System

Abstract

A system is provided.SOLUTION: A system comprising: sensor means for measuring environmental information such as soil conditions, moisture levels, light levels, and the like; storage means for storing the environmental information collected from the sensors in a cloud database; analysis means for executing a generative AI model to perform an analysis based on the information stored in the cloud database; action plan generation means for generating an action plan for crop management based on the analysis; and prediction means for providing a prediction of future crop production.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional agricultural management requires humans to directly observe and manage the condition of the soil and the health of the crops, which requires a great deal of time and effort. It also requires specialized knowledge and experience, is susceptible to the effects of climate change and pests, and results in unstable yields and quality. There is a need to solve these problems and realize efficient, smart agricultural management. [Means for solving the problem]

[0005] The present invention solves these problems with a system that includes a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity, a storage means for storing environmental data collected from the sensor in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating a crop management action plan based on the analysis results, and a prediction means for providing future crop production forecasts. Furthermore, the generative AI model analyzes past data stored in the cloud database and data acquired in real time, and notifies the user's device of the generated crop management action plan, enabling farmers to achieve efficient and smart agricultural management.

[0006] "Soil conditions" refers to the environmental conditions that affect plant growth, such as soil moisture, fertility, pH level, and temperature.

[0007] "Moisture content" is an index that measures the amount of water contained in soil or the environment.

[0008] "Light intensity" is an index that indicates the intensity and amount of sunlight or artificial light required for plants to perform photosynthesis.

[0009] "Sensor means" refers to a device used to measure environmental data, such as soil condition, moisture content, light level, etc.

[0010] "Storage means" refers to a system or device for storing and managing data collected from sensors.

[0011] "Cloud database" refers to a database system that is stored on and accessible from servers on the Internet.

[0012] "Analytics" refers to systems or software that analyze information based on collected data to derive specific patterns or insights.

[0013] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to analyze large amounts of data.

[0014] "Action plan generation means" refers to a system or software that generates a specific action plan for crop management based on the analysis results.

[0015] "Prediction tools" refers to systems or methods that predict future situations or events based on analytical results or current data.

[0016] "Notification means" refers to a system or device for transmitting the generated action plan and prediction results to the user's terminal.

[0017] "User's terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention aims to realize smart and efficient agricultural management through a system that integrates generative AI models and digital sensors. This system monitors soil conditions and farmland environments in real time, and provides optimal crop management methods, pest prevention, and resource management based on data analysis using generative AI models.

[0040] The server receives data from sensors that measure environmental data such as soil condition, moisture content, light intensity, etc. These sensors are installed in the fields and measure the required data every hour. For example, if the soil moisture content is measured to be 42%, this data is sent from the sensor to the server in real time.

[0041] The server stores the received data in a cloud database and manages the data securely. The data temporarily stored in memory is quickly transferred to the cloud database via storage means. The database stores not only current data, but also past farmland data and environmental data.

[0042] The server, as the analytical tool, runs generative AI models based on historical and real-time data stored in a cloud database. For example, it combines three years of weather data with current soil data to gain specific insights, such as the increased risk of disease when soil moisture falls below 30%. Such analysis results provide farmers with valuable information to take preemptive measures.

[0043] The server has a means to generate an optimal crop management action plan based on the analysis results of the generative AI model. For example, a detailed action plan may be generated, such as "Since the soil moisture content is expected to decrease this week, 0.5 liters of irrigation should be carried out in three days." This plan is stored in a cloud database and notified to the user's device.

[0044] The user's device (smartphone or PC) receives and displays the action plan and prediction results sent from the server. The user can then carry out specific farm work in accordance with the action plan. For example, the user can check the notification and carry out actual irrigation work based on the irrigation schedule for the next two days.

[0045] The server, which acts as a forecasting tool, predicts future crop growth and risks based on current data and analysis results. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes." This information is sent to the user's device, allowing farmers to create production plans based on it.

[0046] In this way, the system of the present invention integrates sensor means, storage means, analysis means, action plan generation means, prediction means, and notification means, thereby enabling farmers to achieve more efficient and smarter agricultural management.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] Sensors are installed in the fields and measure environmental data such as soil condition, moisture content, light intensity, and temperature every hour, and this data is temporarily stored inside the sensors.

[0050] Step 2:

[0051] The sensor sends the measured data to a server via a wireless network. For example, if the soil moisture content is measured at 42%, this data is sent to the server.

[0052] Step 3:

[0053] The server receives the data sent from the sensor and temporarily stores it in the server's memory.

[0054] Step 4:

[0055] The server stores the environmental data stored in the memory in a cloud database, and the soil humidity and other environmental data are quickly transferred to the cloud database through the storage means.

[0056] Step 5:

[0057] Data stored in a cloud database is collected by a server and input into a generative AI model, which then begins analysis based on past and current data.

[0058] Step 6:

[0059] The generative AI model performs the analysis and extracts specific patterns and insights, such as "When humidity drops below 30%, the risk of disease increases."

[0060] Step 7:

[0061] The server generates an optimal crop management action plan based on the analysis results. For example, it might suggest irrigating 0.5 liters every morning for the next two days to maintain moisture levels.

[0062] Step 8:

[0063] The server notifies the user's device of the generated action plan, which is then sent to the smartphone or PC via a notification means.

[0064] Step 9:

[0065] The user's device receives and displays the action plan sent from the server. The user checks the notification and makes preparations to carry out specific farm work.

[0066] Step 10:

[0067] The server uses current data and analysis results to predict future crop growth and risks. For example, it predicts that "temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[0068] Step 11:

[0069] The server notifies the user's terminal of the prediction result, and the server transmits the prediction result to the user's terminal through the prediction means.

[0070] Step 12:

[0071] The user's terminal receives and displays the forecast results sent from the server, allowing the user to efficiently create production plans based on the forecasts.

[0072] Example 1

[0073] 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."

[0074] Conventional agricultural management systems suffer from fragmented data collection and analysis, lacking real-time capabilities, making it difficult to efficiently manage resources and develop preventative measures. Furthermore, they lack the information users need to take appropriate action in a timely manner, resulting in suboptimal productivity and quality of agricultural crops. The present invention aims to solve these problems by providing a system that realizes efficient and smart agricultural management based on real-time data from sensors.

[0075] 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.

[0076] In this invention, the server includes a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity, a storage means for storing the environmental data collected from the sensor in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating a crop management action plan based on the analysis results, a notification means for notifying a user terminal of the generated action plan, a user means for performing crop management based on the action plan notified to the user terminal, and a prediction means for providing a future crop production forecast. This enables precise analysis based on real-time data and the provision of an accurate action plan.

[0077] "Sensor means" refers to a device for measuring environmental data such as soil condition, moisture content, and light intensity.

[0078] "Storage means" refers to equipment or functionality for storing environmental data collected from sensors in a cloud database.

[0079] "Analysis means" refers to a device or function for executing a generative AI model that performs analysis based on data stored in a cloud database.

[0080] A "generative AI model" is an artificial intelligence model that performs analysis based on past data stored in a cloud database and real-time data.

[0081] An "action plan generation means" is a device or function for generating a crop management action plan based on the analysis results of the generative AI model.

[0082] The "notification means" is a facility or function for notifying the user's terminal of the generated action plan.

[0083] The "user means" refers to a person and their operating means for carrying out crop management based on the action plan notified to the user's terminal.

[0084] A "forecasting means" is a device or function for providing a forecast of future crop production.

[0085] A "cloud database" is a database accessible via the Internet, and is a system for storing data collected from sensors and analysis results.

[0086] "Analysis Results" means the output data analyzed by the generative AI model, including insights and recommended actions for crop management.

[0087] "Real-time data" is data that is currently being collected from sensors.

[0088] An "action plan" is a specific plan or instruction for crop management generated based on the analysis results.

[0089] A "terminal" is a device, such as a smartphone or PC, that users use to receive and check information.

[0090] This invention realizes smart and efficient agricultural management through a system that integrates generative AI models and digital sensors. This system is implemented using the following main hardware and software:

[0091] Hardware and software used

[0092] 1. Sensor means

[0093] Digital sensors installed on farmland include soil moisture sensors, light sensors, and temperature sensors, which serve to measure environmental data in real time.

[0094] 2. Server

[0095] The servers used to collect and analyze data are common cloud-based servers (e.g., AWS EC2, Google Cloud Compute Engine).

[0096] 3. Cloud Database

[0097] Databases used to store and manage environmental data include Amazon RDS and Google Cloud Firestore.

[0098] 4. Generative AI Models

[0099] Generative AI models used for data analysis include OpenAI's GPT-3, for example.

[0100] System Overview

[0101] The server first obtains environmental data such as soil condition, moisture content, and light intensity from the sensor means every hour and temporarily stores it in memory. This data is then sent to and managed in a cloud database, which also stores past data and serves as the basis for analysis by the generative AI model.

[0102] The generative AI model performs advanced analysis using historical data stored in a cloud database and real-time data. For example, the following prompt sentence is provided to the generative AI model:

[0103] "Based on the past three years of weather data and current soil data, what are the growth risks for tomatoes over the next two weeks and what management practices are recommended?"

[0104] The generative AI model analyzes this prompt and outputs specific insights. Based on the analysis results, the server generates an action plan for crop management. For example, if a drop in soil moisture is predicted, a specific action plan will be generated, such as "irrigate 0.5 liters in three days." This plan is stored in a cloud database and notified to the user's device.

[0105] The user's device (smartphone or PC) receives and displays the action plan and prediction results sent from the server. The user can then carry out specific farm work based on this information. For example, the user can check the notification on their device and carry out actual irrigation work according to the irrigation schedule.

[0106] Furthermore, as a predictive tool, the server uses current data and analysis results to predict future crop growth and risks. For example, it notifies users of a forecast result such as "Tomatoes will grow faster because temperatures will rise sharply in the next two weeks." This allows users to adjust their future production plans and make agricultural activities more efficient and effective.

[0107] The above is an embodiment of the system of the present invention, which enables farmers to achieve more efficient and smarter farm management and improve crop productivity and quality.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1:

[0110] Collecting data from sensors

[0111] Input: Environmental data such as soil condition, moisture content, and light intensity from digital sensors installed on farmland

[0112] How it works: The sensor measures environmental data every hour.

[0113] Output: Measured environmental data (e.g., soil moisture content 42%)

[0114] Step 2:

[0115] Sending data to the server

[0116] Input: Environmental data collected from sensors

[0117] How it works: The sensor sends measurement data to the server in real time.

[0118] Output: Environmental data sent to the server (with timestamp)

[0119] Step 3:

[0120] Data storage

[0121] Input: Environmental data received by the server

[0122] How it works: The server temporarily stores the data in memory, and then stores it in a cloud database, for example Amazon RDS, using an API call.

[0123] Output: Data stored in a cloud database

[0124] Step 4:

[0125] Analysis using generative AI models

[0126] Input: Historical and real-time environmental data stored in a cloud database

[0127] How it works: The server prompts the generative AI model with the following prompt: "Based on the past three years of climate data and current soil data, what are the growth risks and recommended management practices for tomatoes over the next two weeks?"

[0128] Output: Analysis results from the generative AI model (e.g., disease risk increases when soil moisture falls below 30%)

[0129] Step 5:

[0130] Generate an action plan

[0131] Input: Analysis results of the generative AI model

[0132] How it works: The server generates an action plan for farming based on the analysis results. For example, it creates a specific plan that states, "Because the moisture content will decrease over the next three days, 0.5 liters of irrigation should be carried out in three days."

[0133] Output: Generated action plan

[0134] Step 6:

[0135] User Notification

[0136] Input: Generated Action Plan

[0137] How it works: The server notifies the user of the action plan via push notification or email.

[0138] Output: An action plan displayed on the user's device (e.g., a notification that "watering should be performed in 3 days").

[0139] Step 7:

[0140] User confirmation and execution

[0141] Input: Action plan sent to the user's device

[0142] Operation: The user checks the notification on the device and performs farm work according to the proposed action plan. For example, the user irrigates based on the irrigation schedule. The user then enters the progress of this work into another app or management system.

[0143] Output: Results and progress data of the farming operations performed

[0144] Step 8:

[0145] Providing forecast data

[0146] Input: Analysis results from the generative AI model and current environmental data

[0147] How it works: The server predicts future crop growth and risks and provides that information to the user. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes."

[0148] Output: Forecast information displayed on the user's terminal and data for adjusting production plans

[0149] (Application example 1)

[0150] 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."

[0151] Modern logistics centers are susceptible to a wide range of environmental variables (temperature, humidity, light intensity, vibration, etc.), and require appropriate management to maintain the quality of cargo. However, current systems for monitoring these environmental variables in real time and providing optimal cargo management methods are inadequate. This increases the risk of cargo deterioration and loss, and reduces the efficiency of logistics operations.

[0152] 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.

[0153] In this invention, the server includes a sensor means for measuring environmental data such as temperature, humidity, light intensity, and vibration, a storage means for storing the environmental data collected from the sensors in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating an action plan for package management based on the analysis results, and a prediction means for providing a prediction of future package deterioration. This makes it possible to monitor the environment within the logistics center in real time and achieve optimal package management based on precise data analysis and predictions by the generative AI model.

[0154] "temperature"

[0155] is an index that indicates the thermal energy of a substance, and is an important factor in maintaining the quality of cargo in environmental management within a logistics center.

[0156] "Humidity"

[0157] is an indicator of the proportion of water vapor in the air, and is a factor that affects the storage conditions of cargo in environmental control within logistics centers.

[0158] "Light intensity"

[0159] is an indicator of the intensity of light measured at a specific location, and is an environmental factor that needs to be managed to prevent deterioration within a logistics center.

[0160] "vibration"

[0161] This refers to the phenomenon of an object shaking, and indicates the physical stimulation that cargo in a logistics center receives, making it an important factor in assessing the risk of damage to cargo.

[0162] "Sensor means"

[0163] refers to devices and equipment for measuring and acquiring environmental data.

[0164] "Storage means"

[0165] refers to a system or device for storing environmental data collected from sensors.

[0166] Cloud database

[0167] refers to a remote database service for storing and managing data over the Internet.

[0168] "Analysis means"

[0169] This refers to systems and devices that perform analysis based on data stored in a cloud database.

[0170] "Generative AI model"

[0171] refers to an artificial intelligence system that uses machine learning technology to analyze data and make predictions.

[0172] "Action plan generation method"

[0173] This refers to a system or device that creates specific management methods and countermeasures based on the analysis results.

[0174] "Prediction methods"

[0175] refers to systems and devices that provide predictions regarding future cargo deterioration risks and management.

[0176] "Notification means"

[0177] This refers to a system or device that notifies the user of the generated action plan or prediction results on their device.

[0178] "User's Device"

[0179] refers to a digital device for receiving and displaying generated action plans and forecast results.

[0180] The present invention is a system for achieving optimal cargo management and deterioration prediction in a logistics center. This system includes a sensor means for measuring environmental data such as temperature, humidity, light intensity, and vibration, a storage means for storing the environmental data collected from the sensors in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating an action plan for cargo management based on the analysis results, and a prediction means for providing a prediction of future cargo deterioration.

[0181] Specific implementation methods of the system

[0182] Configuration and operation of the sensor means

[0183] Digital sensors (such as the Bosch BME280 sensor) are installed in the logistics center to measure temperature, humidity, light intensity, vibration, etc. These sensors measure environmental data every hour and are placed in different locations within the center to achieve comprehensive monitoring.

[0184] Data collection and storage methods

[0185] The collected data is sent to a cloud database (e.g., Amazon Web Services (AWS) or Google Cloud) via an internet connection. The data is stored in the cloud database in real time via a storage means.

[0186] Analysis methods and generative AI models

[0187] The server uses a machine learning platform (e.g., TensorFlow or PyTorch) to analyze historical data stored in a cloud database and data acquired in real time. The generative AI model uses environmental data as input to analyze deterioration risks and optimal management methods.

[0188] Action plan generation method

[0189] It also includes a means for generating a specific action plan based on the analysis results. For example, if the temperature exceeds a preset threshold, it generates instructions such as moving the package to a refrigerated area. The generated action plan is stored in a cloud database.

[0190] Prediction and Notification

[0191] The prediction method predicts future risk of cargo deterioration based on current environmental data and analysis results. This predicted information is sent to the user's device (smartphone or PC). Notifications are sent in real time using notification services such as Pushbullet.

[0192] Specific examples

[0193] Users manage their logistics centers using the smartphone application "LogiSmart." The app receives data collected from sensors and provides analysis results, action plans, and forecasts in real time. For example, the following prompts can be used to input data into the generative AI model:

[0194] "If the temperature data is above 25°C, there is a high risk that the quality of package A will deteriorate within a day. Generate an action plan to move it to a refrigerated area until the temperature stabilizes."

[0195] This system allows logistics center managers to effectively maintain the quality of goods and minimize the risk of deterioration.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] The server collects environmental data from digital sensors that measure temperature, humidity, light intensity, vibration, etc. These sensors are installed in the distribution center and collect data every hour. The input data are the values ​​obtained from each sensor and are sent to the server through the sensor API. The output data is a set of environmental data.

[0199] Step 2:

[0200] The server stores the collected environmental data in a cloud database via a storage means. The input data is the environmental data collected in step 1, and the output data is the data converted into a format stored in the cloud database. This data is transmitted via the Internet and protected by security protocols.

[0201] Step 3:

[0202] The server retrieves the historical data and real-time retrieved data stored in the cloud database, where the input data is a set of historical and real-time data retrieved from the cloud database, and the output data is a dataset prepared for analysis.

[0203] Step 4:

[0204] The server performs data analysis using a generative AI model. The input data is the dataset prepared in step 3 and is fed into the generative AI model. The output data is the analysis results, which are the luggage management method and deterioration risk assessment. The generative AI model uses a machine learning platform such as TensorFlow or PyTorch.

[0205] Step 5:

[0206] The server generates a specific action plan based on the analysis results. The input data is the analysis results obtained in step 4, and the output data is a specific action plan, such as "move the package to the refrigerated area immediately." The action plan generation is based on predefined rules and prompt statements.

[0207] Step 6:

[0208] The server notifies the user of the generated action plan and prediction results. The input data is the action plan and prediction results generated in step 5, and the output data is a notification message sent to the user's device. The notification is sent using a notification service such as Pushbullet. Specifically, if the temperature exceeds 25°C, a message stating "there is an increased risk that the quality of package A will deteriorate within one day" is sent to the user's smartphone.

[0209] Step 7:

[0210] After receiving the notification, the user follows the instructions to take specific management actions. The input data is the action plan notified in step 6, and the output data is the actual package management action. For example, if the temperature rises, the user can move the package to a refrigerated area.

[0211] 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.

[0212] This invention realizes smart and efficient agricultural management through a system that integrates generative AI models, digital sensors, and an emotion engine that recognizes user emotions. This system monitors soil conditions and the environment in real time, and provides optimal crop management methods, pest prevention, and resource management based on data analysis using generative AI models, while also incorporating user emotions into the analysis.

[0213] The server receives data from sensors that measure environmental data such as soil condition, moisture content, light intensity, etc. These sensors are installed in the fields and measure the required data every hour. For example, if the soil moisture content is measured to be 42%, this data is sent from the sensor to the server in real time.

[0214] The server stores the received data in a cloud database and manages the data securely. The data temporarily stored in memory is quickly transferred to the cloud database via storage means. The database stores not only current data, but also past farmland data and environmental data.

[0215] The server, which acts as the analytical tool, runs generative AI models based on historical and real-time data stored in a cloud database. For example, it combines three years of weather data with current soil data to obtain specific insights, such as "the risk of disease increases when soil moisture falls below 30%." Such analysis results provide farmers with valuable information to take preemptive measures.

[0216] The server, which acts as the action plan generator, generates an optimal crop management action plan based on the analysis results of the generative AI model. For example, a detailed action plan may be generated stating, "Because the soil moisture content is expected to decrease this week, 0.5 liters of irrigation should be carried out in three days." This plan is stored in a cloud database and notified to the user's device.

[0217] The server, which acts as a forecasting tool, predicts future crop growth and risks based on current data and analysis results. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes." This information is sent to the user's device, allowing farmers to create production plans based on it.

[0218] Furthermore, this invention incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, voice, posture, etc., and stores the emotion data in a cloud database. The emotion engine analyzes the user's emotions in real time and reflects the emotion data in the analysis of the generative AI model, thereby adjusting the action plan according to the user's emotions. For example, if the user is feeling stressed, a plan such as "reduce the amount of work you should do over the next week" is suggested based on that data.

[0219] The server, which acts as a notification means, notifies the user's device of the generated action plan, prediction results, and emotion analysis results. The user can check the notifications via their device (smartphone or PC) and carry out specific farm work or lifestyle plans. For example, the server can notify the user of the irrigation schedule for the next two days and relaxation suggestions based on the user's current emotional state, allowing the user to take appropriate measures based on this information.

[0220] In this way, the system of the present invention integrates sensor means, storage means, analysis means, action plan generation means, prediction means, emotion engine, and notification means to enable farmers to achieve more efficient and smarter agricultural management.

[0221] The processing flow will be explained below.

[0222] Step 1:

[0223] Sensors are installed in agricultural fields and measure environmental data such as soil condition, moisture content, light intensity, and temperature every hour. This data is temporarily stored inside the sensor. For example, the soil moisture content may be measured as 42%.

[0224] Step 2:

[0225] The sensor transmits the measured data to a server via a wireless network. For example, if the moisture content is measured as 42%, the sensor transmits the data to the server in real time.

[0226] Step 3:

[0227] The server receives the data sent from the sensor and temporarily stores it in the server's memory.

[0228] Step 4:

[0229] The server stores the environmental data stored in the memory in a cloud database, and through the storage means, the soil moisture content and other environmental data are quickly transferred to the cloud database.

[0230] Step 5:

[0231] The server collects historical data stored in a cloud database and newly acquired real-time data, which are then input into the generative AI model. The historical data includes, for example, three years of weather and agricultural data.

[0232] Step 6:

[0233] A generative AI model analyzes the data and extracts specific patterns and insights, for example, it may conclude that soil moisture levels below 30% increase the risk of disease.

[0234] Step 7:

[0235] The server generates an optimal crop management action plan based on the analysis results, for example, a specific action plan such as "irrigate 0.5 liters every morning for the next two days."

[0236] Step 8:

[0237] The server notifies the user's device of the generated action plan, and a detailed action plan is sent to the smartphone or PC via the notification means.

[0238] Step 9:

[0239] The device receives the notification from the server and displays it to the user, who can then check the displayed irrigation schedule on their smartphone.

[0240] Step 10:

[0241] The server uses current data and analysis results to predict future crop growth and risks. For example, it predicts that temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster.

[0242] Step 11:

[0243] The server notifies the user's terminal of the prediction result, and the prediction information is transmitted to the terminal through the prediction means.

[0244] Step 12:

[0245] The user's terminal receives and displays the prediction results, allowing the user to efficiently create production plans based on the predictions.

[0246] Step 13:

[0247] The server uses an emotion engine to analyze the user's emotions, which are based on data acquired from the camera and microphone on the user's device.

[0248] Step 14:

[0249] The emotion engine stores the user's emotion data in a cloud database and adds it to the generative AI model. For example, data indicating that the user is feeling stressed is stored.

[0250] Step 15:

[0251] The server adjusts the analysis results of the generative AI model based on the emotional data. For example, if stress is high, an action plan to reduce it will be generated.

[0252] Step 16:

[0253] The server then sends the adjusted action plan and prediction results to the user's device, where the user can check the notification and take action according to their emotional state.

[0254] Example 2

[0255] 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."

[0256] Conventional agricultural management systems are limited to collecting and analyzing environmental data, and are unable to create work plans that take into account the user's emotional state, making efficient agricultural management difficult. Furthermore, it is difficult to provide appropriate crop management and future predictions, which can lead to increased stress and fatigue for users.

[0257] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity, a storage means for storing the environmental data collected from the sensor in a cloud database, and an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database. This not only enables the aggregation and analysis of environmental data in real time, but also enables comprehensive crop management and future predictions that take into account user emotional data.

[0258] "Sensor means" refers to a device that measures environmental data such as soil condition, moisture content, and light intensity.

[0259] "Storage means" refers to a device or method for storing environmental data collected from sensors in a cloud database.

[0260] "Analysis means" refers to a device or method that executes a generative AI model based on data stored in a cloud database to perform data analysis.

[0261] A "generative AI model" is an artificial intelligence technology that analyzes historical and real-time data to generate insights and predictions.

[0262] An "action plan generation means" is a device or method that generates an optimal crop management action plan based on the analysis results of a generative AI model.

[0263] A "prediction means" is a device or method for predicting future crop growth and risks based on current data and analysis results.

[0264] "Emotion analysis means" refers to a device or method for analyzing the user's facial expression, voice, posture, etc., to grasp the user's emotional state.

[0265] The "notification means" is a device or method for notifying the user's terminal of the generated action plan, prediction results, and emotion analysis results.

[0266] A "cloud database" is a database that exists on the Internet and is a system that provides accessible storage resources.

[0267] A "user terminal" is a device that can be directly operated by a user, such as a smartphone or a personal computer.

[0268] The system integrates generative AI models, digital sensors, and an emotion engine to achieve smart and efficient agricultural management. Through interactions between servers, terminals, and users, the system collects and analyzes environmental data, performs optimal crop management, predictions, and notifications.

[0269] Hardware and Software Configuration

[0270] 1. Digital Sensor:

[0271] Digital sensors are installed in farmland and have the ability to measure environmental data such as soil condition (moisture content, pH value, etc.), light intensity, and temperature every hour, and this data is sent to a server in real time.

[0272] 2. Server:

[0273] The server temporarily stores the received environmental data in its memory and then rapidly transfers it to a cloud database using a storage means. The server also includes an analysis means that uses a generative AI model to analyze the historical and real-time data stored in the cloud database. Once the analysis is complete, the server generates an optimal crop management action plan and derives prediction results.

[0274] 3. Cloud Database:

[0275] Historical farmland and environmental data is stored in the cloud database. The data is encrypted and securely stored, and is accessed from the server when necessary for analysis.

[0276] 4. Emotion Engine:

[0277] The emotion engine analyzes the user's facial expressions, voice, posture, etc. to generate emotion data, which is stored in a cloud database and reflected in the analysis of the generative AI model.

[0278] 5. Device (smartphone or PC):

[0279] The user receives the generated action plan, prediction results, and emotion analysis results via their device. The information sent from the server via notification means is displayed in an intuitive, easy-to-understand format.

[0280] Specific examples

[0281] The operation of the system will be explained using the following concrete example.

[0282] 1. Data collection example:

[0283] The server receives environmental data from the sensor, such as "soil moisture content 42%, temperature 25°C, light intensity 800 lux." This data is sent to the server in real time and first stored in memory.

[0284] 2. Data storage examples:

[0285] The server uses a storage means to transfer the collected data to a cloud database for safekeeping.

[0286] 3. Data analysis example:

[0287] The server runs a generative AI model based on data including the past three years' worth of climate data and newly received current soil data. Through analysis, it gains the insight that "the risk of disease increases when soil moisture is below 30%."

[0288] 4. Action plan generation example:

[0289] The server generates an action plan such as "The soil moisture content is expected to decrease this week, so irrigation with 0.5 liters should be carried out in three days," and stores the plan in a cloud database.

[0290] 5. Prediction example:

[0291] Based on future climate data, the server makes predictions such as, "Temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[0292] 6. Emotion analysis example:

[0293] The emotion engine analyzes the stress the user is feeling, sends that data to the server, and reflects it in the analysis of the generative AI model.

[0294] 7. Example of Notice:

[0295] The server then sends these analysis results from a cloud database to the user's device, displaying a watering schedule for the next two days and relaxation suggestions based on the user's current emotional state.

[0296] Prompt Sentence Examples

[0297] "For a field with soil moisture at 42%, should we irrigate 0.5 liters over the next three days? If so, please suggest the best course of action if the user is experiencing stress."

[0298] This system allows farmers to receive optimal farming plans in real time and enables comprehensive agricultural management that also takes emotional data into account.

[0299] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0300] Step 1: Data collection

[0301] Subject: Server

[0302] The server receives environmental data (soil condition, moisture content, light intensity, etc.) every hour from digital sensors installed in the farmland. For example, if a sensor measures the soil moisture level at 42% and the light intensity at 800 lux, the data is sent to the server in real time.

[0303] input:

[0304] Sensor environmental data (e.g. soil moisture 42%, light intensity 800 lux)

[0305] Data processing / calculation:

[0306] Data from the sensor is received in real time and temporarily stored in memory.

[0307] output:

[0308] Environmental data stored in memory

[0309] Step 2: Save data

[0310] Subject: Server

[0311] The server temporarily stores the received environmental data in a memory, and then uses a storage means to quickly transfer the data to a cloud database, thereby ensuring secure management of the data.

[0312] input:

[0313] Environmental data stored in memory

[0314] Data processing / calculation:

[0315] The data is retrieved from the memory and transferred to a cloud database for storage.

[0316] output:

[0317] Environmental data stored in a cloud database

[0318] Step 3: Data analysis

[0319] Subject: Server

[0320] The server runs a generative AI model based on historical and real-time data stored in a cloud database and performs analysis, such as integrating climate data from the past three years with current soil data to obtain insights such as "the risk of disease increases when soil moisture falls below 30%."

[0321] input:

[0322] Historical and real-time environmental data stored in a cloud database

[0323] Data processing / calculation:

[0324] Run generative AI models and analyze historical and real-time data to gain insights.

[0325] output:

[0326] Analysis results (e.g., "The risk of disease increases when soil moisture falls below 30%)

[0327] Step 4: Generate an action plan

[0328] Subject: Server

[0329] The server generates an optimal crop management action plan based on the analysis results of the generative AI model, such as "Since the soil moisture content is expected to decrease this week, irrigation with 0.5 liters should be carried out in three days."

[0330] input:

[0331] Analysis results

[0332] Data processing / calculation:

[0333] An action plan is generated based on the analysis results and stored in a cloud database.

[0334] output:

[0335] Generated action plan (e.g. "0.5 liters of watering should be done in 3 days")

[0336] Step 5: Predict

[0337] Subject: Server

[0338] The server uses current data and analysis results to predict future crop growth and risks, for example, predicting that "temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[0339] input:

[0340] Data and analysis results stored in a cloud database

[0341] Data processing / calculation:

[0342] Predict future crop growth and risks using generative AI models.

[0343] output:

[0344] Prediction results (e.g., "Temperatures will rise sharply over the next two weeks, causing tomatoes to grow faster")

[0345] Step 6: Sentiment Analysis

[0346] Subject: Emotion Engine

[0347] The emotion engine generates emotional data by analyzing the user's facial expressions, voice, posture, etc. For example, if the user is feeling stressed, it will use that data to suggest a plan such as "reduce the amount of work you do next week."

[0348] input:

[0349] User facial expression, voice, and posture data

[0350] Data processing / calculation:

[0351] Emotional data is generated by analyzing facial expressions, voice, posture, etc.

[0352] output:

[0353] User emotion data (e.g., "I feel stressed")

[0354] Step 7: Notification

[0355] Subject: Server

[0356] The server then notifies the user of the generated action plan, prediction results, and emotion analysis results via their smartphone or PC, allowing them to check the notifications and carry out specific farm work and lifestyle plans.

[0357] input:

[0358] Generated action plans, prediction results, and sentiment analysis results

[0359] Data processing / calculation:

[0360] Action plans, prediction results, and emotion analysis results are notified to the user's device.

[0361] output:

[0362] Notifications displayed on the user's device (e.g., "Watering schedule for the next two days" and "Relaxation suggestions")

[0363] Through the above processing steps, the present system provides smart and efficient agricultural management for users.

[0364] (Application example 2)

[0365] 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."

[0366] Conventional production management systems performed optimization based solely on environmental data, but did not take into account the emotional state of employees, which resulted in suboptimal production efficiency and increased employee stress and fatigue. Furthermore, because predictions based on real-time environmental and emotional data could not be made, appropriate maintenance and rest instructions were sometimes delayed.

[0367] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor means for measuring environmental data and employee emotional state data, a storage means for storing the measured environmental data and employee emotional state data in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating a production management action plan based on the analysis results, and a prediction means for providing a prediction of future production efficiency. This enables more accurate production management by taking environmental data and employee emotional states into consideration in an integrated manner. Furthermore, maintenance and rest instructions can be automatically performed at appropriate times, thereby optimizing production efficiency and reducing employee burden.

[0368] "Environmental data" refers to information such as temperature, humidity, and machine operating status inside the factory.

[0369] "Emotional state data" refers to information that indicates stress levels and fatigue levels analyzed from employees' facial expressions, voices, postures, etc.

[0370] "Sensor means" refers to devices for measuring environmental data and emotional state data.

[0371] "Storage means" refers to the system or method for storing measured data in a cloud database.

[0372] "Analysis means" refers to devices or programs for performing analysis using a generative AI model based on stored data.

[0373] A "generative AI model" refers to an artificial intelligence model that analyzes environmental data and emotional state data to generate an action plan.

[0374] "Action plan generation means" refers to a device or program for creating specific measures and schedules for production management based on the analysis results.

[0375] "Prediction means" refers to functions and devices for predicting future production efficiency and risks.

[0376] "Notification means" refers to a system or device for reporting the generated action plan and prediction results to the user's terminal.

[0377] A "cloud database" refers to a digital storage system that stores and makes accessible large amounts of data online.

[0378] This invention relates to a system that uses environmental data and employee emotional state data in an integrated manner to optimize production management within a factory. The program processing and mechanism of this system are explained below.

[0379] Hardware and Software Configuration

[0380] The server collects environmental data and data on employees' emotional states using multiple sensors, including temperature sensors, humidity sensors, and sensors that measure the operating status of machines installed in the factory, as well as cameras and microphones that analyze employees' facial expressions, voices, and postures.

[0381] The collected data is stored by the server in a cloud database, which allows for safe and efficient storage of historical and real-time data.

[0382] The server runs a generative AI model based on environmental and emotional state data stored in a cloud database. This generative AI model combines historical and real-time data for analysis, providing optimal action plans and predictions of future production efficiency.

[0383] Based on the analysis results, the action plan generation means creates a specific production management action plan, such as "The temperature of the machine is abnormally high and the stress level of employees is high, so temporarily stop the machine to cool it down" or "Allow employees to take a break."

[0384] The action plan and prediction results are sent to the user's device (smartphone, smart glasses, PC, etc.). Through the notification means, the user can check the generated action plan and take specific countermeasures.

[0385] Specific examples

[0386] For example, if an employee's emotion engine analyzes an employee's facial expressions, posture, and voice and detects a high overall stress level, the server will take environmental data (e.g., high temperature) into account and use the generative AI model to generate an action plan to "pause the machine and have the employee take a break." This plan will be notified to the user's device, allowing the user to adjust their work in a timely manner.

[0387] Prompt Sentence Examples

[0388] When inputting a prompt sentence to a generative AI model, write it as follows:

[0389] "Generate optimal production management methods when temperatures are high and employee stress levels are high."

[0390] Based on this prompt, the action plan generated is "Taking into account the temperature and stress level, pause the machines and have employees take a break."

[0391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0392] Step 1:

[0393] Sensors measure environmental data (temperature, humidity, machine operating status, etc.) and employee emotional state data (facial expression, voice, posture), and acquire each data. The input is measurement data from the various sensors, and the output is data sent to the server. Specifically, the temperature sensor measures the environmental temperature and sends that value (e.g., 30°C) to the server.

[0394] Step 2:

[0395] The server stores the measured environmental data and emotional state data in a cloud database. The input is the data sent from each sensor, and the output is the data stored in the cloud database. Specifically, the server receives temperature data and employee stress level data and writes it to the cloud database.

[0396] Step 3:

[0397] The server runs the generative AI model based on the data stored in the cloud database and analyzes the data. The input is the past and current data stored in the cloud database, and the output is the analysis results. Specifically, the server runs the generative AI model using the temperature data from the past week and the current stress level as input, detecting abnormal temperature increases and high stress levels.

[0398] Step 4:

[0399] The server generates a production management action plan based on the analysis results. The input is the analysis results from the generative AI model, and the output is an action plan. Specifically, the server generates an action plan that says, "Stop the machines and have employees take a break."

[0400] Step 5:

[0401] The server notifies the user's device of the generated action plan. The input is the generated action plan, and the output is a notification message displayed on the user's device. Specifically, the server sends the generated action plan to the user's smartphone and displays a notification saying, "Pause the machine and have employees take a break."

[0402] Step 6:

[0403] The user checks the notification on the terminal and implements specific production management countermeasures. The input is the notification message displayed on the terminal, and the output is the implemented countermeasure. Specific actions include the user checking the notification, temporarily stopping the machine, and instructing employees to take a break.

[0404] Step 7:

[0405] The server collects the result data of the implemented countermeasures and stores it in a cloud database. The input is the environmental and emotional state data after the countermeasures are implemented, and the output is new data stored in the cloud database. Specifically, the server remeasures the machine's stopped state and the employee's emotional state and stores them in the cloud database.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] [Second embodiment]

[0410] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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."

[0422] This invention aims to realize smart and efficient agricultural management through a system that integrates generative AI models and digital sensors. This system monitors soil conditions and farmland environments in real time, and provides optimal crop management methods, pest prevention, and resource management based on data analysis using generative AI models.

[0423] The server receives data from sensors that measure environmental data such as soil condition, moisture content, light intensity, etc. These sensors are installed in the fields and measure the required data every hour. For example, if the soil moisture content is measured to be 42%, this data is sent from the sensor to the server in real time.

[0424] The server stores the received data in a cloud database and manages the data securely. The data temporarily stored in memory is quickly transferred to the cloud database via storage means. The database stores not only current data, but also past farmland data and environmental data.

[0425] The server, as the analytical tool, runs generative AI models based on historical and real-time data stored in a cloud database. For example, it combines three years of weather data with current soil data to gain specific insights, such as the increased risk of disease when soil moisture falls below 30%. Such analysis results provide farmers with valuable information to take preemptive measures.

[0426] The server has a means to generate an optimal crop management action plan based on the analysis results of the generative AI model. For example, a detailed action plan may be generated, such as "Since the soil moisture content is expected to decrease this week, 0.5 liters of irrigation should be carried out in three days." This plan is stored in a cloud database and notified to the user's device.

[0427] The user's device (smartphone or PC) receives and displays the action plan and prediction results sent from the server. The user can then carry out specific farm work in accordance with the action plan. For example, the user can check the notification and carry out actual irrigation work based on the irrigation schedule for the next two days.

[0428] The server, which acts as a forecasting tool, predicts future crop growth and risks based on current data and analysis results. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes." This information is sent to the user's device, allowing farmers to create production plans based on it.

[0429] In this way, the system of the present invention integrates sensor means, storage means, analysis means, action plan generation means, prediction means, and notification means, thereby enabling farmers to achieve more efficient and smarter agricultural management.

[0430] The processing flow will be explained below.

[0431] Step 1:

[0432] Sensors are installed in the fields and measure environmental data such as soil condition, moisture content, light intensity, and temperature every hour, and this data is temporarily stored inside the sensors.

[0433] Step 2:

[0434] The sensor sends the measured data to a server via a wireless network. For example, if the soil moisture content is measured at 42%, this data is sent to the server.

[0435] Step 3:

[0436] The server receives the data sent from the sensor and temporarily stores it in the server's memory.

[0437] Step 4:

[0438] The server stores the environmental data stored in the memory in a cloud database, and the soil humidity and other environmental data are quickly transferred to the cloud database through the storage means.

[0439] Step 5:

[0440] Data stored in a cloud database is collected by a server and input into a generative AI model, which then begins analysis based on past and current data.

[0441] Step 6:

[0442] The generative AI model performs the analysis and extracts specific patterns and insights, such as "When humidity drops below 30%, the risk of disease increases."

[0443] Step 7:

[0444] The server generates an optimal crop management action plan based on the analysis results. For example, it might suggest irrigating 0.5 liters every morning for the next two days to maintain moisture levels.

[0445] Step 8:

[0446] The server notifies the user's device of the generated action plan, which is then sent to the smartphone or PC via a notification means.

[0447] Step 9:

[0448] The user's device receives and displays the action plan sent from the server. The user checks the notification and makes preparations to carry out specific farm work.

[0449] Step 10:

[0450] The server uses current data and analysis results to predict future crop growth and risks. For example, it predicts that "temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[0451] Step 11:

[0452] The server notifies the user's terminal of the prediction result, and the server transmits the prediction result to the user's terminal through the prediction means.

[0453] Step 12:

[0454] The user's terminal receives and displays the forecast results sent from the server, allowing the user to efficiently create production plans based on the forecasts.

[0455] Example 1

[0456] 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."

[0457] Conventional agricultural management systems suffer from fragmented data collection and analysis, lacking real-time capabilities, making it difficult to efficiently manage resources and develop preventative measures. Furthermore, they lack the information users need to take appropriate action in a timely manner, resulting in suboptimal productivity and quality of agricultural crops. The present invention aims to solve these problems by providing a system that realizes efficient and smart agricultural management based on real-time data from sensors.

[0458] 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.

[0459] In this invention, the server includes a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity, a storage means for storing the environmental data collected from the sensor in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating a crop management action plan based on the analysis results, a notification means for notifying a user terminal of the generated action plan, a user means for performing crop management based on the action plan notified to the user terminal, and a prediction means for providing a future crop production forecast. This enables precise analysis based on real-time data and the provision of an accurate action plan.

[0460] "Sensor means" refers to a device for measuring environmental data such as soil condition, moisture content, and light intensity.

[0461] "Storage means" refers to equipment or functionality for storing environmental data collected from sensors in a cloud database.

[0462] "Analysis means" refers to a device or function for executing a generative AI model that performs analysis based on data stored in a cloud database.

[0463] A "generative AI model" is an artificial intelligence model that performs analysis based on past data stored in a cloud database and real-time data.

[0464] An "action plan generation means" is a device or function for generating a crop management action plan based on the analysis results of the generative AI model.

[0465] The "notification means" is a facility or function for notifying the user's terminal of the generated action plan.

[0466] The "user means" refers to a person and their operating means for carrying out crop management based on the action plan notified to the user's terminal.

[0467] A "forecasting means" is a device or function for providing a forecast of future crop production.

[0468] A "cloud database" is a database accessible via the Internet, and is a system for storing data collected from sensors and analysis results.

[0469] "Analysis Results" means the output data analyzed by the generative AI model, including insights and recommended actions for crop management.

[0470] "Real-time data" is data that is currently being collected from sensors.

[0471] An "action plan" is a specific plan or instruction for crop management generated based on the analysis results.

[0472] A "terminal" is a device, such as a smartphone or PC, that users use to receive and check information.

[0473] This invention realizes smart and efficient agricultural management through a system that integrates generative AI models and digital sensors. This system is implemented using the following main hardware and software:

[0474] Hardware and software used

[0475] 1. Sensor means

[0476] Digital sensors installed on farmland include soil moisture sensors, light sensors, and temperature sensors, which serve to measure environmental data in real time.

[0477] 2. Server

[0478] The servers used to collect and analyze data are common cloud-based servers (e.g., AWS EC2, Google Cloud Compute Engine).

[0479] 3. Cloud Database

[0480] Databases used to store and manage environmental data include Amazon RDS and Google Cloud Firestore.

[0481] 4. Generative AI Models

[0482] Generative AI models used for data analysis include OpenAI's GPT-3, for example.

[0483] System Overview

[0484] The server first obtains environmental data such as soil condition, moisture content, and light intensity from the sensor means every hour and temporarily stores it in memory. This data is then sent to and managed in a cloud database, which also stores past data and serves as the basis for analysis by the generative AI model.

[0485] The generative AI model performs advanced analysis using historical data stored in a cloud database and real-time data. For example, the following prompt sentence is provided to the generative AI model:

[0486] "Based on the past three years of weather data and current soil data, what are the growth risks for tomatoes over the next two weeks and what management practices are recommended?"

[0487] The generative AI model analyzes this prompt and outputs specific insights. Based on the analysis results, the server generates an action plan for crop management. For example, if a drop in soil moisture is predicted, a specific action plan will be generated, such as "irrigate 0.5 liters in three days." This plan is stored in a cloud database and notified to the user's device.

[0488] The user's device (smartphone or PC) receives and displays the action plan and prediction results sent from the server. The user can then carry out specific farm work based on this information. For example, the user can check the notification on their device and carry out actual irrigation work according to the irrigation schedule.

[0489] Furthermore, as a predictive tool, the server uses current data and analysis results to predict future crop growth and risks. For example, it notifies users of a forecast result such as "Tomatoes will grow faster because temperatures will rise sharply in the next two weeks." This allows users to adjust their future production plans and make agricultural activities more efficient and effective.

[0490] The above is an embodiment of the system of the present invention, which enables farmers to achieve more efficient and smarter farm management and improve crop productivity and quality.

[0491] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0492] Step 1:

[0493] Collecting data from sensors

[0494] Input: Environmental data such as soil condition, moisture content, and light intensity from digital sensors installed on farmland

[0495] How it works: The sensor measures environmental data every hour.

[0496] Output: Measured environmental data (e.g., soil moisture content 42%)

[0497] Step 2:

[0498] Sending data to the server

[0499] Input: Environmental data collected from sensors

[0500] How it works: The sensor sends measurement data to the server in real time.

[0501] Output: Environmental data sent to the server (with timestamp)

[0502] Step 3:

[0503] Data storage

[0504] Input: Environmental data received by the server

[0505] How it works: The server temporarily stores the data in memory, and then stores it in a cloud database, for example Amazon RDS, using an API call.

[0506] Output: Data stored in a cloud database

[0507] Step 4:

[0508] Analysis using generative AI models

[0509] Input: Historical and real-time environmental data stored in a cloud database

[0510] How it works: The server prompts the generative AI model with the following prompt: "Based on the past three years of climate data and current soil data, what are the growth risks and recommended management practices for tomatoes over the next two weeks?"

[0511] Output: Analysis results from the generative AI model (e.g., disease risk increases when soil moisture falls below 30%)

[0512] Step 5:

[0513] Generate an action plan

[0514] Input: Analysis results of the generative AI model

[0515] How it works: The server generates an action plan for farming based on the analysis results. For example, it creates a specific plan that states, "Because the moisture content will decrease over the next three days, 0.5 liters of irrigation should be carried out in three days."

[0516] Output: Generated action plan

[0517] Step 6:

[0518] User Notification

[0519] Input: Generated Action Plan

[0520] How it works: The server notifies the user of the action plan via push notification or email.

[0521] Output: An action plan displayed on the user's device (e.g., a notification that "watering should be performed in 3 days").

[0522] Step 7:

[0523] User confirmation and execution

[0524] Input: Action plan sent to the user's device

[0525] Operation: The user checks the notification on the device and performs farm work according to the proposed action plan. For example, the user irrigates based on the irrigation schedule. The user then enters the progress of this work into another app or management system.

[0526] Output: Results and progress data of the farming operations performed

[0527] Step 8:

[0528] Providing forecast data

[0529] Input: Analysis results from the generative AI model and current environmental data

[0530] How it works: The server predicts future crop growth and risks and provides that information to the user. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes."

[0531] Output: Forecast information displayed on the user's terminal and data for adjusting production plans

[0532] (Application example 1)

[0533] 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."

[0534] Modern logistics centers are susceptible to a wide range of environmental variables (temperature, humidity, light intensity, vibration, etc.), and require appropriate management to maintain the quality of cargo. However, current systems for monitoring these environmental variables in real time and providing optimal cargo management methods are inadequate. This increases the risk of cargo deterioration and loss, and reduces the efficiency of logistics operations.

[0535] 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.

[0536] In this invention, the server includes a sensor means for measuring environmental data such as temperature, humidity, light intensity, and vibration, a storage means for storing the environmental data collected from the sensors in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating an action plan for package management based on the analysis results, and a prediction means for providing a prediction of future package deterioration. This makes it possible to monitor the environment within the logistics center in real time and achieve optimal package management based on precise data analysis and predictions by the generative AI model.

[0537] "temperature"

[0538] is an index that indicates the thermal energy of a substance, and is an important factor in maintaining the quality of cargo in environmental management within a logistics center.

[0539] "Humidity"

[0540] is an indicator of the proportion of water vapor in the air, and is a factor that affects the storage conditions of cargo in environmental control within logistics centers.

[0541] "Light intensity"

[0542] is an indicator of the intensity of light measured at a specific location, and is an environmental factor that needs to be managed to prevent deterioration within a logistics center.

[0543] "vibration"

[0544] This refers to the phenomenon of an object shaking, and indicates the physical stimulation that cargo in a logistics center receives, making it an important factor in assessing the risk of damage to cargo.

[0545] "Sensor means"

[0546] refers to devices and equipment for measuring and acquiring environmental data.

[0547] "Storage means"

[0548] refers to a system or device for storing environmental data collected from sensors.

[0549] Cloud database

[0550] refers to a remote database service for storing and managing data over the Internet.

[0551] "Analysis means"

[0552] This refers to systems and devices that perform analysis based on data stored in a cloud database.

[0553] "Generative AI model"

[0554] refers to an artificial intelligence system that uses machine learning technology to analyze data and make predictions.

[0555] "Action plan generation method"

[0556] This refers to a system or device that creates specific management methods and countermeasures based on the analysis results.

[0557] "Prediction methods"

[0558] refers to systems and devices that provide predictions regarding future cargo deterioration risks and management.

[0559] "Notification means"

[0560] This refers to a system or device that notifies the user of the generated action plan or prediction results on their device.

[0561] "User's Device"

[0562] refers to a digital device for receiving and displaying generated action plans and forecast results.

[0563] The present invention is a system for achieving optimal cargo management and deterioration prediction in a logistics center. This system includes a sensor means for measuring environmental data such as temperature, humidity, light intensity, and vibration, a storage means for storing the environmental data collected from the sensors in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating an action plan for cargo management based on the analysis results, and a prediction means for providing a prediction of future cargo deterioration.

[0564] Specific implementation methods of the system

[0565] Configuration and operation of the sensor means

[0566] Digital sensors (such as the Bosch BME280 sensor) are installed in the logistics center to measure temperature, humidity, light intensity, vibration, etc. These sensors measure environmental data every hour and are placed in different locations within the center to achieve comprehensive monitoring.

[0567] Data collection and storage methods

[0568] The collected data is sent to a cloud database (e.g., Amazon Web Services (AWS) or Google Cloud) via an internet connection. The data is stored in the cloud database in real time via a storage means.

[0569] Analysis methods and generative AI models

[0570] The server uses a machine learning platform (e.g., TensorFlow or PyTorch) to analyze historical data stored in a cloud database and data acquired in real time. The generative AI model uses environmental data as input to analyze deterioration risks and optimal management methods.

[0571] Action plan generation method

[0572] It also includes a means for generating a specific action plan based on the analysis results. For example, if the temperature exceeds a preset threshold, it generates instructions such as moving the package to a refrigerated area. The generated action plan is stored in a cloud database.

[0573] Prediction and Notification

[0574] The prediction method predicts future risk of cargo deterioration based on current environmental data and analysis results. This predicted information is sent to the user's device (smartphone or PC). Notifications are sent in real time using notification services such as Pushbullet.

[0575] Specific examples

[0576] Users manage their logistics centers using the smartphone application "LogiSmart." The app receives data collected from sensors and provides analysis results, action plans, and forecasts in real time. For example, the following prompts can be used to input data into the generative AI model:

[0577] "If the temperature data is above 25°C, there is a high risk that the quality of package A will deteriorate within a day. Generate an action plan to move it to a refrigerated area until the temperature stabilizes."

[0578] This system allows logistics center managers to effectively maintain the quality of goods and minimize the risk of deterioration.

[0579] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0580] Step 1:

[0581] The server collects environmental data from digital sensors that measure temperature, humidity, light intensity, vibration, etc. These sensors are installed in the distribution center and collect data every hour. The input data are the values ​​obtained from each sensor and are sent to the server through the sensor API. The output data is a set of environmental data.

[0582] Step 2:

[0583] The server stores the collected environmental data in a cloud database via a storage means. The input data is the environmental data collected in step 1, and the output data is the data converted into a format stored in the cloud database. This data is transmitted via the Internet and protected by security protocols.

[0584] Step 3:

[0585] The server retrieves the historical data and real-time retrieved data stored in the cloud database, where the input data is a set of historical and real-time data retrieved from the cloud database, and the output data is a dataset prepared for analysis.

[0586] Step 4:

[0587] The server performs data analysis using a generative AI model. The input data is the dataset prepared in step 3 and is fed into the generative AI model. The output data is the analysis results, which are the luggage management method and deterioration risk assessment. The generative AI model uses a machine learning platform such as TensorFlow or PyTorch.

[0588] Step 5:

[0589] The server generates a specific action plan based on the analysis results. The input data is the analysis results obtained in step 4, and the output data is a specific action plan, such as "move the package to the refrigerated area immediately." The action plan generation is based on predefined rules and prompt statements.

[0590] Step 6:

[0591] The server notifies the user of the generated action plan and prediction results. The input data is the action plan and prediction results generated in step 5, and the output data is a notification message sent to the user's device. The notification is sent using a notification service such as Pushbullet. Specifically, if the temperature exceeds 25°C, a message stating "there is an increased risk that the quality of package A will deteriorate within one day" is sent to the user's smartphone.

[0592] Step 7:

[0593] After receiving the notification, the user follows the instructions to take specific management actions. The input data is the action plan notified in step 6, and the output data is the actual package management action. For example, if the temperature rises, the user can move the package to a refrigerated area.

[0594] 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.

[0595] This invention realizes smart and efficient agricultural management through a system that integrates generative AI models, digital sensors, and an emotion engine that recognizes user emotions. This system monitors soil conditions and the environment in real time, and provides optimal crop management methods, pest prevention, and resource management based on data analysis using generative AI models, while also incorporating user emotions into the analysis.

[0596] The server receives data from sensors that measure environmental data such as soil condition, moisture content, light intensity, etc. These sensors are installed in the fields and measure the required data every hour. For example, if the soil moisture content is measured to be 42%, this data is sent from the sensor to the server in real time.

[0597] The server stores the received data in a cloud database and manages the data securely. The data temporarily stored in memory is quickly transferred to the cloud database via storage means. The database stores not only current data, but also past farmland data and environmental data.

[0598] The server, which acts as the analytical tool, runs generative AI models based on historical and real-time data stored in a cloud database. For example, it combines three years of weather data with current soil data to obtain specific insights, such as "the risk of disease increases when soil moisture falls below 30%." Such analysis results provide farmers with valuable information to take preemptive measures.

[0599] The server, which acts as the action plan generator, generates an optimal crop management action plan based on the analysis results of the generative AI model. For example, a detailed action plan may be generated stating, "Because the soil moisture content is expected to decrease this week, 0.5 liters of irrigation should be carried out in three days." This plan is stored in a cloud database and notified to the user's device.

[0600] The server, which acts as a forecasting tool, predicts future crop growth and risks based on current data and analysis results. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes." This information is sent to the user's device, allowing farmers to create production plans based on it.

[0601] Furthermore, this invention incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, voice, posture, etc., and stores the emotion data in a cloud database. The emotion engine analyzes the user's emotions in real time and reflects the emotion data in the analysis of the generative AI model, thereby adjusting the action plan according to the user's emotions. For example, if the user is feeling stressed, a plan such as "reduce the amount of work you should do over the next week" is suggested based on that data.

[0602] The server, which acts as a notification means, notifies the user's device of the generated action plan, prediction results, and emotion analysis results. The user can check the notifications via their device (smartphone or PC) and carry out specific farm work or lifestyle plans. For example, the server can notify the user of the irrigation schedule for the next two days and relaxation suggestions based on the user's current emotional state, allowing the user to take appropriate measures based on this information.

[0603] In this way, the system of the present invention integrates sensor means, storage means, analysis means, action plan generation means, prediction means, emotion engine, and notification means to enable farmers to achieve more efficient and smarter agricultural management.

[0604] The processing flow will be explained below.

[0605] Step 1:

[0606] Sensors are installed in agricultural fields and measure environmental data such as soil condition, moisture content, light intensity, and temperature every hour. This data is temporarily stored inside the sensor. For example, the soil moisture content may be measured as 42%.

[0607] Step 2:

[0608] The sensor transmits the measured data to a server via a wireless network. For example, if the moisture content is measured as 42%, the sensor transmits the data to the server in real time.

[0609] Step 3:

[0610] The server receives the data sent from the sensor and temporarily stores it in the server's memory.

[0611] Step 4:

[0612] The server stores the environmental data stored in the memory in a cloud database, and through the storage means, the soil moisture content and other environmental data are quickly transferred to the cloud database.

[0613] Step 5:

[0614] The server collects historical data stored in a cloud database and newly acquired real-time data, which are then input into the generative AI model. The historical data includes, for example, three years of weather and agricultural data.

[0615] Step 6:

[0616] A generative AI model analyzes the data and extracts specific patterns and insights, for example, it may conclude that soil moisture levels below 30% increase the risk of disease.

[0617] Step 7:

[0618] The server generates an optimal crop management action plan based on the analysis results, for example, a specific action plan such as "irrigate 0.5 liters every morning for the next two days."

[0619] Step 8:

[0620] The server notifies the user's device of the generated action plan, and a detailed action plan is sent to the smartphone or PC via the notification means.

[0621] Step 9:

[0622] The device receives the notification from the server and displays it to the user, who can then check the displayed irrigation schedule on their smartphone.

[0623] Step 10:

[0624] The server uses current data and analysis results to predict future crop growth and risks. For example, it predicts that temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster.

[0625] Step 11:

[0626] The server notifies the user's terminal of the prediction result, and the prediction information is transmitted to the terminal through the prediction means.

[0627] Step 12:

[0628] The user's terminal receives and displays the prediction results, allowing the user to efficiently create production plans based on the predictions.

[0629] Step 13:

[0630] The server uses an emotion engine to analyze the user's emotions, which are based on data acquired from the camera and microphone on the user's device.

[0631] Step 14:

[0632] The emotion engine stores the user's emotion data in a cloud database and adds it to the generative AI model. For example, data indicating that the user is feeling stressed is stored.

[0633] Step 15:

[0634] The server adjusts the analysis results of the generative AI model based on the emotional data. For example, if stress is high, an action plan to reduce it will be generated.

[0635] Step 16:

[0636] The server then sends the adjusted action plan and prediction results to the user's device, where the user can check the notification and take action according to their emotional state.

[0637] Example 2

[0638] 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."

[0639] Conventional agricultural management systems are limited to collecting and analyzing environmental data, and are unable to create work plans that take into account the user's emotional state, making efficient agricultural management difficult. Furthermore, it is difficult to provide appropriate crop management and future predictions, which can lead to increased stress and fatigue for users.

[0640] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity, a storage means for storing the environmental data collected from the sensor in a cloud database, and an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database. This not only enables the aggregation and analysis of environmental data in real time, but also enables comprehensive crop management and future predictions that take into account user emotional data.

[0641] "Sensor means" refers to a device that measures environmental data such as soil condition, moisture content, and light intensity.

[0642] "Storage means" refers to a device or method for storing environmental data collected from sensors in a cloud database.

[0643] "Analysis means" refers to a device or method that executes a generative AI model based on data stored in a cloud database to perform data analysis.

[0644] A "generative AI model" is an artificial intelligence technology that analyzes historical and real-time data to generate insights and predictions.

[0645] An "action plan generation means" is a device or method that generates an optimal crop management action plan based on the analysis results of a generative AI model.

[0646] A "prediction means" is a device or method for predicting future crop growth and risks based on current data and analysis results.

[0647] "Emotion analysis means" refers to a device or method for analyzing the user's facial expression, voice, posture, etc., to grasp the user's emotional state.

[0648] The "notification means" is a device or method for notifying the user's terminal of the generated action plan, prediction results, and emotion analysis results.

[0649] A "cloud database" is a database that exists on the Internet and is a system that provides accessible storage resources.

[0650] A "user terminal" is a device that can be directly operated by a user, such as a smartphone or a personal computer.

[0651] The system integrates generative AI models, digital sensors, and an emotion engine to achieve smart and efficient agricultural management. Through interactions between servers, terminals, and users, the system collects and analyzes environmental data, performs optimal crop management, predictions, and notifications.

[0652] Hardware and Software Configuration

[0653] 1. Digital Sensor:

[0654] Digital sensors are installed in farmland and have the ability to measure environmental data such as soil condition (moisture content, pH value, etc.), light intensity, and temperature every hour, and this data is sent to a server in real time.

[0655] 2. Server:

[0656] The server temporarily stores the received environmental data in its memory and then rapidly transfers it to a cloud database using a storage means. The server also includes an analysis means that uses a generative AI model to analyze the historical and real-time data stored in the cloud database. Once the analysis is complete, the server generates an optimal crop management action plan and derives prediction results.

[0657] 3. Cloud Database:

[0658] Historical farmland and environmental data is stored in the cloud database. The data is encrypted and securely stored, and is accessed from the server when necessary for analysis.

[0659] 4. Emotion Engine:

[0660] The emotion engine analyzes the user's facial expressions, voice, posture, etc. to generate emotion data, which is stored in a cloud database and reflected in the analysis of the generative AI model.

[0661] 5. Device (smartphone or PC):

[0662] The user receives the generated action plan, prediction results, and emotion analysis results via their device. The information sent from the server via notification means is displayed in an intuitive, easy-to-understand format.

[0663] Specific examples

[0664] The operation of the system will be explained using the following concrete example.

[0665] 1. Data collection example:

[0666] The server receives environmental data from the sensor, such as "soil moisture content 42%, temperature 25°C, light intensity 800 lux." This data is sent to the server in real time and first stored in memory.

[0667] 2. Data storage examples:

[0668] The server uses a storage means to transfer the collected data to a cloud database for safekeeping.

[0669] 3. Data analysis example:

[0670] The server runs a generative AI model based on data including the past three years' worth of climate data and newly received current soil data. Through analysis, it gains the insight that "the risk of disease increases when soil moisture is below 30%."

[0671] 4. Action plan generation example:

[0672] The server generates an action plan such as "The soil moisture content is expected to decrease this week, so irrigation with 0.5 liters should be carried out in three days," and stores the plan in a cloud database.

[0673] 5. Prediction example:

[0674] Based on future climate data, the server makes predictions such as, "Temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[0675] 6. Emotion analysis example:

[0676] The emotion engine analyzes the stress the user is feeling, sends that data to the server, and reflects it in the analysis of the generative AI model.

[0677] 7. Example of Notice:

[0678] The server then sends these analysis results from a cloud database to the user's device, displaying a watering schedule for the next two days and relaxation suggestions based on the user's current emotional state.

[0679] Prompt Sentence Examples

[0680] "For a field with soil moisture at 42%, should we irrigate 0.5 liters over the next three days? If so, please suggest the best course of action if the user is experiencing stress."

[0681] This system allows farmers to receive optimal farming plans in real time and enables comprehensive agricultural management that also takes emotional data into account.

[0682] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0683] Step 1: Data collection

[0684] Subject: Server

[0685] The server receives environmental data (soil condition, moisture content, light intensity, etc.) every hour from digital sensors installed in the farmland. For example, if a sensor measures the soil moisture level at 42% and the light intensity at 800 lux, the data is sent to the server in real time.

[0686] input:

[0687] Sensor environmental data (e.g. soil moisture 42%, light intensity 800 lux)

[0688] Data processing / calculation:

[0689] Data from the sensor is received in real time and temporarily stored in memory.

[0690] output:

[0691] Environmental data stored in memory

[0692] Step 2: Save data

[0693] Subject: Server

[0694] The server temporarily stores the received environmental data in a memory, and then uses a storage means to quickly transfer the data to a cloud database, thereby ensuring secure management of the data.

[0695] input:

[0696] Environmental data stored in memory

[0697] Data processing / calculation:

[0698] The data is retrieved from the memory and transferred to a cloud database for storage.

[0699] output:

[0700] Environmental data stored in a cloud database

[0701] Step 3: Data analysis

[0702] Subject: Server

[0703] The server runs a generative AI model based on historical and real-time data stored in a cloud database and performs analysis, such as integrating climate data from the past three years with current soil data to obtain insights such as "the risk of disease increases when soil moisture falls below 30%."

[0704] input:

[0705] Historical and real-time environmental data stored in a cloud database

[0706] Data processing / calculation:

[0707] Run generative AI models and analyze historical and real-time data to gain insights.

[0708] output:

[0709] Analysis results (e.g., "The risk of disease increases when soil moisture falls below 30%)

[0710] Step 4: Generate an action plan

[0711] Subject: Server

[0712] The server generates an optimal crop management action plan based on the analysis results of the generative AI model, such as "Since the soil moisture content is expected to decrease this week, irrigation with 0.5 liters should be carried out in three days."

[0713] input:

[0714] Analysis results

[0715] Data processing / calculation:

[0716] An action plan is generated based on the analysis results and stored in a cloud database.

[0717] output:

[0718] Generated action plan (e.g. "0.5 liters of watering should be done in 3 days")

[0719] Step 5: Predict

[0720] Subject: Server

[0721] The server uses current data and analysis results to predict future crop growth and risks, for example, predicting that "temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[0722] input:

[0723] Data and analysis results stored in a cloud database

[0724] Data processing / calculation:

[0725] Predict future crop growth and risks using generative AI models.

[0726] output:

[0727] Prediction results (e.g., "Temperatures will rise sharply over the next two weeks, causing tomatoes to grow faster")

[0728] Step 6: Sentiment Analysis

[0729] Subject: Emotion Engine

[0730] The emotion engine generates emotional data by analyzing the user's facial expressions, voice, posture, etc. For example, if the user is feeling stressed, it will use that data to suggest a plan such as "reduce the amount of work you do next week."

[0731] input:

[0732] User facial expression, voice, and posture data

[0733] Data processing / calculation:

[0734] Emotional data is generated by analyzing facial expressions, voice, posture, etc.

[0735] output:

[0736] User emotion data (e.g., "I feel stressed")

[0737] Step 7: Notification

[0738] Subject: Server

[0739] The server then notifies the user of the generated action plan, prediction results, and emotion analysis results via their smartphone or PC, allowing them to check the notifications and carry out specific farm work and lifestyle plans.

[0740] input:

[0741] Generated action plans, prediction results, and sentiment analysis results

[0742] Data processing / calculation:

[0743] Action plans, prediction results, and emotion analysis results are notified to the user's device.

[0744] output:

[0745] Notifications displayed on the user's device (e.g., "Watering schedule for the next two days" and "Relaxation suggestions")

[0746] Through the above processing steps, the present system provides smart and efficient agricultural management for users.

[0747] (Application example 2)

[0748] 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."

[0749] Conventional production management systems performed optimization based solely on environmental data, but did not take into account the emotional state of employees, which resulted in suboptimal production efficiency and increased employee stress and fatigue. Furthermore, because predictions based on real-time environmental and emotional data could not be made, appropriate maintenance and rest instructions were sometimes delayed.

[0750] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor means for measuring environmental data and employee emotional state data, a storage means for storing the measured environmental data and employee emotional state data in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating a production management action plan based on the analysis results, and a prediction means for providing a prediction of future production efficiency. This enables more accurate production management by taking environmental data and employee emotional states into consideration in an integrated manner. Furthermore, maintenance and rest instructions can be automatically performed at appropriate times, thereby optimizing production efficiency and reducing employee burden.

[0751] "Environmental data" refers to information such as temperature, humidity, and machine operating status inside the factory.

[0752] "Emotional state data" refers to information that indicates stress levels and fatigue levels analyzed from employees' facial expressions, voices, postures, etc.

[0753] "Sensor means" refers to devices for measuring environmental data and emotional state data.

[0754] "Storage means" refers to the system or method for storing measured data in a cloud database.

[0755] "Analysis means" refers to devices or programs for performing analysis using a generative AI model based on stored data.

[0756] A "generative AI model" refers to an artificial intelligence model that analyzes environmental data and emotional state data to generate an action plan.

[0757] "Action plan generation means" refers to a device or program for creating specific measures and schedules for production management based on the analysis results.

[0758] "Prediction means" refers to functions and devices for predicting future production efficiency and risks.

[0759] "Notification means" refers to a system or device for reporting the generated action plan and prediction results to the user's terminal.

[0760] A "cloud database" refers to a digital storage system that stores and makes accessible large amounts of data online.

[0761] This invention relates to a system that uses environmental data and employee emotional state data in an integrated manner to optimize production management within a factory. The program processing and mechanism of this system are explained below.

[0762] Hardware and Software Configuration

[0763] The server collects environmental data and data on employees' emotional states using multiple sensors, including temperature sensors, humidity sensors, and sensors that measure the operating status of machines installed in the factory, as well as cameras and microphones that analyze employees' facial expressions, voices, and postures.

[0764] The collected data is stored by the server in a cloud database, which allows for safe and efficient storage of historical and real-time data.

[0765] The server runs a generative AI model based on environmental and emotional state data stored in a cloud database. This generative AI model combines historical and real-time data for analysis, providing optimal action plans and predictions of future production efficiency.

[0766] Based on the analysis results, the action plan generation means creates a specific production management action plan, such as "The temperature of the machine is abnormally high and the stress level of employees is high, so temporarily stop the machine to cool it down" or "Allow employees to take a break."

[0767] The action plan and prediction results are sent to the user's device (smartphone, smart glasses, PC, etc.). Through the notification means, the user can check the generated action plan and take specific countermeasures.

[0768] Specific examples

[0769] For example, if an employee's emotion engine analyzes an employee's facial expressions, posture, and voice and detects a high overall stress level, the server will take environmental data (e.g., high temperature) into account and use the generative AI model to generate an action plan to "pause the machine and have the employee take a break." This plan will be notified to the user's device, allowing the user to adjust their work in a timely manner.

[0770] Prompt Sentence Examples

[0771] When inputting a prompt sentence to a generative AI model, write it as follows:

[0772] "Generate optimal production management methods when temperatures are high and employee stress levels are high."

[0773] Based on this prompt, the action plan generated is "Taking into account the temperature and stress level, pause the machines and have employees take a break."

[0774] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0775] Step 1:

[0776] Sensors measure environmental data (temperature, humidity, machine operating status, etc.) and employee emotional state data (facial expression, voice, posture), and acquire each data. The input is measurement data from the various sensors, and the output is data sent to the server. Specifically, the temperature sensor measures the environmental temperature and sends that value (e.g., 30°C) to the server.

[0777] Step 2:

[0778] The server stores the measured environmental data and emotional state data in a cloud database. The input is the data sent from each sensor, and the output is the data stored in the cloud database. Specifically, the server receives temperature data and employee stress level data and writes it to the cloud database.

[0779] Step 3:

[0780] The server runs the generative AI model based on the data stored in the cloud database and analyzes the data. The input is the past and current data stored in the cloud database, and the output is the analysis results. Specifically, the server runs the generative AI model using the temperature data from the past week and the current stress level as input, detecting abnormal temperature increases and high stress levels.

[0781] Step 4:

[0782] The server generates a production management action plan based on the analysis results. The input is the analysis results from the generative AI model, and the output is an action plan. Specifically, the server generates an action plan that says, "Stop the machines and have employees take a break."

[0783] Step 5:

[0784] The server notifies the user's device of the generated action plan. The input is the generated action plan, and the output is a notification message displayed on the user's device. Specifically, the server sends the generated action plan to the user's smartphone and displays a notification saying, "Pause the machine and have employees take a break."

[0785] Step 6:

[0786] The user checks the notification on the terminal and implements specific production management countermeasures. The input is the notification message displayed on the terminal, and the output is the implemented countermeasure. Specific actions include the user checking the notification, temporarily stopping the machine, and instructing employees to take a break.

[0787] Step 7:

[0788] The server collects the result data of the implemented countermeasures and stores it in a cloud database. The input is the environmental and emotional state data after the countermeasures are implemented, and the output is new data stored in the cloud database. Specifically, the server remeasures the machine's stopped state and the employee's emotional state and stores them in the cloud database.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] [Third embodiment]

[0793] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0794] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0795] 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).

[0796] 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.

[0797] 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.

[0798] 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).

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] This invention aims to realize smart and efficient agricultural management through a system that integrates generative AI models and digital sensors. This system monitors soil conditions and farmland environments in real time, and provides optimal crop management methods, pest prevention, and resource management based on data analysis using generative AI models.

[0806] The server receives data from sensors that measure environmental data such as soil condition, moisture content, light intensity, etc. These sensors are installed in the fields and measure the required data every hour. For example, if the soil moisture content is measured to be 42%, this data is sent from the sensor to the server in real time.

[0807] The server stores the received data in a cloud database and manages the data securely. The data temporarily stored in memory is quickly transferred to the cloud database via storage means. The database stores not only current data, but also past farmland data and environmental data.

[0808] The server, as the analytical tool, runs generative AI models based on historical and real-time data stored in a cloud database. For example, it combines three years of weather data with current soil data to gain specific insights, such as the increased risk of disease when soil moisture falls below 30%. Such analysis results provide farmers with valuable information to take preemptive measures.

[0809] The server has a means to generate an optimal crop management action plan based on the analysis results of the generative AI model. For example, a detailed action plan may be generated, such as "Since the soil moisture content is expected to decrease this week, 0.5 liters of irrigation should be carried out in three days." This plan is stored in a cloud database and notified to the user's device.

[0810] The user's device (smartphone or PC) receives and displays the action plan and prediction results sent from the server. The user can then carry out specific farm work in accordance with the action plan. For example, the user can check the notification and carry out actual irrigation work based on the irrigation schedule for the next two days.

[0811] The server, which acts as a forecasting tool, predicts future crop growth and risks based on current data and analysis results. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes." This information is sent to the user's device, allowing farmers to create production plans based on it.

[0812] In this way, the system of the present invention integrates sensor means, storage means, analysis means, action plan generation means, prediction means, and notification means, thereby enabling farmers to achieve more efficient and smarter agricultural management.

[0813] The processing flow will be explained below.

[0814] Step 1:

[0815] Sensors are installed in the fields and measure environmental data such as soil condition, moisture content, light intensity, and temperature every hour, and this data is temporarily stored inside the sensors.

[0816] Step 2:

[0817] The sensor sends the measured data to a server via a wireless network. For example, if the soil moisture content is measured at 42%, this data is sent to the server.

[0818] Step 3:

[0819] The server receives the data sent from the sensor and temporarily stores it in the server's memory.

[0820] Step 4:

[0821] The server stores the environmental data stored in the memory in a cloud database, and the soil humidity and other environmental data are quickly transferred to the cloud database through the storage means.

[0822] Step 5:

[0823] Data stored in a cloud database is collected by a server and input into a generative AI model, which then begins analysis based on past and current data.

[0824] Step 6:

[0825] The generative AI model performs the analysis and extracts specific patterns and insights, such as "When humidity drops below 30%, the risk of disease increases."

[0826] Step 7:

[0827] The server generates an optimal crop management action plan based on the analysis results. For example, it might suggest irrigating 0.5 liters every morning for the next two days to maintain moisture levels.

[0828] Step 8:

[0829] The server notifies the user's device of the generated action plan, which is then sent to the smartphone or PC via a notification means.

[0830] Step 9:

[0831] The user's device receives and displays the action plan sent from the server. The user checks the notification and makes preparations to carry out specific farm work.

[0832] Step 10:

[0833] The server uses current data and analysis results to predict future crop growth and risks. For example, it predicts that "temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[0834] Step 11:

[0835] The server notifies the user's terminal of the prediction result, and the server transmits the prediction result to the user's terminal through the prediction means.

[0836] Step 12:

[0837] The user's terminal receives and displays the forecast results sent from the server, allowing the user to efficiently create production plans based on the forecasts.

[0838] Example 1

[0839] 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."

[0840] Conventional agricultural management systems suffer from fragmented data collection and analysis, lacking real-time capabilities, making it difficult to efficiently manage resources and develop preventative measures. Furthermore, they lack the information users need to take appropriate action in a timely manner, resulting in suboptimal productivity and quality of agricultural crops. The present invention aims to solve these problems by providing a system that realizes efficient and smart agricultural management based on real-time data from sensors.

[0841] 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.

[0842] In this invention, the server includes a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity, a storage means for storing the environmental data collected from the sensor in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating a crop management action plan based on the analysis results, a notification means for notifying a user terminal of the generated action plan, a user means for performing crop management based on the action plan notified to the user terminal, and a prediction means for providing a future crop production forecast. This enables precise analysis based on real-time data and the provision of an accurate action plan.

[0843] "Sensor means" refers to a device for measuring environmental data such as soil condition, moisture content, and light intensity.

[0844] "Storage means" refers to equipment or functionality for storing environmental data collected from sensors in a cloud database.

[0845] "Analysis means" refers to a device or function for executing a generative AI model that performs analysis based on data stored in a cloud database.

[0846] A "generative AI model" is an artificial intelligence model that performs analysis based on past data stored in a cloud database and real-time data.

[0847] An "action plan generation means" is a device or function for generating a crop management action plan based on the analysis results of the generative AI model.

[0848] The "notification means" is a facility or function for notifying the user's terminal of the generated action plan.

[0849] The "user means" refers to a person and their operating means for carrying out crop management based on the action plan notified to the user's terminal.

[0850] A "forecasting means" is a device or function for providing a forecast of future crop production.

[0851] A "cloud database" is a database accessible via the Internet, and is a system for storing data collected from sensors and analysis results.

[0852] "Analysis Results" means the output data analyzed by the generative AI model, including insights and recommended actions for crop management.

[0853] "Real-time data" is data that is currently being collected from sensors.

[0854] An "action plan" is a specific plan or instruction for crop management generated based on the analysis results.

[0855] A "terminal" is a device, such as a smartphone or PC, that users use to receive and check information.

[0856] This invention realizes smart and efficient agricultural management through a system that integrates generative AI models and digital sensors. This system is implemented using the following main hardware and software:

[0857] Hardware and software used

[0858] 1. Sensor means

[0859] Digital sensors installed on farmland include soil moisture sensors, light sensors, and temperature sensors, which serve to measure environmental data in real time.

[0860] 2. Server

[0861] The servers used to collect and analyze data are common cloud-based servers (e.g., AWS EC2, Google Cloud Compute Engine).

[0862] 3. Cloud Database

[0863] Databases used to store and manage environmental data include Amazon RDS and Google Cloud Firestore.

[0864] 4. Generative AI Models

[0865] Generative AI models used for data analysis include OpenAI's GPT-3, for example.

[0866] System Overview

[0867] The server first obtains environmental data such as soil condition, moisture content, and light intensity from the sensor means every hour and temporarily stores it in memory. This data is then sent to and managed in a cloud database, which also stores past data and serves as the basis for analysis by the generative AI model.

[0868] The generative AI model performs advanced analysis using historical data stored in a cloud database and real-time data. For example, the following prompt sentence is provided to the generative AI model:

[0869] "Based on the past three years of weather data and current soil data, what are the growth risks for tomatoes over the next two weeks and what management practices are recommended?"

[0870] The generative AI model analyzes this prompt and outputs specific insights. Based on the analysis results, the server generates an action plan for crop management. For example, if a drop in soil moisture is predicted, a specific action plan will be generated, such as "irrigate 0.5 liters in three days." This plan is stored in a cloud database and notified to the user's device.

[0871] The user's device (smartphone or PC) receives and displays the action plan and prediction results sent from the server. The user can then carry out specific farm work based on this information. For example, the user can check the notification on their device and carry out actual irrigation work according to the irrigation schedule.

[0872] Furthermore, as a predictive tool, the server uses current data and analysis results to predict future crop growth and risks. For example, it notifies users of a forecast result such as "Tomatoes will grow faster because temperatures will rise sharply in the next two weeks." This allows users to adjust their future production plans and make agricultural activities more efficient and effective.

[0873] The above is an embodiment of the system of the present invention, which enables farmers to achieve more efficient and smarter farm management and improve crop productivity and quality.

[0874] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0875] Step 1:

[0876] Collecting data from sensors

[0877] Input: Environmental data such as soil condition, moisture content, and light intensity from digital sensors installed on farmland

[0878] How it works: The sensor measures environmental data every hour.

[0879] Output: Measured environmental data (e.g., soil moisture content 42%)

[0880] Step 2:

[0881] Sending data to the server

[0882] Input: Environmental data collected from sensors

[0883] How it works: The sensor sends measurement data to the server in real time.

[0884] Output: Environmental data sent to the server (with timestamp)

[0885] Step 3:

[0886] Data storage

[0887] Input: Environmental data received by the server

[0888] How it works: The server temporarily stores the data in memory, and then stores it in a cloud database, for example Amazon RDS, using an API call.

[0889] Output: Data stored in a cloud database

[0890] Step 4:

[0891] Analysis using generative AI models

[0892] Input: Historical and real-time environmental data stored in a cloud database

[0893] How it works: The server prompts the generative AI model with the following prompt: "Based on the past three years of climate data and current soil data, what are the growth risks and recommended management practices for tomatoes over the next two weeks?"

[0894] Output: Analysis results from the generative AI model (e.g., disease risk increases when soil moisture falls below 30%)

[0895] Step 5:

[0896] Generate an action plan

[0897] Input: Analysis results of the generative AI model

[0898] How it works: The server generates an action plan for farming based on the analysis results. For example, it creates a specific plan that states, "Because the moisture content will decrease over the next three days, 0.5 liters of irrigation should be carried out in three days."

[0899] Output: Generated action plan

[0900] Step 6:

[0901] User Notification

[0902] Input: Generated Action Plan

[0903] How it works: The server notifies the user of the action plan via push notification or email.

[0904] Output: An action plan displayed on the user's device (e.g., a notification that "watering should be performed in 3 days").

[0905] Step 7:

[0906] User confirmation and execution

[0907] Input: Action plan sent to the user's device

[0908] Operation: The user checks the notification on the device and performs farm work according to the proposed action plan. For example, the user irrigates based on the irrigation schedule. The user then enters the progress of this work into another app or management system.

[0909] Output: Results and progress data of the farming operations performed

[0910] Step 8:

[0911] Providing forecast data

[0912] Input: Analysis results from the generative AI model and current environmental data

[0913] How it works: The server predicts future crop growth and risks and provides that information to the user. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes."

[0914] Output: Forecast information displayed on the user's terminal and data for adjusting production plans

[0915] (Application example 1)

[0916] 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."

[0917] Modern logistics centers are susceptible to a wide range of environmental variables (temperature, humidity, light intensity, vibration, etc.), and require appropriate management to maintain the quality of cargo. However, current systems for monitoring these environmental variables in real time and providing optimal cargo management methods are inadequate. This increases the risk of cargo deterioration and loss, and reduces the efficiency of logistics operations.

[0918] 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.

[0919] In this invention, the server includes a sensor means for measuring environmental data such as temperature, humidity, light intensity, and vibration, a storage means for storing the environmental data collected from the sensors in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating an action plan for package management based on the analysis results, and a prediction means for providing a prediction of future package deterioration. This makes it possible to monitor the environment within the logistics center in real time and achieve optimal package management based on precise data analysis and predictions by the generative AI model.

[0920] "temperature"

[0921] is an index that indicates the thermal energy of a substance, and is an important factor in maintaining the quality of cargo in environmental management within a logistics center.

[0922] "Humidity"

[0923] is an indicator of the proportion of water vapor in the air, and is a factor that affects the storage conditions of cargo in environmental control within logistics centers.

[0924] "Light intensity"

[0925] is an indicator of the intensity of light measured at a specific location, and is an environmental factor that needs to be managed to prevent deterioration within a logistics center.

[0926] "vibration"

[0927] This refers to the phenomenon of an object shaking, and indicates the physical stimulation that cargo in a logistics center receives, making it an important factor in assessing the risk of damage to cargo.

[0928] "Sensor means"

[0929] refers to devices and equipment for measuring and acquiring environmental data.

[0930] "Storage means"

[0931] refers to a system or device for storing environmental data collected from sensors.

[0932] Cloud database

[0933] refers to a remote database service for storing and managing data over the Internet.

[0934] "Analysis means"

[0935] This refers to systems and devices that perform analysis based on data stored in a cloud database.

[0936] "Generative AI model"

[0937] refers to an artificial intelligence system that uses machine learning technology to analyze data and make predictions.

[0938] "Action plan generation method"

[0939] This refers to a system or device that creates specific management methods and countermeasures based on the analysis results.

[0940] "Prediction methods"

[0941] refers to systems and devices that provide predictions regarding future cargo deterioration risks and management.

[0942] "Notification means"

[0943] This refers to a system or device that notifies the user of the generated action plan or prediction results on their device.

[0944] "User's Device"

[0945] refers to a digital device for receiving and displaying generated action plans and forecast results.

[0946] The present invention is a system for achieving optimal cargo management and deterioration prediction in a logistics center. This system includes a sensor means for measuring environmental data such as temperature, humidity, light intensity, and vibration, a storage means for storing the environmental data collected from the sensors in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating an action plan for cargo management based on the analysis results, and a prediction means for providing a prediction of future cargo deterioration.

[0947] Specific implementation methods of the system

[0948] Configuration and operation of the sensor means

[0949] Digital sensors (such as the Bosch BME280 sensor) are installed in the logistics center to measure temperature, humidity, light intensity, vibration, etc. These sensors measure environmental data every hour and are placed in different locations within the center to achieve comprehensive monitoring.

[0950] Data collection and storage methods

[0951] The collected data is sent to a cloud database (e.g., Amazon Web Services (AWS) or Google Cloud) via an internet connection. The data is stored in the cloud database in real time via a storage means.

[0952] Analysis methods and generative AI models

[0953] The server uses a machine learning platform (e.g., TensorFlow or PyTorch) to analyze historical data stored in a cloud database and data acquired in real time. The generative AI model uses environmental data as input to analyze deterioration risks and optimal management methods.

[0954] Action plan generation method

[0955] It also includes a means for generating a specific action plan based on the analysis results. For example, if the temperature exceeds a preset threshold, it generates instructions such as moving the package to a refrigerated area. The generated action plan is stored in a cloud database.

[0956] Prediction and Notification

[0957] The prediction method predicts future risk of cargo deterioration based on current environmental data and analysis results. This predicted information is sent to the user's device (smartphone or PC). Notifications are sent in real time using notification services such as Pushbullet.

[0958] Specific examples

[0959] Users manage their logistics centers using the smartphone application "LogiSmart." The app receives data collected from sensors and provides analysis results, action plans, and forecasts in real time. For example, the following prompts can be used to input data into the generative AI model:

[0960] "If the temperature data is above 25°C, there is a high risk that the quality of package A will deteriorate within a day. Generate an action plan to move it to a refrigerated area until the temperature stabilizes."

[0961] This system allows logistics center managers to effectively maintain the quality of goods and minimize the risk of deterioration.

[0962] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0963] Step 1:

[0964] The server collects environmental data from digital sensors that measure temperature, humidity, light intensity, vibration, etc. These sensors are installed in the distribution center and collect data every hour. The input data are the values ​​obtained from each sensor and are sent to the server through the sensor API. The output data is a set of environmental data.

[0965] Step 2:

[0966] The server stores the collected environmental data in a cloud database via a storage means. The input data is the environmental data collected in step 1, and the output data is the data converted into a format stored in the cloud database. This data is transmitted via the Internet and protected by security protocols.

[0967] Step 3:

[0968] The server retrieves the historical data and real-time retrieved data stored in the cloud database, where the input data is a set of historical and real-time data retrieved from the cloud database, and the output data is a dataset prepared for analysis.

[0969] Step 4:

[0970] The server performs data analysis using a generative AI model. The input data is the dataset prepared in step 3 and is fed into the generative AI model. The output data is the analysis results, which are the luggage management method and deterioration risk assessment. The generative AI model uses a machine learning platform such as TensorFlow or PyTorch.

[0971] Step 5:

[0972] The server generates a specific action plan based on the analysis results. The input data is the analysis results obtained in step 4, and the output data is a specific action plan, such as "move the package to the refrigerated area immediately." The action plan generation is based on predefined rules and prompt statements.

[0973] Step 6:

[0974] The server notifies the user of the generated action plan and prediction results. The input data is the action plan and prediction results generated in step 5, and the output data is a notification message sent to the user's device. The notification is sent using a notification service such as Pushbullet. Specifically, if the temperature exceeds 25°C, a message stating "there is an increased risk that the quality of package A will deteriorate within one day" is sent to the user's smartphone.

[0975] Step 7:

[0976] After receiving the notification, the user follows the instructions to take specific management actions. The input data is the action plan notified in step 6, and the output data is the actual package management action. For example, if the temperature rises, the user can move the package to a refrigerated area.

[0977] 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.

[0978] This invention realizes smart and efficient agricultural management through a system that integrates generative AI models, digital sensors, and an emotion engine that recognizes user emotions. This system monitors soil conditions and the environment in real time, and provides optimal crop management methods, pest prevention, and resource management based on data analysis using generative AI models, while also incorporating user emotions into the analysis.

[0979] The server receives data from sensors that measure environmental data such as soil condition, moisture content, light intensity, etc. These sensors are installed in the fields and measure the required data every hour. For example, if the soil moisture content is measured to be 42%, this data is sent from the sensor to the server in real time.

[0980] The server stores the received data in a cloud database and manages the data securely. The data temporarily stored in memory is quickly transferred to the cloud database via storage means. The database stores not only current data, but also past farmland data and environmental data.

[0981] The server, which acts as the analytical tool, runs generative AI models based on historical and real-time data stored in a cloud database. For example, it combines three years of weather data with current soil data to obtain specific insights, such as "the risk of disease increases when soil moisture falls below 30%." Such analysis results provide farmers with valuable information to take preemptive measures.

[0982] The server, which acts as the action plan generator, generates an optimal crop management action plan based on the analysis results of the generative AI model. For example, a detailed action plan may be generated stating, "Because the soil moisture content is expected to decrease this week, 0.5 liters of irrigation should be carried out in three days." This plan is stored in a cloud database and notified to the user's device.

[0983] The server, which acts as a forecasting tool, predicts future crop growth and risks based on current data and analysis results. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes." This information is sent to the user's device, allowing farmers to create production plans based on it.

[0984] Furthermore, this invention incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, voice, posture, etc., and stores the emotion data in a cloud database. The emotion engine analyzes the user's emotions in real time and reflects the emotion data in the analysis of the generative AI model, thereby adjusting the action plan according to the user's emotions. For example, if the user is feeling stressed, a plan such as "reduce the amount of work you should do over the next week" is suggested based on that data.

[0985] The server, which acts as a notification means, notifies the user's device of the generated action plan, prediction results, and emotion analysis results. The user can check the notifications via their device (smartphone or PC) and carry out specific farm work or lifestyle plans. For example, the server can notify the user of the irrigation schedule for the next two days and relaxation suggestions based on the user's current emotional state, allowing the user to take appropriate measures based on this information.

[0986] In this way, the system of the present invention integrates sensor means, storage means, analysis means, action plan generation means, prediction means, emotion engine, and notification means to enable farmers to achieve more efficient and smarter agricultural management.

[0987] The processing flow will be explained below.

[0988] Step 1:

[0989] Sensors are installed in agricultural fields and measure environmental data such as soil condition, moisture content, light intensity, and temperature every hour. This data is temporarily stored inside the sensor. For example, the soil moisture content may be measured as 42%.

[0990] Step 2:

[0991] The sensor transmits the measured data to a server via a wireless network. For example, if the moisture content is measured as 42%, the sensor transmits the data to the server in real time.

[0992] Step 3:

[0993] The server receives the data sent from the sensor and temporarily stores it in the server's memory.

[0994] Step 4:

[0995] The server stores the environmental data stored in the memory in a cloud database, and through the storage means, the soil moisture content and other environmental data are quickly transferred to the cloud database.

[0996] Step 5:

[0997] The server collects historical data stored in a cloud database and newly acquired real-time data, which are then input into the generative AI model. The historical data includes, for example, three years of weather and agricultural data.

[0998] Step 6:

[0999] A generative AI model analyzes the data and extracts specific patterns and insights, for example, it may conclude that soil moisture levels below 30% increase the risk of disease.

[1000] Step 7:

[1001] The server generates an optimal crop management action plan based on the analysis results, for example, a specific action plan such as "irrigate 0.5 liters every morning for the next two days."

[1002] Step 8:

[1003] The server notifies the user's device of the generated action plan, and a detailed action plan is sent to the smartphone or PC via the notification means.

[1004] Step 9:

[1005] The device receives the notification from the server and displays it to the user, who can then check the displayed irrigation schedule on their smartphone.

[1006] Step 10:

[1007] The server uses current data and analysis results to predict future crop growth and risks. For example, it predicts that temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster.

[1008] Step 11:

[1009] The server notifies the user's terminal of the prediction result, and the prediction information is transmitted to the terminal through the prediction means.

[1010] Step 12:

[1011] The user's terminal receives and displays the prediction results, allowing the user to efficiently create production plans based on the predictions.

[1012] Step 13:

[1013] The server uses an emotion engine to analyze the user's emotions, which are based on data acquired from the camera and microphone on the user's device.

[1014] Step 14:

[1015] The emotion engine stores the user's emotion data in a cloud database and adds it to the generative AI model. For example, data indicating that the user is feeling stressed is stored.

[1016] Step 15:

[1017] The server adjusts the analysis results of the generative AI model based on the emotional data. For example, if stress is high, an action plan to reduce it will be generated.

[1018] Step 16:

[1019] The server then sends the adjusted action plan and prediction results to the user's device, where the user can check the notification and take action according to their emotional state.

[1020] Example 2

[1021] 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."

[1022] Conventional agricultural management systems are limited to collecting and analyzing environmental data, and are unable to create work plans that take into account the user's emotional state, making efficient agricultural management difficult. Furthermore, it is difficult to provide appropriate crop management and future predictions, which can lead to increased stress and fatigue for users.

[1023] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity, a storage means for storing the environmental data collected from the sensor in a cloud database, and an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database. This not only enables the aggregation and analysis of environmental data in real time, but also enables comprehensive crop management and future predictions that take into account user emotional data.

[1024] "Sensor means" refers to a device that measures environmental data such as soil condition, moisture content, and light intensity.

[1025] "Storage means" refers to a device or method for storing environmental data collected from sensors in a cloud database.

[1026] "Analysis means" refers to a device or method that executes a generative AI model based on data stored in a cloud database to perform data analysis.

[1027] A "generative AI model" is an artificial intelligence technology that analyzes historical and real-time data to generate insights and predictions.

[1028] An "action plan generation means" is a device or method that generates an optimal crop management action plan based on the analysis results of a generative AI model.

[1029] A "prediction means" is a device or method for predicting future crop growth and risks based on current data and analysis results.

[1030] "Emotion analysis means" refers to a device or method for analyzing the user's facial expression, voice, posture, etc., to grasp the user's emotional state.

[1031] The "notification means" is a device or method for notifying the user's terminal of the generated action plan, prediction results, and emotion analysis results.

[1032] A "cloud database" is a database that exists on the Internet and is a system that provides accessible storage resources.

[1033] A "user terminal" is a device that can be directly operated by a user, such as a smartphone or a personal computer.

[1034] The system integrates generative AI models, digital sensors, and an emotion engine to achieve smart and efficient agricultural management. Through interactions between servers, terminals, and users, the system collects and analyzes environmental data, performs optimal crop management, predictions, and notifications.

[1035] Hardware and Software Configuration

[1036] 1. Digital Sensor:

[1037] Digital sensors are installed in farmland and have the ability to measure environmental data such as soil condition (moisture content, pH value, etc.), light intensity, and temperature every hour, and this data is sent to a server in real time.

[1038] 2. Server:

[1039] The server temporarily stores the received environmental data in its memory and then rapidly transfers it to a cloud database using a storage means. The server also includes an analysis means that uses a generative AI model to analyze the historical and real-time data stored in the cloud database. Once the analysis is complete, the server generates an optimal crop management action plan and derives prediction results.

[1040] 3. Cloud Database:

[1041] Historical farmland and environmental data is stored in the cloud database. The data is encrypted and securely stored, and is accessed from the server when necessary for analysis.

[1042] 4. Emotion Engine:

[1043] The emotion engine analyzes the user's facial expressions, voice, posture, etc. to generate emotion data, which is stored in a cloud database and reflected in the analysis of the generative AI model.

[1044] 5. Device (smartphone or PC):

[1045] The user receives the generated action plan, prediction results, and emotion analysis results via their device. The information sent from the server via notification means is displayed in an intuitive, easy-to-understand format.

[1046] Specific examples

[1047] The operation of the system will be explained using the following concrete example.

[1048] 1. Data collection example:

[1049] The server receives environmental data from the sensor, such as "soil moisture content 42%, temperature 25°C, light intensity 800 lux." This data is sent to the server in real time and first stored in memory.

[1050] 2. Data storage examples:

[1051] The server uses a storage means to transfer the collected data to a cloud database for safekeeping.

[1052] 3. Data analysis example:

[1053] The server runs a generative AI model based on data including the past three years' worth of climate data and newly received current soil data. Through analysis, it gains the insight that "the risk of disease increases when soil moisture is below 30%."

[1054] 4. Action plan generation example:

[1055] The server generates an action plan such as "The soil moisture content is expected to decrease this week, so irrigation with 0.5 liters should be carried out in three days," and stores the plan in a cloud database.

[1056] 5. Prediction example:

[1057] Based on future climate data, the server makes predictions such as, "Temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[1058] 6. Emotion analysis example:

[1059] The emotion engine analyzes the stress the user is feeling, sends that data to the server, and reflects it in the analysis of the generative AI model.

[1060] 7. Example of Notice:

[1061] The server then sends these analysis results from a cloud database to the user's device, displaying a watering schedule for the next two days and relaxation suggestions based on the user's current emotional state.

[1062] Prompt Sentence Examples

[1063] "For a field with soil moisture at 42%, should we irrigate 0.5 liters over the next three days? If so, please suggest the best course of action if the user is experiencing stress."

[1064] This system allows farmers to receive optimal farming plans in real time and enables comprehensive agricultural management that also takes emotional data into account.

[1065] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1066] Step 1: Data collection

[1067] Subject: Server

[1068] The server receives environmental data (soil condition, moisture content, light intensity, etc.) every hour from digital sensors installed in the farmland. For example, if a sensor measures the soil moisture level at 42% and the light intensity at 800 lux, the data is sent to the server in real time.

[1069] input:

[1070] Sensor environmental data (e.g. soil moisture 42%, light intensity 800 lux)

[1071] Data processing / calculation:

[1072] Data from the sensor is received in real time and temporarily stored in memory.

[1073] output:

[1074] Environmental data stored in memory

[1075] Step 2: Save data

[1076] Subject: Server

[1077] The server temporarily stores the received environmental data in a memory, and then uses a storage means to quickly transfer the data to a cloud database, thereby ensuring secure management of the data.

[1078] input:

[1079] Environmental data stored in memory

[1080] Data processing / calculation:

[1081] The data is retrieved from the memory and transferred to a cloud database for storage.

[1082] output:

[1083] Environmental data stored in a cloud database

[1084] Step 3: Data analysis

[1085] Subject: Server

[1086] The server runs a generative AI model based on historical and real-time data stored in a cloud database and performs analysis, such as integrating climate data from the past three years with current soil data to obtain insights such as "the risk of disease increases when soil moisture falls below 30%."

[1087] input:

[1088] Historical and real-time environmental data stored in a cloud database

[1089] Data processing / calculation:

[1090] Run generative AI models and analyze historical and real-time data to gain insights.

[1091] output:

[1092] Analysis results (e.g., "The risk of disease increases when soil moisture falls below 30%)

[1093] Step 4: Generate an action plan

[1094] Subject: Server

[1095] The server generates an optimal crop management action plan based on the analysis results of the generative AI model, such as "Since the soil moisture content is expected to decrease this week, irrigation with 0.5 liters should be carried out in three days."

[1096] input:

[1097] Analysis results

[1098] Data processing / calculation:

[1099] An action plan is generated based on the analysis results and stored in a cloud database.

[1100] output:

[1101] Generated action plan (e.g. "0.5 liters of watering should be done in 3 days")

[1102] Step 5: Predict

[1103] Subject: Server

[1104] The server uses current data and analysis results to predict future crop growth and risks, for example, predicting that "temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[1105] input:

[1106] Data and analysis results stored in a cloud database

[1107] Data processing / calculation:

[1108] Predict future crop growth and risks using generative AI models.

[1109] output:

[1110] Prediction results (e.g., "Temperatures will rise sharply over the next two weeks, causing tomatoes to grow faster")

[1111] Step 6: Sentiment Analysis

[1112] Subject: Emotion Engine

[1113] The emotion engine generates emotional data by analyzing the user's facial expressions, voice, posture, etc. For example, if the user is feeling stressed, it will use that data to suggest a plan such as "reduce the amount of work you do next week."

[1114] input:

[1115] User facial expression, voice, and posture data

[1116] Data processing / calculation:

[1117] Emotional data is generated by analyzing facial expressions, voice, posture, etc.

[1118] output:

[1119] User emotion data (e.g., "I feel stressed")

[1120] Step 7: Notification

[1121] Subject: Server

[1122] The server then notifies the user of the generated action plan, prediction results, and emotion analysis results via their smartphone or PC, allowing them to check the notifications and carry out specific farm work and lifestyle plans.

[1123] input:

[1124] Generated action plans, prediction results, and sentiment analysis results

[1125] Data processing / calculation:

[1126] Action plans, prediction results, and emotion analysis results are notified to the user's device.

[1127] output:

[1128] Notifications displayed on the user's device (e.g., "Watering schedule for the next two days" and "Relaxation suggestions")

[1129] Through the above processing steps, the present system provides smart and efficient agricultural management for users.

[1130] (Application example 2)

[1131] 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."

[1132] Conventional production management systems performed optimization based solely on environmental data, but did not take into account the emotional state of employees, which resulted in suboptimal production efficiency and increased employee stress and fatigue. Furthermore, because predictions based on real-time environmental and emotional data could not be made, appropriate maintenance and rest instructions were sometimes delayed.

[1133] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor means for measuring environmental data and employee emotional state data, a storage means for storing the measured environmental data and employee emotional state data in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating a production management action plan based on the analysis results, and a prediction means for providing a prediction of future production efficiency. This enables more accurate production management by taking environmental data and employee emotional states into consideration in an integrated manner. Furthermore, maintenance and rest instructions can be automatically performed at appropriate times, thereby optimizing production efficiency and reducing employee burden.

[1134] "Environmental data" refers to information such as temperature, humidity, and machine operating status inside the factory.

[1135] "Emotional state data" refers to information that indicates stress levels and fatigue levels analyzed from employees' facial expressions, voices, postures, etc.

[1136] "Sensor means" refers to devices for measuring environmental data and emotional state data.

[1137] "Storage means" refers to the system or method for storing measured data in a cloud database.

[1138] "Analysis means" refers to devices or programs for performing analysis using a generative AI model based on stored data.

[1139] A "generative AI model" refers to an artificial intelligence model that analyzes environmental data and emotional state data to generate an action plan.

[1140] "Action plan generation means" refers to a device or program for creating specific measures and schedules for production management based on the analysis results.

[1141] "Prediction means" refers to functions and devices for predicting future production efficiency and risks.

[1142] "Notification means" refers to a system or device for reporting the generated action plan and prediction results to the user's terminal.

[1143] A "cloud database" refers to a digital storage system that stores and makes accessible large amounts of data online.

[1144] This invention relates to a system that uses environmental data and employee emotional state data in an integrated manner to optimize production management within a factory. The program processing and mechanism of this system are explained below.

[1145] Hardware and Software Configuration

[1146] The server collects environmental data and data on employees' emotional states using multiple sensors, including temperature sensors, humidity sensors, and sensors that measure the operating status of machines installed in the factory, as well as cameras and microphones that analyze employees' facial expressions, voices, and postures.

[1147] The collected data is stored by the server in a cloud database, which allows for safe and efficient storage of historical and real-time data.

[1148] The server runs a generative AI model based on environmental and emotional state data stored in a cloud database. This generative AI model combines historical and real-time data for analysis, providing optimal action plans and predictions of future production efficiency.

[1149] Based on the analysis results, the action plan generation means creates a specific production management action plan, such as "The temperature of the machine is abnormally high and the stress level of employees is high, so temporarily stop the machine to cool it down" or "Allow employees to take a break."

[1150] The action plan and prediction results are sent to the user's device (smartphone, smart glasses, PC, etc.). Through the notification means, the user can check the generated action plan and take specific countermeasures.

[1151] Specific examples

[1152] For example, if an employee's emotion engine analyzes an employee's facial expressions, posture, and voice and detects a high overall stress level, the server will take environmental data (e.g., high temperature) into account and use the generative AI model to generate an action plan to "pause the machine and have the employee take a break." This plan will be notified to the user's device, allowing the user to adjust their work in a timely manner.

[1153] Prompt Sentence Examples

[1154] When inputting a prompt sentence to a generative AI model, write it as follows:

[1155] "Generate optimal production management methods when temperatures are high and employee stress levels are high."

[1156] Based on this prompt, the action plan generated is "Taking into account the temperature and stress level, pause the machines and have employees take a break."

[1157] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1158] Step 1:

[1159] Sensors measure environmental data (temperature, humidity, machine operating status, etc.) and employee emotional state data (facial expression, voice, posture), and acquire each data. The input is measurement data from the various sensors, and the output is data sent to the server. Specifically, the temperature sensor measures the environmental temperature and sends that value (e.g., 30°C) to the server.

[1160] Step 2:

[1161] The server stores the measured environmental data and emotional state data in a cloud database. The input is the data sent from each sensor, and the output is the data stored in the cloud database. Specifically, the server receives temperature data and employee stress level data and writes it to the cloud database.

[1162] Step 3:

[1163] The server runs the generative AI model based on the data stored in the cloud database and analyzes the data. The input is the past and current data stored in the cloud database, and the output is the analysis results. Specifically, the server runs the generative AI model using the temperature data from the past week and the current stress level as input, detecting abnormal temperature increases and high stress levels.

[1164] Step 4:

[1165] The server generates a production management action plan based on the analysis results. The input is the analysis results from the generative AI model, and the output is an action plan. Specifically, the server generates an action plan that says, "Stop the machines and have employees take a break."

[1166] Step 5:

[1167] The server notifies the user's device of the generated action plan. The input is the generated action plan, and the output is a notification message displayed on the user's device. Specifically, the server sends the generated action plan to the user's smartphone and displays a notification saying, "Pause the machine and have employees take a break."

[1168] Step 6:

[1169] The user checks the notification on the terminal and implements specific production management countermeasures. The input is the notification message displayed on the terminal, and the output is the implemented countermeasure. Specific actions include the user checking the notification, temporarily stopping the machine, and instructing employees to take a break.

[1170] Step 7:

[1171] The server collects the result data of the implemented countermeasures and stores it in a cloud database. The input is the environmental and emotional state data after the countermeasures are implemented, and the output is new data stored in the cloud database. Specifically, the server remeasures the machine's stopped state and the employee's emotional state and stores them in the cloud database.

[1172] 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.

[1173] 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.

[1174] 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.

[1175] [Fourth embodiment]

[1176] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1177] 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.

[1178] 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).

[1179] 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.

[1180] 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.

[1181] 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).

[1182] 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.

[1183] 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.

[1184] 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.

[1185] 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.

[1186] 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.

[1187] 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.

[1188] 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."

[1189] This invention aims to realize smart and efficient agricultural management through a system that integrates generative AI models and digital sensors. This system monitors soil conditions and farmland environments in real time, and provides optimal crop management methods, pest prevention, and resource management based on data analysis using generative AI models.

[1190] The server receives data from sensors that measure environmental data such as soil condition, moisture content, light intensity, etc. These sensors are installed in the fields and measure the required data every hour. For example, if the soil moisture content is measured to be 42%, this data is sent from the sensor to the server in real time.

[1191] The server stores the received data in a cloud database and manages the data securely. The data temporarily stored in memory is quickly transferred to the cloud database via storage means. The database stores not only current data, but also past farmland data and environmental data.

[1192] The server, as the analytical tool, runs generative AI models based on historical and real-time data stored in a cloud database. For example, it combines three years of weather data with current soil data to gain specific insights, such as the increased risk of disease when soil moisture falls below 30%. Such analysis results provide farmers with valuable information to take preemptive measures.

[1193] The server has a means to generate an optimal crop management action plan based on the analysis results of the generative AI model. For example, a detailed action plan may be generated, such as "Since the soil moisture content is expected to decrease this week, 0.5 liters of irrigation should be carried out in three days." This plan is stored in a cloud database and notified to the user's device.

[1194] The user's device (smartphone or PC) receives and displays the action plan and prediction results sent from the server. The user can then carry out specific farm work in accordance with the action plan. For example, the user can check the notification and carry out actual irrigation work based on the irrigation schedule for the next two days.

[1195] The server, which acts as a forecasting tool, predicts future crop growth and risks based on current data and analysis results. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes." This information is sent to the user's device, allowing farmers to create production plans based on it.

[1196] In this way, the system of the present invention integrates sensor means, storage means, analysis means, action plan generation means, prediction means, and notification means, thereby enabling farmers to achieve more efficient and smarter agricultural management.

[1197] The processing flow will be explained below.

[1198] Step 1:

[1199] Sensors are installed in the fields and measure environmental data such as soil condition, moisture content, light intensity, and temperature every hour, and this data is temporarily stored inside the sensors.

[1200] Step 2:

[1201] The sensor sends the measured data to a server via a wireless network. For example, if the soil moisture content is measured at 42%, this data is sent to the server.

[1202] Step 3:

[1203] The server receives the data sent from the sensor and temporarily stores it in the server's memory.

[1204] Step 4:

[1205] The server stores the environmental data stored in the memory in a cloud database, and the soil humidity and other environmental data are quickly transferred to the cloud database through the storage means.

[1206] Step 5:

[1207] Data stored in a cloud database is collected by a server and input into a generative AI model, which then begins analysis based on past and current data.

[1208] Step 6:

[1209] The generative AI model performs the analysis and extracts specific patterns and insights, such as "When humidity drops below 30%, the risk of disease increases."

[1210] Step 7:

[1211] The server generates an optimal crop management action plan based on the analysis results. For example, it might suggest irrigating 0.5 liters every morning for the next two days to maintain moisture levels.

[1212] Step 8:

[1213] The server notifies the user's device of the generated action plan, which is then sent to the smartphone or PC via a notification means.

[1214] Step 9:

[1215] The user's device receives and displays the action plan sent from the server. The user checks the notification and makes preparations to carry out specific farm work.

[1216] Step 10:

[1217] The server uses current data and analysis results to predict future crop growth and risks. For example, it predicts that "temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[1218] Step 11:

[1219] The server notifies the user's terminal of the prediction result, and the server transmits the prediction result to the user's terminal through the prediction means.

[1220] Step 12:

[1221] The user's terminal receives and displays the forecast results sent from the server, allowing the user to efficiently create production plans based on the forecasts.

[1222] Example 1

[1223] 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."

[1224] Conventional agricultural management systems suffer from fragmented data collection and analysis, lacking real-time capabilities, making it difficult to efficiently manage resources and develop preventative measures. Furthermore, they lack the information users need to take appropriate action in a timely manner, resulting in suboptimal productivity and quality of agricultural crops. The present invention aims to solve these problems by providing a system that realizes efficient and smart agricultural management based on real-time data from sensors.

[1225] 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.

[1226] In this invention, the server includes a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity, a storage means for storing the environmental data collected from the sensor in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating a crop management action plan based on the analysis results, a notification means for notifying a user terminal of the generated action plan, a user means for performing crop management based on the action plan notified to the user terminal, and a prediction means for providing a future crop production forecast. This enables precise analysis based on real-time data and the provision of an accurate action plan.

[1227] "Sensor means" refers to a device for measuring environmental data such as soil condition, moisture content, and light intensity.

[1228] "Storage means" refers to equipment or functionality for storing environmental data collected from sensors in a cloud database.

[1229] "Analysis means" refers to a device or function for executing a generative AI model that performs analysis based on data stored in a cloud database.

[1230] A "generative AI model" is an artificial intelligence model that performs analysis based on past data stored in a cloud database and real-time data.

[1231] An "action plan generation means" is a device or function for generating a crop management action plan based on the analysis results of the generative AI model.

[1232] The "notification means" is a facility or function for notifying the user's terminal of the generated action plan.

[1233] The "user means" refers to a person and their operating means for carrying out crop management based on the action plan notified to the user's terminal.

[1234] A "forecasting means" is a device or function for providing a forecast of future crop production.

[1235] A "cloud database" is a database accessible via the Internet, and is a system for storing data collected from sensors and analysis results.

[1236] "Analysis Results" means the output data analyzed by the generative AI model, including insights and recommended actions for crop management.

[1237] "Real-time data" is data that is currently being collected from sensors.

[1238] An "action plan" is a specific plan or instruction for crop management generated based on the analysis results.

[1239] A "terminal" is a device, such as a smartphone or PC, that users use to receive and check information.

[1240] This invention realizes smart and efficient agricultural management through a system that integrates generative AI models and digital sensors. This system is implemented using the following main hardware and software:

[1241] Hardware and software used

[1242] 1. Sensor means

[1243] Digital sensors installed on farmland include soil moisture sensors, light sensors, and temperature sensors, which serve to measure environmental data in real time.

[1244] 2. Server

[1245] The servers used to collect and analyze data are common cloud-based servers (e.g., AWS EC2, Google Cloud Compute Engine).

[1246] 3. Cloud Database

[1247] Databases used to store and manage environmental data include Amazon RDS and Google Cloud Firestore.

[1248] 4. Generative AI Models

[1249] Generative AI models used for data analysis include OpenAI's GPT-3, for example.

[1250] System Overview

[1251] The server first obtains environmental data such as soil condition, moisture content, and light intensity from the sensor means every hour and temporarily stores it in memory. This data is then sent to and managed in a cloud database, which also stores past data and serves as the basis for analysis by the generative AI model.

[1252] The generative AI model performs advanced analysis using historical data stored in a cloud database and real-time data. For example, the following prompt sentence is provided to the generative AI model:

[1253] "Based on the past three years of weather data and current soil data, what are the growth risks for tomatoes over the next two weeks and what management practices are recommended?"

[1254] The generative AI model analyzes this prompt and outputs specific insights. Based on the analysis results, the server generates an action plan for crop management. For example, if a drop in soil moisture is predicted, a specific action plan will be generated, such as "irrigate 0.5 liters in three days." This plan is stored in a cloud database and notified to the user's device.

[1255] The user's device (smartphone or PC) receives and displays the action plan and prediction results sent from the server. The user can then carry out specific farm work based on this information. For example, the user can check the notification on their device and carry out actual irrigation work according to the irrigation schedule.

[1256] Furthermore, as a predictive tool, the server uses current data and analysis results to predict future crop growth and risks. For example, it notifies users of a forecast result such as "Tomatoes will grow faster because temperatures will rise sharply in the next two weeks." This allows users to adjust their future production plans and make agricultural activities more efficient and effective.

[1257] The above is an embodiment of the system of the present invention, which enables farmers to achieve more efficient and smarter farm management and improve crop productivity and quality.

[1258] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1259] Step 1:

[1260] Collecting data from sensors

[1261] Input: Environmental data such as soil condition, moisture content, and light intensity from digital sensors installed on farmland

[1262] How it works: The sensor measures environmental data every hour.

[1263] Output: Measured environmental data (e.g., soil moisture content 42%)

[1264] Step 2:

[1265] Sending data to the server

[1266] Input: Environmental data collected from sensors

[1267] How it works: The sensor sends measurement data to the server in real time.

[1268] Output: Environmental data sent to the server (with timestamp)

[1269] Step 3:

[1270] Data storage

[1271] Input: Environmental data received by the server

[1272] How it works: The server temporarily stores the data in memory, and then stores it in a cloud database, for example Amazon RDS, using an API call.

[1273] Output: Data stored in a cloud database

[1274] Step 4:

[1275] Analysis using generative AI models

[1276] Input: Historical and real-time environmental data stored in a cloud database

[1277] How it works: The server prompts the generative AI model with the following prompt: "Based on the past three years of climate data and current soil data, what are the growth risks and recommended management practices for tomatoes over the next two weeks?"

[1278] Output: Analysis results from the generative AI model (e.g., disease risk increases when soil moisture falls below 30%)

[1279] Step 5:

[1280] Generate an action plan

[1281] Input: Analysis results of the generative AI model

[1282] How it works: The server generates an action plan for farming based on the analysis results. For example, it creates a specific plan that states, "Because the moisture content will decrease over the next three days, 0.5 liters of irrigation should be carried out in three days."

[1283] Output: Generated action plan

[1284] Step 6:

[1285] User Notification

[1286] Input: Generated Action Plan

[1287] How it works: The server notifies the user of the action plan via push notification or email.

[1288] Output: An action plan displayed on the user's device (e.g., a notification that "watering should be performed in 3 days").

[1289] Step 7:

[1290] User confirmation and execution

[1291] Input: Action plan sent to the user's device

[1292] Operation: The user checks the notification on the device and performs farm work according to the proposed action plan. For example, the user irrigates based on the irrigation schedule. The user then enters the progress of this work into another app or management system.

[1293] Output: Results and progress data of the farming operations performed

[1294] Step 8:

[1295] Providing forecast data

[1296] Input: Analysis results from the generative AI model and current environmental data

[1297] How it works: The server predicts future crop growth and risks and provides that information to the user. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes."

[1298] Output: Forecast information displayed on the user's terminal and data for adjusting production plans

[1299] (Application example 1)

[1300] 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."

[1301] Modern logistics centers are susceptible to a wide range of environmental variables (temperature, humidity, light intensity, vibration, etc.), and require appropriate management to maintain the quality of cargo. However, current systems for monitoring these environmental variables in real time and providing optimal cargo management methods are inadequate. This increases the risk of cargo deterioration and loss, and reduces the efficiency of logistics operations.

[1302] 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.

[1303] In this invention, the server includes a sensor means for measuring environmental data such as temperature, humidity, light intensity, and vibration, a storage means for storing the environmental data collected from the sensors in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating an action plan for package management based on the analysis results, and a prediction means for providing a prediction of future package deterioration. This makes it possible to monitor the environment within the logistics center in real time and achieve optimal package management based on precise data analysis and predictions by the generative AI model.

[1304] "temperature"

[1305] is an index that indicates the thermal energy of a substance, and is an important factor in maintaining the quality of cargo in environmental management within a logistics center.

[1306] "Humidity"

[1307] is an indicator of the proportion of water vapor in the air, and is a factor that affects the storage conditions of cargo in environmental control within logistics centers.

[1308] "Light intensity"

[1309] is an indicator of the intensity of light measured at a specific location, and is an environmental factor that needs to be managed to prevent deterioration within a logistics center.

[1310] "vibration"

[1311] This refers to the phenomenon of an object shaking, and indicates the physical stimulation that cargo in a logistics center receives, making it an important factor in assessing the risk of damage to cargo.

[1312] "Sensor means"

[1313] refers to devices and equipment for measuring and acquiring environmental data.

[1314] "Storage means"

[1315] refers to a system or device for storing environmental data collected from sensors.

[1316] Cloud database

[1317] refers to a remote database service for storing and managing data over the Internet.

[1318] "Analysis means"

[1319] This refers to systems and devices that perform analysis based on data stored in a cloud database.

[1320] "Generative AI model"

[1321] refers to an artificial intelligence system that uses machine learning technology to analyze data and make predictions.

[1322] "Action plan generation method"

[1323] This refers to a system or device that creates specific management methods and countermeasures based on the analysis results.

[1324] "Prediction methods"

[1325] refers to systems and devices that provide predictions regarding future cargo deterioration risks and management.

[1326] "Notification means"

[1327] This refers to a system or device that notifies the user of the generated action plan or prediction results on their device.

[1328] "User's Device"

[1329] refers to a digital device for receiving and displaying generated action plans and forecast results.

[1330] The present invention is a system for achieving optimal cargo management and deterioration prediction in a logistics center. This system includes a sensor means for measuring environmental data such as temperature, humidity, light intensity, and vibration, a storage means for storing the environmental data collected from the sensors in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating an action plan for cargo management based on the analysis results, and a prediction means for providing a prediction of future cargo deterioration.

[1331] Specific implementation methods of the system

[1332] Configuration and operation of the sensor means

[1333] Digital sensors (such as the Bosch BME280 sensor) are installed in the logistics center to measure temperature, humidity, light intensity, vibration, etc. These sensors measure environmental data every hour and are placed in different locations within the center to achieve comprehensive monitoring.

[1334] Data collection and storage methods

[1335] The collected data is sent to a cloud database (e.g., Amazon Web Services (AWS) or Google Cloud) via an internet connection. The data is stored in the cloud database in real time via a storage means.

[1336] Analysis methods and generative AI models

[1337] The server uses a machine learning platform (e.g., TensorFlow or PyTorch) to analyze historical data stored in a cloud database and data acquired in real time. The generative AI model uses environmental data as input to analyze deterioration risks and optimal management methods.

[1338] Action plan generation method

[1339] It also includes a means for generating a specific action plan based on the analysis results. For example, if the temperature exceeds a preset threshold, it generates instructions such as moving the package to a refrigerated area. The generated action plan is stored in a cloud database.

[1340] Prediction and Notification

[1341] The prediction method predicts future risk of cargo deterioration based on current environmental data and analysis results. This predicted information is sent to the user's device (smartphone or PC). Notifications are sent in real time using notification services such as Pushbullet.

[1342] Specific examples

[1343] Users manage their logistics centers using the smartphone application "LogiSmart." The app receives data collected from sensors and provides analysis results, action plans, and forecasts in real time. For example, the following prompts can be used to input data into the generative AI model:

[1344] "If the temperature data is above 25°C, there is a high risk that the quality of package A will deteriorate within a day. Generate an action plan to move it to a refrigerated area until the temperature stabilizes."

[1345] This system allows logistics center managers to effectively maintain the quality of goods and minimize the risk of deterioration.

[1346] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1347] Step 1:

[1348] The server collects environmental data from digital sensors that measure temperature, humidity, light intensity, vibration, etc. These sensors are installed in the distribution center and collect data every hour. The input data are the values ​​obtained from each sensor and are sent to the server through the sensor API. The output data is a set of environmental data.

[1349] Step 2:

[1350] The server stores the collected environmental data in a cloud database via a storage means. The input data is the environmental data collected in step 1, and the output data is the data converted into a format stored in the cloud database. This data is transmitted via the Internet and protected by security protocols.

[1351] Step 3:

[1352] The server retrieves the historical data and real-time retrieved data stored in the cloud database, where the input data is a set of historical and real-time data retrieved from the cloud database, and the output data is a dataset prepared for analysis.

[1353] Step 4:

[1354] The server performs data analysis using a generative AI model. The input data is the dataset prepared in step 3 and is fed into the generative AI model. The output data is the analysis results, which are the luggage management method and deterioration risk assessment. The generative AI model uses a machine learning platform such as TensorFlow or PyTorch.

[1355] Step 5:

[1356] The server generates a specific action plan based on the analysis results. The input data is the analysis results obtained in step 4, and the output data is a specific action plan, such as "move the package to the refrigerated area immediately." The action plan generation is based on predefined rules and prompt statements.

[1357] Step 6:

[1358] The server notifies the user of the generated action plan and prediction results. The input data is the action plan and prediction results generated in step 5, and the output data is a notification message sent to the user's device. The notification is sent using a notification service such as Pushbullet. Specifically, if the temperature exceeds 25°C, a message stating "there is an increased risk that the quality of package A will deteriorate within one day" is sent to the user's smartphone.

[1359] Step 7:

[1360] After receiving the notification, the user follows the instructions to take specific management actions. The input data is the action plan notified in step 6, and the output data is the actual package management action. For example, if the temperature rises, the user can move the package to a refrigerated area.

[1361] 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.

[1362] This invention realizes smart and efficient agricultural management through a system that integrates generative AI models, digital sensors, and an emotion engine that recognizes user emotions. This system monitors soil conditions and the environment in real time, and provides optimal crop management methods, pest prevention, and resource management based on data analysis using generative AI models, while also incorporating user emotions into the analysis.

[1363] The server receives data from sensors that measure environmental data such as soil condition, moisture content, light intensity, etc. These sensors are installed in the fields and measure the required data every hour. For example, if the soil moisture content is measured to be 42%, this data is sent from the sensor to the server in real time.

[1364] The server stores the received data in a cloud database and manages the data securely. The data temporarily stored in memory is quickly transferred to the cloud database via storage means. The database stores not only current data, but also past farmland data and environmental data.

[1365] The server, which acts as the analytical tool, runs generative AI models based on historical and real-time data stored in a cloud database. For example, it combines three years of weather data with current soil data to obtain specific insights, such as "the risk of disease increases when soil moisture falls below 30%." Such analysis results provide farmers with valuable information to take preemptive measures.

[1366] The server, which acts as the action plan generator, generates an optimal crop management action plan based on the analysis results of the generative AI model. For example, a detailed action plan may be generated stating, "Because the soil moisture content is expected to decrease this week, 0.5 liters of irrigation should be carried out in three days." This plan is stored in a cloud database and notified to the user's device.

[1367] The server, which acts as a forecasting tool, predicts future crop growth and risks based on current data and analysis results. For example, it predicts that "temperatures will rise sharply in the next two weeks, which will accelerate the growth of tomatoes." This information is sent to the user's device, allowing farmers to create production plans based on it.

[1368] Furthermore, this invention incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, voice, posture, etc., and stores the emotion data in a cloud database. The emotion engine analyzes the user's emotions in real time and reflects the emotion data in the analysis of the generative AI model, thereby adjusting the action plan according to the user's emotions. For example, if the user is feeling stressed, a plan such as "reduce the amount of work you should do over the next week" is suggested based on that data.

[1369] The server, which acts as a notification means, notifies the user's device of the generated action plan, prediction results, and emotion analysis results. The user can check the notifications via their device (smartphone or PC) and carry out specific farm work or lifestyle plans. For example, the server can notify the user of the irrigation schedule for the next two days and relaxation suggestions based on the user's current emotional state, allowing the user to take appropriate measures based on this information.

[1370] In this way, the system of the present invention integrates sensor means, storage means, analysis means, action plan generation means, prediction means, emotion engine, and notification means to enable farmers to achieve more efficient and smarter agricultural management.

[1371] The processing flow will be explained below.

[1372] Step 1:

[1373] Sensors are installed in agricultural fields and measure environmental data such as soil condition, moisture content, light intensity, and temperature every hour. This data is temporarily stored inside the sensor. For example, the soil moisture content may be measured as 42%.

[1374] Step 2:

[1375] The sensor transmits the measured data to a server via a wireless network. For example, if the moisture content is measured as 42%, the sensor transmits the data to the server in real time.

[1376] Step 3:

[1377] The server receives the data sent from the sensor and temporarily stores it in the server's memory.

[1378] Step 4:

[1379] The server stores the environmental data stored in the memory in a cloud database, and through the storage means, the soil moisture content and other environmental data are quickly transferred to the cloud database.

[1380] Step 5:

[1381] The server collects historical data stored in a cloud database and newly acquired real-time data, which are then input into the generative AI model. The historical data includes, for example, three years of weather and agricultural data.

[1382] Step 6:

[1383] A generative AI model analyzes the data and extracts specific patterns and insights, for example, it may conclude that soil moisture levels below 30% increase the risk of disease.

[1384] Step 7:

[1385] The server generates an optimal crop management action plan based on the analysis results, for example, a specific action plan such as "irrigate 0.5 liters every morning for the next two days."

[1386] Step 8:

[1387] The server notifies the user's device of the generated action plan, and a detailed action plan is sent to the smartphone or PC via the notification means.

[1388] Step 9:

[1389] The device receives the notification from the server and displays it to the user, who can then check the displayed irrigation schedule on their smartphone.

[1390] Step 10:

[1391] The server uses current data and analysis results to predict future crop growth and risks. For example, it predicts that temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster.

[1392] Step 11:

[1393] The server notifies the user's terminal of the prediction result, and the prediction information is transmitted to the terminal through the prediction means.

[1394] Step 12:

[1395] The user's terminal receives and displays the prediction results, allowing the user to efficiently create production plans based on the predictions.

[1396] Step 13:

[1397] The server uses an emotion engine to analyze the user's emotions, which are based on data acquired from the camera and microphone on the user's device.

[1398] Step 14:

[1399] The emotion engine stores the user's emotion data in a cloud database and adds it to the generative AI model. For example, data indicating that the user is feeling stressed is stored.

[1400] Step 15:

[1401] The server adjusts the analysis results of the generative AI model based on the emotional data. For example, if stress is high, an action plan to reduce it will be generated.

[1402] Step 16:

[1403] The server then sends the adjusted action plan and prediction results to the user's device, where the user can check the notification and take action according to their emotional state.

[1404] Example 2

[1405] 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."

[1406] Conventional agricultural management systems are limited to collecting and analyzing environmental data, and are unable to create work plans that take into account the user's emotional state, making efficient agricultural management difficult. Furthermore, it is difficult to provide appropriate crop management and future predictions, which can lead to increased stress and fatigue for users.

[1407] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity, a storage means for storing the environmental data collected from the sensor in a cloud database, and an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database. This not only enables the aggregation and analysis of environmental data in real time, but also enables comprehensive crop management and future predictions that take into account user emotional data.

[1408] "Sensor means" refers to a device that measures environmental data such as soil condition, moisture content, and light intensity.

[1409] "Storage means" refers to a device or method for storing environmental data collected from sensors in a cloud database.

[1410] "Analysis means" refers to a device or method that executes a generative AI model based on data stored in a cloud database to perform data analysis.

[1411] A "generative AI model" is an artificial intelligence technology that analyzes historical and real-time data to generate insights and predictions.

[1412] An "action plan generation means" is a device or method that generates an optimal crop management action plan based on the analysis results of a generative AI model.

[1413] A "prediction means" is a device or method for predicting future crop growth and risks based on current data and analysis results.

[1414] "Emotion analysis means" refers to a device or method for analyzing the user's facial expression, voice, posture, etc., to grasp the user's emotional state.

[1415] The "notification means" is a device or method for notifying the user's terminal of the generated action plan, prediction results, and emotion analysis results.

[1416] A "cloud database" is a database that exists on the Internet and is a system that provides accessible storage resources.

[1417] A "user terminal" is a device that can be directly operated by a user, such as a smartphone or a personal computer.

[1418] The system integrates generative AI models, digital sensors, and an emotion engine to achieve smart and efficient agricultural management. Through interactions between servers, terminals, and users, the system collects and analyzes environmental data, performs optimal crop management, predictions, and notifications.

[1419] Hardware and Software Configuration

[1420] 1. Digital Sensor:

[1421] Digital sensors are installed in farmland and have the ability to measure environmental data such as soil condition (moisture content, pH value, etc.), light intensity, and temperature every hour, and this data is sent to a server in real time.

[1422] 2. Server:

[1423] The server temporarily stores the received environmental data in its memory and then rapidly transfers it to a cloud database using a storage means. The server also includes an analysis means that uses a generative AI model to analyze the historical and real-time data stored in the cloud database. Once the analysis is complete, the server generates an optimal crop management action plan and derives prediction results.

[1424] 3. Cloud Database:

[1425] Historical farmland and environmental data is stored in the cloud database. The data is encrypted and securely stored, and is accessed from the server when necessary for analysis.

[1426] 4. Emotion Engine:

[1427] The emotion engine analyzes the user's facial expressions, voice, posture, etc. to generate emotion data, which is stored in a cloud database and reflected in the analysis of the generative AI model.

[1428] 5. Device (smartphone or PC):

[1429] The user receives the generated action plan, prediction results, and emotion analysis results via their device. The information sent from the server via notification means is displayed in an intuitive, easy-to-understand format.

[1430] Specific examples

[1431] The operation of the system will be explained using the following concrete example.

[1432] 1. Data collection example:

[1433] The server receives environmental data from the sensor, such as "soil moisture content 42%, temperature 25°C, light intensity 800 lux." This data is sent to the server in real time and first stored in memory.

[1434] 2. Data storage examples:

[1435] The server uses a storage means to transfer the collected data to a cloud database for safekeeping.

[1436] 3. Data analysis example:

[1437] The server runs a generative AI model based on data including the past three years' worth of climate data and newly received current soil data. Through analysis, it gains the insight that "the risk of disease increases when soil moisture is below 30%."

[1438] 4. Action plan generation example:

[1439] The server generates an action plan such as "The soil moisture content is expected to decrease this week, so irrigation with 0.5 liters should be carried out in three days," and stores the plan in a cloud database.

[1440] 5. Prediction example:

[1441] Based on future climate data, the server makes predictions such as, "Temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[1442] 6. Emotion analysis example:

[1443] The emotion engine analyzes the stress the user is feeling, sends that data to the server, and reflects it in the analysis of the generative AI model.

[1444] 7. Example of Notice:

[1445] The server then sends these analysis results from a cloud database to the user's device, displaying a watering schedule for the next two days and relaxation suggestions based on the user's current emotional state.

[1446] Prompt Sentence Examples

[1447] "For a field with soil moisture at 42%, should we irrigate 0.5 liters over the next three days? If so, please suggest the best course of action if the user is experiencing stress."

[1448] This system allows farmers to receive optimal farming plans in real time and enables comprehensive agricultural management that also takes emotional data into account.

[1449] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1450] Step 1: Data collection

[1451] Subject: Server

[1452] The server receives environmental data (soil condition, moisture content, light intensity, etc.) every hour from digital sensors installed in the farmland. For example, if a sensor measures the soil moisture level at 42% and the light intensity at 800 lux, the data is sent to the server in real time.

[1453] input:

[1454] Sensor environmental data (e.g. soil moisture 42%, light intensity 800 lux)

[1455] Data processing / calculation:

[1456] Data from the sensor is received in real time and temporarily stored in memory.

[1457] output:

[1458] Environmental data stored in memory

[1459] Step 2: Save data

[1460] Subject: Server

[1461] The server temporarily stores the received environmental data in a memory, and then uses a storage means to quickly transfer the data to a cloud database, thereby ensuring secure management of the data.

[1462] input:

[1463] Environmental data stored in memory

[1464] Data processing / calculation:

[1465] The data is retrieved from the memory and transferred to a cloud database for storage.

[1466] output:

[1467] Environmental data stored in a cloud database

[1468] Step 3: Data analysis

[1469] Subject: Server

[1470] The server runs a generative AI model based on historical and real-time data stored in a cloud database and performs analysis, such as integrating climate data from the past three years with current soil data to obtain insights such as "the risk of disease increases when soil moisture falls below 30%."

[1471] input:

[1472] Historical and real-time environmental data stored in a cloud database

[1473] Data processing / calculation:

[1474] Run generative AI models and analyze historical and real-time data to gain insights.

[1475] output:

[1476] Analysis results (e.g., "The risk of disease increases when soil moisture falls below 30%)

[1477] Step 4: Generate an action plan

[1478] Subject: Server

[1479] The server generates an optimal crop management action plan based on the analysis results of the generative AI model, such as "Since the soil moisture content is expected to decrease this week, irrigation with 0.5 liters should be carried out in three days."

[1480] input:

[1481] Analysis results

[1482] Data processing / calculation:

[1483] An action plan is generated based on the analysis results and stored in a cloud database.

[1484] output:

[1485] Generated action plan (e.g. "0.5 liters of watering should be done in 3 days")

[1486] Step 5: Predict

[1487] Subject: Server

[1488] The server uses current data and analysis results to predict future crop growth and risks, for example, predicting that "temperatures will rise sharply in the next two weeks, causing tomatoes to grow faster."

[1489] input:

[1490] Data and analysis results stored in a cloud database

[1491] Data processing / calculation:

[1492] Predict future crop growth and risks using generative AI models.

[1493] output:

[1494] Prediction results (e.g., "Temperatures will rise sharply over the next two weeks, causing tomatoes to grow faster")

[1495] Step 6: Sentiment Analysis

[1496] Subject: Emotion Engine

[1497] The emotion engine generates emotional data by analyzing the user's facial expressions, voice, posture, etc. For example, if the user is feeling stressed, it will use that data to suggest a plan such as "reduce the amount of work you do next week."

[1498] input:

[1499] User facial expression, voice, and posture data

[1500] Data processing / calculation:

[1501] Emotional data is generated by analyzing facial expressions, voice, posture, etc.

[1502] output:

[1503] User emotion data (e.g., "I feel stressed")

[1504] Step 7: Notification

[1505] Subject: Server

[1506] The server then notifies the user of the generated action plan, prediction results, and emotion analysis results via their smartphone or PC, allowing them to check the notifications and carry out specific farm work and lifestyle plans.

[1507] input:

[1508] Generated action plans, prediction results, and sentiment analysis results

[1509] Data processing / calculation:

[1510] Action plans, prediction results, and emotion analysis results are notified to the user's device.

[1511] output:

[1512] Notifications displayed on the user's device (e.g., "Watering schedule for the next two days" and "Relaxation suggestions")

[1513] Through the above processing steps, the present system provides smart and efficient agricultural management for users.

[1514] (Application example 2)

[1515] 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."

[1516] Conventional production management systems performed optimization based solely on environmental data, but did not take into account the emotional state of employees, which resulted in suboptimal production efficiency and increased employee stress and fatigue. Furthermore, because predictions based on real-time environmental and emotional data could not be made, appropriate maintenance and rest instructions were sometimes delayed.

[1517] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor means for measuring environmental data and employee emotional state data, a storage means for storing the measured environmental data and employee emotional state data in a cloud database, an analysis means for executing a generative AI model that performs analysis based on the data stored in the cloud database, an action plan generation means for generating a production management action plan based on the analysis results, and a prediction means for providing a prediction of future production efficiency. This enables more accurate production management by taking environmental data and employee emotional states into consideration in an integrated manner. Furthermore, maintenance and rest instructions can be automatically performed at appropriate times, thereby optimizing production efficiency and reducing employee burden.

[1518] "Environmental data" refers to information such as temperature, humidity, and machine operating status inside the factory.

[1519] "Emotional state data" refers to information that indicates stress levels and fatigue levels analyzed from employees' facial expressions, voices, postures, etc.

[1520] "Sensor means" refers to devices for measuring environmental data and emotional state data.

[1521] "Storage means" refers to the system or method for storing measured data in a cloud database.

[1522] "Analysis means" refers to devices or programs for performing analysis using a generative AI model based on stored data.

[1523] A "generative AI model" refers to an artificial intelligence model that analyzes environmental data and emotional state data to generate an action plan.

[1524] "Action plan generation means" refers to a device or program for creating specific measures and schedules for production management based on the analysis results.

[1525] "Prediction means" refers to functions and devices for predicting future production efficiency and risks.

[1526] "Notification means" refers to a system or device for reporting the generated action plan and prediction results to the user's terminal.

[1527] A "cloud database" refers to a digital storage system that stores and makes accessible large amounts of data online.

[1528] This invention relates to a system that uses environmental data and employee emotional state data in an integrated manner to optimize production management within a factory. The program processing and mechanism of this system are explained below.

[1529] Hardware and Software Configuration

[1530] The server collects environmental data and data on employees' emotional states using multiple sensors, including temperature sensors, humidity sensors, and sensors that measure the operating status of machines installed in the factory, as well as cameras and microphones that analyze employees' facial expressions, voices, and postures.

[1531] The collected data is stored by the server in a cloud database, which allows for safe and efficient storage of historical and real-time data.

[1532] The server runs a generative AI model based on environmental and emotional state data stored in a cloud database. This generative AI model combines historical and real-time data for analysis, providing optimal action plans and predictions of future production efficiency.

[1533] Based on the analysis results, the action plan generation means creates a specific production management action plan, such as "The temperature of the machine is abnormally high and the stress level of employees is high, so temporarily stop the machine to cool it down" or "Allow employees to take a break."

[1534] The action plan and prediction results are sent to the user's device (smartphone, smart glasses, PC, etc.). Through the notification means, the user can check the generated action plan and take specific countermeasures.

[1535] Specific examples

[1536] For example, if an employee's emotion engine analyzes an employee's facial expressions, posture, and voice and detects a high overall stress level, the server will take environmental data (e.g., high temperature) into account and use the generative AI model to generate an action plan to "pause the machine and have the employee take a break." This plan will be notified to the user's device, allowing the user to adjust their work in a timely manner.

[1537] Prompt Sentence Examples

[1538] When inputting a prompt sentence to a generative AI model, write it as follows:

[1539] "Generate optimal production management methods when temperatures are high and employee stress levels are high."

[1540] Based on this prompt, the action plan generated is "Taking into account the temperature and stress level, pause the machines and have employees take a break."

[1541] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1542] Step 1:

[1543] Sensors measure environmental data (temperature, humidity, machine operating status, etc.) and employee emotional state data (facial expression, voice, posture), and acquire each data. The input is measurement data from the various sensors, and the output is data sent to the server. Specifically, the temperature sensor measures the environmental temperature and sends that value (e.g., 30°C) to the server.

[1544] Step 2:

[1545] The server stores the measured environmental data and emotional state data in a cloud database. The input is the data sent from each sensor, and the output is the data stored in the cloud database. Specifically, the server receives temperature data and employee stress level data and writes it to the cloud database.

[1546] Step 3:

[1547] The server runs the generative AI model based on the data stored in the cloud database and analyzes the data. The input is the past and current data stored in the cloud database, and the output is the analysis results. Specifically, the server runs the generative AI model using the temperature data from the past week and the current stress level as input, detecting abnormal temperature increases and high stress levels.

[1548] Step 4:

[1549] The server generates a production management action plan based on the analysis results. The input is the analysis results from the generative AI model, and the output is an action plan. Specifically, the server generates an action plan that says, "Stop the machines and have employees take a break."

[1550] Step 5:

[1551] The server notifies the user's device of the generated action plan. The input is the generated action plan, and the output is a notification message displayed on the user's device. Specifically, the server sends the generated action plan to the user's smartphone and displays a notification saying, "Pause the machine and have employees take a break."

[1552] Step 6:

[1553] The user checks the notification on the terminal and implements specific production management countermeasures. The input is the notification message displayed on the terminal, and the output is the implemented countermeasure. Specific actions include the user checking the notification, temporarily stopping the machine, and instructing employees to take a break.

[1554] Step 7:

[1555] The server collects the result data of the implemented countermeasures and stores it in a cloud database. The input is the environmental and emotional state data after the countermeasures are implemented, and the output is new data stored in the cloud database. Specifically, the server remeasures the machine's stopped state and the employee's emotional state and stores them in the cloud database.

[1556] 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.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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).

[1563] 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.

[1564] 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."

[1565] 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.

[1566] 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).

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] The following is further disclosed regarding the above embodiment.

[1578] (Claim 1)

[1579] a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity;

[1580] a storage means for storing the environmental data collected from the sensors in a cloud database;

[1581] An analysis means for executing a generative AI model that performs analysis based on data stored in a cloud database;

[1582] an action plan generation means for generating an action plan for crop management based on the analysis results;

[1583] a forecasting means for providing future crop production forecasts;

[1584] A system including:

[1585] (Claim 2)

[1586] The system of claim 1, wherein the generative AI model performs analysis based on past data stored in a cloud database and data acquired in real time.

[1587] (Claim 3)

[1588] 2. The system according to claim 1, further comprising a notification means for notifying a user's terminal of the generated crop management action plan.

[1589] "Example 1"

[1590] (Claim 1)

[1591] a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity;

[1592] a storage means for storing the environmental data collected from the sensors in a cloud database;

[1593] An analysis means for executing a generative AI model that performs analysis based on data stored in a cloud database;

[1594] an action plan generation means for generating an action plan for crop management based on the analysis results;

[1595] a notification means for notifying a user terminal of the generated action plan;

[1596] a user means for executing crop management based on the action plan notified to the user's terminal;

[1597] a forecasting means for providing future crop production forecasts;

[1598] A system including:

[1599] (Claim 2)

[1600] The system of claim 1, wherein the generative AI model performs analysis based on past data stored in a cloud database and data acquired in real time.

[1601] (Claim 3)

[1602] 2. The system according to claim 1, further comprising a user means for notifying a user terminal of the generated crop management action plan, allowing the user to carry out work based on the plan.

[1603] "Application Example 1"

[1604] (Claim 1)

[1605] a sensor means for measuring environmental data such as temperature, humidity, light intensity, and vibration;

[1606] a storage means for storing the environmental data collected from the sensors in a cloud database;

[1607] An analysis means for executing a generative AI model that performs analysis based on data stored in a cloud database;

[1608] an action plan generation means for generating an action plan for cargo management based on the analysis results;

[1609] a prediction means for providing a future luggage deterioration prediction;

[1610] A system including:

[1611] (Claim 2)

[1612] The system of claim 1, wherein the generative AI model performs analysis based on past data stored in a cloud database and data acquired in real time.

[1613] (Claim 3)

[1614] 2. The system according to claim 1, further comprising a notification means for notifying a user terminal of the created action plan for managing luggage.

[1615] "Example 2: Combining Emotion Engines"

[1616] (Claim 1)

[1617] a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity;

[1618] a storage means for storing the environmental data collected from the sensors in a cloud database;

[1619] An analysis means for executing a generative AI model that performs analysis based on data stored in a cloud database;

[1620] an action plan generation means for generating an action plan for crop management based on the analysis results;

[1621] a forecasting means for providing future crop production forecasts;

[1622] emotion analysis means for generating user emotion data and reflecting the data in an analysis result;

[1623] a notification means for notifying the user of the generated action plan, prediction results, and emotion analysis results;

[1624] A system including:

[1625] (Claim 2)

[1626] The system of claim 1, wherein the generative AI model performs analysis based on past data stored in a cloud database and data acquired in real time.

[1627] (Claim 3)

[1628] 2. The system according to claim 1, further comprising a notification means for notifying a user's terminal of the generated crop management action plan.

[1629] "Application example 2 when combining emotion engines"

[1630] (Claim 1)

[1631] a sensor means for measuring environmental data and employee emotional state data;

[1632] a storage means for storing the measured environmental data and emotional state data in a cloud database;

[1633] An analysis means for executing a generative AI model that performs analysis based on data stored in a cloud database;

[1634] an action plan generation means for generating an action plan for production management based on the analysis results;

[1635] a forecasting means for providing future production efficiency forecasts;

[1636] A system including:

[1637] (Claim 2)

[1638] The system of claim 1, wherein the generative AI model performs analysis based on past data stored in a cloud database and data acquired in real time.

[1639] (Claim 3)

[1640] 2. The system according to claim 1, further comprising a notification means for notifying a user terminal of the generated action plan for production management. [Explanation of symbols]

[1641] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a sensor means for measuring environmental data such as soil condition, moisture content, and light intensity; a storage means for storing the environmental data collected from the sensors in a cloud database; An analysis means for executing a generative AI model that performs analysis based on data stored in a cloud database; an action plan generation means for generating an action plan for crop management based on the analysis results; a forecasting means for providing future crop production forecasts; A system including:

2. The system of claim 1, wherein the generative AI model performs analysis based on past data stored in a cloud database and data acquired in real time.

3. 2. The system according to claim 1, further comprising a notification means for notifying a user terminal of the generated crop management action plan.

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