alerts such as "High humidity will continue, so be careful of specific disease-causing bacteria."

[0112] Providing advice

[0113] The server sends the generated advice to each device (audio output device or mobile information terminal application). The device (audio output device) uses a TTS (Text to Speech) engine to convert the received text data into speech. A voice notification such as "Water your plants now" is given. Meanwhile, the user checks the advice in text format using the mobile information terminal application. When the user opens the app, specific advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0114] Example prompts for generative AI models

[0115] "Enter your latest environmental data. Generate crop management advice based on temperature, humidity, sunshine hours, and soil moisture. For example, what is the best way to manage a crop when the temperature is 25°C, humidity is 65%, sunshine hours are 5 hours, and soil moisture is 40%?"

[0116] As a result, the present invention provides farmers with a system that supports efficient and sustainable agricultural management.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1: Data collection

[0119] The device collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture content through sensors installed in the farmland. The input is real-time environmental data, and the output is a collection of this data. For example, at 8:00 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%. The collected data is temporarily stored in the device's internal memory.

[0120] Step 2: Send data

[0121] The device assembles the temporarily stored data into data packets. The input is the environmental data stored in the device's internal memory, and the output is the data packets. These data packets are sent to the server via an Internet connection. HTTP or MQTT is used as the communication protocol. For example, data collected at 8:00 a.m. is immediately sent to the server.

[0122] Step 3: Data aggregation and storage

[0123] The server receives data packets from each device. The received data is stored in a temporary storage area (such as a cache or queue). The input is the data packets received from each device, and the output is the data stored in the temporary storage area. The server then checks the timestamp, checks for outliers, and checks for missing data, and stores only valid data in the central database. For example, data received at 8:00 a.m. is checked for timestamps and outliers before being stored in the central database.

[0124] Step 4: Data analysis and advice generation

[0125] The server inputs the latest environmental data stored in a central database into the generative AI model. The input is the environmental data stored in the central database, and the output is the analysis results. The generative AI model analyzes trends and anomalies based on past and real-time data to generate optimal crop management advice. Based on the input data, it generates advice such as "Watering at 3 p.m. is effective" or a warning such as "Be careful of certain disease-causing bacteria as high humidity continues."

[0126] Step 5: Providing advice

[0127] The server sends the generated advice to each device (audio output device or portable information terminal application). The input is the advice generated based on the analysis, and the output is the advice sent to each device. The device (audio output device) converts the received text data into voice and notifies the farmer. For example, the voice notification may say, "Please water now." Meanwhile, the user can check the advice in text format using the portable information terminal application. For example, when opening the app, specific advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0128] (Application example 1)

[0129] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0130] There is a growing need for systems that can collect and analyze environmental data and machine operation data in factories in real time and provide workers with accurate management advice. However, current systems tend to be slow in collecting and analyzing data, making it difficult to provide appropriate advice in a timely manner. Furthermore, the lack of a flexible information provision mechanism via voice notifications or smartphone apps makes efficient production management difficult.

[0131] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0132] In this invention, the server includes means for collecting environmental data, means for transmitting the collected environmental data to a central database, a generative AI model for analyzing the data stored in the central database, means for generating management advice based on the analysis results, and means for providing the generated advice to workers. This makes it possible to collect and analyze various data within the factory in real time and provide accurate production management advice to workers in a timely manner.

[0133] "Environmental data" refers to data that indicates the temperature, humidity, lighting, vibration, and machine operating status within the factory.

[0134] "Means of collection" refers to devices and systems that collect environmental data within the factory using sensors and monitoring equipment.

[0135] "Transmission means" refers to a communication device or protocol for transmitting collected environmental data to a server via the Internet or a dedicated line.

[0136] The "central database" is a database system for centrally storing and managing collected environmental data.

[0137] An "analytical generative AI model" is an artificial intelligence model that analyzes data stored in a central database and generates appropriate management advice.

[0138] The "means for generating management advice" refers to an algorithm or program for creating appropriate management advice based on the results of analysis by the generative AI model.

[0139] The "means of providing" refers to a device or platform for notifying workers of the generated management advice through an audio output device or a smartphone app.

[0140] "Workers" are the staff who work in the factory and receive the management advice provided by the generative AI model.

[0141] This invention relates to a system that collects and analyzes environmental data and machine operation data within a factory and provides timely management advice. This system consists of the following main components:

[0142] 1. Means of collecting environmental data

[0143] IoT devices installed in factories (such as temperature sensors, humidity sensors, vibration sensors, and machine operation status monitoring devices) periodically collect environmental data such as temperature, humidity, vibration, and lighting, and this data is sent to a server via Wi-Fi or a dedicated line.

[0144] 2. Data aggregation and storage

[0145] The server stores data packets received from each IoT device in a temporary storage area. After verifying the data integrity, it stores the correct data in a central database. Data integrity is verified by checking timestamps and filtering outliers.

[0146] 3. Data analysis and advice generation

[0147] The server inputs the latest environmental data stored in the database into the generative AI model. Based on past and real-time data, the generative AI model analyzes temperature, humidity, vibration levels, machine operating status, and other factors, detects trends and anomalies, and generates optimal management advice. For example, it might generate advice such as "Machine A's vibration level is high, so maintenance is required" or "The humidity is high, so please ventilate."

[0148] 4. Providing advice

[0149] The server provides the generated advice to the worker via a voice output device or smartphone app. The voice output device converts the received text data into voice and notifies the worker. Voice advice such as "Machine A requires maintenance" is provided. Meanwhile, the smartphone app displays the generated advice in text format to the worker.

[0150] As a specific example, let's say that at 8:00 a.m., an IoT device in a factory collects data monitoring the operating status of Machine A at a temperature of 25°C, humidity of 60%, and vibration level of 0.2g. This data is sent to a server, where it is checked for consistency and then stored in a central database. The server analyzes this data and generates advice such as, "Machine A has a high vibration level, so maintenance is recommended." The generated advice is notified to the worker via a voice output device, and can also be viewed on a smartphone app.

[0151] Examples of prompts to input to a generative AI model include:

[0152] "Based on data from the past seven days, analyze environmental data such as temperature, humidity, vibration levels, and machine operation status to generate optimal management advice for your factory."

[0153] The system of this invention makes it possible to collect and analyze environmental data within a factory in real time and provide accurate production management advice to workers in a timely manner. This allows for the early detection of equipment abnormalities and the implementation of appropriate maintenance, thereby improving the production efficiency and safety of the entire factory.

[0154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0155] Step 1:

[0156] The __terminal__ collects environmental data within the factory. Specifically, it obtains data on temperature, humidity, vibration, lighting, etc. using temperature sensors, humidity sensors, vibration sensors, and machine operation status monitoring devices.

[0157] __Input:__ Environmental data from various sensors in the factory

[0158] __Output:__ Collected environmental data packet

[0159] Step 2:

[0160] The environmental data collected by the device is compiled into data packets and sent to the server via Wi-Fi or a dedicated line.

[0161] __Input:__ Environmental data packets collected from the device

[0162] __Output:__ Environment data packet sent to the server

[0163] Step 3:

[0164] The server stores the received environmental data packet in a temporary storage area, then checks the timestamp and filters out any outliers to ensure data integrity.

[0165] __Input:__ Environment data packet sent to the server

[0166] __Output:__ Environmental data with integrity verified

[0167] Step 4:

[0168] The server stores the verified data in a central database for further analysis.

[0169] __Input:__ Environmental data whose integrity has been verified

[0170] __Output:__ Environmental data stored in a central database

[0171] Step 5:

[0172] The server inputs the latest environmental data stored in a central database into the generative AI model, analyzes the data, and generates appropriate management advice.

[0173] __Input:__ Latest environmental data stored in a central database

[0174] __Output:__ Generated management advice

[0175] Step 6:

[0176] The server sends the generated management advice to an audio output device or smartphone app.

[0177] __Input:__ Generated management advice

[0178] __Output:__ Management advice sent to audio output devices and smartphone apps

[0179] Step 7:

[0180] The audio output device converts the received advice into audio and notifies the user. The smartphone app also displays the generated advice in text format to the user.

[0181] __Input:__ Management advice received by audio output device and smartphone app

[0182] Output: The audio notification and text display provided to the user.

[0183] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0184] This invention relates to a system that collects environmental data, analyzes it with a generative AI model, and provides crop management advice. It is an agricultural support system that also combines an emotion engine that recognizes the user's emotions. This system helps farmers perform efficient and optimal farm work.

[0185] System Configuration

[0186] The system consists of the following main components:

[0187] 1. Terminal (IoT device)

[0188] 2. Server

[0189] 3. Users (farmers)

[0190] 4. Emotion Engine

[0191] 5. Audio output devices and smartphone apps

[0192] Program processing

[0193] Data collection

[0194] Terminals (IoT devices) are installed in farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is acquired through sensors, compiled into data packets, and sent to a server. For example, at 8 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[0195] Data Aggregation and Storage

[0196] The server stores the data packets received from the terminal in a temporary storage area, checks the integrity of the data, and then stores the correct data in the central database.

[0197] Data analysis and advice generation

[0198] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model. The generative AI model performs analysis based on past and real-time data. Based on the analysis results, it generates optimal crop management advice. For example, advice such as "Watering at 3 p.m. is effective" or "There is no need to add fertilizer for the next three days" is generated.

[0199] emotion recognition

[0200] The emotion engine collects emotion data from the farmers' voices and facial expressions, analyzes the emotions, and sends the collected emotion data to the server.

[0201] Use of Emotional Data

[0202] The server stores the emotion data received from the emotion engine in a central database. The generative AI model uses the emotion data along with environmental data for analysis. For example, if a farmer is feeling anxious, it generates reassuring advice such as, "There are no particular points to be careful of at this stage."

[0203] Providing advice

[0204] The server sends the generated advice to a terminal (audio output device or smartphone app). The terminal (audio output device) converts the received advice into audio and notifies the farmer. For example, advice such as "Water your fields now" is provided as audio. The user can also open the smartphone app to check a text version of the advice. The app notification function allows the user to receive advice in real time. Advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0205] Specific examples

[0206] For example, suppose that at 8:00 a.m., a device collects data showing a temperature of 25°C, humidity of 65%, five hours of sunshine, and soil moisture of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. The advice generated is, "It is effective to water at 3:00 p.m." The emotion engine then collects the farmer's emotional data and sends it to the server. The generative AI model takes into account the data that the farmer is concerned about, and supplements it with reassuring advice such as, "There are no particular points to be careful of at this stage." The advice is communicated via voice through the device (audio output device), and the user can also check it on their smartphone app.

[0207] The present invention is a sustainable agricultural support system that provides farmers with optimal advice based on collected environmental data and emotional data, thereby providing efficient farming and a sense of security.

[0208] The processing flow will be explained below.

[0209] Step 1:

[0210] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture through sensors installed in farmland. For example, at 8 a.m., the data collected is: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[0211] Step 2:

[0212] The device assembles the collected environmental data into a data packet, which includes the sensor ID, a timestamp, and the acquired data.

[0213] Step 3:

[0214] The terminal sends data packets to the server using a communication protocol (e.g., MQTT, HTTP).

[0215] Step 4:

[0216] The server stores the data packets received from each terminal in a temporary storage area.

[0217] Step 5:

[0218] The server verifies the integrity of the data received, checking for timestamps, invalid or missing values.

[0219] Step 6:

[0220] The server then stores the data that has been verified as consistent in a central database, where it is managed together with other past data.

[0221] Step 7:

[0222] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model.

[0223] Step 8:

[0224] The server runs a generative AI model and performs analysis based on the acquired data, comparing historical data with real-time data to detect trends and anomalies.

[0225] Step 9:

[0226] The server generates optimal crop management advice based on the analysis results of the generative AI model, such as advice on when to water at 3 p.m. and the need for additional fertilizer for the next three days.

[0227] Step 10:

[0228] The emotion engine collects emotion data from farmers' voices and facial expressions, including their stress levels and emotional states.

[0229] Step 11:

[0230] The emotion engine assembles the collected emotion data into a data packet and sends it to the server.

[0231] Step 12:

[0232] The server stores the emotion data in a central database, which the generative AI model uses for analysis.

[0233] Step 13:

[0234] The server then tailors crop management advice based on the analysis results, including emotional data. For example, if a farmer is feeling anxious, it adds reassuring advice.

[0235] Step 14:

[0236] The server sends the generated advice to the device (audio output device or smartphone app). For example, the advice may say, "There are no particular points to be aware of at this stage."

[0237] Step 15:

[0238] The audio output device converts the received advice into audio and notifies the farmer, saying, "Please water your plants now."

[0239] Step 16:

[0240] Users can open the smartphone app and see a text version of the advice, which may include, "You don't need to add fertilizer for the next three days."

[0241] Through these specific processing steps, the present invention is a system that provides optimal advice to farmers based on collected environmental data and emotional data, and supports them in carrying out farm work efficiently and safely.

[0242] Example 2

[0243] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0244] In modern agriculture, providing advice based solely on environmental data poses a challenge, as it does not take into account the psychological state of farmers, making it difficult to provide optimal crop management. Furthermore, because the collected data is diverse and complex, analyzing it and generating useful advice can require a great deal of time and effort.

[0245] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data, means for transmitting the collected environmental data to a central database, a generation AI model means for analyzing the data stored in the central database, means for generating crop management advice based on the analysis results, means for collecting farmer's emotion data, means for transmitting the collected emotion data to the central database, means for generating crop management advice that takes the emotion data into account based on the analysis results, and means for providing the generated advice to the farmer. This makes it possible to provide optimal crop management advice that takes the farmer's psychological state into account, thereby enabling farm work to be carried out efficiently and with peace of mind.

[0246] "Environmental data" refers to information such as temperature, humidity, hours of sunlight, and soil moisture in the agricultural environment.

[0247] "Collection means" refers to a device or method for acquiring environmental data and emotional data using sensors, IoT devices, etc.

[0248] "Central database" refers to a data storage system that integrates and stores collected environmental data and emotional data.

[0249] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and generates optimal crop management advice.

[0250] "Analysis means" means a method or device for analyzing data using a generative AI model to generate crop management advice.

[0251] "Emotional data" refers to information indicating the psychological state of farmers obtained from their voices, facial expressions, etc.

[0252] "Provision means" refers to a method or device (such as an audio output device or smartphone app) for notifying farmers of the generated advice.

[0253] This invention relates to a system that collects environmental and emotional data, analyzes it using a generative AI model, and provides crop management advice, helping farmers carry out their farm work efficiently and safely.

[0254] System Configuration

[0255] The system consists of the following main components:

[0256] 1. Terminal (IoT device)

[0257] 2. Server

[0258] 3. Users (farmers)

[0259] 4. Emotion Engine

[0260] 5. Audio output devices and smartphone apps

[0261] Program processing

[0262] Data collection

[0263] Terminals (IoT devices) are installed in farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is acquired through sensors, compiled into data packets, and sent to a server. For example, at 8 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[0264] Data Aggregation and Storage

[0265] The server stores the data packets received from the terminal in a primary storage area, checks the integrity of the data, and then stores the correct data in the central database.

[0266] Data analysis and advice generation

[0267] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model. The generative AI model performs analysis based on past and real-time data. Based on the analysis results, it generates optimal crop management advice. For example, advice such as "Watering at 3 p.m. is effective" or "There is no need to add fertilizer for the next three days" is generated.

[0268] emotion recognition

[0269] The emotion engine collects emotion data from the farmers' voices and facial expressions, and this data is sent to the server.

[0270] Use of Emotional Data

[0271] The server stores the emotion data in a central database. The generative AI model uses the environmental and emotion data for analysis. For example, if a farmer is feeling anxious, it generates reassuring advice such as, "There are no particular precautions to take at this stage."

[0272] Providing advice

[0273] The server sends the generated advice to a device (audio output device or smartphone app). The audio output device converts the advice into audio and notifies the user. For example, advice such as "Water your plants now" is provided as audio. The user can also check a text version of the advice using the smartphone app. The app notification function displays advice in real time, such as "There is no need to add fertilizer for the next three days."

[0274] This system will provide farmers with optimal advice based on collected environmental and emotional data, realizing a sustainable agricultural support system that provides efficient farming and peace of mind.

[0275] Specific examples

[0276] For example, suppose that at 8:00 a.m., a device collects data on a temperature of 25°C, humidity of 65%, sunshine for 5 hours, and soil moisture content of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. The resulting advice is, "It is effective to water at 3:00 p.m." The emotion engine then collects the farmer's emotional data and sends it to the server. The generative AI model considers the data that the farmer is concerned about, and supplements it with reassuring advice such as, "There are no particular points to be aware of at this stage." The advice is communicated via voice through the device (audio output device), and the user can also check it on a smartphone app.

[0277] Prompt Sentence Examples

[0278] Here are some example prompts to input to a generative AI model:

[0279] "Provide today's crop management advice based on the latest environmental data."

[0280] "Please propose a work plan for the next three days taking into account the current agri-environmental data."

[0281] "Generate optimal advice when farmers are feeling anxious."

[0282] Such prompt sentences enable the system to efficiently generate and provide crop management advice to farmers.

[0283] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0284] Step 1: Data collection

[0285] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture using sensors installed in the farmland. Specific devices used include temperature sensors, humidity sensors, light sensors, and soil moisture sensors. At 8:00 AM, the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%. The terminal compiles this data into data packets and sends them to the server. The input is data from the environmental sensors, and the output is the generated data packets.

[0286] Step 2: Data aggregation and storage

[0287] The server stores data packets received from the terminal in a primary storage area. It then checks the input data for format and abnormal values ​​to confirm data integrity. For example, it checks whether the data format is correct and whether the values ​​are within the acceptable range. Once the server confirms that the data is normal, it stores the data in a central database. The input is the data packet sent from the terminal, and the output is data whose integrity has been confirmed.

[0288] Step 3: Data analysis and advice generation

[0289] The server retrieves the latest environmental data stored in the central database and inputs it into the generative AI model. Specifically, it provides the generative AI model with the following prompt: "Please provide today's crop management advice based on the latest environmental data." The generative AI model analyzes past and real-time data to generate optimal crop management advice. For example, the generated advice might be, "It is effective to water at 3 p.m." The input is the latest environmental data from the central database, and the output is the generated crop management advice.

[0290] Step 4: Emotion Recognition

[0291] The emotion engine collects emotion data from the farmers' voices and facial expressions. Specifically, it uses voice recognition and facial expression analysis technologies. The emotion engine compiles the collected emotion data into data packets and sends them to the server. The input is the farmers' voices and facial expressions, and the output is the generated emotion data packets.

[0292] Step 5: Use emotion data

[0293] The server stores the emotion data received from the emotion engine in a central database. The generative AI model analyzes the combined environmental data and emotion data. For example, if a farmer is feeling anxious, reassuring advice such as "There are no particular points to be aware of at this stage" is generated. A specific prompt used is "Please provide the best advice based on the farmer's emotion data." The input is emotion data and environmental data, and the output is crop management advice that takes emotion into consideration.

[0294] Step 6: Providing advice

[0295] The server sends the generated advice to a terminal (audio output device or smartphone app). The audio output device converts the received advice into audio and notifies the user with advice such as "Water your fields now." The user can also open the smartphone app to check the text version of the advice. The app notification function is used to display advice in real time, such as "There is no need to add fertilizer for the next three days." The input is the generated advice, and the output is a notification to the farmer.

[0296] (Application example 2)

[0297] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0298] Conventional agricultural support systems can only provide advice based on environmental data, which means they lack comprehensive support that takes into account the emotional state of farmers. They also struggle to provide real-time information that farmers can respond to immediately. This has created a challenge in simultaneously ensuring the efficiency of farm work and farmers' mental well-being.

[0299] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0300] In this invention, the server includes a means for collecting environmental data and emotional data, a means for transmitting the collected environmental data and emotional data to a central database, a generative AI model for analyzing the data stored in the central database, a means for generating crop management advice based on the analysis results, a means for providing the generated advice to users, and a smart device for displaying data in real time. This allows for the provision of comprehensive advice in real time that takes into account the emotional state of farmers, thereby improving the efficiency of farm work and increasing farmers' peace of mind.

[0301] "Environmental data" is information about the physical and environmental conditions of agricultural land, such as temperature, humidity, hours of sunshine, and soil moisture.

[0302] "Emotion data" is information about the emotional state of the farmer as recognized from his voice, facial expression, etc.

[0303] The "central database" is a database system that centrally stores collected environmental data and emotional data and uses them for analysis and management.

[0304] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates optimal advice based on past and real-time data.

[0305] "Advice provision means" refers to the means for communicating the advice generated by the generative AI model to farmers, and includes audio output devices and smart devices.

[0306] "Real-time display means" refers to a means of instantly visualizing collected data and advice, enabling farmers to respond quickly.

[0307] "Smart devices" are electronic devices with advanced functions such as smartphones, tablets, smart glasses, and head-mounted displays.

[0308] The present invention relates to a system that collects environmental data and emotional data, analyzes them using a generative AI model, and provides farmers with optimal crop management advice. This system helps farmers perform efficient and optimal farm work and provides them with peace of mind. Detailed embodiments for specifically implementing the present invention are described below.

[0309] System Configuration

[0310] The system consists of the following main components:

[0311] 1. Terminal: This terminal contains environmental sensors and emotion recognition devices and is installed in the farmland. For example, it uses a DHT22 sensor to measure temperature and humidity, and a soil moisture sensor to measure the amount of moisture in the soil.

[0312] 2. Server: Responsible for data aggregation, storage, and analysis. Collected environmental and emotional data are stored in a central database.

[0313] 3. User: Farmers receive advice via their smartphones or smart devices.

[0314] 4. Emotion Engine: This engine has the ability to analyze the user's voice and facial expressions and generate emotion data. This emotion data is collected using voice and camera.

[0315] 5. Generative AI model: Analyzes environmental and emotional data to generate optimal crop management advice. This model uses a generative AI model such as GPT-3 (registered trademark).

[0316] 6. Audio output device and smartphone app: Generated advice is communicated to farmers via voice and text.

[0317] Program processing

[0318] The server does the following:

[0319] 1. Data collection: The terminals are installed in farmland to periodically collect environmental data such as temperature, humidity, sunshine hours, and soil moisture content, and also use an emotion engine to collect farmers' sentiment data.

[0320] 2. Data transmission: The collected environmental data and emotion data are transmitted from the device to the server.

[0321] 3. Data aggregation and storage: The server stores the received data in a central database and checks the integrity of the data.

[0322] 4. Data analysis and advice generation: The server inputs environmental data and emotional data into the generative AI model for analysis. The generative AI model generates crop management advice based on multiple data sets. An example of a prompt is, "Please provide optimal crop management advice based on the following environmental and emotional data. Environmental data: Temperature: 25°C, Humidity: 65%, Sunshine hours: 5 hours, Soil moisture: 40% Emotional data: The manager's emotion is 'anxious'."

[0323] 5. Providing advice: The generated advice is sent from the server to the device (audio output device or smartphone app). For example, advice such as "Watering at 3 p.m. is effective" or "There are no particular points to be careful of at this stage" is provided.

[0324] Specific examples

[0325] For example, at 8 a.m., the device collects environmental data such as a temperature of 25 degrees, humidity of 65%, five hours of sunshine, and soil moisture of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. Furthermore, the emotion engine collects the farmer's emotional data and sends it to the server. Based on this data, the generative AI model generates advice such as "It would be effective to water at 3 p.m." It also provides additional reassuring advice such as "There are no particular precautions to take at this stage." This advice is communicated to the farmer via voice or text.

[0326] In this way, the present invention is a sustainable agricultural support system that not only improves the work efficiency of farmers but also provides them with a sense of psychological security.

[0327] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0328] Step 1:

[0329] The device collects environmental and emotional data using environmental sensors and an emotion recognition device. Specifically, it measures temperature and humidity using a DHT22 sensor, and soil moisture using a soil moisture sensor. It also collects emotional data from farmers using voice and camera recordings. For example, at 8 a.m., the device obtains data showing a temperature of 25°C, humidity of 65%, sunshine for 5 hours, soil moisture content of 40%, and an emotion of "anxiety."

[0330] Step 2:

[0331] The terminal assembles the collected environmental and emotional data into data packets and transmits them to the server. Specifically, the collected data is properly formatted and transmitted using a secure communication protocol. The transmitted data includes temperature, humidity, sunshine hours, soil moisture content, and the farmer's emotional information.

[0332] Step 3:

[0333] The server temporarily stores the received data packets in a database and checks the data integrity. Specifically, it checks the format of the received data and verifies whether it contains any invalid data. Normal data is stored in the central database.

[0334] Step 4:

[0335] The server retrieves the latest environmental and emotional data stored in the central database and inputs it into the generative AI model. Specifically, it extracts data from the database for a specified time range and formats the data in a format compatible with the generative AI model. For example, it prepares data for the past 24 hours.

[0336] Step 5:

[0337] The generative AI model analyzes the acquired data and generates optimal crop management advice. Specifically, it compares it with weather patterns and past data and performs analysis using machine learning algorithms. An example of a prompt is, "Please provide optimal crop management advice based on the following environmental and emotional data. Environmental data: Temperature: 25°C, Humidity: 65%, Sunshine hours: 5 hours, Soil moisture: 40% Emotional data: Manager's emotion is 'anxious'."

[0338] Step 6:

[0339] The server receives the advice obtained from the generative AI model and converts it into an appropriate output format. Specifically, it converts the generated advice into speech text and formats it in a format that can be sent to a voice output device or smartphone app. For example, it generates advice such as "It is effective to water plants at 3 p.m."

[0340] Step 7:

[0341] The user checks the received advice in real time using an audio output device or smartphone app. Specifically, the audio output device notifies the user of the advice by voice, and the smartphone app displays the advice in text format. The user then performs farm work in accordance with the advice provided. The user can also check past advice and corresponding environmental data within the app.

[0342] 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.

[0343] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0344] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0345] [Second embodiment]

[0346] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0347] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0348] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0349] 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.

[0350] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0351] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0352] 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. 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.

[0353] 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.

[0354] 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 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.

[0355] 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.

[0356] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0357] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0358] This invention relates to an agricultural support system that uses IoT devices and generative AI models to collect and analyze environmental data to provide optimal crop management advice.

[0359] System Configuration

[0360] The system consists of the following main components:

[0361] 1. Terminal (IoT device)

[0362] 2. Server

[0363] 3. Users (farmers)

[0364] 4. Audio output devices and smartphone apps

[0365] Program processing

[0366] Data collection

[0367] Terminals (IoT devices) are installed on farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is obtained through sensors. For example, at 8:00 a.m., the collected data is 25°C temperature, 65% humidity, 5 hours of sunlight, and 40% soil moisture. The terminals assemble this data into data packets and send them to a server.

[0368] Data Aggregation and Storage

[0369] The server stores the data packets received from each device in a temporary storage area. It then checks the integrity of the data and stores only valid data in the central database. When checking the integrity, it checks the timestamp and checks for abnormal values.

[0370] Data analysis and advice generation

[0371] The server inputs the latest environmental data stored in the database into the generative AI model. The generative AI model analyzes trends and anomalies based on past and real-time data. As a result, optimal crop management advice is generated. The advice might be, for example, "Watering at 3 p.m. is effective" or "No additional fertilizer is required for the next three days." Weather data is also integrated to generate long-term forecasts and disease warnings. Warnings include, "Beware of certain disease-causing fungi due to prolonged high humidity."

[0372] Providing advice

[0373] The server sends the generated advice to each terminal (audio output device or smartphone app). The terminal (audio output device) converts the received text data into voice and notifies the farmer. Voice advice such as "Water now" is provided to the farmer. Meanwhile, the user can also check the advice in text format using the smartphone app. When the app is opened, advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0374] Specific examples

[0375] For example, suppose that at 8:00 AM, a device collects data showing a temperature of 25°C, humidity of 65%, sunshine for 5 hours, and soil moisture content of 40%. This data is sent to a server, where it is checked for consistency and then stored in a central database. The server analyzes the data and generates advice such as, "It is effective to water plants at 3:00 PM." The generated advice is announced via voice output by the device, and the user can also check the advice on a smartphone app.

[0376] Through the above process, the present invention is a system that supports efficient farming for farmers and helps achieve sustainable agriculture.

[0377] The processing flow will be explained below.

[0378] Step 1:

[0379] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, sunshine hours, and soil moisture content through sensors installed in farmland. For example, at 8 a.m., the data obtained is: temperature 25°C, humidity 65%, sunshine hours 5 hours, soil moisture content 40%.

[0380] Step 2:

[0381] The device assembles the collected environmental data into a data packet, which includes the sensor ID, a timestamp, and the acquired data.

[0382] Step 3:

[0383] The terminal sends data packets to the server using a communication protocol (e.g., MQTT, HTTP).

[0384] Step 4:

[0385] The server stores the data packets received from each terminal in a temporary storage area.

[0386] Step 5:

[0387] The server verifies the integrity of the data it receives by checking the timestamps in the data packets and for invalid or missing values.

[0388] Step 6:

[0389] The server then stores the data that has been verified as consistent in a central database, where it is managed together with other past data.

[0390] Step 7:

[0391] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model.

[0392] Step 8:

[0393] The server runs a generative AI model and performs analysis based on the acquired data, comparing historical data with real-time data to detect trends and anomalies.

[0394] Step 9:

[0395] The server generates optimal crop management advice based on the analysis results of the generative AI model. The advice includes information on watering timing, adding fertilizer, and disease prevention measures. Examples include "Watering at 3 p.m. is effective" and "There is no need to add fertilizer for the next three days."

[0396] Step 10:

[0397] The server also integrates meteorological data to generate weather forecasts and disease warnings, such as "Beware of certain disease pathogens as high humidity continues."

[0398] Step 11:

[0399] The server transmits the generated advice to each device (audio output device or smartphone app), including text data of the audio advice.

[0400] Step 12:

[0401] The terminal (audio output device) converts the received text data into audio and notifies the farmer, providing advice such as "Please water your plants now."

[0402] Step 13:

[0403] Users open the smartphone app to view text-based crop management advice. The app's notifications allow users to receive advice in real time. For example, the advice might say, "You don't need to add fertilizer for the next three days."

[0404] Example 1

[0405] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0406] Previous agricultural support systems had issues with the collection, analysis, and provision of environmental data, such as insufficient data consistency verification, elimination of time lags, and insufficient convenience through multiple notification methods. Additionally, the quality of crop management advice based on analysis results was low, and accurate information for farmers to carry out their farm work efficiently was not provided.

[0407] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0408] In this invention, the server includes means for collecting environmental data, means for assembling the collected environmental data into data packets and transmitting them to a central database, means for verifying the consistency of the data stored in the central database, a generative AI model for analyzing the data whose consistency has been verified, means for generating crop management advice based on the analysis results of the generative AI model, and means for providing the generated advice to farmers. This enables the collection and analysis of accurate environmental data in real time, and makes it possible to provide appropriate and timely crop management advice to farmers.

[0409] "Environmental data" refers to data such as temperature, humidity, hours of sunlight, and soil moisture collected at a specific location such as farmland.

[0410] "Collection means" refers to the sensor-equipped equipment or devices used to collect environmental data.

[0411] A "data packet" is a unit of data that is converted into a certain format in order to transmit collected environmental data collectively.

[0412] The "central database" is a database system for centrally storing and managing collected environmental data.

[0413] "Integrity checks" means the functions or processes used to check data for accuracy and integrity before it is stored in the central database.

[0414] A "generative AI model" is an artificial intelligence model that analyzes collected environmental data and generates crop management advice.

[0415] "Means for generating advice" refers to the process or algorithm for creating crop management advice based on the analysis results of the generative AI model.

[0416] An "agricultural worker" is someone who works in agriculture, that is, someone who is responsible for the work of growing crops.

[0417] The "audio output device" refers to a device that transmits the generated crop management advice as audio.

[0418] A "portable information terminal application" is application software that runs on a portable device such as a smartphone or tablet and provides crop management advice.

[0419] MODE FOR CARRYING OUT THE INVENTION

[0420] This invention is a system for agricultural support using IoT devices and generative AI models. The system aims to provide farmers with optimal crop management advice by collecting environmental data and analyzing the collected data.

[0421] System Configuration

[0422] The system consists of the following main components:

[0423] 1. Terminal: An IoT device that collects environmental data, such as temperature, humidity, hours of sunlight, and soil moisture.

[0424] 2. Server: Refers to the computer system that receives the collected data, verifies the data integrity, and analyzes the data using generative AI models.

[0425] 3. User: Refers to the farmer who uses the system.

[0426] 4. Audio output devices and handheld information terminal applications: Devices for providing crop management advice.

[0427] Examples of data collection

[0428] The device acquires environmental data from sensors installed in the farmland. For example, data on temperature, humidity, hours of sunlight, and soil moisture is collected periodically. At 8:00 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5, and soil moisture 40%. This data is measured in real time by the sensors and temporarily stored in the device's internal memory.

[0429] Specific examples of data transmission

[0430] The device collects data and assembles it into data packets. Then, it sends the resulting data packets to a server via an internet connection. HTTP or MQTT is typically used as the communication protocol. For example, data collected at 8:00 AM can be sent immediately to the server.

[0431] Data Aggregation and Storage

[0432] The server receives data packets from each device. The received data is stored in a temporary storage area (cache, queue, etc.). The server then checks the integrity of the data, verifying timestamps, checking for outliers, and inspecting for missing data, and stores only normal data in the central database. For example, at 8 a.m., data such as a temperature of 25 degrees and humidity of 65% is confirmed to be normal and stored in the database.

[0433] Data analysis and advice generation

[0434] The server inputs the latest environmental data stored in a central database into the generative AI model. The generative AI model analyzes trends and anomalies based on past and real-time data to generate optimal crop management advice. For example, the generative AI model analyzes humidity and temperature trends and generates specific advice such as "Watering at 3 p.m." It also predicts the risk of specific diseases and generates warning alerts such as "High humidity will continue, so be careful of specific disease-causing bacteria."

[0435] Providing advice

[0436] The server sends the generated advice to each device (audio output device or mobile information terminal application). The device (audio output device) uses a TTS (Text to Speech) engine to convert the received text data into speech. A voice notification such as "Water your plants now" is given. Meanwhile, the user checks the advice in text format using the mobile information terminal application. When the user opens the app, specific advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0437] Example prompts for generative AI models

[0438] "Enter your latest environmental data. Generate crop management advice based on temperature, humidity, sunshine hours, and soil moisture. For example, what is the best way to manage a crop when the temperature is 25°C, humidity is 65%, sunshine hours are 5 hours, and soil moisture is 40%?"

[0439] As a result, the present invention provides farmers with a system that supports efficient and sustainable agricultural management.

[0440] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0441] Step 1: Data collection

[0442] The device collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture content through sensors installed in the farmland. The input is real-time environmental data, and the output is a collection of this data. For example, at 8:00 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%. The collected data is temporarily stored in the device's internal memory.

[0443] Step 2: Send data

[0444] The device assembles the temporarily stored data into data packets. The input is the environmental data stored in the device's internal memory, and the output is the data packets. These data packets are sent to the server via an Internet connection. HTTP or MQTT is used as the communication protocol. For example, data collected at 8:00 a.m. is immediately sent to the server.

[0445] Step 3: Data aggregation and storage

[0446] The server receives data packets from each device. The received data is stored in a temporary storage area (such as a cache or queue). The input is the data packets received from each device, and the output is the data stored in the temporary storage area. The server then checks the timestamp, checks for outliers, and checks for missing data, and stores only valid data in the central database. For example, data received at 8:00 a.m. is checked for timestamps and outliers before being stored in the central database.

[0447] Step 4: Data analysis and advice generation

[0448] The server inputs the latest environmental data stored in a central database into the generative AI model. The input is the environmental data stored in the central database, and the output is the analysis results. The generative AI model analyzes trends and anomalies based on past and real-time data to generate optimal crop management advice. Based on the input data, it generates advice such as "Watering at 3 p.m. is effective" or a warning such as "Be careful of certain disease-causing bacteria as high humidity continues."

[0449] Step 5: Providing advice

[0450] The server sends the generated advice to each device (audio output device or portable information terminal application). The input is the advice generated based on the analysis, and the output is the advice sent to each device. The device (audio output device) converts the received text data into voice and notifies the farmer. For example, the voice notification may say, "Please water now." Meanwhile, the user can check the advice in text format using the portable information terminal application. For example, when opening the app, specific advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0451] (Application example 1)

[0452] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0453] There is a growing need for systems that can collect and analyze environmental data and machine operation data in factories in real time and provide workers with accurate management advice. However, current systems tend to be slow in collecting and analyzing data, making it difficult to provide appropriate advice in a timely manner. Furthermore, the lack of a flexible information provision mechanism via voice notifications or smartphone apps makes efficient production management difficult.

[0454] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0455] In this invention, the server includes means for collecting environmental data, means for transmitting the collected environmental data to a central database, a generative AI model for analyzing the data stored in the central database, means for generating management advice based on the analysis results, and means for providing the generated advice to workers. This makes it possible to collect and analyze various data within the factory in real time and provide accurate production management advice to workers in a timely manner.

[0456] "Environmental data" refers to data that indicates the temperature, humidity, lighting, vibration, and machine operating status within the factory.

[0457] "Means of collection" refers to devices and systems that collect environmental data within the factory using sensors and monitoring equipment.

[0458] "Transmission means" refers to a communication device or protocol for transmitting collected environmental data to a server via the Internet or a dedicated line.

[0459] The "central database" is a database system for centrally storing and managing collected environmental data.

[0460] An "analytical generative AI model" is an artificial intelligence model that analyzes data stored in a central database and generates appropriate management advice.

[0461] The "means for generating management advice" refers to an algorithm or program for creating appropriate management advice based on the results of analysis by the generative AI model.

[0462] The "means of providing" refers to a device or platform for notifying workers of the generated management advice through an audio output device or a smartphone app.

[0463] "Workers" are the staff who work in the factory and receive the management advice provided by the generative AI model.

[0464] This invention relates to a system that collects and analyzes environmental data and machine operation data within a factory and provides timely management advice. This system consists of the following main components:

[0465] 1. Means of collecting environmental data

[0466] IoT devices installed in factories (such as temperature sensors, humidity sensors, vibration sensors, and machine operation status monitoring devices) periodically collect environmental data such as temperature, humidity, vibration, and lighting, and this data is sent to a server via Wi-Fi or a dedicated line.

[0467] 2. Data aggregation and storage

[0468] The server stores data packets received from each IoT device in a temporary storage area. After verifying the data integrity, it stores the correct data in a central database. Data integrity is verified by checking timestamps and filtering outliers.

[0469] 3. Data analysis and advice generation

[0470] The server inputs the latest environmental data stored in the database into the generative AI model. Based on past and real-time data, the generative AI model analyzes temperature, humidity, vibration levels, machine operating status, and other factors, detects trends and anomalies, and generates optimal management advice. For example, it might generate advice such as "Machine A's vibration level is high, so maintenance is required" or "The humidity is high, so please ventilate."

[0471] 4. Providing advice

[0472] The server provides the generated advice to the worker via a voice output device or smartphone app. The voice output device converts the received text data into voice and notifies the worker. Voice advice such as "Machine A requires maintenance" is provided. Meanwhile, the smartphone app displays the generated advice in text format to the worker.

[0473] As a specific example, let's say that at 8:00 a.m., an IoT device in a factory collects data monitoring the operating status of Machine A at a temperature of 25°C, humidity of 60%, and vibration level of 0.2g. This data is sent to a server, where it is checked for consistency and then stored in a central database. The server analyzes this data and generates advice such as, "Machine A has a high vibration level, so maintenance is recommended." The generated advice is notified to the worker via a voice output device, and can also be viewed on a smartphone app.

[0474] Examples of prompts to input to a generative AI model include:

[0475] "Based on data from the past seven days, analyze environmental data such as temperature, humidity, vibration levels, and machine operation status to generate optimal management advice for your factory."

[0476] The system of this invention makes it possible to collect and analyze environmental data within a factory in real time and provide accurate production management advice to workers in a timely manner. This allows for the early detection of equipment abnormalities and the implementation of appropriate maintenance, thereby improving the production efficiency and safety of the entire factory.

[0477] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0478] Step 1:

[0479] The __terminal__ collects environmental data within the factory. Specifically, it obtains data on temperature, humidity, vibration, lighting, etc. using temperature sensors, humidity sensors, vibration sensors, and machine operation status monitoring devices.

[0480] __Input:__ Environmental data from various sensors in the factory

[0481] __Output:__ Collected environmental data packet

[0482] Step 2:

[0483] The environmental data collected by the device is compiled into data packets and sent to the server via Wi-Fi or a dedicated line.

[0484] __Input:__ Environmental data packets collected from the device

[0485] __Output:__ Environment data packet sent to the server

[0486] Step 3:

[0487] The server stores the received environmental data packet in a temporary storage area, then checks the timestamp and filters out any outliers to ensure data integrity.

[0488] __Input:__ Environment data packet sent to the server

[0489] __Output:__ Environmental data with integrity verified

[0490] Step 4:

[0491] The server stores the verified data in a central database for further analysis.

[0492] __Input:__ Environmental data whose integrity has been verified

[0493] __Output:__ Environmental data stored in a central database

[0494] Step 5:

[0495] The server inputs the latest environmental data stored in a central database into the generative AI model, analyzes the data, and generates appropriate management advice.

[0496] __Input:__ Latest environmental data stored in a central database

[0497] __Output:__ Generated management advice

[0498] Step 6:

[0499] The server sends the generated management advice to an audio output device or smartphone app.

[0500] __Input:__ Generated management advice

[0501] __Output:__ Management advice sent to audio output devices and smartphone apps

[0502] Step 7:

[0503] The audio output device converts the received advice into audio and notifies the user. The smartphone app also displays the generated advice in text format to the user.

[0504] __Input:__ Management advice received by audio output device and smartphone app

[0505] Output: The audio notification and text display provided to the user.

[0506] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0507] This invention relates to a system that collects environmental data, analyzes it with a generative AI model, and provides crop management advice. It is an agricultural support system that also combines an emotion engine that recognizes the user's emotions. This system helps farmers perform efficient and optimal farm work.

[0508] System Configuration

[0509] The system consists of the following main components:

[0510] 1. Terminal (IoT device)

[0511] 2. Server

[0512] 3. Users (farmers)

[0513] 4. Emotion Engine

[0514] 5. Audio output devices and smartphone apps

[0515] Program processing

[0516] Data collection

[0517] Terminals (IoT devices) are installed in farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is acquired through sensors, compiled into data packets, and sent to a server. For example, at 8 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[0518] Data Aggregation and Storage

[0519] The server stores the data packets received from the terminal in a temporary storage area, checks the integrity of the data, and then stores the correct data in the central database.

[0520] Data analysis and advice generation

[0521] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model. The generative AI model performs analysis based on past and real-time data. Based on the analysis results, it generates optimal crop management advice. For example, advice such as "Watering at 3 p.m. is effective" or "There is no need to add fertilizer for the next three days" is generated.

[0522] emotion recognition

[0523] The emotion engine collects emotion data from the farmers' voices and facial expressions, analyzes the emotions, and sends the collected emotion data to the server.

[0524] Use of Emotional Data

[0525] The server stores the emotion data received from the emotion engine in a central database. The generative AI model uses the emotion data along with environmental data for analysis. For example, if a farmer is feeling anxious, it generates reassuring advice such as, "There are no particular points to be careful of at this stage."

[0526] Providing advice

[0527] The server sends the generated advice to a terminal (audio output device or smartphone app). The terminal (audio output device) converts the received advice into audio and notifies the farmer. For example, advice such as "Water your fields now" is provided as audio. The user can also open the smartphone app to check a text version of the advice. The app notification function allows the user to receive advice in real time. Advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0528] Specific examples

[0529] For example, suppose that at 8:00 a.m., a device collects data showing a temperature of 25°C, humidity of 65%, five hours of sunshine, and soil moisture of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. The advice generated is, "It is effective to water at 3:00 p.m." The emotion engine then collects the farmer's emotional data and sends it to the server. The generative AI model takes into account the data that the farmer is concerned about, and supplements it with reassuring advice such as, "There are no particular points to be careful of at this stage." The advice is communicated via voice through the device (audio output device), and the user can also check it on their smartphone app.

[0530] The present invention is a sustainable agricultural support system that provides farmers with optimal advice based on collected environmental data and emotional data, thereby providing efficient farming and a sense of security.

[0531] The processing flow will be explained below.

[0532] Step 1:

[0533] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture through sensors installed in farmland. For example, at 8 a.m., the data collected is: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[0534] Step 2:

[0535] The device assembles the collected environmental data into a data packet, which includes the sensor ID, a timestamp, and the acquired data.

[0536] Step 3:

[0537] The terminal sends data packets to the server using a communication protocol (e.g., MQTT, HTTP).

[0538] Step 4:

[0539] The server stores the data packets received from each terminal in a temporary storage area.

[0540] Step 5:

[0541] The server verifies the integrity of the data received, checking for timestamps, invalid or missing values.

[0542] Step 6:

[0543] The server then stores the data that has been verified as consistent in a central database, where it is managed together with other past data.

[0544] Step 7:

[0545] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model.

[0546] Step 8:

[0547] The server runs a generative AI model and performs analysis based on the acquired data, comparing historical data with real-time data to detect trends and anomalies.

[0548] Step 9:

[0549] The server generates optimal crop management advice based on the analysis results of the generative AI model, such as advice on when to water at 3 p.m. and the need for additional fertilizer for the next three days.

[0550] Step 10:

[0551] The emotion engine collects emotion data from farmers' voices and facial expressions, including their stress levels and emotional states.

[0552] Step 11:

[0553] The emotion engine assembles the collected emotion data into a data packet and sends it to the server.

[0554] Step 12:

[0555] The server stores the emotion data in a central database, which the generative AI model uses for analysis.

[0556] Step 13:

[0557] The server then tailors crop management advice based on the analysis results, including emotional data. For example, if a farmer is feeling anxious, it adds reassuring advice.

[0558] Step 14:

[0559] The server sends the generated advice to the device (audio output device or smartphone app). For example, the advice may say, "There are no particular points to be aware of at this stage."

[0560] Step 15:

[0561] The audio output device converts the received advice into audio and notifies the farmer, saying, "Please water your plants now."

[0562] Step 16:

[0563] Users can open the smartphone app and see a text version of the advice, which may include, "You don't need to add fertilizer for the next three days."

[0564] Through these specific processing steps, the present invention is a system that provides optimal advice to farmers based on collected environmental data and emotional data, and supports them in carrying out farm work efficiently and safely.

[0565] Example 2

[0566] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0567] In modern agriculture, providing advice based solely on environmental data poses a challenge, as it does not take into account the psychological state of farmers, making it difficult to provide optimal crop management. Furthermore, because the collected data is diverse and complex, analyzing it and generating useful advice can require a great deal of time and effort.

[0568] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data, means for transmitting the collected environmental data to a central database, a generation AI model means for analyzing the data stored in the central database, means for generating crop management advice based on the analysis results, means for collecting farmer's emotion data, means for transmitting the collected emotion data to the central database, means for generating crop management advice that takes the emotion data into account based on the analysis results, and means for providing the generated advice to the farmer. This makes it possible to provide optimal crop management advice that takes the farmer's psychological state into account, thereby enabling farm work to be carried out efficiently and with peace of mind.

[0569] "Environmental data" refers to information such as temperature, humidity, hours of sunlight, and soil moisture in the agricultural environment.

[0570] "Collection means" refers to a device or method for acquiring environmental data and emotional data using sensors, IoT devices, etc.

[0571] "Central database" refers to a data storage system that integrates and stores collected environmental data and emotional data.

[0572] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and generates optimal crop management advice.

[0573] "Analysis means" means a method or device for analyzing data using a generative AI model to generate crop management advice.

[0574] "Emotional data" refers to information indicating the psychological state of farmers obtained from their voices, facial expressions, etc.

[0575] "Provision means" refers to a method or device (such as an audio output device or smartphone app) for notifying farmers of the generated advice.

[0576] This invention relates to a system that collects environmental and emotional data, analyzes it using a generative AI model, and provides crop management advice, helping farmers carry out their farm work efficiently and safely.

[0577] System Configuration

[0578] The system consists of the following main components:

[0579] 1. Terminal (IoT device)

[0580] 2. Server

[0581] 3. Users (farmers)

[0582] 4. Emotion Engine

[0583] 5. Audio output devices and smartphone apps

[0584] Program processing

[0585] Data collection

[0586] Terminals (IoT devices) are installed in farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is acquired through sensors, compiled into data packets, and sent to a server. For example, at 8 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[0587] Data Aggregation and Storage

[0588] The server stores the data packets received from the terminal in a primary storage area, checks the integrity of the data, and then stores the correct data in the central database.

[0589] Data analysis and advice generation

[0590] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model. The generative AI model performs analysis based on past and real-time data. Based on the analysis results, it generates optimal crop management advice. For example, advice such as "Watering at 3 p.m. is effective" or "There is no need to add fertilizer for the next three days" is generated.

[0591] emotion recognition

[0592] The emotion engine collects emotion data from the farmers' voices and facial expressions, and this data is sent to the server.

[0593] Use of Emotional Data

[0594] The server stores the emotion data in a central database. The generative AI model uses the environmental and emotion data for analysis. For example, if a farmer is feeling anxious, it generates reassuring advice such as, "There are no particular precautions to take at this stage."

[0595] Providing advice

[0596] The server sends the generated advice to a device (audio output device or smartphone app). The audio output device converts the advice into audio and notifies the user. For example, advice such as "Water your plants now" is provided as audio. The user can also check a text version of the advice using the smartphone app. The app notification function displays advice in real time, such as "There is no need to add fertilizer for the next three days."

[0597] This system will provide farmers with optimal advice based on collected environmental and emotional data, realizing a sustainable agricultural support system that provides efficient farming and peace of mind.

[0598] Specific examples

[0599] For example, suppose that at 8:00 a.m., a device collects data on a temperature of 25°C, humidity of 65%, sunshine for 5 hours, and soil moisture content of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. The resulting advice is, "It is effective to water at 3:00 p.m." The emotion engine then collects the farmer's emotional data and sends it to the server. The generative AI model considers the data that the farmer is concerned about, and supplements it with reassuring advice such as, "There are no particular points to be aware of at this stage." The advice is communicated via voice through the device (audio output device), and the user can also check it on a smartphone app.

[0600] Prompt Sentence Examples

[0601] Here are some example prompts to input to a generative AI model:

[0602] "Provide today's crop management advice based on the latest environmental data."

[0603] "Please propose a work plan for the next three days taking into account the current agri-environmental data."

[0604] "Generate optimal advice when farmers are feeling anxious."

[0605] Such prompt sentences enable the system to efficiently generate and provide crop management advice to farmers.

[0606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0607] Step 1: Data collection

[0608] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture using sensors installed in the farmland. Specific devices used include temperature sensors, humidity sensors, light sensors, and soil moisture sensors. At 8:00 AM, the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%. The terminal compiles this data into data packets and sends them to the server. The input is data from the environmental sensors, and the output is the generated data packets.

[0609] Step 2: Data aggregation and storage

[0610] The server stores data packets received from the terminal in a primary storage area. It then checks the input data for format and abnormal values ​​to confirm data integrity. For example, it checks whether the data format is correct and whether the values ​​are within the acceptable range. Once the server confirms that the data is normal, it stores the data in a central database. The input is the data packet sent from the terminal, and the output is data whose integrity has been confirmed.

[0611] Step 3: Data analysis and advice generation

[0612] The server retrieves the latest environmental data stored in the central database and inputs it into the generative AI model. Specifically, it provides the generative AI model with the following prompt: "Please provide today's crop management advice based on the latest environmental data." The generative AI model analyzes past and real-time data to generate optimal crop management advice. For example, the generated advice might be, "It is effective to water at 3 p.m." The input is the latest environmental data from the central database, and the output is the generated crop management advice.

[0613] Step 4: Emotion Recognition

[0614] The emotion engine collects emotion data from the farmers' voices and facial expressions. Specifically, it uses voice recognition and facial expression analysis technologies. The emotion engine compiles the collected emotion data into data packets and sends them to the server. The input is the farmers' voices and facial expressions, and the output is the generated emotion data packets.

[0615] Step 5: Use emotion data

[0616] The server stores the emotion data received from the emotion engine in a central database. The generative AI model analyzes the combined environmental data and emotion data. For example, if a farmer is feeling anxious, reassuring advice such as "There are no particular points to be aware of at this stage" is generated. A specific prompt used is "Please provide the best advice based on the farmer's emotion data." The input is emotion data and environmental data, and the output is crop management advice that takes emotion into consideration.

[0617] Step 6: Providing advice

[0618] The server sends the generated advice to a terminal (audio output device or smartphone app). The audio output device converts the received advice into audio and notifies the user with advice such as "Water your fields now." The user can also open the smartphone app to check the text version of the advice. The app notification function is used to display advice in real time, such as "There is no need to add fertilizer for the next three days." The input is the generated advice, and the output is a notification to the farmer.

[0619] (Application example 2)

[0620] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0621] Conventional agricultural support systems can only provide advice based on environmental data, which means they lack comprehensive support that takes into account the emotional state of farmers. They also struggle to provide real-time information that farmers can respond to immediately. This has created a challenge in simultaneously ensuring the efficiency of farm work and farmers' mental well-being.

[0622] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0623] In this invention, the server includes a means for collecting environmental data and emotional data, a means for transmitting the collected environmental data and emotional data to a central database, a generative AI model for analyzing the data stored in the central database, a means for generating crop management advice based on the analysis results, a means for providing the generated advice to users, and a smart device for displaying data in real time. This allows for the provision of comprehensive advice in real time that takes into account the emotional state of farmers, thereby improving the efficiency of farm work and increasing farmers' peace of mind.

[0624] "Environmental data" is information about the physical and environmental conditions of agricultural land, such as temperature, humidity, hours of sunshine, and soil moisture.

[0625] "Emotion data" is information about the emotional state of the farmer as recognized from his voice, facial expression, etc.

[0626] The "central database" is a database system that centrally stores collected environmental data and emotional data and uses them for analysis and management.

[0627] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates optimal advice based on past and real-time data.

[0628] "Advice provision means" refers to the means for communicating the advice generated by the generative AI model to farmers, and includes audio output devices and smart devices.

[0629] "Real-time display means" refers to a means of instantly visualizing collected data and advice, enabling farmers to respond quickly.

[0630] "Smart devices" are electronic devices with advanced functions such as smartphones, tablets, smart glasses, and head-mounted displays.

[0631] The present invention relates to a system that collects environmental data and emotional data, analyzes them using a generative AI model, and provides farmers with optimal crop management advice. This system helps farmers perform efficient and optimal farm work and provides them with peace of mind. Detailed embodiments for specifically implementing the present invention are described below.

[0632] System Configuration

[0633] The system consists of the following main components:

[0634] 1. Terminal: This terminal contains environmental sensors and emotion recognition devices and is installed in the farmland. For example, it uses a DHT22 sensor to measure temperature and humidity, and a soil moisture sensor to measure the amount of moisture in the soil.

[0635] 2. Server: Responsible for data aggregation, storage, and analysis. Collected environmental and emotional data are stored in a central database.

[0636] 3. User: Farmers receive advice via their smartphones or smart devices.

[0637] 4. Emotion Engine: This engine has the ability to analyze the user's voice and facial expressions and generate emotion data. This emotion data is collected using voice and camera.

[0638] 5. Generative AI model: Analyzes environmental and emotional data to generate optimal crop management advice. This model uses a generative AI model such as GPT-3.

[0639] 6. Audio output device and smartphone app: Generated advice is communicated to farmers via voice and text.

[0640] Program processing

[0641] The server does the following:

[0642] 1. Data collection: The terminals are installed in farmland to periodically collect environmental data such as temperature, humidity, sunshine hours, and soil moisture content, and also use an emotion engine to collect farmers' sentiment data.

[0643] 2. Data transmission: The collected environmental data and emotion data are transmitted from the device to the server.

[0644] 3. Data aggregation and storage: The server stores the received data in a central database and checks the integrity of the data.

[0645] 4. Data analysis and advice generation: The server inputs environmental data and emotional data into the generative AI model for analysis. The generative AI model generates crop management advice based on multiple data sets. An example of a prompt is, "Please provide optimal crop management advice based on the following environmental and emotional data. Environmental data: Temperature: 25°C, Humidity: 65%, Sunshine hours: 5 hours, Soil moisture: 40% Emotional data: The manager's emotion is 'anxious'."

[0646] 5. Providing advice: The generated advice is sent from the server to the device (audio output device or smartphone app). For example, advice such as "Watering at 3 p.m. is effective" or "There are no particular points to be careful of at this stage" is provided.

[0647] Specific examples

[0648] For example, at 8 a.m., the device collects environmental data such as a temperature of 25 degrees, humidity of 65%, five hours of sunshine, and soil moisture of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. Furthermore, the emotion engine collects the farmer's emotional data and sends it to the server. Based on this data, the generative AI model generates advice such as "It would be effective to water at 3 p.m." It also provides additional reassuring advice such as "There are no particular precautions to take at this stage." This advice is communicated to the farmer via voice or text.

[0649] In this way, the present invention is a sustainable agricultural support system that not only improves the work efficiency of farmers but also provides them with a sense of psychological security.

[0650] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0651] Step 1:

[0652] The device collects environmental and emotional data using environmental sensors and an emotion recognition device. Specifically, it measures temperature and humidity using a DHT22 sensor, and soil moisture using a soil moisture sensor. It also collects emotional data from farmers using voice and camera recordings. For example, at 8 a.m., the device obtains data showing a temperature of 25°C, humidity of 65%, sunshine for 5 hours, soil moisture content of 40%, and an emotion of "anxiety."

[0653] Step 2:

[0654] The terminal assembles the collected environmental and emotional data into data packets and transmits them to the server. Specifically, the collected data is properly formatted and transmitted using a secure communication protocol. The transmitted data includes temperature, humidity, sunshine hours, soil moisture content, and the farmer's emotional information.

[0655] Step 3:

[0656] The server temporarily stores the received data packets in a database and checks the data integrity. Specifically, it checks the format of the received data and verifies whether it contains any invalid data. Normal data is stored in the central database.

[0657] Step 4:

[0658] The server retrieves the latest environmental and emotional data stored in the central database and inputs it into the generative AI model. Specifically, it extracts data from the database for a specified time range and formats the data in a format compatible with the generative AI model. For example, it prepares data for the past 24 hours.

[0659] Step 5:

[0660] The generative AI model analyzes the acquired data and generates optimal crop management advice. Specifically, it compares it with weather patterns and past data and performs analysis using machine learning algorithms. An example of a prompt is, "Please provide optimal crop management advice based on the following environmental and emotional data. Environmental data: Temperature: 25°C, Humidity: 65%, Sunshine hours: 5 hours, Soil moisture: 40% Emotional data: Manager's emotion is 'anxious'."

[0661] Step 6:

[0662] The server receives the advice obtained from the generative AI model and converts it into an appropriate output format. Specifically, it converts the generated advice into speech text and formats it in a format that can be sent to a voice output device or smartphone app. For example, it generates advice such as "It is effective to water plants at 3 p.m."

[0663] Step 7:

[0664] The user checks the received advice in real time using an audio output device or smartphone app. Specifically, the audio output device notifies the user of the advice by voice, and the smartphone app displays the advice in text format. The user then performs farm work in accordance with the advice provided. The user can also check past advice and corresponding environmental data within the app.

[0665] 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.

[0666] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0667] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0668] [Third embodiment]

[0669] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0670] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0671] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0672] 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.

[0673] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0674] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0675] 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. 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.

[0676] 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.

[0677] 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 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.

[0678] 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.

[0679] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0680] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0681] This invention relates to an agricultural support system that uses IoT devices and generative AI models to collect and analyze environmental data to provide optimal crop management advice.

[0682] System Configuration

[0683] The system consists of the following main components:

[0684] 1. Terminal (IoT device)

[0685] 2. Server

[0686] 3. Users (farmers)

[0687] 4. Audio output devices and smartphone apps

[0688] Program processing

[0689] Data collection

[0690] Terminals (IoT devices) are installed on farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is obtained through sensors. For example, at 8:00 a.m., the collected data is 25°C temperature, 65% humidity, 5 hours of sunlight, and 40% soil moisture. The terminals assemble this data into data packets and send them to a server.

[0691] Data Aggregation and Storage

[0692] The server stores the data packets received from each device in a temporary storage area. It then checks the integrity of the data and stores only valid data in the central database. When checking the integrity, it checks the timestamp and checks for abnormal values.

[0693] Data analysis and advice generation

[0694] The server inputs the latest environmental data stored in the database into the generative AI model. The generative AI model analyzes trends and anomalies based on past and real-time data. As a result, optimal crop management advice is generated. The advice might be, for example, "Watering at 3 p.m. is effective" or "No additional fertilizer is required for the next three days." Weather data is also integrated to generate long-term forecasts and disease warnings. Warnings include, "Beware of certain disease-causing fungi due to prolonged high humidity."

[0695] Providing advice

[0696] The server sends the generated advice to each terminal (audio output device or smartphone app). The terminal (audio output device) converts the received text data into voice and notifies the farmer. Voice advice such as "Water now" is provided to the farmer. Meanwhile, the user can also check the advice in text format using the smartphone app. When the app is opened, advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0697] Specific examples

[0698] For example, suppose that at 8:00 AM, a device collects data showing a temperature of 25°C, humidity of 65%, sunshine for 5 hours, and soil moisture content of 40%. This data is sent to a server, where it is checked for consistency and then stored in a central database. The server analyzes the data and generates advice such as, "It is effective to water plants at 3:00 PM." The generated advice is announced via voice output by the device, and the user can also check the advice on a smartphone app.

[0699] Through the above process, the present invention is a system that supports efficient farming for farmers and helps achieve sustainable agriculture.

[0700] The processing flow will be explained below.

[0701] Step 1:

[0702] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, sunshine hours, and soil moisture content through sensors installed in farmland. For example, at 8 a.m., the data obtained is: temperature 25°C, humidity 65%, sunshine hours 5 hours, soil moisture content 40%.

[0703] Step 2:

[0704] The device assembles the collected environmental data into a data packet, which includes the sensor ID, a timestamp, and the acquired data.

[0705] Step 3:

[0706] The terminal sends data packets to the server using a communication protocol (e.g., MQTT, HTTP).

[0707] Step 4:

[0708] The server stores the data packets received from each terminal in a temporary storage area.

[0709] Step 5:

[0710] The server verifies the integrity of the data it receives by checking the timestamps in the data packets and for invalid or missing values.

[0711] Step 6:

[0712] The server then stores the data that has been verified as consistent in a central database, where it is managed together with other past data.

[0713] Step 7:

[0714] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model.

[0715] Step 8:

[0716] The server runs a generative AI model and performs analysis based on the acquired data, comparing historical data with real-time data to detect trends and anomalies.

[0717] Step 9:

[0718] The server generates optimal crop management advice based on the analysis results of the generative AI model. The advice includes information on watering timing, adding fertilizer, and disease prevention measures. Examples include "Watering at 3 p.m. is effective" and "There is no need to add fertilizer for the next three days."

[0719] Step 10:

[0720] The server also integrates meteorological data to generate weather forecasts and disease warnings, such as "Beware of certain disease pathogens as high humidity continues."

[0721] Step 11:

[0722] The server transmits the generated advice to each device (audio output device or smartphone app), including text data of the audio advice.

[0723] Step 12:

[0724] The terminal (audio output device) converts the received text data into audio and notifies the farmer, providing advice such as "Please water your plants now."

[0725] Step 13:

[0726] Users open the smartphone app to view text-based crop management advice. The app's notifications allow users to receive advice in real time. For example, the advice might say, "You don't need to add fertilizer for the next three days."

[0727] Example 1

[0728] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0729] Previous agricultural support systems had issues with the collection, analysis, and provision of environmental data, such as insufficient data consistency verification, elimination of time lags, and insufficient convenience through multiple notification methods. Additionally, the quality of crop management advice based on analysis results was low, and accurate information for farmers to carry out their farm work efficiently was not provided.

[0730] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0731] In this invention, the server includes means for collecting environmental data, means for assembling the collected environmental data into data packets and transmitting them to a central database, means for verifying the consistency of the data stored in the central database, a generative AI model for analyzing the data whose consistency has been verified, means for generating crop management advice based on the analysis results of the generative AI model, and means for providing the generated advice to farmers. This enables the collection and analysis of accurate environmental data in real time, and makes it possible to provide appropriate and timely crop management advice to farmers.

[0732] "Environmental data" refers to data such as temperature, humidity, hours of sunlight, and soil moisture collected at a specific location such as farmland.

[0733] "Collection means" refers to the sensor-equipped equipment or devices used to collect environmental data.

[0734] A "data packet" is a unit of data that is converted into a certain format in order to transmit collected environmental data collectively.

[0735] The "central database" is a database system for centrally storing and managing collected environmental data.

[0736] "Integrity checks" means the functions or processes used to check data for accuracy and integrity before it is stored in the central database.

[0737] A "generative AI model" is an artificial intelligence model that analyzes collected environmental data and generates crop management advice.

[0738] "Means for generating advice" refers to the process or algorithm for creating crop management advice based on the analysis results of the generative AI model.

[0739] An "agricultural worker" is someone who works in agriculture, that is, someone who is responsible for the work of growing crops.

[0740] The "audio output device" refers to a device that transmits the generated crop management advice as audio.

[0741] A "portable information terminal application" is application software that runs on a portable device such as a smartphone or tablet and provides crop management advice.

[0742] MODE FOR CARRYING OUT THE INVENTION

[0743] This invention is a system for agricultural support using IoT devices and generative AI models. The system aims to provide farmers with optimal crop management advice by collecting environmental data and analyzing the collected data.

[0744] System Configuration

[0745] The system consists of the following main components:

[0746] 1. Terminal: An IoT device that collects environmental data, such as temperature, humidity, hours of sunlight, and soil moisture.

[0747] 2. Server: Refers to the computer system that receives the collected data, verifies the data integrity, and analyzes the data using generative AI models.

[0748] 3. User: Refers to the farmer who uses the system.

[0749] 4. Audio output devices and handheld information terminal applications: Devices for providing crop management advice.

[0750] Examples of data collection

[0751] The device acquires environmental data from sensors installed in the farmland. For example, data on temperature, humidity, hours of sunlight, and soil moisture is collected periodically. At 8:00 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5, and soil moisture 40%. This data is measured in real time by the sensors and temporarily stored in the device's internal memory.

[0752] Specific examples of data transmission

[0753] The device collects data and assembles it into data packets. Then, it sends the resulting data packets to a server via an internet connection. HTTP or MQTT is typically used as the communication protocol. For example, data collected at 8:00 AM can be sent immediately to the server.

[0754] Data Aggregation and Storage

[0755] The server receives data packets from each device. The received data is stored in a temporary storage area (cache, queue, etc.). The server then checks the integrity of the data, verifying timestamps, checking for outliers, and inspecting for missing data, and stores only normal data in the central database. For example, at 8 a.m., data such as a temperature of 25 degrees and humidity of 65% is confirmed to be normal and stored in the database.

[0756] Data analysis and advice generation

[0757] The server inputs the latest environmental data stored in a central database into the generative AI model. The generative AI model analyzes trends and anomalies based on past and real-time data to generate optimal crop management advice. For example, the generative AI model analyzes humidity and temperature trends and generates specific advice such as "Watering at 3 p.m." It also predicts the risk of specific diseases and generates warning alerts such as "High humidity will continue, so be careful of specific disease-causing bacteria."

[0758] Providing advice

[0759] The server sends the generated advice to each device (audio output device or mobile information terminal application). The device (audio output device) uses a TTS (Text to Speech) engine to convert the received text data into speech. A voice notification such as "Water your plants now" is given. Meanwhile, the user checks the advice in text format using the mobile information terminal application. When the user opens the app, specific advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0760] Example prompts for generative AI models

[0761] "Enter your latest environmental data. Generate crop management advice based on temperature, humidity, sunshine hours, and soil moisture. For example, what is the best way to manage a crop when the temperature is 25°C, humidity is 65%, sunshine hours are 5 hours, and soil moisture is 40%?"

[0762] As a result, the present invention provides farmers with a system that supports efficient and sustainable agricultural management.

[0763] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0764] Step 1: Data collection

[0765] The device collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture content through sensors installed in the farmland. The input is real-time environmental data, and the output is a collection of this data. For example, at 8:00 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%. The collected data is temporarily stored in the device's internal memory.

[0766] Step 2: Send data

[0767] The device assembles the temporarily stored data into data packets. The input is the environmental data stored in the device's internal memory, and the output is the data packets. These data packets are sent to the server via an Internet connection. HTTP or MQTT is used as the communication protocol. For example, data collected at 8:00 a.m. is immediately sent to the server.

[0768] Step 3: Data aggregation and storage

[0769] The server receives data packets from each device. The received data is stored in a temporary storage area (such as a cache or queue). The input is the data packets received from each device, and the output is the data stored in the temporary storage area. The server then checks the timestamp, checks for outliers, and checks for missing data, and stores only valid data in the central database. For example, data received at 8:00 a.m. is checked for timestamps and outliers before being stored in the central database.

[0770] Step 4: Data analysis and advice generation

[0771] The server inputs the latest environmental data stored in a central database into the generative AI model. The input is the environmental data stored in the central database, and the output is the analysis results. The generative AI model analyzes trends and anomalies based on past and real-time data to generate optimal crop management advice. Based on the input data, it generates advice such as "Watering at 3 p.m. is effective" or a warning such as "Be careful of certain disease-causing bacteria as high humidity continues."

[0772] Step 5: Providing advice

[0773] The server sends the generated advice to each device (audio output device or portable information terminal application). The input is the advice generated based on the analysis, and the output is the advice sent to each device. The device (audio output device) converts the received text data into voice and notifies the farmer. For example, the voice notification may say, "Please water now." Meanwhile, the user can check the advice in text format using the portable information terminal application. For example, when opening the app, specific advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0774] (Application example 1)

[0775] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0776] There is a growing need for systems that can collect and analyze environmental data and machine operation data in factories in real time and provide workers with accurate management advice. However, current systems tend to be slow in collecting and analyzing data, making it difficult to provide appropriate advice in a timely manner. Furthermore, the lack of a flexible information provision mechanism via voice notifications or smartphone apps makes efficient production management difficult.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0778] In this invention, the server includes means for collecting environmental data, means for transmitting the collected environmental data to a central database, a generative AI model for analyzing the data stored in the central database, means for generating management advice based on the analysis results, and means for providing the generated advice to workers. This makes it possible to collect and analyze various data within the factory in real time and provide accurate production management advice to workers in a timely manner.

[0779] "Environmental data" refers to data that indicates the temperature, humidity, lighting, vibration, and machine operating status within the factory.

[0780] "Means of collection" refers to devices and systems that collect environmental data within the factory using sensors and monitoring equipment.

[0781] "Transmission means" refers to a communication device or protocol for transmitting collected environmental data to a server via the Internet or a dedicated line.

[0782] The "central database" is a database system for centrally storing and managing collected environmental data.

[0783] An "analytical generative AI model" is an artificial intelligence model that analyzes data stored in a central database and generates appropriate management advice.

[0784] The "means for generating management advice" refers to an algorithm or program for creating appropriate management advice based on the results of analysis by the generative AI model.

[0785] The "means of providing" refers to a device or platform for notifying workers of the generated management advice through an audio output device or a smartphone app.

[0786] "Workers" are the staff who work in the factory and receive the management advice provided by the generative AI model.

[0787] This invention relates to a system that collects and analyzes environmental data and machine operation data within a factory and provides timely management advice. This system consists of the following main components:

[0788] 1. Means of collecting environmental data

[0789] IoT devices installed in factories (such as temperature sensors, humidity sensors, vibration sensors, and machine operation status monitoring devices) periodically collect environmental data such as temperature, humidity, vibration, and lighting, and this data is sent to a server via Wi-Fi or a dedicated line.

[0790] 2. Data aggregation and storage

[0791] The server stores data packets received from each IoT device in a temporary storage area. After verifying the data integrity, it stores the correct data in a central database. Data integrity is verified by checking timestamps and filtering outliers.

[0792] 3. Data analysis and advice generation

[0793] The server inputs the latest environmental data stored in the database into the generative AI model. Based on past and real-time data, the generative AI model analyzes temperature, humidity, vibration levels, machine operating status, and other factors, detects trends and anomalies, and generates optimal management advice. For example, it might generate advice such as "Machine A's vibration level is high, so maintenance is required" or "The humidity is high, so please ventilate."

[0794] 4. Providing advice

[0795] The server provides the generated advice to the worker via a voice output device or smartphone app. The voice output device converts the received text data into voice and notifies the worker. Voice advice such as "Machine A requires maintenance" is provided. Meanwhile, the smartphone app displays the generated advice in text format to the worker.

[0796] As a specific example, let's say that at 8:00 a.m., an IoT device in a factory collects data monitoring the operating status of Machine A at a temperature of 25°C, humidity of 60%, and vibration level of 0.2g. This data is sent to a server, where it is checked for consistency and then stored in a central database. The server analyzes this data and generates advice such as, "Machine A has a high vibration level, so maintenance is recommended." The generated advice is notified to the worker via a voice output device, and can also be viewed on a smartphone app.

[0797] Examples of prompts to input to a generative AI model include:

[0798] "Based on data from the past seven days, analyze environmental data such as temperature, humidity, vibration levels, and machine operation status to generate optimal management advice for your factory."

[0799] The system of this invention makes it possible to collect and analyze environmental data within a factory in real time and provide accurate production management advice to workers in a timely manner. This allows for the early detection of equipment abnormalities and the implementation of appropriate maintenance, thereby improving the production efficiency and safety of the entire factory.

[0800] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0801] Step 1:

[0802] The __terminal__ collects environmental data within the factory. Specifically, it obtains data on temperature, humidity, vibration, lighting, etc. using temperature sensors, humidity sensors, vibration sensors, and machine operation status monitoring devices.

[0803] __Input:__ Environmental data from various sensors in the factory

[0804] __Output:__ Collected environmental data packet

[0805] Step 2:

[0806] The environmental data collected by the device is compiled into data packets and sent to the server via Wi-Fi or a dedicated line.

[0807] __Input:__ Environmental data packets collected from the device

[0808] __Output:__ Environment data packet sent to the server

[0809] Step 3:

[0810] The server stores the received environmental data packet in a temporary storage area, then checks the timestamp and filters out any outliers to ensure data integrity.

[0811] __Input:__ Environment data packet sent to the server

[0812] __Output:__ Environmental data with integrity verified

[0813] Step 4:

[0814] The server stores the verified data in a central database for further analysis.

[0815] __Input:__ Environmental data whose integrity has been verified

[0816] __Output:__ Environmental data stored in a central database

[0817] Step 5:

[0818] The server inputs the latest environmental data stored in a central database into the generative AI model, analyzes the data, and generates appropriate management advice.

[0819] __Input:__ Latest environmental data stored in a central database

[0820] __Output:__ Generated management advice

[0821] Step 6:

[0822] The server sends the generated management advice to an audio output device or smartphone app.

[0823] __Input:__ Generated management advice

[0824] __Output:__ Management advice sent to audio output devices and smartphone apps

[0825] Step 7:

[0826] The audio output device converts the received advice into audio and notifies the user. The smartphone app also displays the generated advice in text format to the user.

[0827] __Input:__ Management advice received by audio output device and smartphone app

[0828] Output: The audio notification and text display provided to the user.

[0829] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0830] This invention relates to a system that collects environmental data, analyzes it with a generative AI model, and provides crop management advice. It is an agricultural support system that also combines an emotion engine that recognizes the user's emotions. This system helps farmers perform efficient and optimal farm work.

[0831] System Configuration

[0832] The system consists of the following main components:

[0833] 1. Terminal (IoT device)

[0834] 2. Server

[0835] 3. Users (farmers)

[0836] 4. Emotion Engine

[0837] 5. Audio output devices and smartphone apps

[0838] Program processing

[0839] Data collection

[0840] Terminals (IoT devices) are installed in farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is acquired through sensors, compiled into data packets, and sent to a server. For example, at 8 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[0841] Data Aggregation and Storage

[0842] The server stores the data packets received from the terminal in a temporary storage area, checks the integrity of the data, and then stores the correct data in the central database.

[0843] Data analysis and advice generation

[0844] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model. The generative AI model performs analysis based on past and real-time data. Based on the analysis results, it generates optimal crop management advice. For example, advice such as "Watering at 3 p.m. is effective" or "There is no need to add fertilizer for the next three days" is generated.

[0845] emotion recognition

[0846] The emotion engine collects emotion data from the farmers' voices and facial expressions, analyzes the emotions, and sends the collected emotion data to the server.

[0847] Use of Emotional Data

[0848] The server stores the emotion data received from the emotion engine in a central database. The generative AI model uses the emotion data along with environmental data for analysis. For example, if a farmer is feeling anxious, it generates reassuring advice such as, "There are no particular points to be careful of at this stage."

[0849] Providing advice

[0850] The server sends the generated advice to a terminal (audio output device or smartphone app). The terminal (audio output device) converts the received advice into audio and notifies the farmer. For example, advice such as "Water your fields now" is provided as audio. The user can also open the smartphone app to check a text version of the advice. The app notification function allows the user to receive advice in real time. Advice such as "There is no need to add fertilizer for the next three days" is displayed.

[0851] Specific examples

[0852] For example, suppose that at 8:00 a.m., a device collects data showing a temperature of 25°C, humidity of 65%, five hours of sunshine, and soil moisture of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. The advice generated is, "It is effective to water at 3:00 p.m." The emotion engine then collects the farmer's emotional data and sends it to the server. The generative AI model takes into account the data that the farmer is concerned about, and supplements it with reassuring advice such as, "There are no particular points to be careful of at this stage." The advice is communicated via voice through the device (audio output device), and the user can also check it on their smartphone app.

[0853] The present invention is a sustainable agricultural support system that provides farmers with optimal advice based on collected environmental data and emotional data, thereby providing efficient farming and a sense of security.

[0854] The processing flow will be explained below.

[0855] Step 1:

[0856] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture through sensors installed in farmland. For example, at 8 a.m., the data collected is: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[0857] Step 2:

[0858] The device assembles the collected environmental data into a data packet, which includes the sensor ID, a timestamp, and the acquired data.

[0859] Step 3:

[0860] The terminal sends data packets to the server using a communication protocol (e.g., MQTT, HTTP).

[0861] Step 4:

[0862] The server stores the data packets received from each terminal in a temporary storage area.

[0863] Step 5:

[0864] The server verifies the integrity of the data received, checking for timestamps, invalid or missing values.

[0865] Step 6:

[0866] The server then stores the data that has been verified as consistent in a central database, where it is managed together with other past data.

[0867] Step 7:

[0868] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model.

[0869] Step 8:

[0870] The server runs a generative AI model and performs analysis based on the acquired data, comparing historical data with real-time data to detect trends and anomalies.

[0871] Step 9:

[0872] The server generates optimal crop management advice based on the analysis results of the generative AI model, such as advice on when to water at 3 p.m. and the need for additional fertilizer for the next three days.

[0873] Step 10:

[0874] The emotion engine collects emotion data from farmers' voices and facial expressions, including their stress levels and emotional states.

[0875] Step 11:

[0876] The emotion engine assembles the collected emotion data into a data packet and sends it to the server.

[0877] Step 12:

[0878] The server stores the emotion data in a central database, which the generative AI model uses for analysis.

[0879] Step 13:

[0880] The server then tailors crop management advice based on the analysis results, including emotional data. For example, if a farmer is feeling anxious, it adds reassuring advice.

[0881] Step 14:

[0882] The server sends the generated advice to the device (audio output device or smartphone app). For example, the advice may say, "There are no particular points to be aware of at this stage."

[0883] Step 15:

[0884] The audio output device converts the received advice into audio and notifies the farmer, saying, "Please water your plants now."

[0885] Step 16:

[0886] Users can open the smartphone app and see a text version of the advice, which may include, "You don't need to add fertilizer for the next three days."

[0887] Through these specific processing steps, the present invention is a system that provides optimal advice to farmers based on collected environmental data and emotional data, and supports them in carrying out farm work efficiently and safely.

[0888] Example 2

[0889] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0890] In modern agriculture, providing advice based solely on environmental data poses a challenge, as it does not take into account the psychological state of farmers, making it difficult to provide optimal crop management. Furthermore, because the collected data is diverse and complex, analyzing it and generating useful advice can require a great deal of time and effort.

[0891] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data, means for transmitting the collected environmental data to a central database, a generation AI model means for analyzing the data stored in the central database, means for generating crop management advice based on the analysis results, means for collecting farmer's emotion data, means for transmitting the collected emotion data to the central database, means for generating crop management advice that takes the emotion data into account based on the analysis results, and means for providing the generated advice to the farmer. This makes it possible to provide optimal crop management advice that takes the farmer's psychological state into account, thereby enabling farm work to be carried out efficiently and with peace of mind.

[0892] "Environmental data" refers to information such as temperature, humidity, hours of sunlight, and soil moisture in the agricultural environment.

[0893] "Collection means" refers to a device or method for acquiring environmental data and emotional data using sensors, IoT devices, etc.

[0894] "Central database" refers to a data storage system that integrates and stores collected environmental data and emotional data.

[0895] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and generates optimal crop management advice.

[0896] "Analysis means" means a method or device for analyzing data using a generative AI model to generate crop management advice.

[0897] "Emotional data" refers to information indicating the psychological state of farmers obtained from their voices, facial expressions, etc.

[0898] "Provision means" refers to a method or device (such as an audio output device or smartphone app) for notifying farmers of the generated advice.

[0899] This invention relates to a system that collects environmental and emotional data, analyzes it using a generative AI model, and provides crop management advice, helping farmers carry out their farm work efficiently and safely.

[0900] System Configuration

[0901] The system consists of the following main components:

[0902] 1. Terminal (IoT device)

[0903] 2. Server

[0904] 3. Users (farmers)

[0905] 4. Emotion Engine

[0906] 5. Audio output devices and smartphone apps

[0907] Program processing

[0908] Data collection

[0909] Terminals (IoT devices) are installed in farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is acquired through sensors, compiled into data packets, and sent to a server. For example, at 8 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[0910] Data Aggregation and Storage

[0911] The server stores the data packets received from the terminal in a primary storage area, checks the integrity of the data, and then stores the correct data in the central database.

[0912] Data analysis and advice generation

[0913] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model. The generative AI model performs analysis based on past and real-time data. Based on the analysis results, it generates optimal crop management advice. For example, advice such as "Watering at 3 p.m. is effective" or "There is no need to add fertilizer for the next three days" is generated.

[0914] emotion recognition

[0915] The emotion engine collects emotion data from the farmers' voices and facial expressions, and this data is sent to the server.

[0916] Use of Emotional Data

[0917] The server stores the emotion data in a central database. The generative AI model uses the environmental and emotion data for analysis. For example, if a farmer is feeling anxious, it generates reassuring advice such as, "There are no particular precautions to take at this stage."

[0918] Providing advice

[0919] The server sends the generated advice to a device (audio output device or smartphone app). The audio output device converts the advice into audio and notifies the user. For example, advice such as "Water your plants now" is provided as audio. The user can also check a text version of the advice using the smartphone app. The app notification function displays advice in real time, such as "There is no need to add fertilizer for the next three days."

[0920] This system will provide farmers with optimal advice based on collected environmental and emotional data, realizing a sustainable agricultural support system that provides efficient farming and peace of mind.

[0921] Specific examples

[0922] For example, suppose that at 8:00 a.m., a device collects data on a temperature of 25°C, humidity of 65%, sunshine for 5 hours, and soil moisture content of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. The resulting advice is, "It is effective to water at 3:00 p.m." The emotion engine then collects the farmer's emotional data and sends it to the server. The generative AI model considers the data that the farmer is concerned about, and supplements it with reassuring advice such as, "There are no particular points to be aware of at this stage." The advice is communicated via voice through the device (audio output device), and the user can also check it on a smartphone app.

[0923] Prompt Sentence Examples

[0924] Here are some example prompts to input to a generative AI model:

[0925] "Provide today's crop management advice based on the latest environmental data."

[0926] "Please propose a work plan for the next three days taking into account the current agri-environmental data."

[0927] "Generate optimal advice when farmers are feeling anxious."

[0928] Such prompt sentences enable the system to efficiently generate and provide crop management advice to farmers.

[0929] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0930] Step 1: Data collection

[0931] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture using sensors installed in the farmland. Specific devices used include temperature sensors, humidity sensors, light sensors, and soil moisture sensors. At 8:00 AM, the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%. The terminal compiles this data into data packets and sends them to the server. The input is data from the environmental sensors, and the output is the generated data packets.

[0932] Step 2: Data aggregation and storage

[0933] The server stores data packets received from the terminal in a primary storage area. It then checks the input data for format and abnormal values ​​to confirm data integrity. For example, it checks whether the data format is correct and whether the values ​​are within the acceptable range. Once the server confirms that the data is normal, it stores the data in a central database. The input is the data packet sent from the terminal, and the output is data whose integrity has been confirmed.

[0934] Step 3: Data analysis and advice generation

[0935] The server retrieves the latest environmental data stored in the central database and inputs it into the generative AI model. Specifically, it provides the generative AI model with the following prompt: "Please provide today's crop management advice based on the latest environmental data." The generative AI model analyzes past and real-time data to generate optimal crop management advice. For example, the generated advice might be, "It is effective to water at 3 p.m." The input is the latest environmental data from the central database, and the output is the generated crop management advice.

[0936] Step 4: Emotion Recognition

[0937] The emotion engine collects emotion data from the farmers' voices and facial expressions. Specifically, it uses voice recognition and facial expression analysis technologies. The emotion engine compiles the collected emotion data into data packets and sends them to the server. The input is the farmers' voices and facial expressions, and the output is the generated emotion data packets.

[0938] Step 5: Use emotion data

[0939] The server stores the emotion data received from the emotion engine in a central database. The generative AI model analyzes the combined environmental data and emotion data. For example, if a farmer is feeling anxious, reassuring advice such as "There are no particular points to be aware of at this stage" is generated. A specific prompt used is "Please provide the best advice based on the farmer's emotion data." The input is emotion data and environmental data, and the output is crop management advice that takes emotion into consideration.

[0940] Step 6: Providing advice

[0941] The server sends the generated advice to a terminal (audio output device or smartphone app). The audio output device converts the received advice into audio and notifies the user with advice such as "Water your fields now." The user can also open the smartphone app to check the text version of the advice. The app notification function is used to display advice in real time, such as "There is no need to add fertilizer for the next three days." The input is the generated advice, and the output is a notification to the farmer.

[0942] (Application example 2)

[0943] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0944] Conventional agricultural support systems can only provide advice based on environmental data, which means they lack comprehensive support that takes into account the emotional state of farmers. They also struggle to provide real-time information that farmers can respond to immediately. This has created a challenge in simultaneously ensuring the efficiency of farm work and farmers' mental well-being.

[0945] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0946] In this invention, the server includes a means for collecting environmental data and emotional data, a means for transmitting the collected environmental data and emotional data to a central database, a generative AI model for analyzing the data stored in the central database, a means for generating crop management advice based on the analysis results, a means for providing the generated advice to users, and a smart device for displaying data in real time. This allows for the provision of comprehensive advice in real time that takes into account the emotional state of farmers, thereby improving the efficiency of farm work and increasing farmers' peace of mind.

[0947] "Environmental data" is information about the physical and environmental conditions of agricultural land, such as temperature, humidity, hours of sunshine, and soil moisture.

[0948] "Emotion data" is information about the emotional state of the farmer as recognized from his voice, facial expression, etc.

[0949] The "central database" is a database system that centrally stores collected environmental data and emotional data and uses them for analysis and management.

[0950] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates optimal advice based on past and real-time data.

[0951] "Advice provision means" refers to the means for communicating the advice generated by the generative AI model to farmers, and includes audio output devices and smart devices.

[0952] "Real-time display means" refers to a means of instantly visualizing collected data and advice, enabling farmers to respond quickly.

[0953] "Smart devices" are electronic devices with advanced functions such as smartphones, tablets, smart glasses, and head-mounted displays.

[0954] The present invention relates to a system that collects environmental data and emotional data, analyzes them using a generative AI model, and provides farmers with optimal crop management advice. This system helps farmers perform efficient and optimal farm work and provides them with peace of mind. Detailed embodiments for specifically implementing the present invention are described below.

[0955] System Configuration

[0956] The system consists of the following main components:

[0957] 1. Terminal: This terminal contains environmental sensors and emotion recognition devices and is installed in the farmland. For example, it uses a DHT22 sensor to measure temperature and humidity, and a soil moisture sensor to measure the amount of moisture in the soil.

[0958] 2. Server: Responsible for data aggregation, storage, and analysis. Collected environmental and emotional data are stored in a central database.

[0959] 3. User: Farmers receive advice via their smartphones or smart devices.

[0960] 4. Emotion Engine: This engine has the ability to analyze the user's voice and facial expressions and generate emotion data. This emotion data is collected using voice and camera.

[0961] 5. Generative AI model: Analyzes environmental and emotional data to generate optimal crop management advice. This model uses a generative AI model such as GPT-3.

[0962] 6. Audio output device and smartphone app: Generated advice is communicated to farmers via voice and text.

[0963] Program processing

[0964] The server does the following:

[0965] 1. Data collection: The terminals are installed in farmland to periodically collect environmental data such as temperature, humidity, sunshine hours, and soil moisture content, and also use an emotion engine to collect farmers' sentiment data.

[0966] 2. Data transmission: The collected environmental data and emotion data are transmitted from the device to the server.

[0967] 3. Data aggregation and storage: The server stores the received data in a central database and checks the integrity of the data.

[0968] 4. Data analysis and advice generation: The server inputs environmental data and emotional data into the generative AI model for analysis. The generative AI model generates crop management advice based on multiple data sets. An example of a prompt is, "Please provide optimal crop management advice based on the following environmental and emotional data. Environmental data: Temperature: 25°C, Humidity: 65%, Sunshine hours: 5 hours, Soil moisture: 40% Emotional data: The manager's emotion is 'anxious'."

[0969] 5. Providing advice: The generated advice is sent from the server to the device (audio output device or smartphone app). For example, advice such as "Watering at 3 p.m. is effective" or "There are no particular points to be careful of at this stage" is provided.

[0970] Specific examples

[0971] For example, at 8 a.m., the device collects environmental data such as a temperature of 25 degrees, humidity of 65%, five hours of sunshine, and soil moisture of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. Furthermore, the emotion engine collects the farmer's emotional data and sends it to the server. Based on this data, the generative AI model generates advice such as "It would be effective to water at 3 p.m." It also provides additional reassuring advice such as "There are no particular precautions to take at this stage." This advice is communicated to the farmer via voice or text.

[0972] In this way, the present invention is a sustainable agricultural support system that not only improves the work efficiency of farmers but also provides them with a sense of psychological security.

[0973] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0974] Step 1:

[0975] The device collects environmental and emotional data using environmental sensors and an emotion recognition device. Specifically, it measures temperature and humidity using a DHT22 sensor, and soil moisture using a soil moisture sensor. It also collects emotional data from farmers using voice and camera recordings. For example, at 8 a.m., the device obtains data showing a temperature of 25°C, humidity of 65%, sunshine for 5 hours, soil moisture content of 40%, and an emotion of "anxiety."

[0976] Step 2:

[0977] The terminal assembles the collected environmental and emotional data into data packets and transmits them to the server. Specifically, the collected data is properly formatted and transmitted using a secure communication protocol. The transmitted data includes temperature, humidity, sunshine hours, soil moisture content, and the farmer's emotional information.

[0978] Step 3:

[0979] The server temporarily stores the received data packets in a database and checks the data integrity. Specifically, it checks the format of the received data and verifies whether it contains any invalid data. Normal data is stored in the central database.

[0980] Step 4:

[0981] The server retrieves the latest environmental and emotional data stored in the central database and inputs it into the generative AI model. Specifically, it extracts data from the database for a specified time range and formats the data in a format compatible with the generative AI model. For example, it prepares data for the past 24 hours.

[0982] Step 5:

[0983] The generative AI model analyzes the acquired data and generates optimal crop management advice. Specifically, it compares it with weather patterns and past data and performs analysis using machine learning algorithms. An example of a prompt is, "Please provide optimal crop management advice based on the following environmental and emotional data. Environmental data: Temperature: 25°C, Humidity: 65%, Sunshine hours: 5 hours, Soil moisture: 40% Emotional data: Manager's emotion is 'anxious'."

[0984] Step 6:

[0985] The server receives the advice obtained from the generative AI model and converts it into an appropriate output format. Specifically, it converts the generated advice into speech text and formats it in a format that can be sent to a voice output device or smartphone app. For example, it generates advice such as "It is effective to water plants at 3 p.m."

[0986] Step 7:

[0987] The user checks the received advice in real time using an audio output device or smartphone app. Specifically, the audio output device notifies the user of the advice by voice, and the smartphone app displays the advice in text format. The user then performs farm work in accordance with the advice provided. The user can also check past advice and corresponding environmental data within the app.

[0988] 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.

[0989] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0990] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0991] [Fourth embodiment]

[0992] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0993] 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.

[0994] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0995] 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.

[0996] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0997] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0998] 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. 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.

[0999] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1000] 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.

[1001] 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 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.

[1002] 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.

[1003] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1004] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1005] This invention relates to an agricultural support system that uses IoT devices and generative AI models to collect and analyze environmental data to provide optimal crop management advice.

[1006] System Configuration

[1007] The system consists of the following main components:

[1008] 1. Terminal (IoT device)

[1009] 2. Server

[1010] 3. Users (farmers)

[1011] 4. Audio output devices and smartphone apps

[1012] Program processing

[1013] Data collection

[1014] Terminals (IoT devices) are installed on farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is obtained through sensors. For example, at 8:00 a.m., the collected data is 25°C temperature, 65% humidity, 5 hours of sunlight, and 40% soil moisture. The terminals assemble this data into data packets and send them to a server.

[1015] Data Aggregation and Storage

[1016] The server stores the data packets received from each device in a temporary storage area. It then checks the integrity of the data and stores only valid data in the central database. When checking the integrity, it checks the timestamp and checks for abnormal values.

[1017] Data analysis and advice generation

[1018] The server inputs the latest environmental data stored in the database into the generative AI model. The generative AI model analyzes trends and anomalies based on past and real-time data. As a result, optimal crop management advice is generated. The advice might be, for example, "Watering at 3 p.m. is effective" or "No additional fertilizer is required for the next three days." Weather data is also integrated to generate long-term forecasts and disease warnings. Warnings include, "Beware of certain disease-causing fungi due to prolonged high humidity."

[1019] Providing advice

[1020] The server sends the generated advice to each terminal (audio output device or smartphone app). The terminal (audio output device) converts the received text data into voice and notifies the farmer. Voice advice such as "Water now" is provided to the farmer. Meanwhile, the user can also check the advice in text format using the smartphone app. When the app is opened, advice such as "There is no need to add fertilizer for the next three days" is displayed.

[1021] Specific examples

[1022] For example, suppose that at 8:00 AM, a device collects data showing a temperature of 25°C, humidity of 65%, sunshine for 5 hours, and soil moisture content of 40%. This data is sent to a server, where it is checked for consistency and then stored in a central database. The server analyzes the data and generates advice such as, "It is effective to water plants at 3:00 PM." The generated advice is announced via voice output by the device, and the user can also check the advice on a smartphone app.

[1023] Through the above process, the present invention is a system that supports efficient farming for farmers and helps achieve sustainable agriculture.

[1024] The processing flow will be explained below.

[1025] Step 1:

[1026] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, sunshine hours, and soil moisture content through sensors installed in farmland. For example, at 8 a.m., the data obtained is: temperature 25°C, humidity 65%, sunshine hours 5 hours, soil moisture content 40%.

[1027] Step 2:

[1028] The device assembles the collected environmental data into a data packet, which includes the sensor ID, a timestamp, and the acquired data.

[1029] Step 3:

[1030] The terminal sends data packets to the server using a communication protocol (e.g., MQTT, HTTP).

[1031] Step 4:

[1032] The server stores the data packets received from each terminal in a temporary storage area.

[1033] Step 5:

[1034] The server verifies the integrity of the data it receives by checking the timestamps in the data packets and for invalid or missing values.

[1035] Step 6:

[1036] The server then stores the data that has been verified as consistent in a central database, where it is managed together with other past data.

[1037] Step 7:

[1038] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model.

[1039] Step 8:

[1040] The server runs a generative AI model and performs analysis based on the acquired data, comparing historical data with real-time data to detect trends and anomalies.

[1041] Step 9:

[1042] The server generates optimal crop management advice based on the analysis results of the generative AI model. The advice includes information on watering timing, adding fertilizer, and disease prevention measures. Examples include "Watering at 3 p.m. is effective" and "There is no need to add fertilizer for the next three days."

[1043] Step 10:

[1044] The server also integrates meteorological data to generate weather forecasts and disease warnings, such as "Beware of certain disease pathogens as high humidity continues."

[1045] Step 11:

[1046] The server transmits the generated advice to each device (audio output device or smartphone app), including text data of the audio advice.

[1047] Step 12:

[1048] The terminal (audio output device) converts the received text data into audio and notifies the farmer, providing advice such as "Please water your plants now."

[1049] Step 13:

[1050] Users open the smartphone app to view text-based crop management advice. The app's notifications allow users to receive advice in real time. For example, the advice might say, "You don't need to add fertilizer for the next three days."

[1051] Example 1

[1052] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1053] Previous agricultural support systems had issues with the collection, analysis, and provision of environmental data, such as insufficient data consistency verification, elimination of time lags, and insufficient convenience through multiple notification methods. Additionally, the quality of crop management advice based on analysis results was low, and accurate information for farmers to carry out their farm work efficiently was not provided.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1055] In this invention, the server includes means for collecting environmental data, means for assembling the collected environmental data into data packets and transmitting them to a central database, means for verifying the consistency of the data stored in the central database, a generative AI model for analyzing the data whose consistency has been verified, means for generating crop management advice based on the analysis results of the generative AI model, and means for providing the generated advice to farmers. This enables the collection and analysis of accurate environmental data in real time, and makes it possible to provide appropriate and timely crop management advice to farmers.

[1056] "Environmental data" refers to data such as temperature, humidity, hours of sunlight, and soil moisture collected at a specific location such as farmland.

[1057] "Collection means" refers to the sensor-equipped equipment or devices used to collect environmental data.

[1058] A "data packet" is a unit of data that is converted into a certain format in order to transmit collected environmental data collectively.

[1059] The "central database" is a database system for centrally storing and managing collected environmental data.

[1060] "Integrity checks" means the functions or processes used to check data for accuracy and integrity before it is stored in the central database.

[1061] A "generative AI model" is an artificial intelligence model that analyzes collected environmental data and generates crop management advice.

[1062] "Means for generating advice" refers to the process or algorithm for creating crop management advice based on the analysis results of the generative AI model.

[1063] An "agricultural worker" is someone who works in agriculture, that is, someone who is responsible for the work of growing crops.

[1064] The "audio output device" refers to a device that transmits the generated crop management advice as audio.

[1065] A "portable information terminal application" is application software that runs on a portable device such as a smartphone or tablet and provides crop management advice.

[1066] MODE FOR CARRYING OUT THE INVENTION

[1067] This invention is a system for agricultural support using IoT devices and generative AI models. The system aims to provide farmers with optimal crop management advice by collecting environmental data and analyzing the collected data.

[1068] System Configuration

[1069] The system consists of the following main components:

[1070] 1. Terminal: An IoT device that collects environmental data, such as temperature, humidity, hours of sunlight, and soil moisture.

[1071] 2. Server: Refers to the computer system that receives the collected data, verifies the data integrity, and analyzes the data using generative AI models.

[1072] 3. User: Refers to the farmer who uses the system.

[1073] 4. Audio output devices and handheld information terminal applications: Devices for providing crop management advice.

[1074] Examples of data collection

[1075] The device acquires environmental data from sensors installed in the farmland. For example, data on temperature, humidity, hours of sunlight, and soil moisture is collected periodically. At 8:00 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5, and soil moisture 40%. This data is measured in real time by the sensors and temporarily stored in the device's internal memory.

[1076] Specific examples of data transmission

[1077] The device collects data and assembles it into data packets. Then, it sends the resulting data packets to a server via an internet connection. HTTP or MQTT is typically used as the communication protocol. For example, data collected at 8:00 AM can be sent immediately to the server.

[1078] Data Aggregation and Storage

[1079] The server receives data packets from each device. The received data is stored in a temporary storage area (cache, queue, etc.). The server then checks the integrity of the data, verifying timestamps, checking for outliers, and inspecting for missing data, and stores only normal data in the central database. For example, at 8 a.m., data such as a temperature of 25 degrees and humidity of 65% is confirmed to be normal and stored in the database.

[1080] Data analysis and advice generation

[1081] The server inputs the latest environmental data stored in a central database into the generative AI model. The generative AI model analyzes trends and anomalies based on past and real-time data to generate optimal crop management advice. For example, the generative AI model analyzes humidity and temperature trends and generates specific advice such as "Watering at 3 p.m." It also predicts the risk of specific diseases and generates warning alerts such as "High humidity will continue, so be careful of specific disease-causing bacteria."

[1082] Providing advice

[1083] The server sends the generated advice to each device (audio output device or mobile information terminal application). The device (audio output device) uses a TTS (Text to Speech) engine to convert the received text data into speech. A voice notification such as "Water your plants now" is given. Meanwhile, the user checks the advice in text format using the mobile information terminal application. When the user opens the app, specific advice such as "There is no need to add fertilizer for the next three days" is displayed.

[1084] Example prompts for generative AI models

[1085] "Enter your latest environmental data. Generate crop management advice based on temperature, humidity, sunshine hours, and soil moisture. For example, what is the best way to manage a crop when the temperature is 25°C, humidity is 65%, sunshine hours are 5 hours, and soil moisture is 40%?"

[1086] As a result, the present invention provides farmers with a system that supports efficient and sustainable agricultural management.

[1087] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1088] Step 1: Data collection

[1089] The device collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture content through sensors installed in the farmland. The input is real-time environmental data, and the output is a collection of this data. For example, at 8:00 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%. The collected data is temporarily stored in the device's internal memory.

[1090] Step 2: Send data

[1091] The device assembles the temporarily stored data into data packets. The input is the environmental data stored in the device's internal memory, and the output is the data packets. These data packets are sent to the server via an Internet connection. HTTP or MQTT is used as the communication protocol. For example, data collected at 8:00 a.m. is immediately sent to the server.

[1092] Step 3: Data aggregation and storage

[1093] The server receives data packets from each device. The received data is stored in a temporary storage area (such as a cache or queue). The input is the data packets received from each device, and the output is the data stored in the temporary storage area. The server then checks the timestamp, checks for outliers, and checks for missing data, and stores only valid data in the central database. For example, data received at 8:00 a.m. is checked for timestamps and outliers before being stored in the central database.

[1094] Step 4: Data analysis and advice generation

[1095] The server inputs the latest environmental data stored in a central database into the generative AI model. The input is the environmental data stored in the central database, and the output is the analysis results. The generative AI model analyzes trends and anomalies based on past and real-time data to generate optimal crop management advice. Based on the input data, it generates advice such as "Watering at 3 p.m. is effective" or a warning such as "Be careful of certain disease-causing bacteria as high humidity continues."

[1096] Step 5: Providing advice

[1097] The server sends the generated advice to each device (audio output device or portable information terminal application). The input is the advice generated based on the analysis, and the output is the advice sent to each device. The device (audio output device) converts the received text data into voice and notifies the farmer. For example, the voice notification may say, "Please water now." Meanwhile, the user can check the advice in text format using the portable information terminal application. For example, when opening the app, specific advice such as "There is no need to add fertilizer for the next three days" is displayed.

[1098] (Application example 1)

[1099] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1100] There is a growing need for systems that can collect and analyze environmental data and machine operation data in factories in real time and provide workers with accurate management advice. However, current systems tend to be slow in collecting and analyzing data, making it difficult to provide appropriate advice in a timely manner. Furthermore, the lack of a flexible information provision mechanism via voice notifications or smartphone apps makes efficient production management difficult.

[1101] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1102] In this invention, the server includes means for collecting environmental data, means for transmitting the collected environmental data to a central database, a generative AI model for analyzing the data stored in the central database, means for generating management advice based on the analysis results, and means for providing the generated advice to workers. This makes it possible to collect and analyze various data within the factory in real time and provide accurate production management advice to workers in a timely manner.

[1103] "Environmental data" refers to data that indicates the temperature, humidity, lighting, vibration, and machine operating status within the factory.

[1104] "Means of collection" refers to devices and systems that collect environmental data within the factory using sensors and monitoring equipment.

[1105] "Transmission means" refers to a communication device or protocol for transmitting collected environmental data to a server via the Internet or a dedicated line.

[1106] The "central database" is a database system for centrally storing and managing collected environmental data.

[1107] An "analytical generative AI model" is an artificial intelligence model that analyzes data stored in a central database and generates appropriate management advice.

[1108] The "means for generating management advice" refers to an algorithm or program for creating appropriate management advice based on the results of analysis by the generative AI model.

[1109] The "means of providing" refers to a device or platform for notifying workers of the generated management advice through an audio output device or a smartphone app.

[1110] "Workers" are the staff who work in the factory and receive the management advice provided by the generative AI model.

[1111] This invention relates to a system that collects and analyzes environmental data and machine operation data within a factory and provides timely management advice. This system consists of the following main components:

[1112] 1. Means of collecting environmental data

[1113] IoT devices installed in factories (such as temperature sensors, humidity sensors, vibration sensors, and machine operation status monitoring devices) periodically collect environmental data such as temperature, humidity, vibration, and lighting, and this data is sent to a server via Wi-Fi or a dedicated line.

[1114] 2. Data aggregation and storage

[1115] The server stores data packets received from each IoT device in a temporary storage area. After verifying the data integrity, it stores the correct data in a central database. Data integrity is verified by checking timestamps and filtering outliers.

[1116] 3. Data analysis and advice generation

[1117] The server inputs the latest environmental data stored in the database into the generative AI model. Based on past and real-time data, the generative AI model analyzes temperature, humidity, vibration levels, machine operating status, and other factors, detects trends and anomalies, and generates optimal management advice. For example, it might generate advice such as "Machine A's vibration level is high, so maintenance is required" or "The humidity is high, so please ventilate."

[1118] 4. Providing advice

[1119] The server provides the generated advice to the worker via a voice output device or smartphone app. The voice output device converts the received text data into voice and notifies the worker. Voice advice such as "Machine A requires maintenance" is provided. Meanwhile, the smartphone app displays the generated advice in text format to the worker.

[1120] As a specific example, let's say that at 8:00 a.m., an IoT device in a factory collects data monitoring the operating status of Machine A at a temperature of 25°C, humidity of 60%, and vibration level of 0.2g. This data is sent to a server, where it is checked for consistency and then stored in a central database. The server analyzes this data and generates advice such as, "Machine A has a high vibration level, so maintenance is recommended." The generated advice is notified to the worker via a voice output device, and can also be viewed on a smartphone app.

[1121] Examples of prompts to input to a generative AI model include:

[1122] "Based on data from the past seven days, analyze environmental data such as temperature, humidity, vibration levels, and machine operation status to generate optimal management advice for your factory."

[1123] The system of this invention makes it possible to collect and analyze environmental data within a factory in real time and provide accurate production management advice to workers in a timely manner. This allows for the early detection of equipment abnormalities and the implementation of appropriate maintenance, thereby improving the production efficiency and safety of the entire factory.

[1124] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1125] Step 1:

[1126] The __terminal__ collects environmental data within the factory. Specifically, it obtains data on temperature, humidity, vibration, lighting, etc. using temperature sensors, humidity sensors, vibration sensors, and machine operation status monitoring devices.

[1127] __Input:__ Environmental data from various sensors in the factory

[1128] __Output:__ Collected environmental data packet

[1129] Step 2:

[1130] The environmental data collected by the device is compiled into data packets and sent to the server via Wi-Fi or a dedicated line.

[1131] __Input:__ Environmental data packets collected from the device

[1132] __Output:__ Environment data packet sent to the server

[1133] Step 3:

[1134] The server stores the received environmental data packet in a temporary storage area, then checks the timestamp and filters out any outliers to ensure data integrity.

[1135] __Input:__ Environment data packet sent to the server

[1136] __Output:__ Environmental data with integrity verified

[1137] Step 4:

[1138] The server stores the verified data in a central database for further analysis.

[1139] __Input:__ Environmental data whose integrity has been verified

[1140] __Output:__ Environmental data stored in a central database

[1141] Step 5:

[1142] The server inputs the latest environmental data stored in a central database into the generative AI model, analyzes the data, and generates appropriate management advice.

[1143] __Input:__ Latest environmental data stored in a central database

[1144] __Output:__ Generated management advice

[1145] Step 6:

[1146] The server sends the generated management advice to an audio output device or smartphone app.

[1147] __Input:__ Generated management advice

[1148] __Output:__ Management advice sent to audio output devices and smartphone apps

[1149] Step 7:

[1150] The audio output device converts the received advice into audio and notifies the user. The smartphone app also displays the generated advice in text format to the user.

[1151] __Input:__ Management advice received by audio output device and smartphone app

[1152] Output: The audio notification and text display provided to the user.

[1153] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1154] This invention relates to a system that collects environmental data, analyzes it with a generative AI model, and provides crop management advice. It is an agricultural support system that also combines an emotion engine that recognizes the user's emotions. This system helps farmers perform efficient and optimal farm work.

[1155] System Configuration

[1156] The system consists of the following main components:

[1157] 1. Terminal (IoT device)

[1158] 2. Server

[1159] 3. Users (farmers)

[1160] 4. Emotion Engine

[1161] 5. Audio output devices and smartphone apps

[1162] Program processing

[1163] Data collection

[1164] Terminals (IoT devices) are installed in farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is acquired through sensors, compiled into data packets, and sent to a server. For example, at 8 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[1165] Data Aggregation and Storage

[1166] The server stores the data packets received from the terminal in a temporary storage area, checks the integrity of the data, and then stores the correct data in the central database.

[1167] Data analysis and advice generation

[1168] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model. The generative AI model performs analysis based on past and real-time data. Based on the analysis results, it generates optimal crop management advice. For example, advice such as "Watering at 3 p.m. is effective" or "There is no need to add fertilizer for the next three days" is generated.

[1169] emotion recognition

[1170] The emotion engine collects emotion data from the farmers' voices and facial expressions, analyzes the emotions, and sends the collected emotion data to the server.

[1171] Use of Emotional Data

[1172] The server stores the emotion data received from the emotion engine in a central database. The generative AI model uses the emotion data along with environmental data for analysis. For example, if a farmer is feeling anxious, it generates reassuring advice such as, "There are no particular points to be careful of at this stage."

[1173] Providing advice

[1174] The server sends the generated advice to a terminal (audio output device or smartphone app). The terminal (audio output device) converts the received advice into audio and notifies the farmer. For example, advice such as "Water your fields now" is provided as audio. The user can also open the smartphone app to check a text version of the advice. The app notification function allows the user to receive advice in real time. Advice such as "There is no need to add fertilizer for the next three days" is displayed.

[1175] Specific examples

[1176] For example, suppose that at 8:00 a.m., a device collects data showing a temperature of 25°C, humidity of 65%, five hours of sunshine, and soil moisture of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. The advice generated is, "It is effective to water at 3:00 p.m." The emotion engine then collects the farmer's emotional data and sends it to the server. The generative AI model takes into account the data that the farmer is concerned about, and supplements it with reassuring advice such as, "There are no particular points to be careful of at this stage." The advice is communicated via voice through the device (audio output device), and the user can also check it on their smartphone app.

[1177] The present invention is a sustainable agricultural support system that provides farmers with optimal advice based on collected environmental data and emotional data, thereby providing efficient farming and a sense of security.

[1178] The processing flow will be explained below.

[1179] Step 1:

[1180] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture through sensors installed in farmland. For example, at 8 a.m., the data collected is: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[1181] Step 2:

[1182] The device assembles the collected environmental data into a data packet, which includes the sensor ID, a timestamp, and the acquired data.

[1183] Step 3:

[1184] The terminal sends data packets to the server using a communication protocol (e.g., MQTT, HTTP).

[1185] Step 4:

[1186] The server stores the data packets received from each terminal in a temporary storage area.

[1187] Step 5:

[1188] The server verifies the integrity of the data received, checking for timestamps, invalid or missing values.

[1189] Step 6:

[1190] The server then stores the data that has been verified as consistent in a central database, where it is managed together with other past data.

[1191] Step 7:

[1192] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model.

[1193] Step 8:

[1194] The server runs a generative AI model and performs analysis based on the acquired data, comparing historical data with real-time data to detect trends and anomalies.

[1195] Step 9:

[1196] The server generates optimal crop management advice based on the analysis results of the generative AI model, such as advice on when to water at 3 p.m. and the need for additional fertilizer for the next three days.

[1197] Step 10:

[1198] The emotion engine collects emotion data from farmers' voices and facial expressions, including their stress levels and emotional states.

[1199] Step 11:

[1200] The emotion engine assembles the collected emotion data into a data packet and sends it to the server.

[1201] Step 12:

[1202] The server stores the emotion data in a central database, which the generative AI model uses for analysis.

[1203] Step 13:

[1204] The server then tailors crop management advice based on the analysis results, including emotional data. For example, if a farmer is feeling anxious, it adds reassuring advice.

[1205] Step 14:

[1206] The server sends the generated advice to the device (audio output device or smartphone app). For example, the advice may say, "There are no particular points to be aware of at this stage."

[1207] Step 15:

[1208] The audio output device converts the received advice into audio and notifies the farmer, saying, "Please water your plants now."

[1209] Step 16:

[1210] Users can open the smartphone app and see a text version of the advice, which may include, "You don't need to add fertilizer for the next three days."

[1211] Through these specific processing steps, the present invention is a system that provides optimal advice to farmers based on collected environmental data and emotional data, and supports them in carrying out farm work efficiently and safely.

[1212] Example 2

[1213] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1214] In modern agriculture, providing advice based solely on environmental data poses a challenge, as it does not take into account the psychological state of farmers, making it difficult to provide optimal crop management. Furthermore, because the collected data is diverse and complex, analyzing it and generating useful advice can require a great deal of time and effort.

[1215] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data, means for transmitting the collected environmental data to a central database, a generation AI model means for analyzing the data stored in the central database, means for generating crop management advice based on the analysis results, means for collecting farmer's emotion data, means for transmitting the collected emotion data to the central database, means for generating crop management advice that takes the emotion data into account based on the analysis results, and means for providing the generated advice to the farmer. This makes it possible to provide optimal crop management advice that takes the farmer's psychological state into account, thereby enabling farm work to be carried out efficiently and with peace of mind.

[1216] "Environmental data" refers to information such as temperature, humidity, hours of sunlight, and soil moisture in the agricultural environment.

[1217] "Collection means" refers to a device or method for acquiring environmental data and emotional data using sensors, IoT devices, etc.

[1218] "Central database" refers to a data storage system that integrates and stores collected environmental data and emotional data.

[1219] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and generates optimal crop management advice.

[1220] "Analysis means" means a method or device for analyzing data using a generative AI model to generate crop management advice.

[1221] "Emotional data" refers to information indicating the psychological state of farmers obtained from their voices, facial expressions, etc.

[1222] "Provision means" refers to a method or device (such as an audio output device or smartphone app) for notifying farmers of the generated advice.

[1223] This invention relates to a system that collects environmental and emotional data, analyzes it using a generative AI model, and provides crop management advice, helping farmers carry out their farm work efficiently and safely.

[1224] System Configuration

[1225] The system consists of the following main components:

[1226] 1. Terminal (IoT device)

[1227] 2. Server

[1228] 3. Users (farmers)

[1229] 4. Emotion Engine

[1230] 5. Audio output devices and smartphone apps

[1231] Program processing

[1232] Data collection

[1233] Terminals (IoT devices) are installed in farmland and periodically collect environmental data such as temperature, humidity, hours of sunlight, and soil moisture. This environmental data is acquired through sensors, compiled into data packets, and sent to a server. For example, at 8 a.m., the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%.

[1234] Data Aggregation and Storage

[1235] The server stores the data packets received from the terminal in a primary storage area, checks the integrity of the data, and then stores the correct data in the central database.

[1236] Data analysis and advice generation

[1237] The server retrieves the latest environmental data stored in a central database and inputs it into the generative AI model. The generative AI model performs analysis based on past and real-time data. Based on the analysis results, it generates optimal crop management advice. For example, advice such as "Watering at 3 p.m. is effective" or "There is no need to add fertilizer for the next three days" is generated.

[1238] emotion recognition

[1239] The emotion engine collects emotion data from the farmers' voices and facial expressions, and this data is sent to the server.

[1240] Use of Emotional Data

[1241] The server stores the emotion data in a central database. The generative AI model uses the environmental and emotion data for analysis. For example, if a farmer is feeling anxious, it generates reassuring advice such as, "There are no particular precautions to take at this stage."

[1242] Providing advice

[1243] The server sends the generated advice to a device (audio output device or smartphone app). The audio output device converts the advice into audio and notifies the user. For example, advice such as "Water your plants now" is provided as audio. The user can also check a text version of the advice using the smartphone app. The app notification function displays advice in real time, such as "There is no need to add fertilizer for the next three days."

[1244] This system will provide farmers with optimal advice based on collected environmental and emotional data, realizing a sustainable agricultural support system that provides efficient farming and peace of mind.

[1245] Specific examples

[1246] For example, suppose that at 8:00 a.m., a device collects data on a temperature of 25°C, humidity of 65%, sunshine for 5 hours, and soil moisture content of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. The resulting advice is, "It is effective to water at 3:00 p.m." The emotion engine then collects the farmer's emotional data and sends it to the server. The generative AI model considers the data that the farmer is concerned about, and supplements it with reassuring advice such as, "There are no particular points to be aware of at this stage." The advice is communicated via voice through the device (audio output device), and the user can also check it on a smartphone app.

[1247] Prompt Sentence Examples

[1248] Here are some example prompts to input to a generative AI model:

[1249] "Provide today's crop management advice based on the latest environmental data."

[1250] "Please propose a work plan for the next three days taking into account the current agri-environmental data."

[1251] "Generate optimal advice when farmers are feeling anxious."

[1252] Such prompt sentences enable the system to efficiently generate and provide crop management advice to farmers.

[1253] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1254] Step 1: Data collection

[1255] The terminal (IoT device) periodically collects environmental data such as temperature, humidity, hours of sunlight, and soil moisture using sensors installed in the farmland. Specific devices used include temperature sensors, humidity sensors, light sensors, and soil moisture sensors. At 8:00 AM, the following data is collected: temperature 25°C, humidity 65%, hours of sunlight 5 hours, and soil moisture 40%. The terminal compiles this data into data packets and sends them to the server. The input is data from the environmental sensors, and the output is the generated data packets.

[1256] Step 2: Data aggregation and storage

[1257] The server stores data packets received from the terminal in a primary storage area. It then checks the input data for format and abnormal values ​​to confirm data integrity. For example, it checks whether the data format is correct and whether the values ​​are within the acceptable range. Once the server confirms that the data is normal, it stores the data in a central database. The input is the data packet sent from the terminal, and the output is data whose integrity has been confirmed.

[1258] Step 3: Data analysis and advice generation

[1259] The server retrieves the latest environmental data stored in the central database and inputs it into the generative AI model. Specifically, it provides the generative AI model with the following prompt: "Please provide today's crop management advice based on the latest environmental data." The generative AI model analyzes past and real-time data to generate optimal crop management advice. For example, the generated advice might be, "It is effective to water at 3 p.m." The input is the latest environmental data from the central database, and the output is the generated crop management advice.

[1260] Step 4: Emotion Recognition

[1261] The emotion engine collects emotion data from the farmers' voices and facial expressions. Specifically, it uses voice recognition and facial expression analysis technologies. The emotion engine compiles the collected emotion data into data packets and sends them to the server. The input is the farmers' voices and facial expressions, and the output is the generated emotion data packets.

[1262] Step 5: Use emotion data

[1263] The server stores the emotion data received from the emotion engine in a central database. The generative AI model analyzes the combined environmental data and emotion data. For example, if a farmer is feeling anxious, reassuring advice such as "There are no particular points to be aware of at this stage" is generated. A specific prompt used is "Please provide the best advice based on the farmer's emotion data." The input is emotion data and environmental data, and the output is crop management advice that takes emotion into consideration.

[1264] Step 6: Providing advice

[1265] The server sends the generated advice to a terminal (audio output device or smartphone app). The audio output device converts the received advice into audio and notifies the user with advice such as "Water your fields now." The user can also open the smartphone app to check the text version of the advice. The app notification function is used to display advice in real time, such as "There is no need to add fertilizer for the next three days." The input is the generated advice, and the output is a notification to the farmer.

[1266] (Application example 2)

[1267] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1268] Conventional agricultural support systems can only provide advice based on environmental data, which means they lack comprehensive support that takes into account the emotional state of farmers. They also struggle to provide real-time information that farmers can respond to immediately. This has created a challenge in simultaneously ensuring the efficiency of farm work and farmers' mental well-being.

[1269] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1270] In this invention, the server includes a means for collecting environmental data and emotional data, a means for transmitting the collected environmental data and emotional data to a central database, a generative AI model for analyzing the data stored in the central database, a means for generating crop management advice based on the analysis results, a means for providing the generated advice to users, and a smart device for displaying data in real time. This allows for the provision of comprehensive advice in real time that takes into account the emotional state of farmers, thereby improving the efficiency of farm work and increasing farmers' peace of mind.

[1271] "Environmental data" is information about the physical and environmental conditions of agricultural land, such as temperature, humidity, hours of sunshine, and soil moisture.

[1272] "Emotion data" is information about the emotional state of the farmer as recognized from his voice, facial expression, etc.

[1273] The "central database" is a database system that centrally stores collected environmental data and emotional data and uses them for analysis and management.

[1274] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates optimal advice based on past and real-time data.

[1275] "Advice provision means" refers to the means for communicating the advice generated by the generative AI model to farmers, and includes audio output devices and smart devices.

[1276] "Real-time display means" refers to a means of instantly visualizing collected data and advice, enabling farmers to respond quickly.

[1277] "Smart devices" are electronic devices with advanced functions such as smartphones, tablets, smart glasses, and head-mounted displays.

[1278] The present invention relates to a system that collects environmental data and emotional data, analyzes them using a generative AI model, and provides farmers with optimal crop management advice. This system helps farmers perform efficient and optimal farm work and provides them with peace of mind. Detailed embodiments for specifically implementing the present invention are described below.

[1279] System Configuration

[1280] The system consists of the following main components:

[1281] 1. Terminal: This terminal contains environmental sensors and emotion recognition devices and is installed in the farmland. For example, it uses a DHT22 sensor to measure temperature and humidity, and a soil moisture sensor to measure the amount of moisture in the soil.

[1282] 2. Server: Responsible for data aggregation, storage, and analysis. Collected environmental and emotional data are stored in a central database.

[1283] 3. User: Farmers receive advice via their smartphones or smart devices.

[1284] 4. Emotion Engine: This engine has the ability to analyze the user's voice and facial expressions and generate emotion data. This emotion data is collected using voice and camera.

[1285] 5. Generative AI model: Analyzes environmental and emotional data to generate optimal crop management advice. This model uses a generative AI model such as GPT-3.

[1286] 6. Audio output device and smartphone app: Generated advice is communicated to farmers via voice and text.

[1287] Program processing

[1288] The server does the following:

[1289] 1. Data collection: The terminals are installed in farmland to periodically collect environmental data such as temperature, humidity, sunshine hours, and soil moisture content, and also use an emotion engine to collect farmers' sentiment data.

[1290] 2. Data transmission: The collected environmental data and emotion data are transmitted from the device to the server.

[1291] 3. Data aggregation and storage: The server stores the received data in a central database and checks the integrity of the data.

[1292] 4. Data analysis and advice generation: The server inputs environmental data and emotional data into the generative AI model for analysis. The generative AI model generates crop management advice based on multiple data sets. An example of a prompt is, "Please provide optimal crop management advice based on the following environmental and emotional data. Environmental data: Temperature: 25°C, Humidity: 65%, Sunshine hours: 5 hours, Soil moisture: 40% Emotional data: The manager's emotion is 'anxious'."

[1293] 5. Providing advice: The generated advice is sent from the server to the device (audio output device or smartphone app). For example, advice such as "Watering at 3 p.m. is effective" or "There are no particular points to be careful of at this stage" is provided.

[1294] Specific examples

[1295] For example, at 8 a.m., the device collects environmental data such as a temperature of 25 degrees, humidity of 65%, five hours of sunshine, and soil moisture of 40%, and sends it to the server. The server stores the data in a central database and analyzes it using a generative AI model. Furthermore, the emotion engine collects the farmer's emotional data and sends it to the server. Based on this data, the generative AI model generates advice such as "It would be effective to water at 3 p.m." It also provides additional reassuring advice such as "There are no particular precautions to take at this stage." This advice is communicated to the farmer via voice or text.

[1296] In this way, the present invention is a sustainable agricultural support system that not only improves the work efficiency of farmers but also provides them with a sense of psychological security.

[1297] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1298] Step 1:

[1299] The device collects environmental and emotional data using environmental sensors and an emotion recognition device. Specifically, it measures temperature and humidity using a DHT22 sensor, and soil moisture using a soil moisture sensor. It also collects emotional data from farmers using voice and camera recordings. For example, at 8 a.m., the device obtains data showing a temperature of 25°C, humidity of 65%, sunshine for 5 hours, soil moisture content of 40%, and an emotion of "anxiety."

[1300] Step 2:

[1301] The terminal assembles the collected environmental and emotional data into data packets and transmits them to the server. Specifically, the collected data is properly formatted and transmitted using a secure communication protocol. The transmitted data includes temperature, humidity, sunshine hours, soil moisture content, and the farmer's emotional information.

[1302] Step 3:

[1303] The server temporarily stores the received data packets in a database and checks the data integrity. Specifically, it checks the format of the received data and verifies whether it contains any invalid data. Normal data is stored in the central database.

[1304] Step 4:

[1305] The server retrieves the latest environmental and emotional data stored in the central database and inputs it into the generative AI model. Specifically, it extracts data from the database for a specified time range and formats the data in a format compatible with the generative AI model. For example, it prepares data for the past 24 hours.

[1306] Step 5:

[1307] The generative AI model analyzes the acquired data and generates optimal crop management advice. Specifically, it compares it with weather patterns and past data and performs analysis using machine learning algorithms. An example of a prompt is, "Please provide optimal crop management advice based on the following environmental and emotional data. Environmental data: Temperature: 25°C, Humidity: 65%, Sunshine hours: 5 hours, Soil moisture: 40% Emotional data: Manager's emotion is 'anxious'."

[1308] Step 6:

[1309] The server receives the advice obtained from the generative AI model and converts it into an appropriate output format. Specifically, it converts the generated advice into speech text and formats it in a format that can be sent to a voice output device or smartphone app. For example, it generates advice such as "It is effective to water plants at 3 p.m."

[1310] Step 7:

[1311] The user checks the received advice in real time using an audio output device or smartphone app. Specifically, the audio output device notifies the user of the advice by voice, and the smartphone app displays the advice in text format. The user then performs farm work in accordance with the advice provided. The user can also check past advice and corresponding environmental data within the app.

[1312] 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.

[1313] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1314] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1315] 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.

[1316] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1317] 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.

[1318] 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).

[1319] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1320] 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."

[1321] 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.

[1322] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1323] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1324] 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.

[1325] 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.

[1326] 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.

[1327] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1328] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] The following is further disclosed regarding the above embodiment.

[1334] (Claim 1)

[1335] a means for collecting environmental data;

[1336] means for transmitting the collected environmental data to a central database;

[1337] A generative AI model that analyzes data stored in a central database;

[1338] means for generating crop management advice based on the analysis results;

[1339] a means for providing the generated advice to the farmer;

[1340] A system including:

[1341] (Claim 2)

[1342] 10. The system of claim 1, wherein the system notifies the farmer of crop management advice through an audio output device.

[1343] (Claim 3)

[1344] The system of claim 1 notifies farmers of crop management advice via a smartphone app.

[1345] (Claim 4)

[1346] 10. The system of claim 1, including means for integrating and analyzing weather data to generate long-term forecasts and disease warnings.

[1347] (Claim 5)

[1348] 10. The system of claim 1, including means for comparing historical data with real-time data to optimize crop management advice.

[1349] "Example 1"

[1350] (Claim 1)

[1351] a means for collecting environmental data;

[1352] means for assembling the collected environmental data into data packets and transmitting them to a central database;

[1353] a means for verifying the integrity of the data stored in the central database;

[1354] A generative AI model that analyzes the data whose integrity has been confirmed, and

[1355] A means for generating crop management advice based on the analysis results of the generative AI model;

[1356] a means for providing the generated advice to farmers;

[1357] A system including:

[1358] (Claim 2)

[1359] 10. The system of claim 1, wherein the system notifies the farmer of crop management advice through an audio output device.

[1360] (Claim 3)

[1361] 10. The system of claim 1, wherein the system notifies the farmer of crop management advice via a portable information terminal application.

[1362] "Application Example 1"

[1363] (Claim 1)

[1364] a means for collecting environmental data;

[1365] means for transmitting the collected environmental data to a central database;

[1366] A generative AI model that analyzes data stored in a central database;

[1367] means for generating management advice based on the analysis results;

[1368] a means for providing the generated advice to a worker;

[1369] A system including:

[1370] (Claim 2)

[1371] 10. The system of claim 1, wherein the management advice is communicated to the worker through an audio output device.

[1372] (Claim 3)

[1373] 10. The system of claim 1, wherein the management advice is communicated to the worker via a smartphone app.

[1374] "Example 2: Combining Emotion Engines"

[1375] (Claim 1)

[1376] a means for collecting environmental data;

[1377] means for transmitting the collected environmental data to a central database;

[1378] A generative AI model that analyzes data stored in a central database;

[1379] means for generating crop management advice based on the analysis results;

[1380] a means of collecting farmer sentiment data;

[1381] means for transmitting the collected emotion data to a central database;

[1382] A means for generating crop management advice taking emotion data into consideration based on the analysis results;

[1383] a means for providing the generated advice to the farmer;

[1384] A system including:

[1385] (Claim 2)

[1386] 10. The system of claim 1, wherein the system notifies the farmer of crop management advice through an audio output device.

[1387] (Claim 3)

[1388] The system of claim 1 notifies farmers of crop management advice via a smartphone app.

[1389] "Application example 2 when combining emotion engines"

[1390] (Claim 1)

[1391] means for collecting environmental and emotional data;

[1392] means for transmitting the collected environmental data and emotion data to a central database;

[1393] A generative AI model that analyzes data stored in a central database;

[1394] means for generating crop management advice based on the analysis results;

[1395] a means for providing the generated advice to a user;

[1396] Smart devices that display data in real time,

[1397] A system including:

[1398] (Claim 2)

[1399] 10. The system of claim 1, wherein the crop management advice is communicated to the user through an audio output device.

[1400] (Claim 3)

[1401] The system of claim 1, which notifies users of crop management advice via a smart device. [Explanation of symbols]

[1402] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting environmental data; means for transmitting the collected environmental data to a central database; A generative AI model that analyzes data stored in a central database; means for generating crop management advice based on the analysis results; a means for providing the generated advice to the farmer; A system including:

2. 10. The system of claim 1, wherein the system notifies the farmer of crop management advice through an audio output device.

3. The system of claim 1 , wherein the system notifies the farmer of crop management advice via a smartphone app.

4. 10. The system of claim 1, further comprising means for integrating and analyzing meteorological data to generate long-range forecasts and disease warnings.

5. 10. The system of claim 1, further comprising means for comparing historical data with real-time data to optimize crop management advice.

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