system

Abstract

Provide a system. [Solution] Sensor means for collecting weather information and environmental data; image acquisition means for capturing an image of the crop; data storage means for receiving and storing data collected by said sensor means and said image acquisition means; data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and risks of pest outbreaks; a calculation means for creating an agricultural plan based on the results of the data analysis means; a notification means for notifying a terminal of the agricultural plan created by the calculation means; A system including:

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 modern agriculture, it is important to collect environmental data, predict the occurrence of pests and diseases, and formulate appropriate agricultural plans. However, with traditional methods, these tasks are carried out individually, requiring a great deal of effort and specialized knowledge. This makes efficient agricultural management difficult, and small-scale farmers in particular face the challenge of finding appropriate responses. Furthermore, there is a need to accurately predict weather fluctuations and the occurrence of pests and diseases, and to respond quickly. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides the following means. First, a sensor means is installed to collect weather information and environmental data, and an image acquisition means is provided to capture images of crops. A data accumulation means is provided to receive and store the data collected by these means, and a data analysis means is provided to analyze the accumulated data. The data analysis means has a calculation means to evaluate weather forecasts and the risk of pest and disease outbreaks, and to create an agricultural plan based on the analysis results. The calculated agricultural plan is notified to the user's terminal via a notification means. This allows farmers to always receive optimal agricultural plans based on the latest weather information and pest and disease risks, enabling efficient agricultural management.

[0006] "Weather information" refers to numerical information related to weather, such as temperature, precipitation, wind speed, wind direction, humidity, and air pressure.

[0007] "Environmental data" refers to information about the farmland and its surrounding environment, typically including humidity, temperature, soil condition, etc.

[0008] "Sensor means" refers to sensor devices used to collect environmental data, including humidity sensors, temperature sensors, soil sensors, and the like.

[0009] "Image acquisition means" refers to devices such as cameras and drones used to photograph the condition of crops.

[0010] "Data storage means" refers to a database or memory device for receiving and storing data collected by the sensor means and image acquisition means.

[0011] "Data analysis means" refers to software and hardware configurations for analyzing data stored in data storage means and obtaining specific calculation results or prediction results.

[0012] A "weather forecast" refers to predictive information about future weather conditions, typically generated using specialized weather models.

[0013] "Pests" refers to diseases and harmful insects that cause damage to agricultural crops.

[0014] "Risk of occurrence" refers to a probabilistic indicator that assesses the likelihood of a pest or disease occurring under specific conditions.

[0015] "Computational means" refers to algorithms and software for creating agricultural plans based on the results of data analysis means.

[0016] An "agricultural plan" refers to a schedule that includes specific action proposals for each stage of farming (sowing, irrigation, fertilization, harvesting, etc.).

[0017] "Notification means" refers to a communication means and interface for notifying the user's terminal of the created agricultural plan.

[0018] A "terminal" is a device used by a user, including a smartphone, tablet, PC, etc. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention relates to a system for automatically processing weather information, environmental data, pest and disease information, and crop quality analysis to create an appropriate agricultural plan. The following aspects can be used to implement this system.

[0041] System configuration

[0042] 1. Sensor means

[0043] Terminal: Humidity, temperature, and soil sensors installed in the field periodically measure environmental data (humidity, temperature, and soil condition).

[0044] Terminal: Drones fly over the fields periodically, taking pictures of the crops' condition with high-resolution cameras.

[0045] 2. Data storage means

[0046] Server: A database that receives and stores collected environmental and image data.

[0047] 3. Data Analysis Methods

[0048] Server: Analyzes the received data and makes a comprehensive assessment of weather forecasts, risk of pest and disease outbreaks, and soil conditions.

[0049] Server: Connects to a weather database to retrieve current and forecast weather information.

[0050] 4. Means of calculation

[0051] Server: Based on the analysis results, it creates specific agricultural plans, including the timing of sowing, irrigation, fertilization, and harvesting.

[0052] 5. Means of notification

[0053] Server: A communication means for notifying the user's device of the created agricultural plan.

[0054] Device: Display a notification message on the user's smartphone or PC.

[0055] System operation example

[0056] Example 1: Creating an irrigation plan

[0057] 1. Terminal: An underground moisture sensor measures the current soil moisture.

[0058] 2. Server: Analyzes data from sensors and combines it with upcoming weather forecasts to calculate optimal irrigation timings.

[0059] 3. Server: Predicts that the field will likely be dehydrated at noon tomorrow and suggests a time to irrigate.

[0060] 4. Terminal: Notifies the user when to irrigate.

[0061] Example 2: Pest control measures

[0062] 1. Terminal: The drone flies over the field and takes images of the crops.

[0063] 2. Server: Uses image analysis technology to detect traces of specific pests and diseases.

[0064] 3. Server: Determines whether there is an increased risk of pest or disease outbreak and proposes specific control measures (e.g., the use of specific pesticides).

[0065] 4. Terminal: Notify the user of prevention measures and encourage them to take them.

[0066] Example 3: Creating a seeding plan

[0067] 1. User: You are considering sowing a new crop.

[0068] 2. Terminal: Collects current weather information, soil conditions, and historical crop growth data.

[0069] 3. Server: Based on all the data, calculates the optimal sowing time and method.

[0070] 4. Server: Notifies the user's terminal of the seeding plan.

[0071] 5. Terminal: The user checks and executes the seeding plan.

[0072] With this configuration and operation, the present invention can realize efficient and effective agricultural management. Users can receive optimal agricultural plans based on the latest data and implement them to improve productivity and reduce risks. Furthermore, analysis of environmental data and pest risks is performed using machine learning algorithms, resulting in high accuracy and speed. This significantly reduces manual labor and improves the efficiency of agricultural management.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Drones also fly periodically, taking photos of the crop condition with high-resolution cameras.

[0076] Step 2:

[0077] Terminal: Sends collected sensor data and image data to the server.

[0078] Step 3:

[0079] Server: Stores the received sensor data and image data in a database. The stored data includes the measurement date and time, sensor readings, image files, etc.

[0080] Step 4:

[0081] Server: Connects to a weather database to retrieve the latest weather forecast data, including temperature, precipitation, wind speed, etc.

[0082] Step 5:

[0083] Server: Uses collected environmental and weather forecast data to assess weather forecasts and pest risk using machine learning algorithms and references to historical data and expert models.

[0084] Step 6:

[0085] Server: Based on the analysis results, it calculates the optimal timing for future agricultural actions (e.g., irrigation, fertilization, harvesting, etc.) using algorithms that determine the optimal actions under specific conditions.

[0086] Step 7:

[0087] Server: Based on the calculation results, a specific agricultural plan is created, detailing the timing and method of each action.

[0088] Step 8:

[0089] Server: Sends the created farming plan to the user's device. The notification also includes details of the plan and the reason for its implementation.

[0090] Step 9:

[0091] Device: A notification message will be displayed on the user's smartphone or PC, allowing the user to review the plan and request adjustments if necessary.

[0092] Step 10:

[0093] User: Implements the proposed agricultural plan and, if necessary, sends a request to adjust the plan to the server via the terminal.

[0094] Step 11:

[0095] Terminal: Sends user feedback to the server.

[0096] Step 12:

[0097] Server: Recalculates the farming plan based on user feedback, makes necessary adjustments, and notifies the user of the adjusted plan again.

[0098] Step 13:

[0099] Terminal: Sensors in the fields and drones continue to collect data and send it to the server.

[0100] Step 14:

[0101] Server: Continuously analyzes the received data, updates the agricultural plan as needed, and notifies the user immediately if an updated plan is available.

[0102] In this way, the system can support agricultural activities sustainably and efficiently.

[0103] Example 1

[0104] 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."

[0105] In modern agriculture, production risks due to weather fluctuations and pest outbreaks remain major challenges, and there is a need to quickly and accurately understand these and take appropriate measures. However, traditional methods involve a large amount of manual work involved in data collection, analysis, and planning, which is not efficient. Another problem is that it is difficult to comprehensively evaluate environmental data and crop conditions and quickly create accurate agricultural plans.

[0106] 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.

[0107] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for taking images of crops, a data storage means for receiving and storing the data collected by the sensor means and the image acquisition means, a data analysis means for analyzing the data stored in the data storage means using a machine learning algorithm to evaluate the weather forecast and the risk of pest and disease outbreaks, a calculation means for creating an agricultural plan based on the results of the data analysis means, and a notification means for notifying a terminal of the agricultural plan created by the calculation means. This makes it possible to quickly respond to weather fluctuations and pest and disease risks and to create an efficient and appropriate agricultural plan.

[0108] "Sensor means" is a device for collecting weather information and environmental data (humidity, temperature, soil conditions).

[0109] The "image acquisition means" is a device equipped with a high-resolution camera for photographing the condition of the crops.

[0110] "Data storage means" is a database system for receiving and storing data collected from the sensor means and image acquisition means.

[0111] The "data analysis means" is a device or program that analyzes the data stored in the data storage means using a machine learning algorithm and evaluates weather forecasts and the risk of pest and disease outbreaks.

[0112] "Calculation means" refers to a device or program for creating an agricultural plan based on the results of the data analysis means.

[0113] The "notification means" is a communication device or program for notifying the terminal of the agricultural plan created by the calculation means.

[0114] A "terminal" is a device that allows a user to receive information and perform operations, and includes smartphones, PCs, etc.

[0115] "Weather information" is information about current and forecast weather conditions.

[0116] "Environmental data" is data relating to weather and earth surface conditions such as humidity, temperature, soil conditions, etc.

[0117] A "machine learning algorithm" is a form of artificial intelligence used in data analysis, analyzing large amounts of data to generate patterns and predictive models.

[0118] An "agricultural plan" is a plan that includes the optimal timing and methods for carrying out agricultural operations such as sowing, irrigation, fertilization, and harvesting.

[0119] A "weather information database" is a system for collecting, storing, and providing weather forecasts and related meteorological data.

[0120] "Cloud messaging technology" is a technology for sending data and notifications to devices via the Internet.

[0121] The present invention relates to a system for automatically processing weather information, environmental data, pest and disease information, and crop quality analysis to create specific and appropriate agricultural plans. The system can be implemented in the following forms.

[0122] System configuration

[0123] The system includes a sensor means, an image acquisition means, a data storage means, a data analysis means, a calculation means, a notification means, and a terminal.

[0124] 1. Sensor means

[0125] The terminal uses humidity, temperature, and soil sensors installed in the field to periodically measure environmental data (humidity, temperature, and soil condition), which enables appropriate measures to be taken in response to crop growth conditions and environmental fluctuations.

[0126] The terminal uses a drone that periodically takes photos of the state of the crops using a high-resolution camera, and the drone's flight schedule is managed by a server.

[0127] 2. Data storage means

[0128] The server receives the environmental data sent from the sensors and stores it in a database system, using a commonly used relational database (for example, MySQL (registered trademark) or PostgreSQL).

[0129] The server also stores image data sent from the drone in a database.

[0130] 3. Data Analysis Methods

[0131] The server uses the Python programming language and machine learning libraries (such as TENSORFLOW® and scikit-learn) to analyze stored environmental and image data, thereby assessing weather forecasts and the risk of pest and disease outbreaks.

[0132] The server interacts with a weather API (e.g., OpenWeatherMap API) to obtain weather forecast data.

[0133] 4. Means of calculation

[0134] Based on the results of the data analysis, the server creates specific agricultural plans for sowing, irrigation, fertilization, harvesting, etc. These calculations are performed using Python's NumPy and Pandas libraries.

[0135] 5. Means of notification

[0136] The server uses cloud messaging technologies such as Firebase Cloud Messaging (FCM) and Amazon SNS to notify the device of the created agricultural plan.

[0137] The device will display a notification message on the user's smartphone or PC, allowing the user to check the plan.

[0138] System operation example

[0139] The following scenario can be considered as a specific example of the system's operation.

[0140] Example 1: Creating an irrigation plan

[0141] 1. The device's humidity sensor measures the humidity in the field.

[0142] 2. The server receives the data and combines it with weather forecast data to calculate the optimal irrigation timing.

[0143] 3. Based on the calculation results, the server predicts that irrigation will be necessary at noon the next day and notifies the user.

[0144] 4. The terminal displays a notification message to the user.

[0145] Example prompt sentence:

[0146] A user wants to create an irrigation plan. The current soil moisture is 40%, and tomorrow's weather forecast predicts a temperature of 30 degrees and sunny skies. What kind of irrigation plan should they create?

[0147] Example 2: Pest control measures

[0148] 1. The device's drone flies over the field and takes images of the crops.

[0149] 2. The server uses image analysis technology to detect traces of pests and diseases.

[0150] 3. The server assesses the risk of pest outbreaks and calculates specific control measures.

[0151] 4. The device notifies the user of the prevention measures and encourages them to take them.

[0152] In this way, this system achieves efficient and appropriate agricultural management by consistently and automatically collecting data, analyzing it, creating plans, and sending notifications.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1: Data collection

[0155] The terminal periodically measures environmental data using humidity, temperature, and soil sensors installed in the field.

[0156] Input: Measurements by environmental sensors (humidity, temperature, soil condition)

[0157] Operation: Data from the sensor is sent to the server via the wireless communication module.

[0158] Output: Measured environmental data

[0159] Step 2: Image acquisition

[0160] The device uses a drone to regularly photograph the condition of the crops using a high-resolution camera.

[0161] Input: Crop images taken by a high-resolution camera mounted on a drone

[0162] Operation: The drone automatically flies at the designated time according to the flight schedule, takes pictures, and then sends the image data to the server.

[0163] Output: Image data of the photographed crops

[0164] Step 3: Save data

[0165] The server receives the environmental data sent from the sensors and the image data sent from the drones and stores them in a database.

[0166] Input: Environmental data, image data

[0167] What it does: Stores the received data in a relational database (e.g., MySQL or PostgreSQL).

[0168] Output: Environmental and image data stored in a database

[0169] Step 4: Environmental data analysis

[0170] The server uses Python and a machine learning library (scikit-learn) to analyze the stored environmental data.

[0171] Input: Environmental data stored in a database

[0172] How it works: It uses machine learning algorithms to analyze environmental data and assess its impact on crop growth.

[0173] Output: Evaluation results (crop growth status, water needs, etc.)

[0174] Step 5: Image analysis

[0175] The server analyzes the stored image data using an image analysis library such as TensorFlow.

[0176] Input: Crop image data stored in a database

[0177] How it works: Uses image analysis technology to detect signs of pests and diseases and the health of crops.

[0178] Output: Image analysis results (traces of pests and diseases, health status of crops)

[0179] Step 6: Obtaining weather forecast data

[0180] The server retrieves weather forecast data from a weather API (such as the OpenWeatherMap API).

[0181] Input: Weather forecast data request from the weather API

[0182] What it does: Get current and forecast weather information via API.

[0183] Output: Current and forecast weather information

[0184] Step 7: Create a farm plan

[0185] The server creates a specific agricultural plan based on the results of environmental data analysis, image analysis, and weather forecast data.

[0186] Input: Environmental data analysis results, image analysis results, weather forecast data

[0187] How it works: This data is evaluated comprehensively and optimal timing for irrigation, fertilization, sowing, and harvesting is calculated using Python's NumPy and Pandas libraries.

[0188] Output: The generated farming plan

[0189] Step 8: User Notification

[0190] The server uses Firebase Cloud Messaging (FCM) and Amazon SNS to notify the user's device of the created farming plan.

[0191] Input: Created farming plan

[0192] Operation: The plan is sent to the user's smartphone or PC and a notification message is displayed.

[0193] Output: Notification of agricultural plan displayed on user terminal

[0194] In this way, environmental and image data are collected and stored at each step, analyzed using machine learning algorithms, an overall agricultural plan is formulated, and the plan is notified to the user.

[0195] (Application example 1)

[0196] 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."

[0197] In modern agriculture and manufacturing, it is extremely important to efficiently collect and analyze environmental and quality data to create optimal action plans and production schedules. However, with conventional systems, data collection and analysis are often performed separately, making it difficult to create effective plans in real time. In particular, in the agricultural sector, rapid and accurate decision-making is required in response to the effects of weather and pests and diseases, and in the manufacturing sector, in product quality control. To address these challenges, there is a strong demand for integrated data collection, analysis, and planning systems.

[0198] 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.

[0199] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for taking images of crops, a data storage means, a data analysis means, a calculation means, a notification means, a data analysis means for analyzing sensor data in the factory and evaluating the state of product quality, a calculation means for creating an optimal production schedule and improvement proposals, and a notification means for notifying a terminal of the production schedule and improvement proposals created by the calculation means. This enables the integrated collection and analysis of environmental data and quality data, and enables the creation of effective agricultural plans and production schedules in real time.

[0200] - "Weather information" refers to data on weather conditions such as temperature, humidity, precipitation, and wind speed.

[0201] "Environmental data" refers to data that indicates the ambient conditions related to the cultivation and manufacturing of crops and products, such as temperature, humidity, and soil condition.

[0202] The "sensor means" is a means for collecting environmental data using physical sensor devices such as a temperature sensor, a humidity sensor, and a soil sensor.

[0203] "Image acquisition means" refers to a means for taking images of crops or products using a camera or drone.

[0204] The "data storage means" refers to a database or server for receiving and storing data collected by the sensor means and image acquisition means.

[0205] The "data analysis means" is a means for analyzing accumulated data and evaluating weather forecasts, risk of pest outbreaks, and product quality.

[0206] "Calculation means" refers to the means for creating agricultural plans and production schedules based on the analysis results.

[0207] The "notification means" is a means for notifying the user's terminal of the agricultural plan or production schedule created by the calculation means.

[0208] A "generative AI model" is a model that uses machine learning algorithms to make predictions and evaluations from input data.

[0209] A "prompt" is a document that inputs specific instructions or questions to a generative AI model.

[0210] The present invention relates to a system that automatically processes weather information, environmental data, pest information, and quality analysis data in agriculture and manufacturing to create optimal plans. This system includes the following components and processing procedures.

[0211] System configuration

[0212] 1. Sensor means

[0213] Sensors are physical devices used to collect environmental data, such as temperature sensors, humidity sensors, and soil sensors, and in factories they also collect sensor data related to product quality.

[0214] 2. Image acquisition method

[0215] Image capture methods include high-resolution cameras and drones that periodically capture images of crop and product conditions.

[0216] 3. Data storage means

[0217] The data storage means is a database or server that receives and stores data collected from the sensor means and image acquisition means, and includes cloud storage and local servers.

[0218] 4. Data Analysis Methods

[0219] Data analytics tools analyze stored data to assess weather forecasts, risk of pest and disease outbreaks, and product quality using generative AI models and machine learning algorithms.

[0220] 5. Means of calculation

[0221] The computational means uses the results of the data analysis means to create optimal agricultural plans and production schedules, including sowing, irrigation, fertilization, harvesting, and maintenance plans.

[0222] 6. Means of notification

[0223] The notification means notifies the user of the plan created by the calculation means to the user's terminal, which may include a smartphone, tablet, PC, or the like.

[0224] Example of operation

[0225] Example 1: Creating an irrigation plan

[0226] The server analyzes data from underground moisture sensors, combines it with the latest weather forecast, calculates the optimal irrigation timing, and notifies the terminal.

[0227] Example 2: Pest control measures

[0228] Drones fly over fields and factories, taking pictures of crops and products. The server analyzes the images, assesses the risk of pests and diseases, and notifies the device with specific prevention measures and improvement suggestions.

[0229] Example 3: Creating a production schedule

[0230] The server analyzes sensor data within the factory, evaluates high-risk defect conditions, calculates the optimal production schedule, and notifies the terminal.

[0231] Program Implementation

[0232] The server analyzes the data using a generative AI model, and is built using a programming language such as Python, with corresponding libraries for collecting sensor data and APIs such as Firebase for notifications.

[0233] Natural language explanations

[0234] The server periodically collects data from the sensors and stores it in a database. It then uses a generative AI model to analyze the accumulated data, making predictions and evaluations. Based on the analysis results, it notifies the user device of the optimal action plan and production schedule.

[0235] Examples of concrete examples and prompts

[0236] Example: On a factory production line, the temperature sensor data was 24 degrees, the humidity sensor data was 42%, and the defect probability was 0.7 (70%). The user was notified that there was a high risk of defect, and the next inspection was scheduled for one hour, and the next maintenance was scheduled for 30 days later.

[0237] Example prompts to input to a generative AI model:

[0238] "Based on the factory sensor data (temperature: 24°C, humidity: 42%, defect probability: 0.7), calculate the next inspection and maintenance schedule and generate a defect risk notification message."

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1:

[0241] Sensor data collection

[0242] The server periodically collects environmental data from sensors such as temperature sensors, humidity sensors, and soil sensors. The inputs include the current temperature, humidity, and soil conditions obtained from each sensor, and this data is collected and stored in a database. Specifically, the server performs a process of obtaining data from the sensors via API and recording it in the database.

[0243] Step 2:

[0244] Acquisition of image data

[0245] Drones and high-resolution cameras are used to periodically capture images of crops and products. The captured image data is input, and this is uploaded and stored in cloud storage or a local server. Specifically, the drone flies a set route, captures images with its camera, and transmits the data to the server via a wireless network.

[0246] Step 3:

[0247] Data accumulation

[0248] The server stores the collected environmental data and image data in a database that centrally manages them. The inputs are sensor data and image data, which are stored in the database in an appropriate format. Specific operations include organizing, verifying, and storing the data.

[0249] Step 4:

[0250] Data analysis

[0251] The server performs analysis using the collected and stored data. Inputs include stored environmental data, image data, and weather forecast data, and uses generative AI models and machine learning algorithms to evaluate weather forecasts, risk of pest outbreaks, and product quality. Specific operations include preprocessing each data set, extracting features, applying the analysis model, and interpreting the results.

[0252] Step 5:

[0253] Creating a plan

[0254] The server calculates optimal agricultural plans and production schedules based on the results of data analysis. The inputs are analysis results and past historical data, and the specific output is the creation of schedules for sowing, irrigation, fertilization, and harvesting, as well as factory production and maintenance plans. Specific operations include calculations using a schedule optimization algorithm and generating plans.

[0255] Step 6:

[0256] notification

[0257] The server notifies the user of the created plan. The input includes an optimal agricultural plan and production schedule, and this is sent to the user's smartphone, tablet, PC, etc. Specific operations include generating notification messages and sending push notifications and emails.

[0258] Example prompt sentences

[0259] For example, "Based on the factory sensor data (temperature: 24°C, humidity: 42%, defect probability: 0.7), calculate the next inspection and maintenance schedule and generate a defect risk notification message."

[0260] 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.

[0261] The present invention relates to a system that automatically processes weather information, environmental data, pest and disease information, and crop quality analysis to create appropriate agricultural plans, and further combines it with an emotion engine that recognizes user emotions and reflects them in the agricultural plans. The following forms can be used to implement this system.

[0262] System configuration

[0263] 1. Sensor means

[0264] Terminal: Humidity, temperature, and soil sensors installed in the field periodically measure environmental data (humidity, temperature, and soil condition).

[0265] Terminal: Drones fly over the fields periodically, taking pictures of the crops' condition with high-resolution cameras.

[0266] 2. Data storage means

[0267] Server: A database that receives and stores collected environmental and image data.

[0268] 3. Data Analysis Methods

[0269] Server: Analyzes the received data and makes a comprehensive assessment of weather forecasts, risk of pest and disease outbreaks, and soil conditions.

[0270] Server: Connects to a weather database to retrieve current and forecast weather information.

[0271] 4. Means of calculation

[0272] Server: Based on the analysis results, it creates specific agricultural plans, including the timing of sowing, irrigation, fertilization, and harvesting.

[0273] 5. Means of notification

[0274] Server: A communication means for notifying the user's device of the created agricultural plan.

[0275] Device: Display a notification message on the user's smartphone or PC.

[0276] 6. Emotion Engine

[0277] Server: An emotion engine that recognizes the user's emotions regarding the notified agricultural plan. The emotion engine determines emotions based on the user's voice input, text input, facial expression analysis, etc.

[0278] Server: Readjusts farming plans based on perceived user sentiment.

[0279] System operation example

[0280] Example 1: Irrigation scheduling and emotional feedback

[0281] 1. Terminal: An underground moisture sensor measures the current soil moisture.

[0282] 2. Server: Analyzes data from sensors and combines it with upcoming weather forecasts to calculate optimal irrigation timings.

[0283] 3. Server: Predicts that the field will likely be dehydrated at noon tomorrow and suggests a time to irrigate.

[0284] 4. Server: Notifies the user's device of the proposed plan.

[0285] 5. Device: Display a notification message on the user's smartphone or PC.

[0286] 6. User: Provide emotional feedback (e.g., dissatisfaction or satisfaction) about the proposed plan via voice or text input.

[0287] 7. Server: The emotion engine analyzes the feedback and makes necessary adjustments.

[0288] Example 2: Pest control and emotional feedback

[0289] 1. Terminal: The drone flies over the field and takes images of the crops.

[0290] 2. Server: Uses image analysis technology to detect traces of specific pests and diseases.

[0291] 3. Server: Determines that the risk of pest outbreaks is increasing and proposes specific control measures.

[0292] 4. Server: Notifies the user of the proposed prevention measures.

[0293] 5. Terminal: Notifies the user of prevention measures and encourages them to take them. The user also receives emotional feedback.

[0294] 6. Server: The emotion engine analyzes the feedback and adjusts the defense measures.

[0295] Example 3: Seeding plan creation and emotional feedback

[0296] 1. User: You are considering sowing a new crop.

[0297] 2. Terminal: Collects current weather information, soil conditions, and historical crop growth data.

[0298] 3. Server: Based on all the data, calculates the optimal sowing time and method.

[0299] 4. Server: Notifies the user's terminal of the seeding plan.

[0300] 5. Terminal: The user checks the seeding plan and provides emotional feedback via voice and text.

[0301] 6. Server: The emotion engine analyzes the feedback and makes necessary adjustments.

[0302] With this configuration and operation, the present invention can realize more efficient and user-friendly agricultural operations. By recognizing the user's emotions and reflecting them in the agricultural planning, it is possible to increase user satisfaction and improve the effectiveness of agriculture.

[0303] The processing flow will be explained below.

[0304] Step 1:

[0305] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Drones also fly periodically, taking photos of the crop condition with high-resolution cameras.

[0306] Step 2:

[0307] Terminal: Sends collected sensor data and image data to the server.

[0308] Step 3:

[0309] Server: Stores the received sensor data and image data in a database. The stored data includes the measurement date and time, sensor readings, image files, etc.

[0310] Step 4:

[0311] Server: Connects to a weather database to retrieve the latest weather forecast data, including temperature, precipitation, wind speed, etc.

[0312] Step 5:

[0313] Server: Uses collected environmental and weather forecast data to assess weather forecasts and pest risk using machine learning algorithms and references to historical data and expert models.

[0314] Step 6:

[0315] Server: Based on the analysis results, it calculates the optimal timing for future agricultural actions (e.g., irrigation, fertilization, harvesting, etc.) using algorithms that determine the optimal actions under specific conditions.

[0316] Step 7:

[0317] Server: Based on the calculation results, a specific agricultural plan is created, detailing the timing and method of each action.

[0318] Step 8:

[0319] Server: Sends the created farming plan to the user's device. The notification also includes details of the plan and the reason for its implementation.

[0320] Step 9:

[0321] Device: A notification message will be displayed on the user's smartphone or PC, allowing the user to review the plan and request adjustments if necessary.

[0322] Step 10:

[0323] User: Enter their feelings about the proposed farming plan via voice or text. Examples include, "I think this plan is good" or "This schedule is tough."

[0324] Step 11:

[0325] Terminal: Sends the user's emotional feedback to the server.

[0326] Step 12:

[0327] Server: The emotion engine analyzes user feedback and determines emotions using voice analysis and natural language processing techniques.

[0328] Step 13:

[0329] Server: Based on the results of sentiment analysis, the farming plan is readjusted as needed. For example, if a user expresses dissatisfaction with irrigation timing, that part is revised.

[0330] Step 14:

[0331] Server: The adjusted agricultural plan is sent back to the user's device.

[0332] Step 15:

[0333] Terminal: Sensors in the fields and drones continue to collect data and send it to the server.

[0334] Step 16:

[0335] Server: Continuously analyzes the received data and updates the farming plan as needed. The emotion engine also continuously receives user feedback and optimizes the plan.

[0336] In this way, the system supports agricultural activities sustainably and efficiently, and by reflecting the user's feelings, it is possible to realize more user-friendly agricultural operations.

[0337] Example 2

[0338] 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."

[0339] Conventional agricultural systems can create agricultural plans based on weather information and environmental data, but they only provide one-way notifications without considering the user's feelings, which has the problem of lacking user satisfaction and flexibility in the plans. This leads to plans not being carried out and users often becoming dissatisfied with the plans, and we aim to solve this problem.

[0340] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes sensor means for collecting weather information and environmental data, image acquisition means for capturing images of crops, data storage means for receiving and storing data collected by the sensor means and the image acquisition means, data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and the risk of pest and disease outbreaks, calculation means for creating an agricultural plan based on the results of the data analysis means, notification means for notifying a terminal of the agricultural plan created by the calculation means, and emotion recognition means for recognizing the user's emotions regarding the notified agricultural plan and readjusting the agricultural plan based on the emotions. This enables the creation of a flexible, satisfying agricultural plan that reflects the user's emotions.

[0341] "Sensor means" refers to a device for collecting weather information and environmental data, and includes humidity sensors, temperature sensors, soil sensors, and the like.

[0342] "Image acquisition means" refers to a device that captures images of crops, and includes drones equipped with high-resolution cameras and fixed cameras.

[0343] "Data storage means" refers to a storage device for receiving and storing data collected from the sensor means and image acquisition means, and includes a database.

[0344] The "data analysis means" is a device or program for analyzing the data stored in the data storage means and evaluating the weather forecast and the risk of pest outbreaks.

[0345] "Calculation means" refers to a device or program for creating an agricultural plan based on the results of the data analysis means, and includes machine learning algorithms.

[0346] The "notification means" is a device or program for notifying the user terminal of the agricultural plan created by the calculation means, and includes a communication means.

[0347] The "emotion recognition means" is a device or program that recognizes the user's emotions regarding the notified farming plan and readjusts the farming plan based on the emotions.

[0348] This invention relates to a system that automatically processes weather information, environmental data, pest and disease information, and crop quality analysis to create appropriate agricultural plans. It also combines an emotion engine that recognizes the user's emotions and reflects them in the agricultural plans.

[0349] System configuration

[0350] 1. Sensor means

[0351] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Specifically, the humidity sensor measures data every 15 minutes and sends it to the gateway using Bluetooth or LoRa communication.

[0352] Device: The drone flies over the fields at a set time every day and takes pictures of the crops using a high-resolution camera. The captured image data is then uploaded to a server via Wi-Fi.

[0353] 2. Data storage means

[0354] Server: Receives and stores collected environmental and image data using a database such as MySQL or PostgreSQL. For example, temperature data is recorded in a temperature table, and humidity data is recorded in a humidity table.

[0355] 3. Data Analysis Methods

[0356] Server: Uses Python scripts to analyze the collected data, calling weather forecast APIs (e.g. OpenWeatherMap API) to retrieve current and forecast weather information, and predict trends in soil moisture and temperature.

[0357] Server: Image data is analyzed using image analysis libraries such as OpenCV. The analysis mainly detects pests and diseases and abnormalities in crops, and records the results in a database.

[0358] 4. Means of calculation

[0359] Server: Uses machine learning algorithms (e.g., TensorFlow, Scikit-learn) to create specific agricultural plans based on the latest analysis results. For example, if soil moisture is low, it will plan irrigation at noon the following day.

[0360] 5. Means of notification

[0361] Server: Uses Firebase Cloud Messaging or REST API to notify users of the created farming plan. Generates notification content (e.g., "Irrigation is required tomorrow at noon") and sends it to the user's smartphone.

[0362] Device: The user's smartphone or PC receives the notification and displays its contents, allowing the user to check the notification.

[0363] 6. Emotion recognition means

[0364] User: The user provides emotional feedback on the proposed farming plan (e.g., "This plan is good" or "Needs to be reconsidered") via voice or text. The voice input is converted to text using the Google® Speech-to-Text API.

[0365] Server: An emotion engine (e.g., OpenAI® GPT) analyzes the text feedback and readjusts the farming plan if the user expresses dissatisfaction or if the plan needs to be revised, again using machine learning algorithms.

[0366] Example: Irrigation scheduling and emotional feedback

[0367] 1. Terminal: The underground humidity sensor measures the soil humidity to be 40% at 2:00 pm on July 15th and sends the data to the server via the gateway via LoRa communication.

[0368] 2. Server: A Python script receives the humidity data and uses the OpenWeatherMap API to get the weather forecast for the next day, which predicts sunny skies with temperatures above 30 degrees.

[0369] 3. Server: The script detects the humidity deficit and determines that the best time to irrigate is at noon the next day. It creates a planning file and saves it in the database.

[0370] 4. Server: Use Firebase Cloud Messaging to send a notification to the user's smartphone saying, "Irrigation is required at noon on July 16th."

[0371] 5. Device: The smartphone receives the notification and displays the message. The user gives feedback by saying, "I'm happy with the plan."

[0372] 6. Server: The voice data is converted to text using the Google Speech-to-Text API, and the emotion engine analyzes the feedback such as "satisfied."

[0373] Prompt Sentence Examples

[0374] "Based on the humidity sensor data and weather forecast, please evaluate whether this irrigation plan is appropriate, reflecting the user's emotional feedback."

[0375] By implementing this aspect of the present invention, it becomes possible to create a flexible and satisfying agricultural plan that reflects the user's feelings, which results in improved efficiency and feasibility of farming, and increased user satisfaction.

[0376] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0377] Step 1:

[0378] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). For example, the humidity sensor measures soil humidity every 15 minutes and transmits the data to the gateway using Bluetooth or LoRa communication.

[0379] Input: Current humidity, temperature, soil data

[0380] Output: Transmits humidity, temperature, and soil data

[0381] Step 2:

[0382] Device: The drone flies over the field at a set time every day, taking pictures of the crops with a high-resolution camera. The captured image data is then uploaded to a server via Wi-Fi.

[0383] Input: Crop image

[0384] Output: Upload image data to the server

[0385] Step 3:

[0386] Server: Uses MySQL or PostgreSQL to receive data sent from sensors and drones and store it in a database.

[0387] Input: Sensor data, image data

[0388] Output: Environmental and image data stored in a database

[0389] Step 4:

[0390] Server: Uses Python scripts to analyze the environmental data stored in the database. Uses weather forecast APIs (e.g. OpenWeatherMap API) to retrieve current and forecast weather information and combine it with the environmental data for analysis.

[0391] Input: Environmental data, weather forecast data

[0392] Output: Analysis results based on weather forecasts and environmental data

[0393] Step 5:

[0394] Server: Image data is analyzed using image analysis libraries such as OpenCV to detect pests, diseases, and abnormalities in crops.

[0395] Input: Image data

[0396] Output: Analysis results on risk of pest and disease outbreaks and abnormalities in crops

[0397] Step 6:

[0398] Server: Based on the analysis results, machine learning algorithms (e.g., TensorFlow, Scikit-learn) are used to create agricultural plans. For example, if a humidity deficiency is detected, a plan to irrigate the area at noon the following day is created.

[0399] Input: Environmental data analysis results, image data analysis results

[0400] Output: Agricultural plan

[0401] Step 7:

[0402] Server: Uses Firebase Cloud Messaging or REST API to notify users of the created farming plan, such as a message like "Irrigation is required tomorrow at noon."

[0403] Input: Agricultural Plan

[0404] Output: Notification message to the user's terminal

[0405] Step 8:

[0406] Device: The user's smartphone or PC receives the notification and displays the notification content. The user checks the notification.

[0407] Input: Notification message

[0408] Output: Display notification information to the user

[0409] Step 9:

[0410] User: The user provides emotional feedback on the proposed farming plan via voice or text, for example, by saying "I'm happy with the plan."

[0411] Input: User's emotional feedback

[0412] Output: Send feedback data to the server

[0413] Step 10:

[0414] Server: The voice feedback is converted to text using the Google Speech-to-Text API. The text feedback is analyzed by an emotion engine (e.g., OpenAI GPT) and the farming plan is readjusted as needed based on the user's emotions.

[0415] Input: User sentiment feedback text

[0416] Output: Re-adjusted farming plan or unchanged feedback results

[0417] In this way, the present invention realizes a system that comprehensively utilizes weather information, environmental data, pest and disease information, and user emotions to provide accurate and satisfying agricultural planning.

[0418] (Application example 2)

[0419] 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."

[0420] Conventional agricultural management systems and factory management systems easily created plans based on the collection and analysis of environmental and production data, but lacked the ability to adjust plans while taking into account the emotions of users and employees. As a result, user satisfaction and working conditions were not sufficiently improved, making efficient operations difficult. Furthermore, in factories, one-sided production plans that ignored the emotions of employees often led to dissatisfaction and stress. The objective of this invention is to solve these problems and provide a system that enables the creation and adjustment of plans while taking into account the emotions of users and employees.

[0421] 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.

[0422] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for acquiring images of crops, a data storage means for receiving and storing the data collected by the sensor means and the image acquisition means, a data analysis means for analyzing the data stored in the data storage means and evaluating the weather forecast and the risk of pest infestation, a calculation means for creating an agricultural plan based on the results of the data analysis means, a notification means for notifying a terminal of the agricultural plan created by the calculation means, a means for monitoring and analyzing factory environment data and production equipment data in real time, and an emotion analysis means for analyzing employee emotions and reflecting the results in the production plan. This makes it possible to design and adjust plans that reflect the emotions of users and employees.

[0423] "Weather information" is information relating to weather conditions, including data such as temperature, humidity, precipitation, wind speed, and wind direction.

[0424] "Environmental data" refers to data obtained by measuring environmental factors at a specific location, including temperature, humidity, soil condition, and air quality.

[0425] "Sensor means" refers to a device that detects a specific physical or chemical phenomenon and outputs it as data.

[0426] "Image capture means" refers to a device or method for photographing or capturing an image of an object.

[0427] "Data storage" refers to a system or device that stores collected data and makes it available for retrieval as needed.

[0428] "Data analysis means" refers to a system or method for analyzing collected data to extract meaningful information.

[0429] "Calculation means" refers to a system or device that performs calculations based on the obtained data and generates optimal plans and instructions.

[0430] "Notification means" refers to a system or method for transmitting the created plans and instructions to the user's terminal.

[0431] "Factory environmental data" refers to data related to various environmental factors within a factory (temperature, humidity, air quality, etc.).

[0432] "Production equipment data" refers to data relating to the operating status, operating rate, failure history, etc. of production equipment.

[0433] "Emotion analysis means" refers to a system or method for analyzing the emotions of users or employees and reflecting the analysis results in the system.

[0434] The present invention relates to a management system in a factory or agricultural environment, which analyzes collected data and creates an optimal plan that reflects the feelings of users and employees.

[0435] System configuration

[0436] 1. Environmental data collection methods

[0437] Sensor means:

[0438] Temperature, humidity and air quality sensors installed in factories and farms regularly measure environmental data.

[0439] Sensors are installed in factory production equipment to record operating conditions and failure history.

[0440] 2. Image acquisition method

[0441] Device:

[0442] Drones and fixed cameras are used to regularly capture images of crops in the fields and production equipment in factories.

[0443] 3. Data storage means

[0444] server:

[0445] A database for receiving and storing environmental data from factories and farms, and image data from crops and production equipment.

[0446] For the database, cloud servers (Google Cloud Platform, Amazon Web Services) or on-premise servers are used.

[0447] 4. Data Analysis Methods

[0448] server:

[0449] The collected environmental and image data is analyzed to comprehensively assess weather forecasts and the risk of pest and disease outbreaks.

[0450] At the factory, environmental data and production equipment data are analyzed to develop production plans.

[0451] By using machine learning algorithms (TensorFlow and PyTorch), the accuracy of the analysis is improved, and a plan is created based on the results.

[0452] 5. Means of calculation

[0453] server:

[0454] Based on the analysis results, agricultural and production plans are formulated, and the timing of sowing, irrigation, fertilization, harvesting, production schedules, etc. are calculated.

[0455] 6. Means of notification

[0456] server:

[0457] The created plan is notified to the user device using Apple Push Notification Service (APNs) or GOOGLE FI (registered trademark) rebase Cloud Messaging (FCM).

[0458] Device:

[0459] Notification messages are displayed on the smartphones and head-mounted displays (HMDs) of users and factory employees.

[0460] 7. Emotion analysis method

[0461] server:

[0462] It has an emotion engine for analyzing user and employee emotions, and recognizes emotions using voice input (Apple Siri, Google Assistant), text input (chatbots), and facial expression analysis (Microsoft® Azure® Face API).

[0463] The emotion engine performs analysis using emotion analysis libraries (NLTK's VADER, Microsoft Azure's Text Analytics for Sentiment Analysis) and reflects the results in the plan.

[0464] Specific examples

[0465] For example, if a user is considering sowing a new crop, the optimal sowing time is calculated based on current weather information, soil conditions, and past growth data collected by the sensor means. The results are then sent to the user's smartphone. When the user provides feedback on the plan, the emotion engine analyzes their emotions and adjusts the sowing time as necessary.

[0466] Prompt Sentence Examples

[0467] You are designing a system that analyzes factory environmental data and production equipment data in real time to create optimal production plans. This system also includes an emotion engine that analyzes employee emotions and reflects them in production plans. Measure temperature, humidity, and air quality using sensors in the factory, and collect data on the availability and failure rates of production equipment. Store the collected data on a server and analyze it using a machine learning algorithm. Notify managers and employees of the analysis results and production plans, collect emotional feedback, and adjust the plans accordingly. Please explain the specific steps.

[0468] In this way, the present invention provides a more efficient and user-friendly system by creating and adjusting plans that reflect the feelings of users and employees.

[0469] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0470] Step 1:

[0471] Environmental and image data collection

[0472] Input: Environmental data such as temperature, humidity, and air quality from sensors installed in factories and farms, and image data of crops and production equipment collected periodically.

[0473] How it works: Sensors in factories and farms periodically measure data and transmit it to a central server via wireless or wired connections. Drones and fixed cameras also capture image data and transmit it to the server in real time.

[0474] Step 2:

[0475] Accumulation of collected data

[0476] Input: Environmental data, image data

[0477] Output: Data stored in the database

[0478] Specific operation: The server stores the received environmental data and image data in a database as a data storage means. This database uses a cloud server or an on-premise server.

[0479] Step 3:

[0480] Data analysis

[0481] Input: Environmental data and image data stored in a database

[0482] Output: Information such as weather forecasts, risk of pest outbreaks, and operational status of production facilities

[0483] How it works: The server uses machine learning algorithms to comprehensively analyze the collected data, for example, using TensorFlow or PyTorch to evaluate weather forecasts, risk of pest outbreaks, and the operating status of production facilities.

[0484] Step 4:

[0485] Creating a plan

[0486] Input: Analysis results (weather forecast, pest risk, production facility operation status, etc.)

[0487] Output: Optimal agricultural and production planning

[0488] Specific operations: Based on the analysis results, the server creates specific plans for sowing, irrigation, fertilization, harvesting, production schedules, etc.

[0489] Step 5:

[0490] Plan Notification

[0491] Input: Created plan

[0492] Output: Notification message to the user's terminal

[0493] Specific operation: The server notifies the user and factory employees of the created plan via their devices (smartphones or head-mounted displays) using Apple Push Notification Service (APNs) or Google Firebase Cloud Messaging (FCM).

[0494] Step 6:

[0495] User and employee sentiment analysis

[0496] Input: User / employee voice input, text input, and facial expression data

[0497] Output: Emotion analysis results

[0498] Specific operation: The server collects emotional data from users and employees using voice input (Apple Siri, Google Assistant), text input (chatbot), and facial expression analysis (Microsoft Azure Face API), and analyzes the data using an emotion analysis library (nltk's VADER or Microsoft Azure's Text Analytics for Sentiment Analysis).

[0499] Step 7:

[0500] Re-adjusting plans

[0501] Input: Sentiment analysis results

[0502] Output: Re-adjusted agricultural and production plans

[0503] Specific operation: Based on the results of the sentiment analysis, the server readjusts the agricultural and production plans as necessary and notifies the users and employees again.

[0504] Through the above steps, the present invention can realize efficient and user-friendly agricultural and factory management.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] [Second embodiment]

[0509] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0510] 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.

[0511] 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).

[0512] 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.

[0513] 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.

[0514] 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).

[0515] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0516] 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.

[0517] 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.

[0518] 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.

[0519] 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.

[0520] 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."

[0521] The present invention relates to a system for automatically processing weather information, environmental data, pest and disease information, and crop quality analysis to create an appropriate agricultural plan. The following aspects can be used to implement this system.

[0522] System configuration

[0523] 1. Sensor means

[0524] Terminal: Humidity, temperature, and soil sensors installed in the field periodically measure environmental data (humidity, temperature, and soil condition).

[0525] Terminal: Drones fly over the fields periodically, taking pictures of the crops' condition with high-resolution cameras.

[0526] 2. Data storage means

[0527] Server: A database that receives and stores collected environmental and image data.

[0528] 3. Data Analysis Methods

[0529] Server: Analyzes the received data and makes a comprehensive assessment of weather forecasts, risk of pest and disease outbreaks, and soil conditions.

[0530] Server: Connects to a weather database to retrieve current and forecast weather information.

[0531] 4. Means of calculation

[0532] Server: Based on the analysis results, it creates specific agricultural plans, including the timing of sowing, irrigation, fertilization, and harvesting.

[0533] 5. Means of notification

[0534] Server: A communication means for notifying the user's device of the created agricultural plan.

[0535] Device: Display a notification message on the user's smartphone or PC.

[0536] System operation example

[0537] Example 1: Creating an irrigation plan

[0538] 1. Terminal: An underground moisture sensor measures the current soil moisture.

[0539] 2. Server: Analyzes data from sensors and combines it with upcoming weather forecasts to calculate optimal irrigation timings.

[0540] 3. Server: Predicts that the field will likely be dehydrated at noon tomorrow and suggests a time to irrigate.

[0541] 4. Terminal: Notifies the user when to irrigate.

[0542] Example 2: Pest control measures

[0543] 1. Terminal: The drone flies over the field and takes images of the crops.

[0544] 2. Server: Uses image analysis technology to detect traces of specific pests and diseases.

[0545] 3. Server: Determines whether there is an increased risk of pest or disease outbreak and proposes specific control measures (e.g., the use of specific pesticides).

[0546] 4. Terminal: Notify the user of prevention measures and encourage them to take them.

[0547] Example 3: Creating a seeding plan

[0548] 1. User: You are considering sowing a new crop.

[0549] 2. Terminal: Collects current weather information, soil conditions, and historical crop growth data.

[0550] 3. Server: Based on all the data, calculates the optimal sowing time and method.

[0551] 4. Server: Notifies the user's terminal of the seeding plan.

[0552] 5. Terminal: The user checks and executes the seeding plan.

[0553] With this configuration and operation, the present invention can realize efficient and effective agricultural management. Users can receive optimal agricultural plans based on the latest data and implement them to improve productivity and reduce risks. Furthermore, analysis of environmental data and pest risks is performed using machine learning algorithms, resulting in high accuracy and speed. This significantly reduces manual labor and improves the efficiency of agricultural management.

[0554] The processing flow will be explained below.

[0555] Step 1:

[0556] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Drones also fly periodically, taking photos of the crop condition with high-resolution cameras.

[0557] Step 2:

[0558] Terminal: Sends collected sensor data and image data to the server.

[0559] Step 3:

[0560] Server: Stores the received sensor data and image data in a database. The stored data includes the measurement date and time, sensor readings, image files, etc.

[0561] Step 4:

[0562] Server: Connects to a weather database to retrieve the latest weather forecast data, including temperature, precipitation, wind speed, etc.

[0563] Step 5:

[0564] Server: Uses collected environmental and weather forecast data to assess weather forecasts and pest risk using machine learning algorithms and references to historical data and expert models.

[0565] Step 6:

[0566] Server: Based on the analysis results, it calculates the optimal timing for future agricultural actions (e.g., irrigation, fertilization, harvesting, etc.) using algorithms that determine the optimal actions under specific conditions.

[0567] Step 7:

[0568] Server: Based on the calculation results, a specific agricultural plan is created, detailing the timing and method of each action.

[0569] Step 8:

[0570] Server: Sends the created farming plan to the user's device. The notification also includes details of the plan and the reason for its implementation.

[0571] Step 9:

[0572] Device: A notification message will be displayed on the user's smartphone or PC, allowing the user to review the plan and request adjustments if necessary.

[0573] Step 10:

[0574] User: Implements the proposed agricultural plan and, if necessary, sends a request to adjust the plan to the server via the terminal.

[0575] Step 11:

[0576] Terminal: Sends user feedback to the server.

[0577] Step 12:

[0578] Server: Recalculates the farming plan based on user feedback, makes necessary adjustments, and notifies the user of the adjusted plan again.

[0579] Step 13:

[0580] Terminal: Sensors in the fields and drones continue to collect data and send it to the server.

[0581] Step 14:

[0582] Server: Continuously analyzes the received data, updates the agricultural plan as needed, and notifies the user immediately if an updated plan is available.

[0583] In this way, the system can support agricultural activities sustainably and efficiently.

[0584] Example 1

[0585] 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."

[0586] In modern agriculture, production risks due to weather fluctuations and pest outbreaks remain major challenges, and there is a need to quickly and accurately understand these and take appropriate measures. However, traditional methods involve a large amount of manual work involved in data collection, analysis, and planning, which is not efficient. Another problem is that it is difficult to comprehensively evaluate environmental data and crop conditions and quickly create accurate agricultural plans.

[0587] 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.

[0588] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for taking images of crops, a data storage means for receiving and storing the data collected by the sensor means and the image acquisition means, a data analysis means for analyzing the data stored in the data storage means using a machine learning algorithm to evaluate the weather forecast and the risk of pest and disease outbreaks, a calculation means for creating an agricultural plan based on the results of the data analysis means, and a notification means for notifying a terminal of the agricultural plan created by the calculation means. This makes it possible to quickly respond to weather fluctuations and pest and disease risks and to create an efficient and appropriate agricultural plan.

[0589] "Sensor means" is a device for collecting weather information and environmental data (humidity, temperature, soil conditions).

[0590] The "image acquisition means" is a device equipped with a high-resolution camera for photographing the condition of the crops.

[0591] "Data storage means" is a database system for receiving and storing data collected from the sensor means and image acquisition means.

[0592] The "data analysis means" is a device or program that analyzes the data stored in the data storage means using a machine learning algorithm and evaluates weather forecasts and the risk of pest and disease outbreaks.

[0593] "Calculation means" refers to a device or program for creating an agricultural plan based on the results of the data analysis means.

[0594] The "notification means" is a communication device or program for notifying the terminal of the agricultural plan created by the calculation means.

[0595] A "terminal" is a device that allows a user to receive information and perform operations, and includes smartphones, PCs, etc.

[0596] "Weather information" is information about current and forecast weather conditions.

[0597] "Environmental data" is data relating to weather and earth surface conditions such as humidity, temperature, soil conditions, etc.

[0598] A "machine learning algorithm" is a form of artificial intelligence used in data analysis, analyzing large amounts of data to generate patterns and predictive models.

[0599] An "agricultural plan" is a plan that includes the optimal timing and methods for carrying out agricultural operations such as sowing, irrigation, fertilization, and harvesting.

[0600] A "weather information database" is a system for collecting, storing, and providing weather forecasts and related meteorological data.

[0601] "Cloud messaging technology" is a technology for sending data and notifications to devices via the Internet.

[0602] The present invention relates to a system for automatically processing weather information, environmental data, pest and disease information, and crop quality analysis to create specific and appropriate agricultural plans. The system can be implemented in the following forms.

[0603] System configuration

[0604] The system includes a sensor means, an image acquisition means, a data storage means, a data analysis means, a calculation means, a notification means, and a terminal.

[0605] 1. Sensor means

[0606] The terminal uses humidity, temperature, and soil sensors installed in the field to periodically measure environmental data (humidity, temperature, and soil condition), which enables appropriate measures to be taken in response to crop growth conditions and environmental fluctuations.

[0607] The terminal uses a drone that periodically takes photos of the state of the crops using a high-resolution camera, and the drone's flight schedule is managed by a server.

[0608] 2. Data storage means

[0609] The server receives the environmental data sent from the sensors and stores it in a database system, using a commonly used relational database (e.g., MySQL or PostgreSQL).

[0610] The server also stores image data sent from the drone in a database.

[0611] 3. Data Analysis Methods

[0612] The server uses the Python programming language and machine learning libraries (such as TensorFlow and scikit-learn) to analyze stored environmental and image data, thereby assessing weather forecasts and the risk of pest and disease outbreaks.

[0613] The server interacts with a weather API (e.g., OpenWeatherMap API) to obtain weather forecast data.

[0614] 4. Means of calculation

[0615] Based on the results of the data analysis, the server creates specific agricultural plans for sowing, irrigation, fertilization, harvesting, etc. These calculations are performed using Python's NumPy and Pandas libraries.

[0616] 5. Means of notification

[0617] The server uses cloud messaging technologies such as Firebase Cloud Messaging (FCM) and Amazon SNS to notify the device of the created agricultural plan.

[0618] The device will display a notification message on the user's smartphone or PC, allowing the user to check the plan.

[0619] System operation example

[0620] The following scenario can be considered as a specific example of the system's operation.

[0621] Example 1: Creating an irrigation plan

[0622] 1. The device's humidity sensor measures the humidity in the field.

[0623] 2. The server receives the data and combines it with weather forecast data to calculate the optimal irrigation timing.

[0624] 3. Based on the calculation results, the server predicts that irrigation will be necessary at noon the next day and notifies the user.

[0625] 4. The terminal displays a notification message to the user.

[0626] Example prompt sentence:

[0627] A user wants to create an irrigation plan. The current soil moisture is 40%, and tomorrow's weather forecast predicts a temperature of 30 degrees and sunny skies. What kind of irrigation plan should they create?

[0628] Example 2: Pest control measures

[0629] 1. The device's drone flies over the field and takes images of the crops.

[0630] 2. The server uses image analysis technology to detect traces of pests and diseases.

[0631] 3. The server assesses the risk of pest outbreaks and calculates specific control measures.

[0632] 4. The device notifies the user of the prevention measures and encourages them to take them.

[0633] In this way, this system achieves efficient and appropriate agricultural management by consistently and automatically collecting data, analyzing it, creating plans, and sending notifications.

[0634] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0635] Step 1: Data collection

[0636] The terminal periodically measures environmental data using humidity, temperature, and soil sensors installed in the field.

[0637] Input: Measurements by environmental sensors (humidity, temperature, soil condition)

[0638] Operation: Data from the sensor is sent to the server via the wireless communication module.

[0639] Output: Measured environmental data

[0640] Step 2: Image acquisition

[0641] The device uses a drone to regularly photograph the condition of the crops using a high-resolution camera.

[0642] Input: Crop images taken by a high-resolution camera mounted on a drone

[0643] Operation: The drone automatically flies at the designated time according to the flight schedule, takes pictures, and then sends the image data to the server.

[0644] Output: Image data of the photographed crops

[0645] Step 3: Save data

[0646] The server receives the environmental data sent from the sensors and the image data sent from the drones and stores them in a database.

[0647] Input: Environmental data, image data

[0648] What it does: Stores the received data in a relational database (e.g., MySQL or PostgreSQL).

[0649] Output: Environmental and image data stored in a database

[0650] Step 4: Environmental data analysis

[0651] The server uses Python and a machine learning library (scikit-learn) to analyze the stored environmental data.

[0652] Input: Environmental data stored in a database

[0653] How it works: It uses machine learning algorithms to analyze environmental data and assess its impact on crop growth.

[0654] Output: Evaluation results (crop growth status, water needs, etc.)

[0655] Step 5: Image analysis

[0656] The server analyzes the stored image data using an image analysis library such as TensorFlow.

[0657] Input: Crop image data stored in a database

[0658] How it works: Uses image analysis technology to detect signs of pests and diseases and the health of crops.

[0659] Output: Image analysis results (traces of pests and diseases, health status of crops)

[0660] Step 6: Obtaining weather forecast data

[0661] The server retrieves weather forecast data from a weather API (such as the OpenWeatherMap API).

[0662] Input: Weather forecast data request from the weather API

[0663] What it does: Get current and forecast weather information via API.

[0664] Output: Current and forecast weather information

[0665] Step 7: Create a farm plan

[0666] The server creates a specific agricultural plan based on the results of environmental data analysis, image analysis, and weather forecast data.

[0667] Input: Environmental data analysis results, image analysis results, weather forecast data

[0668] How it works: This data is evaluated comprehensively and optimal timing for irrigation, fertilization, sowing, and harvesting is calculated using Python's NumPy and Pandas libraries.

[0669] Output: The generated farming plan

[0670] Step 8: User Notification

[0671] The server uses Firebase Cloud Messaging (FCM) and Amazon SNS to notify the user's device of the created farming plan.

[0672] Input: Created farming plan

[0673] Operation: The plan is sent to the user's smartphone or PC and a notification message is displayed.

[0674] Output: Notification of agricultural plan displayed on user terminal

[0675] In this way, environmental and image data are collected and stored at each step, analyzed using machine learning algorithms, an overall agricultural plan is formulated, and the plan is notified to the user.

[0676] (Application example 1)

[0677] 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."

[0678] In modern agriculture and manufacturing, it is extremely important to efficiently collect and analyze environmental and quality data to create optimal action plans and production schedules. However, with conventional systems, data collection and analysis are often performed separately, making it difficult to create effective plans in real time. In particular, in the agricultural sector, rapid and accurate decision-making is required in response to the effects of weather and pests and diseases, and in the manufacturing sector, in product quality control. To address these challenges, there is a strong demand for integrated data collection, analysis, and planning systems.

[0679] 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.

[0680] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for taking images of crops, a data storage means, a data analysis means, a calculation means, a notification means, a data analysis means for analyzing sensor data in the factory and evaluating the state of product quality, a calculation means for creating an optimal production schedule and improvement proposals, and a notification means for notifying a terminal of the production schedule and improvement proposals created by the calculation means. This enables the integrated collection and analysis of environmental data and quality data, and enables the creation of effective agricultural plans and production schedules in real time.

[0681] - "Weather information" refers to data on weather conditions such as temperature, humidity, precipitation, and wind speed.

[0682] "Environmental data" refers to data that indicates the ambient conditions related to the cultivation and manufacturing of crops and products, such as temperature, humidity, and soil condition.

[0683] The "sensor means" is a means for collecting environmental data using physical sensor devices such as a temperature sensor, a humidity sensor, and a soil sensor.

[0684] "Image acquisition means" refers to a means for taking images of crops or products using a camera or drone.

[0685] The "data storage means" refers to a database or server for receiving and storing data collected by the sensor means and image acquisition means.

[0686] The "data analysis means" is a means for analyzing accumulated data and evaluating weather forecasts, risk of pest outbreaks, and product quality.

[0687] "Calculation means" refers to the means for creating agricultural plans and production schedules based on the analysis results.

[0688] The "notification means" is a means for notifying the user's terminal of the agricultural plan or production schedule created by the calculation means.

[0689] A "generative AI model" is a model that uses machine learning algorithms to make predictions and evaluations from input data.

[0690] A "prompt" is a document that inputs specific instructions or questions to a generative AI model.

[0691] The present invention relates to a system that automatically processes weather information, environmental data, pest information, and quality analysis data in agriculture and manufacturing to create optimal plans. This system includes the following components and processing procedures.

[0692] System configuration

[0693] 1. Sensor means

[0694] Sensors are physical devices used to collect environmental data, such as temperature sensors, humidity sensors, and soil sensors, and in factories they also collect sensor data related to product quality.

[0695] 2. Image acquisition method

[0696] Image capture methods include high-resolution cameras and drones that periodically capture images of crop and product conditions.

[0697] 3. Data storage means

[0698] The data storage means is a database or server that receives and stores data collected from the sensor means and image acquisition means, and includes cloud storage and local servers.

[0699] 4. Data Analysis Methods

[0700] Data analytics tools analyze stored data to assess weather forecasts, risk of pest and disease outbreaks, and product quality using generative AI models and machine learning algorithms.

[0701] 5. Means of calculation

[0702] The computational means uses the results of the data analysis means to create optimal agricultural plans and production schedules, including sowing, irrigation, fertilization, harvesting, and maintenance plans.

[0703] 6. Means of notification

[0704] The notification means notifies the user of the plan created by the calculation means to the user's terminal, which may include a smartphone, tablet, PC, or the like.

[0705] Example of operation

[0706] Example 1: Creating an irrigation plan

[0707] The server analyzes data from underground moisture sensors, combines it with the latest weather forecast, calculates the optimal irrigation timing, and notifies the terminal.

[0708] Example 2: Pest control measures

[0709] Drones fly over fields and factories, taking pictures of crops and products. The server analyzes the images, assesses the risk of pests and diseases, and notifies the device with specific prevention measures and improvement suggestions.

[0710] Example 3: Creating a production schedule

[0711] The server analyzes sensor data within the factory, evaluates high-risk defect conditions, calculates the optimal production schedule, and notifies the terminal.

[0712] Program Implementation

[0713] The server analyzes the data using a generative AI model, and is built using a programming language such as Python, with corresponding libraries for collecting sensor data and APIs such as Firebase for notifications.

[0714] Natural language explanations

[0715] The server periodically collects data from the sensors and stores it in a database. It then uses a generative AI model to analyze the accumulated data, making predictions and evaluations. Based on the analysis results, it notifies the user device of the optimal action plan and production schedule.

[0716] Examples of concrete examples and prompts

[0717] Example: On a factory production line, the temperature sensor data was 24 degrees, the humidity sensor data was 42%, and the defect probability was 0.7 (70%). The user was notified that there was a high risk of defect, and the next inspection was scheduled for one hour, and the next maintenance was scheduled for 30 days later.

[0718] Example prompts to input to a generative AI model:

[0719] "Based on the factory sensor data (temperature: 24°C, humidity: 42%, defect probability: 0.7), calculate the next inspection and maintenance schedule and generate a defect risk notification message."

[0720] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0721] Step 1:

[0722] Sensor data collection

[0723] The server periodically collects environmental data from sensors such as temperature sensors, humidity sensors, and soil sensors. The inputs include the current temperature, humidity, and soil conditions obtained from each sensor, and this data is collected and stored in a database. Specifically, the server performs a process of obtaining data from the sensors via API and recording it in the database.

[0724] Step 2:

[0725] Acquisition of image data

[0726] Drones and high-resolution cameras are used to periodically capture images of crops and products. The captured image data is input, and this is uploaded and stored in cloud storage or a local server. Specifically, the drone flies a set route, captures images with its camera, and transmits the data to the server via a wireless network.

[0727] Step 3:

[0728] Data accumulation

[0729] The server stores the collected environmental data and image data in a database that centrally manages them. The inputs are sensor data and image data, which are stored in the database in an appropriate format. Specific operations include organizing, verifying, and storing the data.

[0730] Step 4:

[0731] Data analysis

[0732] The server performs analysis using the collected and stored data. Inputs include stored environmental data, image data, and weather forecast data, and uses generative AI models and machine learning algorithms to evaluate weather forecasts, risk of pest outbreaks, and product quality. Specific operations include preprocessing each data set, extracting features, applying the analysis model, and interpreting the results.

[0733] Step 5:

[0734] Creating a plan

[0735] The server calculates optimal agricultural plans and production schedules based on the results of data analysis. The inputs are analysis results and past historical data, and the specific output is the creation of schedules for sowing, irrigation, fertilization, and harvesting, as well as factory production and maintenance plans. Specific operations include calculations using a schedule optimization algorithm and generating plans.

[0736] Step 6:

[0737] notification

[0738] The server notifies the user of the created plan. The input includes an optimal agricultural plan and production schedule, and this is sent to the user's smartphone, tablet, PC, etc. Specific operations include generating notification messages and sending push notifications and emails.

[0739] Example prompt sentences

[0740] For example, "Based on the factory sensor data (temperature: 24°C, humidity: 42%, defect probability: 0.7), calculate the next inspection and maintenance schedule and generate a defect risk notification message."

[0741] 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.

[0742] The present invention relates to a system that automatically processes weather information, environmental data, pest and disease information, and crop quality analysis to create appropriate agricultural plans, and further combines it with an emotion engine that recognizes user emotions and reflects them in the agricultural plans. The following forms can be used to implement this system.

[0743] System configuration

[0744] 1. Sensor means

[0745] Terminal: Humidity, temperature, and soil sensors installed in the field periodically measure environmental data (humidity, temperature, and soil condition).

[0746] Terminal: Drones fly over the fields periodically, taking pictures of the crops' condition with high-resolution cameras.

[0747] 2. Data storage means

[0748] Server: A database that receives and stores collected environmental and image data.

[0749] 3. Data Analysis Methods

[0750] Server: Analyzes the received data and makes a comprehensive assessment of weather forecasts, risk of pest and disease outbreaks, and soil conditions.

[0751] Server: Connects to a weather database to retrieve current and forecast weather information.

[0752] 4. Means of calculation

[0753] Server: Based on the analysis results, it creates specific agricultural plans, including the timing of sowing, irrigation, fertilization, and harvesting.

[0754] 5. Means of notification

[0755] Server: A communication means for notifying the user's device of the created agricultural plan.

[0756] Device: Display a notification message on the user's smartphone or PC.

[0757] 6. Emotion Engine

[0758] Server: An emotion engine that recognizes the user's emotions regarding the notified agricultural plan. The emotion engine determines emotions based on the user's voice input, text input, facial expression analysis, etc.

[0759] Server: Readjusts farming plans based on perceived user sentiment.

[0760] System operation example

[0761] Example 1: Irrigation scheduling and emotional feedback

[0762] 1. Terminal: An underground moisture sensor measures the current soil moisture.

[0763] 2. Server: Analyzes data from sensors and combines it with upcoming weather forecasts to calculate optimal irrigation timings.

[0764] 3. Server: Predicts that the field will likely be dehydrated at noon tomorrow and suggests a time to irrigate.

[0765] 4. Server: Notifies the user's device of the proposed plan.

[0766] 5. Device: Display a notification message on the user's smartphone or PC.

[0767] 6. User: Provide emotional feedback (e.g., dissatisfaction or satisfaction) about the proposed plan via voice or text input.

[0768] 7. Server: The emotion engine analyzes the feedback and makes necessary adjustments.

[0769] Example 2: Pest control and emotional feedback

[0770] 1. Terminal: The drone flies over the field and takes images of the crops.

[0771] 2. Server: Uses image analysis technology to detect traces of specific pests and diseases.

[0772] 3. Server: Determines that the risk of pest outbreaks is increasing and proposes specific control measures.

[0773] 4. Server: Notifies the user of the proposed prevention measures.

[0774] 5. Terminal: Notifies the user of prevention measures and encourages them to take them. The user also receives emotional feedback.

[0775] 6. Server: The emotion engine analyzes the feedback and adjusts the defense measures.

[0776] Example 3: Seeding plan creation and emotional feedback

[0777] 1. User: You are considering sowing a new crop.

[0778] 2. Terminal: Collects current weather information, soil conditions, and historical crop growth data.

[0779] 3. Server: Based on all the data, calculates the optimal sowing time and method.

[0780] 4. Server: Notifies the user's terminal of the seeding plan.

[0781] 5. Terminal: The user checks the seeding plan and provides emotional feedback via voice and text.

[0782] 6. Server: The emotion engine analyzes the feedback and makes necessary adjustments.

[0783] With this configuration and operation, the present invention can realize more efficient and user-friendly agricultural operations. By recognizing the user's emotions and reflecting them in the agricultural planning, it is possible to increase user satisfaction and improve the effectiveness of agriculture.

[0784] The processing flow will be explained below.

[0785] Step 1:

[0786] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Drones also fly periodically, taking photos of the crop condition with high-resolution cameras.

[0787] Step 2:

[0788] Terminal: Sends collected sensor data and image data to the server.

[0789] Step 3:

[0790] Server: Stores the received sensor data and image data in a database. The stored data includes the measurement date and time, sensor readings, image files, etc.

[0791] Step 4:

[0792] Server: Connects to a weather database to retrieve the latest weather forecast data, including temperature, precipitation, wind speed, etc.

[0793] Step 5:

[0794] Server: Uses collected environmental and weather forecast data to assess weather forecasts and pest risk using machine learning algorithms and references to historical data and expert models.

[0795] Step 6:

[0796] Server: Based on the analysis results, it calculates the optimal timing for future agricultural actions (e.g., irrigation, fertilization, harvesting, etc.) using algorithms that determine the optimal actions under specific conditions.

[0797] Step 7:

[0798] Server: Based on the calculation results, a specific agricultural plan is created, detailing the timing and method of each action.

[0799] Step 8:

[0800] Server: Sends the created farming plan to the user's device. The notification also includes details of the plan and the reason for its implementation.

[0801] Step 9:

[0802] Device: A notification message will be displayed on the user's smartphone or PC, allowing the user to review the plan and request adjustments if necessary.

[0803] Step 10:

[0804] User: Enter their feelings about the proposed farming plan via voice or text. Examples include, "I think this plan is good" or "This schedule is tough."

[0805] Step 11:

[0806] Terminal: Sends the user's emotional feedback to the server.

[0807] Step 12:

[0808] Server: The emotion engine analyzes user feedback and determines emotions using voice analysis and natural language processing techniques.

[0809] Step 13:

[0810] Server: Based on the results of sentiment analysis, the farming plan is readjusted as needed. For example, if a user expresses dissatisfaction with irrigation timing, that part is revised.

[0811] Step 14:

[0812] Server: The adjusted agricultural plan is sent back to the user's device.

[0813] Step 15:

[0814] Terminal: Sensors in the fields and drones continue to collect data and send it to the server.

[0815] Step 16:

[0816] Server: Continuously analyzes the received data and updates the farming plan as needed. The emotion engine also continuously receives user feedback and optimizes the plan.

[0817] In this way, the system supports agricultural activities sustainably and efficiently, and by reflecting the user's feelings, it is possible to realize more user-friendly agricultural operations.

[0818] Example 2

[0819] 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."

[0820] Conventional agricultural systems can create agricultural plans based on weather information and environmental data, but they only provide one-way notifications without considering the user's feelings, which has the problem of lacking user satisfaction and flexibility in the plans. This leads to plans not being carried out and users often becoming dissatisfied with the plans, and we aim to solve this problem.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes sensor means for collecting weather information and environmental data, image acquisition means for capturing images of crops, data storage means for receiving and storing data collected by the sensor means and the image acquisition means, data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and the risk of pest and disease outbreaks, calculation means for creating an agricultural plan based on the results of the data analysis means, notification means for notifying a terminal of the agricultural plan created by the calculation means, and emotion recognition means for recognizing the user's emotions regarding the notified agricultural plan and readjusting the agricultural plan based on the emotions. This enables the creation of a flexible, satisfying agricultural plan that reflects the user's emotions.

[0822] "Sensor means" refers to a device for collecting weather information and environmental data, and includes humidity sensors, temperature sensors, soil sensors, and the like.

[0823] "Image acquisition means" refers to a device that captures images of crops, and includes drones equipped with high-resolution cameras and fixed cameras.

[0824] "Data storage means" refers to a storage device for receiving and storing data collected from the sensor means and image acquisition means, and includes a database.

[0825] The "data analysis means" is a device or program for analyzing the data stored in the data storage means and evaluating the weather forecast and the risk of pest outbreaks.

[0826] "Calculation means" refers to a device or program for creating an agricultural plan based on the results of the data analysis means, and includes machine learning algorithms.

[0827] The "notification means" is a device or program for notifying the user terminal of the agricultural plan created by the calculation means, and includes a communication means.

[0828] The "emotion recognition means" is a device or program that recognizes the user's emotions regarding the notified farming plan and readjusts the farming plan based on the emotions.

[0829] This invention relates to a system that automatically processes weather information, environmental data, pest and disease information, and crop quality analysis to create appropriate agricultural plans. It also combines an emotion engine that recognizes the user's emotions and reflects them in the agricultural plans.

[0830] System configuration

[0831] 1. Sensor means

[0832] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Specifically, the humidity sensor measures data every 15 minutes and sends it to the gateway using Bluetooth or LoRa communication.

[0833] Device: The drone flies over the fields at a set time every day and takes pictures of the crops using a high-resolution camera. The captured image data is then uploaded to a server via Wi-Fi.

[0834] 2. Data storage means

[0835] Server: Receives and stores collected environmental and image data using a database such as MySQL or PostgreSQL. For example, temperature data is recorded in a temperature table, and humidity data is recorded in a humidity table.

[0836] 3. Data Analysis Methods

[0837] Server: Uses Python scripts to analyze the collected data, calling weather forecast APIs (e.g. OpenWeatherMap API) to retrieve current and forecast weather information, and predict trends in soil moisture and temperature.

[0838] Server: Image data is analyzed using image analysis libraries such as OpenCV. The analysis mainly detects pests and diseases and abnormalities in crops, and records the results in a database.

[0839] 4. Means of calculation

[0840] Server: Uses machine learning algorithms (e.g., TensorFlow, Scikit-learn) to create specific agricultural plans based on the latest analysis results. For example, if soil moisture is low, it will plan irrigation at noon the following day.

[0841] 5. Means of notification

[0842] Server: Uses Firebase Cloud Messaging or REST API to notify users of the created farming plan. Generates notification content (e.g., "Irrigation is required tomorrow at noon") and sends it to the user's smartphone.

[0843] Device: The user's smartphone or PC receives the notification and displays its contents, allowing the user to check the notification.

[0844] 6. Emotion recognition means

[0845] User: The user provides emotional feedback on the proposed farming plan (e.g., "This plan is good" or "Needs to be reconsidered") via voice or text. The voice input is converted to text using the Google Speech-to-Text API.

[0846] Server: An emotion engine (e.g., OpenAI GPT) analyzes the text feedback and readjusts the farming plan if the user expresses dissatisfaction or if the plan needs to be revised, again using machine learning algorithms.

[0847] Example: Irrigation scheduling and emotional feedback

[0848] 1. Terminal: The underground humidity sensor measures the soil humidity to be 40% at 2:00 pm on July 15th and sends the data to the server via the gateway via LoRa communication.

[0849] 2. Server: A Python script receives the humidity data and uses the OpenWeatherMap API to get the weather forecast for the next day, which predicts sunny skies with temperatures above 30 degrees.

[0850] 3. Server: The script detects the humidity deficit and determines that the best time to irrigate is at noon the next day. It creates a planning file and saves it in the database.

[0851] 4. Server: Use Firebase Cloud Messaging to send a notification to the user's smartphone saying, "Irrigation is required at noon on July 16th."

[0852] 5. Device: The smartphone receives the notification and displays the message. The user gives feedback by saying, "I'm happy with the plan."

[0853] 6. Server: The voice data is converted to text using the Google Speech-to-Text API, and the emotion engine analyzes the feedback such as "satisfied."

[0854] Prompt Sentence Examples

[0855] "Based on the humidity sensor data and weather forecast, please evaluate whether this irrigation plan is appropriate, reflecting the user's emotional feedback."

[0856] By implementing this aspect of the present invention, it becomes possible to create a flexible and satisfying agricultural plan that reflects the user's feelings, which results in improved efficiency and feasibility of farming, and increased user satisfaction.

[0857] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0858] Step 1:

[0859] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). For example, the humidity sensor measures soil humidity every 15 minutes and transmits the data to the gateway using Bluetooth or LoRa communication.

[0860] Input: Current humidity, temperature, soil data

[0861] Output: Transmits humidity, temperature, and soil data

[0862] Step 2:

[0863] Device: The drone flies over the field at a set time every day, taking pictures of the crops with a high-resolution camera. The captured image data is then uploaded to a server via Wi-Fi.

[0864] Input: Crop image

[0865] Output: Upload image data to the server

[0866] Step 3:

[0867] Server: Uses MySQL or PostgreSQL to receive data sent from sensors and drones and store it in a database.

[0868] Input: Sensor data, image data

[0869] Output: Environmental and image data stored in a database

[0870] Step 4:

[0871] Server: Uses Python scripts to analyze the environmental data stored in the database. Uses weather forecast APIs (e.g. OpenWeatherMap API) to retrieve current and forecast weather information and combine it with the environmental data for analysis.

[0872] Input: Environmental data, weather forecast data

[0873] Output: Analysis results based on weather forecasts and environmental data

[0874] Step 5:

[0875] Server: Image data is analyzed using image analysis libraries such as OpenCV to detect pests, diseases, and abnormalities in crops.

[0876] Input: Image data

[0877] Output: Analysis results on risk of pest and disease outbreaks and abnormalities in crops

[0878] Step 6:

[0879] Server: Based on the analysis results, machine learning algorithms (e.g., TensorFlow, Scikit-learn) are used to create agricultural plans. For example, if a humidity deficiency is detected, a plan to irrigate the area at noon the following day is created.

[0880] Input: Environmental data analysis results, image data analysis results

[0881] Output: Agricultural plan

[0882] Step 7:

[0883] Server: Uses Firebase Cloud Messaging or REST API to notify users of the created farming plan, such as a message like "Irrigation is required tomorrow at noon."

[0884] Input: Agricultural Plan

[0885] Output: Notification message to the user's terminal

[0886] Step 8:

[0887] Device: The user's smartphone or PC receives the notification and displays the notification content. The user checks the notification.

[0888] Input: Notification message

[0889] Output: Display notification information to the user

[0890] Step 9:

[0891] User: The user provides emotional feedback on the proposed farming plan via voice or text, for example, by saying "I'm happy with the plan."

[0892] Input: User's emotional feedback

[0893] Output: Send feedback data to the server

[0894] Step 10:

[0895] Server: The voice feedback is converted to text using the Google Speech-to-Text API. The text feedback is analyzed by an emotion engine (e.g., OpenAI GPT) and the farming plan is readjusted as needed based on the user's emotions.

[0896] Input: User sentiment feedback text

[0897] Output: Re-adjusted farming plan or unchanged feedback results

[0898] In this way, the present invention realizes a system that comprehensively utilizes weather information, environmental data, pest and disease information, and user emotions to provide accurate and satisfying agricultural planning.

[0899] (Application example 2)

[0900] 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."

[0901] Conventional agricultural management systems and factory management systems easily created plans based on the collection and analysis of environmental and production data, but lacked the ability to adjust plans while taking into account the emotions of users and employees. As a result, user satisfaction and working conditions were not sufficiently improved, making efficient operations difficult. Furthermore, in factories, one-sided production plans that ignored the emotions of employees often led to dissatisfaction and stress. The objective of this invention is to solve these problems and provide a system that enables the creation and adjustment of plans while taking into account the emotions of users and employees.

[0902] 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.

[0903] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for acquiring images of crops, a data storage means for receiving and storing the data collected by the sensor means and the image acquisition means, a data analysis means for analyzing the data stored in the data storage means and evaluating the weather forecast and the risk of pest infestation, a calculation means for creating an agricultural plan based on the results of the data analysis means, a notification means for notifying a terminal of the agricultural plan created by the calculation means, a means for monitoring and analyzing factory environment data and production equipment data in real time, and an emotion analysis means for analyzing employee emotions and reflecting the results in the production plan. This makes it possible to design and adjust plans that reflect the emotions of users and employees.

[0904] "Weather information" is information relating to weather conditions, including data such as temperature, humidity, precipitation, wind speed, and wind direction.

[0905] "Environmental data" refers to data obtained by measuring environmental factors at a specific location, including temperature, humidity, soil condition, and air quality.

[0906] "Sensor means" refers to a device that detects a specific physical or chemical phenomenon and outputs it as data.

[0907] "Image capture means" refers to a device or method for photographing or capturing an image of an object.

[0908] "Data storage" refers to a system or device that stores collected data and makes it available for retrieval as needed.

[0909] "Data analysis means" refers to a system or method for analyzing collected data to extract meaningful information.

[0910] "Calculation means" refers to a system or device that performs calculations based on the obtained data and generates optimal plans and instructions.

[0911] "Notification means" refers to a system or method for transmitting the created plans and instructions to the user's terminal.

[0912] "Factory environmental data" refers to data related to various environmental factors within a factory (temperature, humidity, air quality, etc.).

[0913] "Production equipment data" refers to data relating to the operating status, operating rate, failure history, etc. of production equipment.

[0914] "Emotion analysis means" refers to a system or method for analyzing the emotions of users or employees and reflecting the analysis results in the system.

[0915] The present invention relates to a management system in a factory or agricultural environment, which analyzes collected data and creates an optimal plan that reflects the feelings of users and employees.

[0916] System configuration

[0917] 1. Environmental data collection methods

[0918] Sensor means:

[0919] Temperature, humidity and air quality sensors installed in factories and farms regularly measure environmental data.

[0920] Sensors are installed in factory production equipment to record operating conditions and failure history.

[0921] 2. Image acquisition method

[0922] Device:

[0923] Drones and fixed cameras are used to regularly capture images of crops in the fields and production equipment in factories.

[0924] 3. Data storage means

[0925] server:

[0926] A database for receiving and storing environmental data from factories and farms, and image data from crops and production equipment.

[0927] For the database, cloud servers (Google Cloud Platform, Amazon Web Services) or on-premise servers are used.

[0928] 4. Data Analysis Methods

[0929] server:

[0930] The collected environmental and image data is analyzed to comprehensively assess weather forecasts and the risk of pest and disease outbreaks.

[0931] At the factory, environmental data and production equipment data are analyzed to develop production plans.

[0932] By using machine learning algorithms (TensorFlow and PyTorch), the accuracy of the analysis is improved, and a plan is created based on the results.

[0933] 5. Means of calculation

[0934] server:

[0935] Based on the analysis results, agricultural and production plans are formulated, and the timing of sowing, irrigation, fertilization, harvesting, production schedules, etc. are calculated.

[0936] 6. Means of notification

[0937] server:

[0938] The created plan is notified to the user's device using Apple Push Notification Service (APNs) or Google Firebase Cloud Messaging (FCM).

[0939] Device:

[0940] Notification messages are displayed on the smartphones and head-mounted displays (HMDs) of users and factory employees.

[0941] 7. Emotion analysis method

[0942] server:

[0943] It has an emotion engine for analyzing user and employee emotions, and recognizes emotions using voice input (Apple Siri, Google Assistant), text input (chatbots), and facial expression analysis (Microsoft Azure Face API).

[0944] The emotion engine performs analysis using emotion analysis libraries (NLTK's VADER, Microsoft Azure's Text Analytics for Sentiment Analysis) and reflects the results in the plan.

[0945] Specific examples

[0946] For example, if a user is considering sowing a new crop, the optimal sowing time is calculated based on current weather information, soil conditions, and past growth data collected by the sensor means. The results are then sent to the user's smartphone. When the user provides feedback on the plan, the emotion engine analyzes their emotions and adjusts the sowing time as necessary.

[0947] Prompt Sentence Examples

[0948] You are designing a system that analyzes factory environmental data and production equipment data in real time to create optimal production plans. This system also includes an emotion engine that analyzes employee emotions and reflects them in production plans. Measure temperature, humidity, and air quality using sensors in the factory, and collect data on the availability and failure rates of production equipment. Store the collected data on a server and analyze it using a machine learning algorithm. Notify managers and employees of the analysis results and production plans, collect emotional feedback, and adjust the plans accordingly. Please explain the specific steps.

[0949] In this way, the present invention provides a more efficient and user-friendly system by creating and adjusting plans that reflect the feelings of users and employees.

[0950] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0951] Step 1:

[0952] Environmental and image data collection

[0953] Input: Environmental data such as temperature, humidity, and air quality from sensors installed in factories and farms, and image data of crops and production equipment collected periodically.

[0954] How it works: Sensors in factories and farms periodically measure data and transmit it to a central server via wireless or wired connections. Drones and fixed cameras also capture image data and transmit it to the server in real time.

[0955] Step 2:

[0956] Accumulation of collected data

[0957] Input: Environmental data, image data

[0958] Output: Data stored in the database

[0959] Specific operation: The server stores the received environmental data and image data in a database as a data storage means. This database uses a cloud server or an on-premise server.

[0960] Step 3:

[0961] Data analysis

[0962] Input: Environmental data and image data stored in a database

[0963] Output: Information such as weather forecasts, risk of pest outbreaks, and operational status of production facilities

[0964] How it works: The server uses machine learning algorithms to comprehensively analyze the collected data, for example, using TensorFlow or PyTorch to evaluate weather forecasts, risk of pest outbreaks, and the operating status of production facilities.

[0965] Step 4:

[0966] Creating a plan

[0967] Input: Analysis results (weather forecast, pest risk, production facility operation status, etc.)

[0968] Output: Optimal agricultural and production planning

[0969] Specific operations: Based on the analysis results, the server creates specific plans for sowing, irrigation, fertilization, harvesting, production schedules, etc.

[0970] Step 5:

[0971] Plan Notification

[0972] Input: Created plan

[0973] Output: Notification message to the user's terminal

[0974] Specific operation: The server notifies the user and factory employees of the created plan via their devices (smartphones or head-mounted displays) using Apple Push Notification Service (APNs) or Google Firebase Cloud Messaging (FCM).

[0975] Step 6:

[0976] User and employee sentiment analysis

[0977] Input: User / employee voice input, text input, and facial expression data

[0978] Output: Emotion analysis results

[0979] Specific operation: The server collects emotional data from users and employees using voice input (Apple Siri, Google Assistant), text input (chatbot), and facial expression analysis (Microsoft Azure Face API), and analyzes the data using an emotion analysis library (nltk's VADER or Microsoft Azure's Text Analytics for Sentiment Analysis).

[0980] Step 7:

[0981] Re-adjusting plans

[0982] Input: Sentiment analysis results

[0983] Output: Re-adjusted agricultural and production plans

[0984] Specific operation: Based on the results of the sentiment analysis, the server readjusts the agricultural and production plans as necessary and notifies the users and employees again.

[0985] Through the above steps, the present invention can realize efficient and user-friendly agricultural and factory management.

[0986] 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.

[0987] 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.

[0988] 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.

[0989] [Third embodiment]

[0990] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0991] 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.

[0992] 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).

[0993] 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.

[0994] 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.

[0995] 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).

[0996] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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."

[1002] The present invention relates to a system for automatically processing weather information, environmental data, pest and disease information, and crop quality analysis to create an appropriate agricultural plan. The following aspects can be used to implement this system.

[1003] System configuration

[1004] 1. Sensor means

[1005] Terminal: Humidity, temperature, and soil sensors installed in the field periodically measure environmental data (humidity, temperature, and soil condition).

[1006] Terminal: Drones fly over the fields periodically, taking pictures of the crops' condition with high-resolution cameras.

[1007] 2. Data storage means

[1008] Server: A database that receives and stores collected environmental and image data.

[1009] 3. Data Analysis Methods

[1010] Server: Analyzes the received data and makes a comprehensive assessment of weather forecasts, risk of pest and disease outbreaks, and soil conditions.

[1011] Server: Connects to a weather database to retrieve current and forecast weather information.

[1012] 4. Means of calculation

[1013] Server: Based on the analysis results, it creates specific agricultural plans, including the timing of sowing, irrigation, fertilization, and harvesting.

[1014] 5. Means of notification

[1015] Server: A communication means for notifying the user's device of the created agricultural plan.

[1016] Device: Display a notification message on the user's smartphone or PC.

[1017] System operation example

[1018] Example 1: Creating an irrigation plan

[1019] 1. Terminal: An underground moisture sensor measures the current soil moisture.

[1020] 2. Server: Analyzes data from sensors and combines it with upcoming weather forecasts to calculate optimal irrigation timings.

[1021] 3. Server: Predicts that the field will likely be dehydrated at noon tomorrow and suggests a time to irrigate.

[1022] 4. Terminal: Notifies the user when to irrigate.

[1023] Example 2: Pest control measures

[1024] 1. Terminal: The drone flies over the field and takes images of the crops.

[1025] 2. Server: Uses image analysis technology to detect traces of specific pests and diseases.

[1026] 3. Server: Determines whether there is an increased risk of pest or disease outbreak and proposes specific control measures (e.g., the use of specific pesticides).

[1027] 4. Terminal: Notify the user of prevention measures and encourage them to take them.

[1028] Example 3: Creating a seeding plan

[1029] 1. User: You are considering sowing a new crop.

[1030] 2. Terminal: Collects current weather information, soil conditions, and historical crop growth data.

[1031] 3. Server: Based on all the data, calculates the optimal sowing time and method.

[1032] 4. Server: Notifies the user's terminal of the seeding plan.

[1033] 5. Terminal: The user checks and executes the seeding plan.

[1034] With this configuration and operation, the present invention can realize efficient and effective agricultural management. Users can receive optimal agricultural plans based on the latest data and implement them to improve productivity and reduce risks. Furthermore, analysis of environmental data and pest risks is performed using machine learning algorithms, resulting in high accuracy and speed. This significantly reduces manual labor and improves the efficiency of agricultural management.

[1035] The processing flow will be explained below.

[1036] Step 1:

[1037] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Drones also fly periodically, taking photos of the crop condition with high-resolution cameras.

[1038] Step 2:

[1039] Terminal: Sends collected sensor data and image data to the server.

[1040] Step 3:

[1041] Server: Stores the received sensor data and image data in a database. The stored data includes the measurement date and time, sensor readings, image files, etc.

[1042] Step 4:

[1043] Server: Connects to a weather database to retrieve the latest weather forecast data, including temperature, precipitation, wind speed, etc.

[1044] Step 5:

[1045] Server: Uses collected environmental and weather forecast data to assess weather forecasts and pest risk using machine learning algorithms and references to historical data and expert models.

[1046] Step 6:

[1047] Server: Based on the analysis results, it calculates the optimal timing for future agricultural actions (e.g., irrigation, fertilization, harvesting, etc.) using algorithms that determine the optimal actions under specific conditions.

[1048] Step 7:

[1049] Server: Based on the calculation results, a specific agricultural plan is created, detailing the timing and method of each action.

[1050] Step 8:

[1051] Server: Sends the created farming plan to the user's device. The notification also includes details of the plan and the reason for its implementation.

[1052] Step 9:

[1053] Device: A notification message will be displayed on the user's smartphone or PC, allowing the user to review the plan and request adjustments if necessary.

[1054] Step 10:

[1055] User: Implements the proposed agricultural plan and, if necessary, sends a request to adjust the plan to the server via the terminal.

[1056] Step 11:

[1057] Terminal: Sends user feedback to the server.

[1058] Step 12:

[1059] Server: Recalculates the farming plan based on user feedback, makes necessary adjustments, and notifies the user of the adjusted plan again.

[1060] Step 13:

[1061] Terminal: Sensors in the fields and drones continue to collect data and send it to the server.

[1062] Step 14:

[1063] Server: Continuously analyzes the received data, updates the agricultural plan as needed, and notifies the user immediately if an updated plan is available.

[1064] In this way, the system can support agricultural activities sustainably and efficiently.

[1065] Example 1

[1066] 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."

[1067] In modern agriculture, production risks due to weather fluctuations and pest outbreaks remain major challenges, and there is a need to quickly and accurately understand these and take appropriate measures. However, traditional methods involve a large amount of manual work involved in data collection, analysis, and planning, which is not efficient. Another problem is that it is difficult to comprehensively evaluate environmental data and crop conditions and quickly create accurate agricultural plans.

[1068] 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.

[1069] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for taking images of crops, a data storage means for receiving and storing the data collected by the sensor means and the image acquisition means, a data analysis means for analyzing the data stored in the data storage means using a machine learning algorithm to evaluate the weather forecast and the risk of pest and disease outbreaks, a calculation means for creating an agricultural plan based on the results of the data analysis means, and a notification means for notifying a terminal of the agricultural plan created by the calculation means. This makes it possible to quickly respond to weather fluctuations and pest and disease risks and to create an efficient and appropriate agricultural plan.

[1070] "Sensor means" is a device for collecting weather information and environmental data (humidity, temperature, soil conditions).

[1071] The "image acquisition means" is a device equipped with a high-resolution camera for photographing the condition of the crops.

[1072] "Data storage means" is a database system for receiving and storing data collected from the sensor means and image acquisition means.

[1073] The "data analysis means" is a device or program that analyzes the data stored in the data storage means using a machine learning algorithm and evaluates weather forecasts and the risk of pest and disease outbreaks.

[1074] "Calculation means" refers to a device or program for creating an agricultural plan based on the results of the data analysis means.

[1075] The "notification means" is a communication device or program for notifying the terminal of the agricultural plan created by the calculation means.

[1076] A "terminal" is a device that allows a user to receive information and perform operations, and includes smartphones, PCs, etc.

[1077] "Weather information" is information about current and forecast weather conditions.

[1078] "Environmental data" is data relating to weather and earth surface conditions such as humidity, temperature, soil conditions, etc.

[1079] A "machine learning algorithm" is a form of artificial intelligence used in data analysis, analyzing large amounts of data to generate patterns and predictive models.

[1080] An "agricultural plan" is a plan that includes the optimal timing and methods for carrying out agricultural operations such as sowing, irrigation, fertilization, and harvesting.

[1081] A "weather information database" is a system for collecting, storing, and providing weather forecasts and related meteorological data.

[1082] "Cloud messaging technology" is a technology for sending data and notifications to devices via the Internet.

[1083] The present invention relates to a system for automatically processing weather information, environmental data, pest and disease information, and crop quality analysis to create specific and appropriate agricultural plans. The system can be implemented in the following forms.

[1084] System configuration

[1085] The system includes a sensor means, an image acquisition means, a data storage means, a data analysis means, a calculation means, a notification means, and a terminal.

[1086] 1. Sensor means

[1087] The terminal uses humidity, temperature, and soil sensors installed in the field to periodically measure environmental data (humidity, temperature, and soil condition), which enables appropriate measures to be taken in response to crop growth conditions and environmental fluctuations.

[1088] The terminal uses a drone that periodically takes photos of the state of the crops using a high-resolution camera, and the drone's flight schedule is managed by a server.

[1089] 2. Data storage means

[1090] The server receives the environmental data sent from the sensors and stores it in a database system, using a commonly used relational database (e.g., MySQL or PostgreSQL).

[1091] The server also stores image data sent from the drone in a database.

[1092] 3. Data Analysis Methods

[1093] The server uses the Python programming language and machine learning libraries (such as TensorFlow and scikit-learn) to analyze stored environmental and image data, thereby assessing weather forecasts and the risk of pest and disease outbreaks.

[1094] The server interacts with a weather API (e.g., OpenWeatherMap API) to obtain weather forecast data.

[1095] 4. Means of calculation

[1096] Based on the results of the data analysis, the server creates specific agricultural plans for sowing, irrigation, fertilization, harvesting, etc. These calculations are performed using Python's NumPy and Pandas libraries.

[1097] 5. Means of notification

[1098] The server uses cloud messaging technologies such as Firebase Cloud Messaging (FCM) and Amazon SNS to notify the device of the created agricultural plan.

[1099] The device will display a notification message on the user's smartphone or PC, allowing the user to check the plan.

[1100] System operation example

[1101] The following scenario can be considered as a specific example of the system's operation.

[1102] Example 1: Creating an irrigation plan

[1103] 1. The device's humidity sensor measures the humidity in the field.

[1104] 2. The server receives the data and combines it with weather forecast data to calculate the optimal irrigation timing.

[1105] 3. Based on the calculation results, the server predicts that irrigation will be necessary at noon the next day and notifies the user.

[1106] 4. The terminal displays a notification message to the user.

[1107] Example prompt sentence:

[1108] A user wants to create an irrigation plan. The current soil moisture is 40%, and tomorrow's weather forecast predicts a temperature of 30 degrees and sunny skies. What kind of irrigation plan should they create?

[1109] Example 2: Pest control measures

[1110] 1. The device's drone flies over the field and takes images of the crops.

[1111] 2. The server uses image analysis technology to detect traces of pests and diseases.

[1112] 3. The server assesses the risk of pest outbreaks and calculates specific control measures.

[1113] 4. The device notifies the user of the prevention measures and encourages them to take them.

[1114] In this way, this system achieves efficient and appropriate agricultural management by consistently and automatically collecting data, analyzing it, creating plans, and sending notifications.

[1115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1116] Step 1: Data collection

[1117] The terminal periodically measures environmental data using humidity, temperature, and soil sensors installed in the field.

[1118] Input: Measurements by environmental sensors (humidity, temperature, soil condition)

[1119] Operation: Data from the sensor is sent to the server via the wireless communication module.

[1120] Output: Measured environmental data

[1121] Step 2: Image acquisition

[1122] The device uses a drone to regularly photograph the condition of the crops using a high-resolution camera.

[1123] Input: Crop images taken by a high-resolution camera mounted on a drone

[1124] Operation: The drone automatically flies at the designated time according to the flight schedule, takes pictures, and then sends the image data to the server.

[1125] Output: Image data of the photographed crops

[1126] Step 3: Save data

[1127] The server receives the environmental data sent from the sensors and the image data sent from the drones and stores them in a database.

[1128] Input: Environmental data, image data

[1129] What it does: Stores the received data in a relational database (e.g., MySQL or PostgreSQL).

[1130] Output: Environmental and image data stored in a database

[1131] Step 4: Environmental data analysis

[1132] The server uses Python and a machine learning library (scikit-learn) to analyze the stored environmental data.

[1133] Input: Environmental data stored in a database

[1134] How it works: It uses machine learning algorithms to analyze environmental data and assess its impact on crop growth.

[1135] Output: Evaluation results (crop growth status, water needs, etc.)

[1136] Step 5: Image analysis

[1137] The server analyzes the stored image data using an image analysis library such as TensorFlow.

[1138] Input: Crop image data stored in a database

[1139] How it works: Uses image analysis technology to detect signs of pests and diseases and the health of crops.

[1140] Output: Image analysis results (traces of pests and diseases, health status of crops)

[1141] Step 6: Obtaining weather forecast data

[1142] The server retrieves weather forecast data from a weather API (such as the OpenWeatherMap API).

[1143] Input: Weather forecast data request from the weather API

[1144] What it does: Get current and forecast weather information via API.

[1145] Output: Current and forecast weather information

[1146] Step 7: Create a farm plan

[1147] The server creates a specific agricultural plan based on the results of environmental data analysis, image analysis, and weather forecast data.

[1148] Input: Environmental data analysis results, image analysis results, weather forecast data

[1149] How it works: This data is evaluated comprehensively and optimal timing for irrigation, fertilization, sowing, and harvesting is calculated using Python's NumPy and Pandas libraries.

[1150] Output: The generated farming plan

[1151] Step 8: User Notification

[1152] The server uses Firebase Cloud Messaging (FCM) and Amazon SNS to notify the user's device of the created farming plan.

[1153] Input: Created farming plan

[1154] Operation: The plan is sent to the user's smartphone or PC and a notification message is displayed.

[1155] Output: Notification of agricultural plan displayed on user terminal

[1156] In this way, environmental and image data are collected and stored at each step, analyzed using machine learning algorithms, an overall agricultural plan is formulated, and the plan is notified to the user.

[1157] (Application example 1)

[1158] 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."

[1159] In modern agriculture and manufacturing, it is extremely important to efficiently collect and analyze environmental and quality data to create optimal action plans and production schedules. However, with conventional systems, data collection and analysis are often performed separately, making it difficult to create effective plans in real time. In particular, in the agricultural sector, rapid and accurate decision-making is required in response to the effects of weather and pests and diseases, and in the manufacturing sector, in product quality control. To address these challenges, there is a strong demand for integrated data collection, analysis, and planning systems.

[1160] 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.

[1161] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for taking images of crops, a data storage means, a data analysis means, a calculation means, a notification means, a data analysis means for analyzing sensor data in the factory and evaluating the state of product quality, a calculation means for creating an optimal production schedule and improvement proposals, and a notification means for notifying a terminal of the production schedule and improvement proposals created by the calculation means. This enables the integrated collection and analysis of environmental data and quality data, and enables the creation of effective agricultural plans and production schedules in real time.

[1162] - "Weather information" refers to data on weather conditions such as temperature, humidity, precipitation, and wind speed.

[1163] "Environmental data" refers to data that indicates the ambient conditions related to the cultivation and manufacturing of crops and products, such as temperature, humidity, and soil condition.

[1164] The "sensor means" is a means for collecting environmental data using physical sensor devices such as a temperature sensor, a humidity sensor, and a soil sensor.

[1165] "Image acquisition means" refers to a means for taking images of crops or products using a camera or drone.

[1166] The "data storage means" refers to a database or server for receiving and storing data collected by the sensor means and image acquisition means.

[1167] The "data analysis means" is a means for analyzing accumulated data and evaluating weather forecasts, risk of pest outbreaks, and product quality.

[1168] "Calculation means" refers to the means for creating agricultural plans and production schedules based on the analysis results.

[1169] The "notification means" is a means for notifying the user's terminal of the agricultural plan or production schedule created by the calculation means.

[1170] A "generative AI model" is a model that uses machine learning algorithms to make predictions and evaluations from input data.

[1171] A "prompt" is a document that inputs specific instructions or questions to a generative AI model.

[1172] The present invention relates to a system that automatically processes weather information, environmental data, pest information, and quality analysis data in agriculture and manufacturing to create optimal plans. This system includes the following components and processing procedures.

[1173] System configuration

[1174] 1. Sensor means

[1175] Sensors are physical devices used to collect environmental data, such as temperature sensors, humidity sensors, and soil sensors, and in factories they also collect sensor data related to product quality.

[1176] 2. Image acquisition method

[1177] Image capture methods include high-resolution cameras and drones that periodically capture images of crop and product conditions.

[1178] 3. Data storage means

[1179] The data storage means is a database or server that receives and stores data collected from the sensor means and image acquisition means, and includes cloud storage and local servers.

[1180] 4. Data Analysis Methods

[1181] Data analytics tools analyze stored data to assess weather forecasts, risk of pest and disease outbreaks, and product quality using generative AI models and machine learning algorithms.

[1182] 5. Means of calculation

[1183] The computational means uses the results of the data analysis means to create optimal agricultural plans and production schedules, including sowing, irrigation, fertilization, harvesting, and maintenance plans.

[1184] 6. Means of notification

[1185] The notification means notifies the user of the plan created by the calculation means to the user's terminal, which may include a smartphone, tablet, PC, or the like.

[1186] Example of operation

[1187] Example 1: Creating an irrigation plan

[1188] The server analyzes data from underground moisture sensors, combines it with the latest weather forecast, calculates the optimal irrigation timing, and notifies the terminal.

[1189] Example 2: Pest control measures

[1190] Drones fly over fields and factories, taking pictures of crops and products. The server analyzes the images, assesses the risk of pests and diseases, and notifies the device with specific prevention measures and improvement suggestions.

[1191] Example 3: Creating a production schedule

[1192] The server analyzes sensor data within the factory, evaluates high-risk defect conditions, calculates the optimal production schedule, and notifies the terminal.

[1193] Program Implementation

[1194] The server analyzes the data using a generative AI model, and is built using a programming language such as Python, with corresponding libraries for collecting sensor data and APIs such as Firebase for notifications.

[1195] Natural language explanations

[1196] The server periodically collects data from the sensors and stores it in a database. It then uses a generative AI model to analyze the accumulated data, making predictions and evaluations. Based on the analysis results, it notifies the user device of the optimal action plan and production schedule.

[1197] Examples of concrete examples and prompts

[1198] Example: On a factory production line, the temperature sensor data was 24 degrees, the humidity sensor data was 42%, and the defect probability was 0.7 (70%). The user was notified that there was a high risk of defect, and the next inspection was scheduled for one hour, and the next maintenance was scheduled for 30 days later.

[1199] Example prompts to input to a generative AI model:

[1200] "Based on the factory sensor data (temperature: 24°C, humidity: 42%, defect probability: 0.7), calculate the next inspection and maintenance schedule and generate a defect risk notification message."

[1201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1202] Step 1:

[1203] Sensor data collection

[1204] The server periodically collects environmental data from sensors such as temperature sensors, humidity sensors, and soil sensors. The inputs include the current temperature, humidity, and soil conditions obtained from each sensor, and this data is collected and stored in a database. Specifically, the server performs a process of obtaining data from the sensors via API and recording it in the database.

[1205] Step 2:

[1206] Acquisition of image data

[1207] Drones and high-resolution cameras are used to periodically capture images of crops and products. The captured image data is input, and this is uploaded and stored in cloud storage or a local server. Specifically, the drone flies a set route, captures images with its camera, and transmits the data to the server via a wireless network.

[1208] Step 3:

[1209] Data accumulation

[1210] The server stores the collected environmental data and image data in a database that centrally manages them. The inputs are sensor data and image data, which are stored in the database in an appropriate format. Specific operations include organizing, verifying, and storing the data.

[1211] Step 4:

[1212] Data analysis

[1213] The server performs analysis using the collected and stored data. Inputs include stored environmental data, image data, and weather forecast data, and uses generative AI models and machine learning algorithms to evaluate weather forecasts, risk of pest outbreaks, and product quality. Specific operations include preprocessing each data set, extracting features, applying the analysis model, and interpreting the results.

[1214] Step 5:

[1215] Creating a plan

[1216] The server calculates optimal agricultural plans and production schedules based on the results of data analysis. The inputs are analysis results and past historical data, and the specific output is the creation of schedules for sowing, irrigation, fertilization, and harvesting, as well as factory production and maintenance plans. Specific operations include calculations using a schedule optimization algorithm and generating plans.

[1217] Step 6:

[1218] notification

[1219] The server notifies the user of the created plan. The input includes an optimal agricultural plan and production schedule, and this is sent to the user's smartphone, tablet, PC, etc. Specific operations include generating notification messages and sending push notifications and emails.

[1220] Example prompt sentences

[1221] For example, "Based on the factory sensor data (temperature: 24°C, humidity: 42%, defect probability: 0.7), calculate the next inspection and maintenance schedule and generate a defect risk notification message."

[1222] 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.

[1223] The present invention relates to a system that automatically processes weather information, environmental data, pest and disease information, and crop quality analysis to create appropriate agricultural plans, and further combines it with an emotion engine that recognizes user emotions and reflects them in the agricultural plans. The following forms can be used to implement this system.

[1224] System configuration

[1225] 1. Sensor means

[1226] Terminal: Humidity, temperature, and soil sensors installed in the field periodically measure environmental data (humidity, temperature, and soil condition).

[1227] Terminal: Drones fly over the fields periodically, taking pictures of the crops' condition with high-resolution cameras.

[1228] 2. Data storage means

[1229] Server: A database that receives and stores collected environmental and image data.

[1230] 3. Data Analysis Methods

[1231] Server: Analyzes the received data and makes a comprehensive assessment of weather forecasts, risk of pest and disease outbreaks, and soil conditions.

[1232] Server: Connects to a weather database to retrieve current and forecast weather information.

[1233] 4. Means of calculation

[1234] Server: Based on the analysis results, it creates specific agricultural plans, including the timing of sowing, irrigation, fertilization, and harvesting.

[1235] 5. Means of notification

[1236] Server: A communication means for notifying the user's device of the created agricultural plan.

[1237] Device: Display a notification message on the user's smartphone or PC.

[1238] 6. Emotion Engine

[1239] Server: An emotion engine that recognizes the user's emotions regarding the notified agricultural plan. The emotion engine determines emotions based on the user's voice input, text input, facial expression analysis, etc.

[1240] Server: Readjusts farming plans based on perceived user sentiment.

[1241] System operation example

[1242] Example 1: Irrigation scheduling and emotional feedback

[1243] 1. Terminal: An underground moisture sensor measures the current soil moisture.

[1244] 2. Server: Analyzes data from sensors and combines it with upcoming weather forecasts to calculate optimal irrigation timings.

[1245] 3. Server: Predicts that the field will likely be dehydrated at noon tomorrow and suggests a time to irrigate.

[1246] 4. Server: Notifies the user's device of the proposed plan.

[1247] 5. Device: Display a notification message on the user's smartphone or PC.

[1248] 6. User: Provide emotional feedback (e.g., dissatisfaction or satisfaction) about the proposed plan via voice or text input.

[1249] 7. Server: The emotion engine analyzes the feedback and makes necessary adjustments.

[1250] Example 2: Pest control and emotional feedback

[1251] 1. Terminal: The drone flies over the field and takes images of the crops.

[1252] 2. Server: Uses image analysis technology to detect traces of specific pests and diseases.

[1253] 3. Server: Determines that the risk of pest outbreaks is increasing and proposes specific control measures.

[1254] 4. Server: Notifies the user of the proposed prevention measures.

[1255] 5. Terminal: Notifies the user of prevention measures and encourages them to take them. The user also receives emotional feedback.

[1256] 6. Server: The emotion engine analyzes the feedback and adjusts the defense measures.

[1257] Example 3: Seeding plan creation and emotional feedback

[1258] 1. User: You are considering sowing a new crop.

[1259] 2. Terminal: Collects current weather information, soil conditions, and historical crop growth data.

[1260] 3. Server: Based on all the data, calculates the optimal sowing time and method.

[1261] 4. Server: Notifies the user's terminal of the seeding plan.

[1262] 5. Terminal: The user checks the seeding plan and provides emotional feedback via voice and text.

[1263] 6. Server: The emotion engine analyzes the feedback and makes necessary adjustments.

[1264] With this configuration and operation, the present invention can realize more efficient and user-friendly agricultural operations. By recognizing the user's emotions and reflecting them in the agricultural planning, it is possible to increase user satisfaction and improve the effectiveness of agriculture.

[1265] The processing flow will be explained below.

[1266] Step 1:

[1267] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Drones also fly periodically, taking photos of the crop condition with high-resolution cameras.

[1268] Step 2:

[1269] Terminal: Sends collected sensor data and image data to the server.

[1270] Step 3:

[1271] Server: Stores the received sensor data and image data in a database. The stored data includes the measurement date and time, sensor readings, image files, etc.

[1272] Step 4:

[1273] Server: Connects to a weather database to retrieve the latest weather forecast data, including temperature, precipitation, wind speed, etc.

[1274] Step 5:

[1275] Server: Uses collected environmental and weather forecast data to assess weather forecasts and pest risk using machine learning algorithms and references to historical data and expert models.

[1276] Step 6:

[1277] Server: Based on the analysis results, it calculates the optimal timing for future agricultural actions (e.g., irrigation, fertilization, harvesting, etc.) using algorithms that determine the optimal actions under specific conditions.

[1278] Step 7:

[1279] Server: Based on the calculation results, a specific agricultural plan is created, detailing the timing and method of each action.

[1280] Step 8:

[1281] Server: Sends the created farming plan to the user's device. The notification also includes details of the plan and the reason for its implementation.

[1282] Step 9:

[1283] Device: A notification message will be displayed on the user's smartphone or PC, allowing the user to review the plan and request adjustments if necessary.

[1284] Step 10:

[1285] User: Enter their feelings about the proposed farming plan via voice or text. Examples include, "I think this plan is good" or "This schedule is tough."

[1286] Step 11:

[1287] Terminal: Sends the user's emotional feedback to the server.

[1288] Step 12:

[1289] Server: The emotion engine analyzes user feedback and determines emotions using voice analysis and natural language processing techniques.

[1290] Step 13:

[1291] Server: Based on the results of sentiment analysis, the farming plan is readjusted as needed. For example, if a user expresses dissatisfaction with irrigation timing, that part is revised.

[1292] Step 14:

[1293] Server: The adjusted agricultural plan is sent back to the user's device.

[1294] Step 15:

[1295] Terminal: Sensors in the fields and drones continue to collect data and send it to the server.

[1296] Step 16:

[1297] Server: Continuously analyzes the received data and updates the farming plan as needed. The emotion engine also continuously receives user feedback and optimizes the plan.

[1298] In this way, the system supports agricultural activities sustainably and efficiently, and by reflecting the user's feelings, it is possible to realize more user-friendly agricultural operations.

[1299] Example 2

[1300] 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."

[1301] Conventional agricultural systems can create agricultural plans based on weather information and environmental data, but they only provide one-way notifications without considering the user's feelings, which has the problem of lacking user satisfaction and flexibility in the plans. This leads to plans not being carried out and users often becoming dissatisfied with the plans, and we aim to solve this problem.

[1302] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes sensor means for collecting weather information and environmental data, image acquisition means for capturing images of crops, data storage means for receiving and storing data collected by the sensor means and the image acquisition means, data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and the risk of pest and disease outbreaks, calculation means for creating an agricultural plan based on the results of the data analysis means, notification means for notifying a terminal of the agricultural plan created by the calculation means, and emotion recognition means for recognizing the user's emotions regarding the notified agricultural plan and readjusting the agricultural plan based on the emotions. This enables the creation of a flexible, satisfying agricultural plan that reflects the user's emotions.

[1303] "Sensor means" refers to a device for collecting weather information and environmental data, and includes humidity sensors, temperature sensors, soil sensors, and the like.

[1304] "Image acquisition means" refers to a device that captures images of crops, and includes drones equipped with high-resolution cameras and fixed cameras.

[1305] "Data storage means" refers to a storage device for receiving and storing data collected from the sensor means and image acquisition means, and includes a database.

[1306] The "data analysis means" is a device or program for analyzing the data stored in the data storage means and evaluating the weather forecast and the risk of pest outbreaks.

[1307] "Calculation means" refers to a device or program for creating an agricultural plan based on the results of the data analysis means, and includes machine learning algorithms.

[1308] The "notification means" is a device or program for notifying the user terminal of the agricultural plan created by the calculation means, and includes a communication means.

[1309] The "emotion recognition means" is a device or program that recognizes the user's emotions regarding the notified farming plan and readjusts the farming plan based on the emotions.

[1310] This invention relates to a system that automatically processes weather information, environmental data, pest and disease information, and crop quality analysis to create appropriate agricultural plans. It also combines an emotion engine that recognizes the user's emotions and reflects them in the agricultural plans.

[1311] System configuration

[1312] 1. Sensor means

[1313] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Specifically, the humidity sensor measures data every 15 minutes and sends it to the gateway using Bluetooth or LoRa communication.

[1314] Device: The drone flies over the fields at a set time every day and takes pictures of the crops using a high-resolution camera. The captured image data is then uploaded to a server via Wi-Fi.

[1315] 2. Data storage means

[1316] Server: Receives and stores collected environmental and image data using a database such as MySQL or PostgreSQL. For example, temperature data is recorded in a temperature table, and humidity data is recorded in a humidity table.

[1317] 3. Data Analysis Methods

[1318] Server: Uses Python scripts to analyze the collected data, calling weather forecast APIs (e.g. OpenWeatherMap API) to retrieve current and forecast weather information, and predict trends in soil moisture and temperature.

[1319] Server: Image data is analyzed using image analysis libraries such as OpenCV. The analysis mainly detects pests and diseases and abnormalities in crops, and records the results in a database.

[1320] 4. Means of calculation

[1321] Server: Uses machine learning algorithms (e.g., TensorFlow, Scikit-learn) to create specific agricultural plans based on the latest analysis results. For example, if soil moisture is low, it will plan irrigation at noon the following day.

[1322] 5. Means of notification

[1323] Server: Uses Firebase Cloud Messaging or REST API to notify users of the created farming plan. Generates notification content (e.g., "Irrigation is required tomorrow at noon") and sends it to the user's smartphone.

[1324] Device: The user's smartphone or PC receives the notification and displays its contents, allowing the user to check the notification.

[1325] 6. Emotion recognition means

[1326] User: The user provides emotional feedback on the proposed farming plan (e.g., "This plan is good" or "Needs to be reconsidered") via voice or text. The voice input is converted to text using the Google Speech-to-Text API.

[1327] Server: An emotion engine (e.g., OpenAI GPT) analyzes the text feedback and readjusts the farming plan if the user expresses dissatisfaction or if the plan needs to be revised, again using machine learning algorithms.

[1328] Example: Irrigation scheduling and emotional feedback

[1329] 1. Terminal: The underground humidity sensor measures the soil humidity to be 40% at 2:00 pm on July 15th and sends the data to the server via the gateway via LoRa communication.

[1330] 2. Server: A Python script receives the humidity data and uses the OpenWeatherMap API to get the weather forecast for the next day, which predicts sunny skies with temperatures above 30 degrees.

[1331] 3. Server: The script detects the humidity deficit and determines that the best time to irrigate is at noon the next day. It creates a planning file and saves it in the database.

[1332] 4. Server: Use Firebase Cloud Messaging to send a notification to the user's smartphone saying, "Irrigation is required at noon on July 16th."

[1333] 5. Device: The smartphone receives the notification and displays the message. The user gives feedback by saying, "I'm happy with the plan."

[1334] 6. Server: The voice data is converted to text using the Google Speech-to-Text API, and the emotion engine analyzes the feedback such as "satisfied."

[1335] Prompt Sentence Examples

[1336] "Based on the humidity sensor data and weather forecast, please evaluate whether this irrigation plan is appropriate, reflecting the user's emotional feedback."

[1337] By implementing this aspect of the present invention, it becomes possible to create a flexible and satisfying agricultural plan that reflects the user's feelings, which results in improved efficiency and feasibility of farming, and increased user satisfaction.

[1338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1339] Step 1:

[1340] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). For example, the humidity sensor measures soil humidity every 15 minutes and transmits the data to the gateway using Bluetooth or LoRa communication.

[1341] Input: Current humidity, temperature, soil data

[1342] Output: Transmits humidity, temperature, and soil data

[1343] Step 2:

[1344] Device: The drone flies over the field at a set time every day, taking pictures of the crops with a high-resolution camera. The captured image data is then uploaded to a server via Wi-Fi.

[1345] Input: Crop image

[1346] Output: Upload image data to the server

[1347] Step 3:

[1348] Server: Uses MySQL or PostgreSQL to receive data sent from sensors and drones and store it in a database.

[1349] Input: Sensor data, image data

[1350] Output: Environmental and image data stored in a database

[1351] Step 4:

[1352] Server: Uses Python scripts to analyze the environmental data stored in the database. Uses weather forecast APIs (e.g. OpenWeatherMap API) to retrieve current and forecast weather information and combine it with the environmental data for analysis.

[1353] Input: Environmental data, weather forecast data

[1354] Output: Analysis results based on weather forecasts and environmental data

[1355] Step 5:

[1356] Server: Image data is analyzed using image analysis libraries such as OpenCV to detect pests, diseases, and abnormalities in crops.

[1357] Input: Image data

[1358] Output: Analysis results on risk of pest and disease outbreaks and abnormalities in crops

[1359] Step 6:

[1360] Server: Based on the analysis results, machine learning algorithms (e.g., TensorFlow, Scikit-learn) are used to create agricultural plans. For example, if a humidity deficiency is detected, a plan to irrigate the area at noon the following day is created.

[1361] Input: Environmental data analysis results, image data analysis results

[1362] Output: Agricultural plan

[1363] Step 7:

[1364] Server: Uses Firebase Cloud Messaging or REST API to notify users of the created farming plan, such as a message like "Irrigation is required tomorrow at noon."

[1365] Input: Agricultural Plan

[1366] Output: Notification message to the user's terminal

[1367] Step 8:

[1368] Device: The user's smartphone or PC receives the notification and displays the notification content. The user checks the notification.

[1369] Input: Notification message

[1370] Output: Display notification information to the user

[1371] Step 9:

[1372] User: The user provides emotional feedback on the proposed farming plan via voice or text, for example, by saying "I'm happy with the plan."

[1373] Input: User's emotional feedback

[1374] Output: Send feedback data to the server

[1375] Step 10:

[1376] Server: The voice feedback is converted to text using the Google Speech-to-Text API. The text feedback is analyzed by an emotion engine (e.g., OpenAI GPT) and the farming plan is readjusted as needed based on the user's emotions.

[1377] Input: User sentiment feedback text

[1378] Output: Re-adjusted farming plan or unchanged feedback results

[1379] In this way, the present invention realizes a system that comprehensively utilizes weather information, environmental data, pest and disease information, and user emotions to provide accurate and satisfying agricultural planning.

[1380] (Application example 2)

[1381] 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."

[1382] Conventional agricultural management systems and factory management systems easily created plans based on the collection and analysis of environmental and production data, but lacked the ability to adjust plans while taking into account the emotions of users and employees. As a result, user satisfaction and working conditions were not sufficiently improved, making efficient operations difficult. Furthermore, in factories, one-sided production plans that ignored the emotions of employees often led to dissatisfaction and stress. The objective of this invention is to solve these problems and provide a system that enables the creation and adjustment of plans while taking into account the emotions of users and employees.

[1383] 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.

[1384] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for acquiring images of crops, a data storage means for receiving and storing the data collected by the sensor means and the image acquisition means, a data analysis means for analyzing the data stored in the data storage means and evaluating the weather forecast and the risk of pest infestation, a calculation means for creating an agricultural plan based on the results of the data analysis means, a notification means for notifying a terminal of the agricultural plan created by the calculation means, a means for monitoring and analyzing factory environment data and production equipment data in real time, and an emotion analysis means for analyzing employee emotions and reflecting the results in the production plan. This makes it possible to design and adjust plans that reflect the emotions of users and employees.

[1385] "Weather information" is information relating to weather conditions, including data such as temperature, humidity, precipitation, wind speed, and wind direction.

[1386] "Environmental data" refers to data obtained by measuring environmental factors at a specific location, including temperature, humidity, soil condition, and air quality.

[1387] "Sensor means" refers to a device that detects a specific physical or chemical phenomenon and outputs it as data.

[1388] "Image capture means" refers to a device or method for photographing or capturing an image of an object.

[1389] "Data storage" refers to a system or device that stores collected data and makes it available for retrieval as needed.

[1390] "Data analysis means" refers to a system or method for analyzing collected data to extract meaningful information.

[1391] "Calculation means" refers to a system or device that performs calculations based on the obtained data and generates optimal plans and instructions.

[1392] "Notification means" refers to a system or method for transmitting the created plans and instructions to the user's terminal.

[1393] "Factory environmental data" refers to data related to various environmental factors within a factory (temperature, humidity, air quality, etc.).

[1394] "Production equipment data" refers to data relating to the operating status, operating rate, failure history, etc. of production equipment.

[1395] "Emotion analysis means" refers to a system or method for analyzing the emotions of users or employees and reflecting the analysis results in the system.

[1396] The present invention relates to a management system in a factory or agricultural environment, which analyzes collected data and creates an optimal plan that reflects the feelings of users and employees.

[1397] System configuration

[1398] 1. Environmental data collection methods

[1399] Sensor means:

[1400] Temperature, humidity and air quality sensors installed in factories and farms regularly measure environmental data.

[1401] Sensors are installed in factory production equipment to record operating conditions and failure history.

[1402] 2. Image acquisition method

[1403] Device:

[1404] Drones and fixed cameras are used to regularly capture images of crops in the fields and production equipment in factories.

[1405] 3. Data storage means

[1406] server:

[1407] A database for receiving and storing environmental data from factories and farms, and image data from crops and production equipment.

[1408] For the database, cloud servers (Google Cloud Platform, Amazon Web Services) or on-premise servers are used.

[1409] 4. Data Analysis Methods

[1410] server:

[1411] The collected environmental and image data is analyzed to comprehensively assess weather forecasts and the risk of pest and disease outbreaks.

[1412] At the factory, environmental data and production equipment data are analyzed to develop production plans.

[1413] By using machine learning algorithms (TensorFlow and PyTorch), the accuracy of the analysis is improved, and a plan is created based on the results.

[1414] 5. Means of calculation

[1415] server:

[1416] Based on the analysis results, agricultural and production plans are formulated, and the timing of sowing, irrigation, fertilization, harvesting, production schedules, etc. are calculated.

[1417] 6. Means of notification

[1418] server:

[1419] The created plan is notified to the user's device using Apple Push Notification Service (APNs) or Google Firebase Cloud Messaging (FCM).

[1420] Device:

[1421] Notification messages are displayed on the smartphones and head-mounted displays (HMDs) of users and factory employees.

[1422] 7. Emotion analysis method

[1423] server:

[1424] It has an emotion engine for analyzing user and employee emotions, and recognizes emotions using voice input (Apple Siri, Google Assistant), text input (chatbots), and facial expression analysis (Microsoft Azure Face API).

[1425] The emotion engine performs analysis using emotion analysis libraries (NLTK's VADER, Microsoft Azure's Text Analytics for Sentiment Analysis) and reflects the results in the plan.

[1426] Specific examples

[1427] For example, if a user is considering sowing a new crop, the optimal sowing time is calculated based on current weather information, soil conditions, and past growth data collected by the sensor means. The results are then sent to the user's smartphone. When the user provides feedback on the plan, the emotion engine analyzes their emotions and adjusts the sowing time as necessary.

[1428] Prompt Sentence Examples

[1429] You are designing a system that analyzes factory environmental data and production equipment data in real time to create optimal production plans. This system also includes an emotion engine that analyzes employee emotions and reflects them in production plans. Measure temperature, humidity, and air quality using sensors in the factory, and collect data on the availability and failure rates of production equipment. Store the collected data on a server and analyze it using a machine learning algorithm. Notify managers and employees of the analysis results and production plans, collect emotional feedback, and adjust the plans accordingly. Please explain the specific steps.

[1430] In this way, the present invention provides a more efficient and user-friendly system by creating and adjusting plans that reflect the feelings of users and employees.

[1431] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1432] Step 1:

[1433] Environmental and image data collection

[1434] Input: Environmental data such as temperature, humidity, and air quality from sensors installed in factories and farms, and image data of crops and production equipment collected periodically.

[1435] How it works: Sensors in factories and farms periodically measure data and transmit it to a central server via wireless or wired connections. Drones and fixed cameras also capture image data and transmit it to the server in real time.

[1436] Step 2:

[1437] Accumulation of collected data

[1438] Input: Environmental data, image data

[1439] Output: Data stored in the database

[1440] Specific operation: The server stores the received environmental data and image data in a database as a data storage means. This database uses a cloud server or an on-premise server.

[1441] Step 3:

[1442] Data analysis

[1443] Input: Environmental data and image data stored in a database

[1444] Output: Information such as weather forecasts, risk of pest outbreaks, and operational status of production facilities

[1445] How it works: The server uses machine learning algorithms to comprehensively analyze the collected data, for example, using TensorFlow or PyTorch to evaluate weather forecasts, risk of pest outbreaks, and the operating status of production facilities.

[1446] Step 4:

[1447] Creating a plan

[1448] Input: Analysis results (weather forecast, pest risk, production facility operation status, etc.)

[1449] Output: Optimal agricultural and production planning

[1450] Specific operations: Based on the analysis results, the server creates specific plans for sowing, irrigation, fertilization, harvesting, production schedules, etc.

[1451] Step 5:

[1452] Plan Notification

[1453] Input: Created plan

[1454] Output: Notification message to the user's terminal

[1455] Specific operation: The server notifies the user and factory employees of the created plan via their devices (smartphones or head-mounted displays) using Apple Push Notification Service (APNs) or Google Firebase Cloud Messaging (FCM).

[1456] Step 6:

[1457] User and employee sentiment analysis

[1458] Input: User / employee voice input, text input, and facial expression data

[1459] Output: Emotion analysis results

[1460] Specific operation: The server collects emotional data from users and employees using voice input (Apple Siri, Google Assistant), text input (chatbot), and facial expression analysis (Microsoft Azure Face API), and analyzes the data using an emotion analysis library (nltk's VADER or Microsoft Azure's Text Analytics for Sentiment Analysis).

[1461] Step 7:

[1462] Re-adjusting plans

[1463] Input: Sentiment analysis results

[1464] Output: Re-adjusted agricultural and production plans

[1465] Specific operation: Based on the results of the sentiment analysis, the server readjusts the agricultural and production plans as necessary and notifies the users and employees again.

[1466] Through the above steps, the present invention can realize efficient and user-friendly agricultural and factory management.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] [Fourth embodiment]

[1471] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1472] 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.

[1473] 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).

[1474] 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.

[1475] 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.

[1476] 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).

[1477] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1478] 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.

[1479] 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.

[1480] 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.

[1481] 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.

[1482] 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.

[1483] 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."

[1484] The present invention relates to a system for automatically processing weather information, environmental data, pest and disease information, and crop quality analysis to create an appropriate agricultural plan. The following aspects can be used to implement this system.

[1485] System configuration

[1486] 1. Sensor means

[1487] Terminal: Humidity, temperature, and soil sensors installed in the field periodically measure environmental data (humidity, temperature, and soil condition).

[1488] Terminal: Drones fly over the fields periodically, taking pictures of the crops' condition with high-resolution cameras.

[1489] 2. Data storage means

[1490] Server: A database that receives and stores collected environmental and image data.

[1491] 3. Data Analysis Methods

[1492] Server: Analyzes the received data and makes a comprehensive assessment of weather forecasts, risk of pest and disease outbreaks, and soil conditions.

[1493] Server: Connects to a weather database to retrieve current and forecast weather information.

[1494] 4. Means of calculation

[1495] Server: Based on the analysis results, it creates specific agricultural plans, including the timing of sowing, irrigation, fertilization, and harvesting.

[1496] 5. Means of notification

[1497] Server: A communication means for notifying the user's device of the created agricultural plan.

[1498] Device: Display a notification message on the user's smartphone or PC.

[1499] System operation example

[1500] Example 1: Creating an irrigation plan

[1501] 1. Terminal: An underground moisture sensor measures the current soil moisture.

[1502] 2. Server: Analyzes data from sensors and combines it with upcoming weather forecasts to calculate optimal irrigation timings.

[1503] 3. Server: Predicts that the field will likely be dehydrated at noon tomorrow and suggests a time to irrigate.

[1504] 4. Terminal: Notifies the user when to irrigate.

[1505] Example 2: Pest control measures

[1506] 1. Terminal: The drone flies over the field and takes images of the crops.

[1507] 2. Server: Uses image analysis technology to detect traces of specific pests and diseases.

[1508] 3. Server: Determines whether there is an increased risk of pest or disease outbreak and proposes specific control measures (e.g., the use of specific pesticides).

[1509] 4. Terminal: Notify the user of prevention measures and encourage them to take them.

[1510] Example 3: Creating a seeding plan

[1511] 1. User: You are considering sowing a new crop.

[1512] 2. Terminal: Collects current weather information, soil conditions, and historical crop growth data.

[1513] 3. Server: Based on all the data, calculates the optimal sowing time and method.

[1514] 4. Server: Notifies the user's terminal of the seeding plan.

[1515] 5. Terminal: The user checks and executes the seeding plan.

[1516] With this configuration and operation, the present invention can realize efficient and effective agricultural management. Users can receive optimal agricultural plans based on the latest data and implement them to improve productivity and reduce risks. Furthermore, analysis of environmental data and pest risks is performed using machine learning algorithms, resulting in high accuracy and speed. This significantly reduces manual labor and improves the efficiency of agricultural management.

[1517] The processing flow will be explained below.

[1518] Step 1:

[1519] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Drones also fly periodically, taking photos of the crop condition with high-resolution cameras.

[1520] Step 2:

[1521] Terminal: Sends collected sensor data and image data to the server.

[1522] Step 3:

[1523] Server: Stores the received sensor data and image data in a database. The stored data includes the measurement date and time, sensor readings, image files, etc.

[1524] Step 4:

[1525] Server: Connects to a weather database to retrieve the latest weather forecast data, including temperature, precipitation, wind speed, etc.

[1526] Step 5:

[1527] Server: Uses collected environmental and weather forecast data to assess weather forecasts and pest risk using machine learning algorithms and references to historical data and expert models.

[1528] Step 6:

[1529] Server: Based on the analysis results, it calculates the optimal timing for future agricultural actions (e.g., irrigation, fertilization, harvesting, etc.) using algorithms that determine the optimal actions under specific conditions.

[1530] Step 7:

[1531] Server: Based on the calculation results, a specific agricultural plan is created, detailing the timing and method of each action.

[1532] Step 8:

[1533] Server: Sends the created farming plan to the user's device. The notification also includes details of the plan and the reason for its implementation.

[1534] Step 9:

[1535] Device: A notification message will be displayed on the user's smartphone or PC, allowing the user to review the plan and request adjustments if necessary.

[1536] Step 10:

[1537] User: Implements the proposed agricultural plan and, if necessary, sends a request to adjust the plan to the server via the terminal.

[1538] Step 11:

[1539] Terminal: Sends user feedback to the server.

[1540] Step 12:

[1541] Server: Recalculates the farming plan based on user feedback, makes necessary adjustments, and notifies the user of the adjusted plan again.

[1542] Step 13:

[1543] Terminal: Sensors in the fields and drones continue to collect data and send it to the server.

[1544] Step 14:

[1545] Server: Continuously analyzes the received data, updates the agricultural plan as needed, and notifies the user immediately if an updated plan is available.

[1546] In this way, the system can support agricultural activities sustainably and efficiently.

[1547] Example 1

[1548] 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."

[1549] In modern agriculture, production risks due to weather fluctuations and pest outbreaks remain major challenges, and there is a need to quickly and accurately understand these and take appropriate measures. However, traditional methods involve a large amount of manual work involved in data collection, analysis, and planning, which is not efficient. Another problem is that it is difficult to comprehensively evaluate environmental data and crop conditions and quickly create accurate agricultural plans.

[1550] 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.

[1551] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for taking images of crops, a data storage means for receiving and storing the data collected by the sensor means and the image acquisition means, a data analysis means for analyzing the data stored in the data storage means using a machine learning algorithm to evaluate the weather forecast and the risk of pest and disease outbreaks, a calculation means for creating an agricultural plan based on the results of the data analysis means, and a notification means for notifying a terminal of the agricultural plan created by the calculation means. This makes it possible to quickly respond to weather fluctuations and pest and disease risks and to create an efficient and appropriate agricultural plan.

[1552] "Sensor means" is a device for collecting weather information and environmental data (humidity, temperature, soil conditions).

[1553] The "image acquisition means" is a device equipped with a high-resolution camera for photographing the condition of the crops.

[1554] "Data storage means" is a database system for receiving and storing data collected from the sensor means and image acquisition means.

[1555] The "data analysis means" is a device or program that analyzes the data stored in the data storage means using a machine learning algorithm and evaluates weather forecasts and the risk of pest and disease outbreaks.

[1556] "Calculation means" refers to a device or program for creating an agricultural plan based on the results of the data analysis means.

[1557] The "notification means" is a communication device or program for notifying the terminal of the agricultural plan created by the calculation means.

[1558] A "terminal" is a device that allows a user to receive information and perform operations, and includes smartphones, PCs, etc.

[1559] "Weather information" is information about current and forecast weather conditions.

[1560] "Environmental data" is data relating to weather and earth surface conditions such as humidity, temperature, soil conditions, etc.

[1561] A "machine learning algorithm" is a form of artificial intelligence used in data analysis, analyzing large amounts of data to generate patterns and predictive models.

[1562] An "agricultural plan" is a plan that includes the optimal timing and methods for carrying out agricultural operations such as sowing, irrigation, fertilization, and harvesting.

[1563] A "weather information database" is a system for collecting, storing, and providing weather forecasts and related meteorological data.

[1564] "Cloud messaging technology" is a technology for sending data and notifications to devices via the Internet.

[1565] The present invention relates to a system for automatically processing weather information, environmental data, pest and disease information, and crop quality analysis to create specific and appropriate agricultural plans. The system can be implemented in the following forms.

[1566] System configuration

[1567] The system includes a sensor means, an image acquisition means, a data storage means, a data analysis means, a calculation means, a notification means, and a terminal.

[1568] 1. Sensor means

[1569] The terminal uses humidity, temperature, and soil sensors installed in the field to periodically measure environmental data (humidity, temperature, and soil condition), which enables appropriate measures to be taken in response to crop growth conditions and environmental fluctuations.

[1570] The terminal uses a drone that periodically takes photos of the state of the crops using a high-resolution camera, and the drone's flight schedule is managed by a server.

[1571] 2. Data storage means

[1572] The server receives the environmental data sent from the sensors and stores it in a database system, using a commonly used relational database (e.g., MySQL or PostgreSQL).

[1573] The server also stores image data sent from the drone in a database.

[1574] 3. Data Analysis Methods

[1575] The server uses the Python programming language and machine learning libraries (such as TensorFlow and scikit-learn) to analyze stored environmental and image data, thereby assessing weather forecasts and the risk of pest and disease outbreaks.

[1576] The server interacts with a weather API (e.g., OpenWeatherMap API) to obtain weather forecast data.

[1577] 4. Means of calculation

[1578] Based on the results of the data analysis, the server creates specific agricultural plans for sowing, irrigation, fertilization, harvesting, etc. These calculations are performed using Python's NumPy and Pandas libraries.

[1579] 5. Means of notification

[1580] The server uses cloud messaging technologies such as Firebase Cloud Messaging (FCM) and Amazon SNS to notify the device of the created agricultural plan.

[1581] The device will display a notification message on the user's smartphone or PC, allowing the user to check the plan.

[1582] System operation example

[1583] The following scenario can be considered as a specific example of the system's operation.

[1584] Example 1: Creating an irrigation plan

[1585] 1. The device's humidity sensor measures the humidity in the field.

[1586] 2. The server receives the data and combines it with weather forecast data to calculate the optimal irrigation timing.

[1587] 3. Based on the calculation results, the server predicts that irrigation will be necessary at noon the next day and notifies the user.

[1588] 4. The terminal displays a notification message to the user.

[1589] Example prompt sentence:

[1590] A user wants to create an irrigation plan. The current soil moisture is 40%, and tomorrow's weather forecast predicts a temperature of 30 degrees and sunny skies. What kind of irrigation plan should they create?

[1591] Example 2: Pest control measures

[1592] 1. The device's drone flies over the field and takes images of the crops.

[1593] 2. The server uses image analysis technology to detect traces of pests and diseases.

[1594] 3. The server assesses the risk of pest outbreaks and calculates specific control measures.

[1595] 4. The device notifies the user of the prevention measures and encourages them to take them.

[1596] In this way, this system achieves efficient and appropriate agricultural management by consistently and automatically collecting data, analyzing it, creating plans, and sending notifications.

[1597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1598] Step 1: Data collection

[1599] The terminal periodically measures environmental data using humidity, temperature, and soil sensors installed in the field.

[1600] Input: Measurements by environmental sensors (humidity, temperature, soil condition)

[1601] Operation: Data from the sensor is sent to the server via the wireless communication module.

[1602] Output: Measured environmental data

[1603] Step 2: Image acquisition

[1604] The device uses a drone to regularly photograph the condition of the crops using a high-resolution camera.

[1605] Input: Crop images taken by a high-resolution camera mounted on a drone

[1606] Operation: The drone automatically flies at the designated time according to the flight schedule, takes pictures, and then sends the image data to the server.

[1607] Output: Image data of the photographed crops

[1608] Step 3: Save data

[1609] The server receives the environmental data sent from the sensors and the image data sent from the drones and stores them in a database.

[1610] Input: Environmental data, image data

[1611] What it does: Stores the received data in a relational database (e.g., MySQL or PostgreSQL).

[1612] Output: Environmental and image data stored in a database

[1613] Step 4: Environmental data analysis

[1614] The server uses Python and a machine learning library (scikit-learn) to analyze the stored environmental data.

[1615] Input: Environmental data stored in a database

[1616] How it works: It uses machine learning algorithms to analyze environmental data and assess its impact on crop growth.

[1617] Output: Evaluation results (crop growth status, water needs, etc.)

[1618] Step 5: Image analysis

[1619] The server analyzes the stored image data using an image analysis library such as TensorFlow.

[1620] Input: Crop image data stored in a database

[1621] How it works: Uses image analysis technology to detect signs of pests and diseases and the health of crops.

[1622] Output: Image analysis results (traces of pests and diseases, health status of crops)

[1623] Step 6: Obtaining weather forecast data

[1624] The server retrieves weather forecast data from a weather API (such as the OpenWeatherMap API).

[1625] Input: Weather forecast data request from the weather API

[1626] What it does: Get current and forecast weather information via API.

[1627] Output: Current and forecast weather information

[1628] Step 7: Create a farm plan

[1629] The server creates a specific agricultural plan based on the results of environmental data analysis, image analysis, and weather forecast data.

[1630] Input: Environmental data analysis results, image analysis results, weather forecast data

[1631] How it works: This data is evaluated comprehensively and optimal timing for irrigation, fertilization, sowing, and harvesting is calculated using Python's NumPy and Pandas libraries.

[1632] Output: The generated farming plan

[1633] Step 8: User Notification

[1634] The server uses Firebase Cloud Messaging (FCM) and Amazon SNS to notify the user's device of the created farming plan.

[1635] Input: Created farming plan

[1636] Operation: The plan is sent to the user's smartphone or PC and a notification message is displayed.

[1637] Output: Notification of agricultural plan displayed on user terminal

[1638] In this way, environmental and image data are collected and stored at each step, analyzed using machine learning algorithms, an overall agricultural plan is formulated, and the plan is notified to the user.

[1639] (Application example 1)

[1640] 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."

[1641] In modern agriculture and manufacturing, it is extremely important to efficiently collect and analyze environmental and quality data to create optimal action plans and production schedules. However, with conventional systems, data collection and analysis are often performed separately, making it difficult to create effective plans in real time. In particular, in the agricultural sector, rapid and accurate decision-making is required in response to the effects of weather and pests and diseases, and in the manufacturing sector, in product quality control. To address these challenges, there is a strong demand for integrated data collection, analysis, and planning systems.

[1642] 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.

[1643] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for taking images of crops, a data storage means, a data analysis means, a calculation means, a notification means, a data analysis means for analyzing sensor data in the factory and evaluating the state of product quality, a calculation means for creating an optimal production schedule and improvement proposals, and a notification means for notifying a terminal of the production schedule and improvement proposals created by the calculation means. This enables the integrated collection and analysis of environmental data and quality data, and enables the creation of effective agricultural plans and production schedules in real time.

[1644] - "Weather information" refers to data on weather conditions such as temperature, humidity, precipitation, and wind speed.

[1645] "Environmental data" refers to data that indicates the ambient conditions related to the cultivation and manufacturing of crops and products, such as temperature, humidity, and soil condition.

[1646] The "sensor means" is a means for collecting environmental data using physical sensor devices such as a temperature sensor, a humidity sensor, and a soil sensor.

[1647] "Image acquisition means" refers to a means for taking images of crops or products using a camera or drone.

[1648] The "data storage means" refers to a database or server for receiving and storing data collected by the sensor means and image acquisition means.

[1649] The "data analysis means" is a means for analyzing accumulated data and evaluating weather forecasts, risk of pest outbreaks, and product quality.

[1650] "Calculation means" refers to the means for creating agricultural plans and production schedules based on the analysis results.

[1651] The "notification means" is a means for notifying the user's terminal of the agricultural plan or production schedule created by the calculation means.

[1652] A "generative AI model" is a model that uses machine learning algorithms to make predictions and evaluations from input data.

[1653] A "prompt" is a document that inputs specific instructions or questions to a generative AI model.

[1654] The present invention relates to a system that automatically processes weather information, environmental data, pest information, and quality analysis data in agriculture and manufacturing to create optimal plans. This system includes the following components and processing procedures.

[1655] System configuration

[1656] 1. Sensor means

[1657] Sensors are physical devices used to collect environmental data, such as temperature sensors, humidity sensors, and soil sensors, and in factories they also collect sensor data related to product quality.

[1658] 2. Image acquisition method

[1659] Image capture methods include high-resolution cameras and drones that periodically capture images of crop and product conditions.

[1660] 3. Data storage means

[1661] The data storage means is a database or server that receives and stores data collected from the sensor means and image acquisition means, and includes cloud storage and local servers.

[1662] 4. Data Analysis Methods

[1663] Data analytics tools analyze stored data to assess weather forecasts, risk of pest and disease outbreaks, and product quality using generative AI models and machine learning algorithms.

[1664] 5. Means of calculation

[1665] The computational means uses the results of the data analysis means to create optimal agricultural plans and production schedules, including sowing, irrigation, fertilization, harvesting, and maintenance plans.

[1666] 6. Means of notification

[1667] The notification means notifies the user of the plan created by the calculation means to the user's terminal, which may include a smartphone, tablet, PC, or the like.

[1668] Example of operation

[1669] Example 1: Creating an irrigation plan

[1670] The server analyzes data from underground moisture sensors, combines it with the latest weather forecast, calculates the optimal irrigation timing, and notifies the terminal.

[1671] Example 2: Pest control measures

[1672] Drones fly over fields and factories, taking pictures of crops and products. The server analyzes the images, assesses the risk of pests and diseases, and notifies the device with specific prevention measures and improvement suggestions.

[1673] Example 3: Creating a production schedule

[1674] The server analyzes sensor data within the factory, evaluates high-risk defect conditions, calculates the optimal production schedule, and notifies the terminal.

[1675] Program Implementation

[1676] The server analyzes the data using a generative AI model, and is built using a programming language such as Python, with corresponding libraries for collecting sensor data and APIs such as Firebase for notifications.

[1677] Natural language explanations

[1678] The server periodically collects data from the sensors and stores it in a database. It then uses a generative AI model to analyze the accumulated data, making predictions and evaluations. Based on the analysis results, it notifies the user device of the optimal action plan and production schedule.

[1679] Examples of concrete examples and prompts

[1680] Example: On a factory production line, the temperature sensor data was 24 degrees, the humidity sensor data was 42%, and the defect probability was 0.7 (70%). The user was notified that there was a high risk of defect, and the next inspection was scheduled for one hour, and the next maintenance was scheduled for 30 days later.

[1681] Example prompts to input to a generative AI model:

[1682] "Based on the factory sensor data (temperature: 24°C, humidity: 42%, defect probability: 0.7), calculate the next inspection and maintenance schedule and generate a defect risk notification message."

[1683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1684] Step 1:

[1685] Sensor data collection

[1686] The server periodically collects environmental data from sensors such as temperature sensors, humidity sensors, and soil sensors. The inputs include the current temperature, humidity, and soil conditions obtained from each sensor, and this data is collected and stored in a database. Specifically, the server performs a process of obtaining data from the sensors via API and recording it in the database.

[1687] Step 2:

[1688] Acquisition of image data

[1689] Drones and high-resolution cameras are used to periodically capture images of crops and products. The captured image data is input, and this is uploaded and stored in cloud storage or a local server. Specifically, the drone flies a set route, captures images with its camera, and transmits the data to the server via a wireless network.

[1690] Step 3:

[1691] Data accumulation

[1692] The server stores the collected environmental data and image data in a database that centrally manages them. The inputs are sensor data and image data, which are stored in the database in an appropriate format. Specific operations include organizing, verifying, and storing the data.

[1693] Step 4:

[1694] Data analysis

[1695] The server performs analysis using the collected and stored data. Inputs include stored environmental data, image data, and weather forecast data, and uses generative AI models and machine learning algorithms to evaluate weather forecasts, risk of pest outbreaks, and product quality. Specific operations include preprocessing each data set, extracting features, applying the analysis model, and interpreting the results.

[1696] Step 5:

[1697] Creating a plan

[1698] The server calculates optimal agricultural plans and production schedules based on the results of data analysis. The inputs are analysis results and past historical data, and the specific output is the creation of schedules for sowing, irrigation, fertilization, and harvesting, as well as factory production and maintenance plans. Specific operations include calculations using a schedule optimization algorithm and generating plans.

[1699] Step 6:

[1700] notification

[1701] The server notifies the user of the created plan. The input includes an optimal agricultural plan and production schedule, and this is sent to the user's smartphone, tablet, PC, etc. Specific operations include generating notification messages and sending push notifications and emails.

[1702] Example prompt sentences

[1703] For example, "Based on the factory sensor data (temperature: 24°C, humidity: 42%, defect probability: 0.7), calculate the next inspection and maintenance schedule and generate a defect risk notification message."

[1704] 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.

[1705] The present invention relates to a system that automatically processes weather information, environmental data, pest and disease information, and crop quality analysis to create appropriate agricultural plans, and further combines it with an emotion engine that recognizes user emotions and reflects them in the agricultural plans. The following forms can be used to implement this system.

[1706] System configuration

[1707] 1. Sensor means

[1708] Terminal: Humidity, temperature, and soil sensors installed in the field periodically measure environmental data (humidity, temperature, and soil condition).

[1709] Terminal: Drones fly over the fields periodically, taking pictures of the crops' condition with high-resolution cameras.

[1710] 2. Data storage means

[1711] Server: A database that receives and stores collected environmental and image data.

[1712] 3. Data Analysis Methods

[1713] Server: Analyzes the received data and makes a comprehensive assessment of weather forecasts, risk of pest and disease outbreaks, and soil conditions.

[1714] Server: Connects to a weather database to retrieve current and forecast weather information.

[1715] 4. Means of calculation

[1716] Server: Based on the analysis results, it creates specific agricultural plans, including the timing of sowing, irrigation, fertilization, and harvesting.

[1717] 5. Means of notification

[1718] Server: A communication means for notifying the user's device of the created agricultural plan.

[1719] Device: Display a notification message on the user's smartphone or PC.

[1720] 6. Emotion Engine

[1721] Server: An emotion engine that recognizes the user's emotions regarding the notified agricultural plan. The emotion engine determines emotions based on the user's voice input, text input, facial expression analysis, etc.

[1722] Server: Readjusts farming plans based on perceived user sentiment.

[1723] System operation example

[1724] Example 1: Irrigation scheduling and emotional feedback

[1725] 1. Terminal: An underground moisture sensor measures the current soil moisture.

[1726] 2. Server: Analyzes data from sensors and combines it with upcoming weather forecasts to calculate optimal irrigation timings.

[1727] 3. Server: Predicts that the field will likely be dehydrated at noon tomorrow and suggests a time to irrigate.

[1728] 4. Server: Notifies the user's device of the proposed plan.

[1729] 5. Device: Display a notification message on the user's smartphone or PC.

[1730] 6. User: Provide emotional feedback (e.g., dissatisfaction or satisfaction) about the proposed plan via voice or text input.

[1731] 7. Server: The emotion engine analyzes the feedback and makes necessary adjustments.

[1732] Example 2: Pest control and emotional feedback

[1733] 1. Terminal: The drone flies over the field and takes images of the crops.

[1734] 2. Server: Uses image analysis technology to detect traces of specific pests and diseases.

[1735] 3. Server: Determines that the risk of pest outbreaks is increasing and proposes specific control measures.

[1736] 4. Server: Notifies the user of the proposed prevention measures.

[1737] 5. Terminal: Notifies the user of prevention measures and encourages them to take them. The user also receives emotional feedback.

[1738] 6. Server: The emotion engine analyzes the feedback and adjusts the defense measures.

[1739] Example 3: Seeding plan creation and emotional feedback

[1740] 1. User: You are considering sowing a new crop.

[1741] 2. Terminal: Collects current weather information, soil conditions, and historical crop growth data.

[1742] 3. Server: Based on all the data, calculates the optimal sowing time and method.

[1743] 4. Server: Notifies the user's terminal of the seeding plan.

[1744] 5. Terminal: The user checks the seeding plan and provides emotional feedback via voice and text.

[1745] 6. Server: The emotion engine analyzes the feedback and makes necessary adjustments.

[1746] With this configuration and operation, the present invention can realize more efficient and user-friendly agricultural operations. By recognizing the user's emotions and reflecting them in the agricultural planning, it is possible to increase user satisfaction and improve the effectiveness of agriculture.

[1747] The processing flow will be explained below.

[1748] Step 1:

[1749] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Drones also fly periodically, taking photos of the crop condition with high-resolution cameras.

[1750] Step 2:

[1751] Terminal: Sends collected sensor data and image data to the server.

[1752] Step 3:

[1753] Server: Stores the received sensor data and image data in a database. The stored data includes the measurement date and time, sensor readings, image files, etc.

[1754] Step 4:

[1755] Server: Connects to a weather database to retrieve the latest weather forecast data, including temperature, precipitation, wind speed, etc.

[1756] Step 5:

[1757] Server: Uses collected environmental and weather forecast data to assess weather forecasts and pest risk using machine learning algorithms and references to historical data and expert models.

[1758] Step 6:

[1759] Server: Based on the analysis results, it calculates the optimal timing for future agricultural actions (e.g., irrigation, fertilization, harvesting, etc.) using algorithms that determine the optimal actions under specific conditions.

[1760] Step 7:

[1761] Server: Based on the calculation results, a specific agricultural plan is created, detailing the timing and method of each action.

[1762] Step 8:

[1763] Server: Sends the created farming plan to the user's device. The notification also includes details of the plan and the reason for its implementation.

[1764] Step 9:

[1765] Device: A notification message will be displayed on the user's smartphone or PC, allowing the user to review the plan and request adjustments if necessary.

[1766] Step 10:

[1767] User: Enter their feelings about the proposed farming plan via voice or text. Examples include, "I think this plan is good" or "This schedule is tough."

[1768] Step 11:

[1769] Terminal: Sends the user's emotional feedback to the server.

[1770] Step 12:

[1771] Server: The emotion engine analyzes user feedback and determines emotions using voice analysis and natural language processing techniques.

[1772] Step 13:

[1773] Server: Based on the results of sentiment analysis, the farming plan is readjusted as needed. For example, if a user expresses dissatisfaction with irrigation timing, that part is revised.

[1774] Step 14:

[1775] Server: The adjusted agricultural plan is sent back to the user's device.

[1776] Step 15:

[1777] Terminal: Sensors in the fields and drones continue to collect data and send it to the server.

[1778] Step 16:

[1779] Server: Continuously analyzes the received data and updates the farming plan as needed. The emotion engine also continuously receives user feedback and optimizes the plan.

[1780] In this way, the system supports agricultural activities sustainably and efficiently, and by reflecting the user's feelings, it is possible to realize more user-friendly agricultural operations.

[1781] Example 2

[1782] 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."

[1783] Conventional agricultural systems can create agricultural plans based on weather information and environmental data, but they only provide one-way notifications without considering the user's feelings, which has the problem of lacking user satisfaction and flexibility in the plans. This leads to plans not being carried out and users often becoming dissatisfied with the plans, and we aim to solve this problem.

[1784] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes sensor means for collecting weather information and environmental data, image acquisition means for capturing images of crops, data storage means for receiving and storing data collected by the sensor means and the image acquisition means, data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and the risk of pest and disease outbreaks, calculation means for creating an agricultural plan based on the results of the data analysis means, notification means for notifying a terminal of the agricultural plan created by the calculation means, and emotion recognition means for recognizing the user's emotions regarding the notified agricultural plan and readjusting the agricultural plan based on the emotions. This enables the creation of a flexible, satisfying agricultural plan that reflects the user's emotions.

[1785] "Sensor means" refers to a device for collecting weather information and environmental data, and includes humidity sensors, temperature sensors, soil sensors, and the like.

[1786] "Image acquisition means" refers to a device that captures images of crops, and includes drones equipped with high-resolution cameras and fixed cameras.

[1787] "Data storage means" refers to a storage device for receiving and storing data collected from the sensor means and image acquisition means, and includes a database.

[1788] The "data analysis means" is a device or program for analyzing the data stored in the data storage means and evaluating the weather forecast and the risk of pest outbreaks.

[1789] "Calculation means" refers to a device or program for creating an agricultural plan based on the results of the data analysis means, and includes machine learning algorithms.

[1790] The "notification means" is a device or program for notifying the user terminal of the agricultural plan created by the calculation means, and includes a communication means.

[1791] The "emotion recognition means" is a device or program that recognizes the user's emotions regarding the notified farming plan and readjusts the farming plan based on the emotions.

[1792] This invention relates to a system that automatically processes weather information, environmental data, pest and disease information, and crop quality analysis to create appropriate agricultural plans. It also combines an emotion engine that recognizes the user's emotions and reflects them in the agricultural plans.

[1793] System configuration

[1794] 1. Sensor means

[1795] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). Specifically, the humidity sensor measures data every 15 minutes and sends it to the gateway using Bluetooth or LoRa communication.

[1796] Device: The drone flies over the fields at a set time every day and takes pictures of the crops using a high-resolution camera. The captured image data is then uploaded to a server via Wi-Fi.

[1797] 2. Data storage means

[1798] Server: Receives and stores collected environmental and image data using a database such as MySQL or PostgreSQL. For example, temperature data is recorded in a temperature table, and humidity data is recorded in a humidity table.

[1799] 3. Data Analysis Methods

[1800] Server: Uses Python scripts to analyze the collected data, calling weather forecast APIs (e.g. OpenWeatherMap API) to retrieve current and forecast weather information, and predict trends in soil moisture and temperature.

[1801] Server: Image data is analyzed using image analysis libraries such as OpenCV. The analysis mainly detects pests and diseases and abnormalities in crops, and records the results in a database.

[1802] 4. Means of calculation

[1803] Server: Uses machine learning algorithms (e.g., TensorFlow, Scikit-learn) to create specific agricultural plans based on the latest analysis results. For example, if soil moisture is low, it will plan irrigation at noon the following day.

[1804] 5. Means of notification

[1805] Server: Uses Firebase Cloud Messaging or REST API to notify users of the created farming plan. Generates notification content (e.g., "Irrigation is required tomorrow at noon") and sends it to the user's smartphone.

[1806] Device: The user's smartphone or PC receives the notification and displays its contents, allowing the user to check the notification.

[1807] 6. Emotion recognition means

[1808] User: The user provides emotional feedback on the proposed farming plan (e.g., "This plan is good" or "Needs to be reconsidered") via voice or text. The voice input is converted to text using the Google Speech-to-Text API.

[1809] Server: An emotion engine (e.g., OpenAI GPT) analyzes the text feedback and readjusts the farming plan if the user expresses dissatisfaction or if the plan needs to be revised, again using machine learning algorithms.

[1810] Example: Irrigation scheduling and emotional feedback

[1811] 1. Terminal: The underground humidity sensor measures the soil humidity to be 40% at 2:00 pm on July 15th and sends the data to the server via the gateway via LoRa communication.

[1812] 2. Server: A Python script receives the humidity data and uses the OpenWeatherMap API to get the weather forecast for the next day, which predicts sunny skies with temperatures above 30 degrees.

[1813] 3. Server: The script detects the humidity deficit and determines that the best time to irrigate is at noon the next day. It creates a planning file and saves it in the database.

[1814] 4. Server: Use Firebase Cloud Messaging to send a notification to the user's smartphone saying, "Irrigation is required at noon on July 16th."

[1815] 5. Device: The smartphone receives the notification and displays the message. The user gives feedback by saying, "I'm happy with the plan."

[1816] 6. Server: The voice data is converted to text using the Google Speech-to-Text API, and the emotion engine analyzes the feedback such as "satisfied."

[1817] Prompt Sentence Examples

[1818] "Based on the humidity sensor data and weather forecast, please evaluate whether this irrigation plan is appropriate, reflecting the user's emotional feedback."

[1819] By implementing this aspect of the present invention, it becomes possible to create a flexible and satisfying agricultural plan that reflects the user's feelings, which results in improved efficiency and feasibility of farming, and increased user satisfaction.

[1820] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1821] Step 1:

[1822] Terminal: Humidity, temperature, and soil sensors installed in the fields periodically measure environmental data (humidity, temperature, and soil condition). For example, the humidity sensor measures soil humidity every 15 minutes and transmits the data to the gateway using Bluetooth or LoRa communication.

[1823] Input: Current humidity, temperature, soil data

[1824] Output: Transmits humidity, temperature, and soil data

[1825] Step 2:

[1826] Device: The drone flies over the field at a set time every day, taking pictures of the crops with a high-resolution camera. The captured image data is then uploaded to a server via Wi-Fi.

[1827] Input: Crop image

[1828] Output: Upload image data to the server

[1829] Step 3:

[1830] Server: Uses MySQL or PostgreSQL to receive data sent from sensors and drones and store it in a database.

[1831] Input: Sensor data, image data

[1832] Output: Environmental and image data stored in a database

[1833] Step 4:

[1834] Server: Uses Python scripts to analyze the environmental data stored in the database. Uses weather forecast APIs (e.g. OpenWeatherMap API) to retrieve current and forecast weather information and combine it with the environmental data for analysis.

[1835] Input: Environmental data, weather forecast data

[1836] Output: Analysis results based on weather forecasts and environmental data

[1837] Step 5:

[1838] Server: Image data is analyzed using image analysis libraries such as OpenCV to detect pests, diseases, and abnormalities in crops.

[1839] Input: Image data

[1840] Output: Analysis results on risk of pest and disease outbreaks and abnormalities in crops

[1841] Step 6:

[1842] Server: Based on the analysis results, machine learning algorithms (e.g., TensorFlow, Scikit-learn) are used to create agricultural plans. For example, if a humidity deficiency is detected, a plan to irrigate the area at noon the following day is created.

[1843] Input: Environmental data analysis results, image data analysis results

[1844] Output: Agricultural plan

[1845] Step 7:

[1846] Server: Uses Firebase Cloud Messaging or REST API to notify users of the created farming plan, such as a message like "Irrigation is required tomorrow at noon."

[1847] Input: Agricultural Plan

[1848] Output: Notification message to the user's terminal

[1849] Step 8:

[1850] Device: The user's smartphone or PC receives the notification and displays the notification content. The user checks the notification.

[1851] Input: Notification message

[1852] Output: Display notification information to the user

[1853] Step 9:

[1854] User: The user provides emotional feedback on the proposed farming plan via voice or text, for example, by saying "I'm happy with the plan."

[1855] Input: User's emotional feedback

[1856] Output: Send feedback data to the server

[1857] Step 10:

[1858] Server: The voice feedback is converted to text using the Google Speech-to-Text API. The text feedback is analyzed by an emotion engine (e.g., OpenAI GPT) and the farming plan is readjusted as needed based on the user's emotions.

[1859] Input: User sentiment feedback text

[1860] Output: Re-adjusted farming plan or unchanged feedback results

[1861] In this way, the present invention realizes a system that comprehensively utilizes weather information, environmental data, pest and disease information, and user emotions to provide accurate and satisfying agricultural planning.

[1862] (Application example 2)

[1863] 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."

[1864] Conventional agricultural management systems and factory management systems easily created plans based on the collection and analysis of environmental and production data, but lacked the ability to adjust plans while taking into account the emotions of users and employees. As a result, user satisfaction and working conditions were not sufficiently improved, making efficient operations difficult. Furthermore, in factories, one-sided production plans that ignored the emotions of employees often led to dissatisfaction and stress. The objective of this invention is to solve these problems and provide a system that enables the creation and adjustment of plans while taking into account the emotions of users and employees.

[1865] 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.

[1866] In this invention, the server includes a sensor means for collecting weather information and environmental data, an image acquisition means for acquiring images of crops, a data storage means for receiving and storing the data collected by the sensor means and the image acquisition means, a data analysis means for analyzing the data stored in the data storage means and evaluating the weather forecast and the risk of pest infestation, a calculation means for creating an agricultural plan based on the results of the data analysis means, a notification means for notifying a terminal of the agricultural plan created by the calculation means, a means for monitoring and analyzing factory environment data and production equipment data in real time, and an emotion analysis means for analyzing employee emotions and reflecting the results in the production plan. This makes it possible to design and adjust plans that reflect the emotions of users and employees.

[1867] "Weather information" is information relating to weather conditions, including data such as temperature, humidity, precipitation, wind speed, and wind direction.

[1868] "Environmental data" refers to data obtained by measuring environmental factors at a specific location, including temperature, humidity, soil condition, and air quality.

[1869] "Sensor means" refers to a device that detects a specific physical or chemical phenomenon and outputs it as data.

[1870] "Image capture means" refers to a device or method for photographing or capturing an image of an object.

[1871] "Data storage" refers to a system or device that stores collected data and makes it available for retrieval as needed.

[1872] "Data analysis means" refers to a system or method for analyzing collected data to extract meaningful information.

[1873] "Calculation means" refers to a system or device that performs calculations based on the obtained data and generates optimal plans and instructions.

[1874] "Notification means" refers to a system or method for transmitting the created plans and instructions to the user's terminal.

[1875] "Factory environmental data" refers to data related to various environmental factors within a factory (temperature, humidity, air quality, etc.).

[1876] "Production equipment data" refers to data relating to the operating status, operating rate, failure history, etc. of production equipment.

[1877] "Emotion analysis means" refers to a system or method for analyzing the emotions of users or employees and reflecting the analysis results in the system.

[1878] The present invention relates to a management system in a factory or agricultural environment, which analyzes collected data and creates an optimal plan that reflects the feelings of users and employees.

[1879] System configuration

[1880] 1. Environmental data collection methods

[1881] Sensor means:

[1882] Temperature, humidity and air quality sensors installed in factories and farms regularly measure environmental data.

[1883] Sensors are installed in factory production equipment to record operating conditions and failure history.

[1884] 2. Image acquisition method

[1885] Device:

[1886] Drones and fixed cameras are used to regularly capture images of crops in the fields and production equipment in factories.

[1887] 3. Data storage means

[1888] server:

[1889] A database for receiving and storing environmental data from factories and farms, and image data from crops and production equipment.

[1890] For the database, cloud servers (Google Cloud Platform, Amazon Web Services) or on-premise servers are used.

[1891] 4. Data Analysis Methods

[1892] server:

[1893] The collected environmental and image data is analyzed to comprehensively assess weather forecasts and the risk of pest and disease outbreaks.

[1894] At the factory, environmental data and production equipment data are analyzed to develop production plans.

[1895] By using machine learning algorithms (TensorFlow and PyTorch), the accuracy of the analysis is improved, and a plan is created based on the results.

[1896] 5. Means of calculation

[1897] server:

[1898] Based on the analysis results, agricultural and production plans are formulated, and the timing of sowing, irrigation, fertilization, harvesting, production schedules, etc. are calculated.

[1899] 6. Means of notification

[1900] server:

[1901] The created plan is notified to the user's device using Apple Push Notification Service (APNs) or Google Firebase Cloud Messaging (FCM).

[1902] Device:

[1903] Notification messages are displayed on the smartphones and head-mounted displays (HMDs) of users and factory employees.

[1904] 7. Emotion analysis method

[1905] server:

[1906] It has an emotion engine for analyzing user and employee emotions, and recognizes emotions using voice input (Apple Siri, Google Assistant), text input (chatbots), and facial expression analysis (Microsoft Azure Face API).

[1907] The emotion engine performs analysis using emotion analysis libraries (NLTK's VADER, Microsoft Azure's Text Analytics for Sentiment Analysis) and reflects the results in the plan.

[1908] Specific examples

[1909] For example, if a user is considering sowing a new crop, the optimal sowing time is calculated based on current weather information, soil conditions, and past growth data collected by the sensor means. The results are then sent to the user's smartphone. When the user provides feedback on the plan, the emotion engine analyzes their emotions and adjusts the sowing time as necessary.

[1910] Prompt Sentence Examples

[1911] You are designing a system that analyzes factory environmental data and production equipment data in real time to create optimal production plans. This system also includes an emotion engine that analyzes employee emotions and reflects them in production plans. Measure temperature, humidity, and air quality using sensors in the factory, and collect data on the availability and failure rates of production equipment. Store the collected data on a server and analyze it using a machine learning algorithm. Notify managers and employees of the analysis results and production plans, collect emotional feedback, and adjust the plans accordingly. Please explain the specific steps.

[1912] In this way, the present invention provides a more efficient and user-friendly system by creating and adjusting plans that reflect the feelings of users and employees.

[1913] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1914] Step 1:

[1915] Environmental and image data collection

[1916] Input: Environmental data such as temperature, humidity, and air quality from sensors installed in factories and farms, and image data of crops and production equipment collected periodically.

[1917] How it works: Sensors in factories and farms periodically measure data and transmit it to a central server via wireless or wired connections. Drones and fixed cameras also capture image data and transmit it to the server in real time.

[1918] Step 2:

[1919] Accumulation of collected data

[1920] Input: Environmental data, image data

[1921] Output: Data stored in the database

[1922] Specific operation: The server stores the received environmental data and image data in a database as a data storage means. This database uses a cloud server or an on-premise server.

[1923] Step 3:

[1924] Data analysis

[1925] Input: Environmental data and image data stored in a database

[1926] Output: Information such as weather forecasts, risk of pest outbreaks, and operational status of production facilities

[1927] How it works: The server uses machine learning algorithms to comprehensively analyze the collected data, for example, using TensorFlow or PyTorch to evaluate weather forecasts, risk of pest outbreaks, and the operating status of production facilities.

[1928] Step 4:

[1929] Creating a plan

[1930] Input: Analysis results (weather forecast, pest risk, production facility operation status, etc.)

[1931] Output: Optimal agricultural and production planning

[1932] Specific operations: Based on the analysis results, the server creates specific plans for sowing, irrigation, fertilization, harvesting, production schedules, etc.

[1933] Step 5:

[1934] Plan Notification

[1935] Input: Created plan

[1936] Output: Notification message to the user's terminal

[1937] Specific operation: The server notifies the user and factory employees of the created plan via their devices (smartphones or head-mounted displays) using Apple Push Notification Service (APNs) or Google Firebase Cloud Messaging (FCM).

[1938] Step 6:

[1939] User and employee sentiment analysis

[1940] Input: User / employee voice input, text input, and facial expression data

[1941] Output: Emotion analysis results

[1942] Specific operation: The server collects emotional data from users and employees using voice input (Apple Siri, Google Assistant), text input (chatbot), and facial expression analysis (Microsoft Azure Face API), and analyzes the data using an emotion analysis library (nltk's VADER or Microsoft Azure's Text Analytics for Sentiment Analysis).

[1943] Step 7:

[1944] Re-adjusting plans

[1945] Input: Sentiment analysis results

[1946] Output: Re-adjusted agricultural and production plans

[1947] Specific operation: Based on the results of the sentiment analysis, the server readjusts the agricultural and production plans as necessary and notifies the users and employees again.

[1948] Through the above steps, the present invention can realize efficient and user-friendly agricultural and factory management.

[1949] 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.

[1950] 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.

[1951] 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.

[1952] 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.

[1953] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion 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.

[1954] 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.

[1955] 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).

[1956] 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.

[1957] 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."

[1958] 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.

[1959] 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).

[1960] 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.

[1961] 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.

[1962] 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.

[1963] 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.

[1964] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another 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.

[1965] 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.

[1966] 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.

[1967] 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.

[1968] 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.

[1969] 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.

[1970] The following is further disclosed regarding the above embodiment.

[1971] (Claim 1)

[1972] Sensor means for collecting weather information and environmental data;

[1973] image acquisition means for capturing an image of the crop;

[1974] data storage means for receiving and storing data collected by said sensor means and said image acquisition means;

[1975] data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and risks of pest outbreaks;

[1976] a calculation means for creating an agricultural plan based on the results of the data analysis means;

[1977] a notification means for notifying a terminal of the agricultural plan created by the calculation means;

[1978] A system including:

[1979] (Claim 2)

[1980] 2. The system of claim 1, wherein said data storage means comprises means connected to a weather forecast database for obtaining current and forecast weather information.

[1981] (Claim 3)

[1982] 2. The system according to claim 1, wherein the data analysis means evaluates the risk of pest infestation using a machine learning algorithm.

[1983] "Example 1"

[1984] (Claim 1)

[1985] Sensor means for collecting weather information and environmental data;

[1986] image acquisition means for capturing an image of the crop;

[1987] data storage means for receiving and storing data collected by said sensor means and said image acquisition means;

[1988] a data analysis means for analyzing the data stored in the data storage means using a machine learning algorithm to evaluate weather forecasts and risks of pest outbreaks;

[1989] a calculation means for creating an agricultural plan based on the results of the data analysis means;

[1990] a notification means for notifying a terminal of the agricultural plan created by the calculation means;

[1991] A system including:

[1992] (Claim 2)

[1993] 2. The system of claim 1, wherein said data storage means is connected to a weather information database and includes means for obtaining current and forecast weather information.

[1994] (Claim 3)

[1995] The system of claim 1, wherein the data analysis means uses a machine learning algorithm to comprehensively evaluate data on humidity, temperature, and soil condition of the field.

[1996] (Claim 4)

[1997] 2. The system of claim 1, wherein the image analysis means uses image analysis techniques to analyze images of the crop and detect signs of pests or diseases.

[1998] (Claim 5)

[1999] 2. The system of claim 1, wherein the notification means uses cloud messaging technology to send the agricultural plan to the user's terminal.

[2000] "Application Example 1"

[2001] (Claim 1)

[2002] Sensor means for collecting weather information and environmental data;

[2003] image acquisition means for capturing an image of the crop;

[2004] data storage means for receiving and storing data collected by said sensor means and said image acquisition means;

[2005] data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and risks of pest outbreaks;

[2006] a calculation means for creating an agricultural plan based on the results of the data analysis means;

[2007] a notification means for notifying a terminal of the agricultural plan created by the calculation means;

[2008] a data analysis means for analyzing sensor data in the factory and evaluating the state of product quality;

[2009] Calculation tools for creating optimal production schedules and improvement proposals;

[2010] a notification means for notifying a terminal of the production schedule and improvement proposals created by the calculation means;

[2011] A system including:

[2012] (Claim 2)

[2013] 2. The system of claim 1, wherein said data storage means comprises means connected to a weather forecast database for obtaining current and forecast weather information.

[2014] (Claim 3)

[2015] 2. The system of claim 1, wherein the data analysis means comprises means for assessing product quality status using a generative AI model.

[2016] "Example 2: Combining Emotion Engines"

[2017] (Claim 1)

[2018] Sensor means for collecting weather information and environmental data;

[2019] image acquisition means for capturing an image of the crop;

[2020] data storage means for receiving and storing data collected by said sensor means and said image acquisition means;

[2021] data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and risks of pest outbreaks;

[2022] a calculation means for creating an agricultural plan based on the results of the data analysis means;

[2023] a notification means for notifying a terminal of the agricultural plan created by the calculation means;

[2024] an emotion recognition means for recognizing the user's emotion regarding the notified agricultural plan and readjusting the agricultural plan based on the emotion;

[2025] A system including:

[2026] (Claim 2)

[2027] 2. The system of claim 1, wherein said data storage means comprises means connected to a weather forecast database for obtaining current and forecast weather information.

[2028] (Claim 3)

[2029] 2. The system according to claim 1, wherein the data analysis means evaluates the risk of pest infestation using a machine learning algorithm.

[2030] "Application example 2 when combining emotion engines"

[2031] (Claim 1)

[2032] Sensor means for collecting weather information and environmental data;

[2033] image acquisition means for acquiring an image of the crop;

[2034] data storage means for receiving and storing data collected by said sensor means and said image acquisition means;

[2035] data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and risks of pest outbreaks;

[2036] a calculation means for creating an agricultural plan based on the results of the data analysis means;

[2037] a notification means for notifying a terminal of the agricultural plan created by the calculation means;

[2038] A means of monitoring and analyzing factory environment data and production equipment data in real time;

[2039] An emotion analysis method that analyzes employee emotions and reflects the results in production plans;

[2040] A system including:

[2041] (Claim 2)

[2042] 2. The system of claim 1, wherein said data storage means comprises means connected to a weather forecast database for obtaining current and forecast weather information.

[2043] (Claim 3)

[2044] 2. The system according to claim 1, wherein the data analysis means evaluates the risk of pest infestation using a machine learning algorithm. [Explanation of symbols]

[2045] 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. Sensor means for collecting weather information and environmental data; image acquisition means for capturing an image of the crop; data storage means for receiving and storing data collected by said sensor means and said image acquisition means; data analysis means for analyzing the data stored in the data storage means and evaluating weather forecasts and risks of pest outbreaks; a calculation means for creating an agricultural plan based on the results of the data analysis means; a notification means for notifying a terminal of the agricultural plan created by the calculation means; A system including:

2. 2. The system of claim 1, wherein said data storage means includes means connected to a weather forecast database for obtaining current and forecast weather information.

3. The system according to claim 1 , wherein the data analysis means evaluates the risk of pest infestation using a machine learning algorithm.

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