system

Abstract

We provide the system. [Solution] A device for collecting environmental data, An information processing device that analyzes environmental data acquired from the aforementioned device, An operating device that executes a work procedure based on the analysis results of the aforementioned information processing device, A display device that uses the analysis results of the aforementioned information processing device to present information to the user in real time and allows the user to change instructions, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of this invention is to solve problems such as a decrease in work efficiency in agriculture, a shortage of successors due to aging, and the difficulty of technology inheritance. Conventional agricultural production depends on labor, and thus there is a problem that productivity is greatly influenced by environmental conditions and human factors. As a result, the stability of agricultural management has decreased, and there is a current situation where developmental technologies are not fully utilized in the production process.

Means for Solving the Problems

[0005] The present invention solves the above-mentioned problems by providing a system comprising electronic components for acquiring environmental information, artificial intelligence for analyzing the acquired environmental information, and a mechanical device for performing physical work based on the analysis results. This system can collect environmental information in real time and derive optimal cultivation conditions by analyzing the data with artificial intelligence. Furthermore, since automated and precise agricultural activities are carried out by the mechanical device, it is possible to improve production efficiency while reducing reliance on human resources.

[0006] "Environmental information" refers to data representing meteorological conditions such as temperature, humidity, and sunshine duration in agricultural environments.

[0007] "Electronic components" refer to devices such as sensors used to acquire information, and have the function of measuring and collecting environmental information.

[0008] "Artificial intelligence" is a general term for algorithms and programs that analyze collected data and perform predictions and optimizations.

[0009] "Analysis results" refer to the output information of data processed and analyzed by artificial intelligence, and serve as the basis for specific actions and decisions.

[0010] "Mechanical equipment" refers to robots and mechanical systems used to perform physical tasks based on analysis results, and possesses the ability to autonomously carry out various agricultural activities.

[0011] "Physical work" refers to specific practical tasks performed in agricultural settings, including activities such as irrigation, fertilization, and harvesting. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The system in this invention is an integrated system that enables efficient management of the entire agricultural environment. It solves various challenges in conventional agricultural production through the acquisition of environmental information, data analysis, and automation of physical tasks.

[0034] At the heart of this system is a server, which aggregates environmental information from multiple electronic components (sensors). The server receives this data in real time and stores it in a database. Next, the server uses its built-in artificial intelligence algorithms to analyze the accumulated data and calculate the optimal environmental conditions for crop growth. For example, the server calculates the timing of irrigation and the appropriate amount of fertilizer based on fluctuations in temperature and humidity.

[0035] Meanwhile, the terminal receives instructions from the server and transmits them to local machinery (agricultural robots and drones). Specifically, the terminal has the function of sending specific operation commands to individual machinery based on the server's analysis results. The terminal also monitors the status of the machinery and the progress of the work, and feeds this information back to the server, thereby improving the efficiency of the entire system.

[0036] Users operate this system via smart devices. Using the application interface, users can view real-time farm monitoring data. This data, including temperature, humidity, and sunshine duration, is displayed in graphs, allowing users to understand the farm's conditions at a glance. Furthermore, users can manually modify work instructions as needed. As a result, users have flexible control over the timing and settings of their work.

[0037] In this way, the system of the present invention can improve the accuracy of agricultural work while increasing agricultural productivity and reducing the burden on human labor. For example, it can respond immediately to changes in weather and supply the water and nutrients that plants need in a timely manner, and concrete attempts can be made to maximize yields.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server acquires environmental information such as temperature, humidity, and sunshine duration in real time from various sensors placed throughout the farm. The collected data is immediately stored in a database.

[0041] Step 2:

[0042] The server uses collected environmental information to apply artificial intelligence algorithms and perform analysis. This allows it to understand the current state of crops and predicted weather conditions. Specifically, optimization calculations are performed based on this data to determine the required amount and timing of irrigation and fertilization.

[0043] Step 3:

[0044] Based on the results obtained from the analysis, the server generates specific work instructions for agricultural robots and drones. For example, this might include a command such as "Start irrigation in the designated area at 8:00 AM."

[0045] Step 4:

[0046] The terminal receives work instructions sent from the server. Upon receiving the instructions, the terminal sends control signals to the robots and drones. These control signals ensure that each machine performs its operations precisely.

[0047] Step 5:

[0048] The terminal monitors the operation of agricultural robots and drones in real time. It acquires additional data from sensors during operation and immediately reports any abnormalities to the server.

[0049] Step 6:

[0050] Users can monitor farm conditions in real time using an operating application via a terminal. Monitoring results are displayed to the user in graphs and numerical data. Users can also manually set or adjust work instructions as needed.

[0051] (Example 1)

[0052] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0053] In modern agriculture, it is essential to respond immediately to changes in environmental conditions and perform tasks at the appropriate time. However, current technology makes the process of collecting and analyzing environmental data and feeding that back into physical work inefficient, resulting in a heavy human burden and making it difficult to maximize productivity. Therefore, an integrated system is needed that efficiently acquires and analyzes environmental information and automates the execution of tasks.

[0054] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0055] In this invention, the server includes an information acquisition means for acquiring environmental data, an intelligent analysis means for analyzing the environmental data acquired from the information acquisition means, and a work mechanism means for executing work actions based on the analysis results of the intelligent analysis means. This enables rapid and efficient agricultural work in response to changes in environmental conditions.

[0056] "Environmental data" refers to information about the natural environment, such as temperature, humidity, soil moisture, and sunlight, which is important for agriculture and other activities.

[0057] "Information acquisition means" refers to the functions and operations performed by devices and sensors placed to collect environmental data.

[0058] "Intelligent analysis means" refers to artificial intelligence algorithms and data processing technologies used to process and analyze acquired environmental data.

[0059] "Work actions" refer to specific physical tasks and operations performed based on the results of environmental data analysis.

[0060] "Work mechanism means" refers to the devices and equipment used to actually perform physical work based on the results of the analysis.

[0061] "Feedback means" refers to a function that monitors the operating status and results of the work mechanism and transmits the information based on that to the server or other system components.

[0062] "Terminal operation means" refers to the interface or functions that allow a user to monitor the system status using a terminal and make adjustments or operations as needed.

[0063] To implement this invention, an integrated agricultural management system is required. This system has a set of functions including the acquisition, analysis, and feedback of environmental data, and the automation of agricultural operations.

[0064] The server uses a wide variety of sensor devices to acquire environmental data. This includes general-purpose environmental sensors for measuring temperature and humidity, and the data is aggregated using wireless communication technology. The acquired data is stored in a database system such as MySQL®.

[0065] The server also incorporates artificial intelligence algorithms such as "TENSORFLOW®" to analyze the collected data. Specifically, it calculates optimal crop growth conditions based on environmental data and predicts future environmental changes using predictive models. Based on these analysis results, the optimal irrigation schedule and fertilizer distribution are determined.

[0066] The terminal uses autonomous agricultural robots and unmanned aerial vehicles to perform physical agricultural tasks based on instructions received from the server. The terminal sends necessary action commands to these machines and feeds back progress to the server. This progress information is used for subsequent data analysis.

[0067] Users can use smart devices to monitor the overall system status and take action as needed. The application interface graphically displays real-time data such as temperature, humidity, and sunlight to support user decision-making. Users can also manually modify work instructions through the application, enabling flexible management.

[0068] One concrete example is a system that can respond immediately to sudden changes in weather and supply crops with the water they need at the appropriate time. An example of a prompt message would be, "Please suggest the optimal amount of fertilizer to apply based on this week's weather data."

[0069] This system makes it possible to improve agricultural productivity and reduce the burden on human resources.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server receives data from a group of sensors to collect environmental information. Inputs include temperature, humidity, soil moisture, and sunlight. This data is transmitted to the server using wireless technology. After receiving this raw data, the server stores it in a MySQL database. The output is time-series data stored in the database. Storing the data in this format allows for efficient use in subsequent analysis steps.

[0073] Step 2:

[0074] The server uses artificial intelligence algorithms such as TensorFlow to analyze environmental data stored in the database. Historical environmental data and current real-time data are used as input. The server analyzes this data to calculate irrigation timing and fertilizer application amounts in order to formulate an optimal farming plan. The output is an optimized farming schedule based on the analysis results. This result is sent to the terminal and used for the next physical task.

[0075] Step 3:

[0076] The terminal sends operational instructions to machinery based on analysis results received from the server. For example, it commands irrigation equipment to water at specified times and drones to spread fertilizer over a specific area. The input is operational instruction data from the server, and the output is the execution of the actual physical work. This automates and efficiently carries out planned agricultural tasks.

[0077] Step 4:

[0078] The terminal monitors the progress of the physical work being performed and sends real-time feedback to the server. The terminal receives status information from the machinery and the status of work completion as input. As output, the progress of the work and the actual data necessary for the next plan are returned to the server. This feedback loop enables more precise instructions in future work plans.

[0079] Step 5:

[0080] Users use smart devices to understand the overall system status and take action as needed. Through the application interface, users receive monitoring data and work schedules as input. Output includes graphs and dashboards that visualize environmental changes and work progress. Based on this information, users can achieve optimal farm management by making manual adjustments as required.

[0081] (Application Example 1)

[0082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] In urban agricultural environments, significant fluctuations in environmental conditions make efficient production management difficult. Furthermore, conventional systems lack sufficient real-time monitoring and adjustment capabilities, leading to increased human workload. This hinders the realization of sustainable urban agriculture.

[0084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0085] In this invention, the server includes means for a device for collecting environmental data, means for an information processing device for analyzing the environmental data acquired from the device, means for an operating device for executing work procedures based on the analysis results of the information processing device, and means for a display device that uses the analysis results of the information processing device to present information to the user in real time and allow instructions to be changed. This enables real-time, efficient environmental adjustment and production management in agricultural activities in urban environments.

[0086] "Environmental data" refers to information related to weather conditions such as temperature, humidity, and light levels in urban areas and agricultural land, as well as land-related information.

[0087] A "device" is an electronic device, such as a sensor or measuring instrument, used to collect environmental data.

[0088] An "information processing device" is a device consisting of a computer and software that analyzes collected environmental data and extracts necessary information.

[0089] An "operating device" is a machine or equipment used to execute specific agricultural tasks or production management procedures based on the results analyzed by an information processing device.

[0090] A "display device" is a device that presents information to the user in real time and provides an interface for changing instructions as needed.

[0091] The system for realizing this invention provides efficient environmental management in urban agriculture. A server collects environmental data through multiple devices. These devices are used to measure weather conditions such as temperature, humidity, and light intensity using sensors.

[0092] The server analyzes environmental data acquired using an information processing device. This analysis utilizes a database management system and computer software to execute AI algorithms. As a result of the analysis, the ideal environmental conditions for crop growth are calculated, and the necessary agricultural activities are identified.

[0093] The operating system automatically executes work procedures based on instructions from the server. This system often includes irrigation systems, fertilizers, and other agricultural machinery. This allows for environmental adjustments without human intervention.

[0094] Users can remotely check the system status using smartphones or display devices. Simultaneously, users can modify instructions based on information provided by the server via the display device. This functionality requires application software for smart devices, allowing users to monitor farm conditions in real time and control the operation of the machinery.

[0095] As a concrete example, if a user is managing a rooftop garden in an urban environment, the application can detect temperature increases and, based on the AI's analysis, suggest appropriate irrigation to the user. The user can then use their smartphone to take action based on this suggestion.

[0096] An example of a prompt would be: "Please provide the best suggestions for creating environmental conditions conducive to plant growth in an urban farm. Clearly indicate any missing elements or points to be aware of."

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server collects weather data such as temperature, humidity, and light intensity from environmental sensors. The signals transmitted from the sensors are received by a receiver and processed as digital data. This serves as the input, and the collected information is stored in the database as environmental data.

[0100] Step 2:

[0101] The server analyzes accumulated environmental data using an information processing device. The input environmental data is analyzed using an AI algorithm to calculate the optimal environmental conditions necessary for crop growth. The output is generated as recommendations for necessary agricultural activities.

[0102] Step 3:

[0103] The server sends instructions to the operating devices based on the analysis results. Specifically, it commands irrigation equipment and fertilizer application equipment on when and how they should operate. The input is the analysis results from the previous step, and the output is the operation command.

[0104] Step 4:

[0105] Users check the system status via smart devices. Output information from the server is displayed on the interface, allowing users to make decisions based on this information. Operating status of the operating equipment and environmental monitoring data are provided as input.

[0106] Step 5:

[0107] The user modifies the instructions via the display device as needed. The server receives the new instructions from the user and resends them to the operating device. Here, the user's operation becomes the new input, and the updated operation command is output.

[0108] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0109] The system according to this invention achieves more advanced agricultural management by collecting and analyzing data related to the agricultural environment and further taking into account the user's emotions. First, the server receives general environmental information from electronic components. This includes temperature, humidity, and sunshine hours. The server stores this data in a database and performs analysis using artificial intelligence. Through this analysis, appropriate irrigation schedules and fertilizer application timings can be determined.

[0110] Furthermore, this system can identify user emotions by incorporating an emotion engine. The emotion engine analyzes the user's facial expressions and voice through cameras and voice input devices to recognize emotional states such as stress and a sense of security. This information is used to consider the psychological impact on the user's productive activities.

[0111] Specifically, the terminal controls the agricultural robot based on analysis results from the server, while also incorporating user feedback obtained from the emotion engine. For example, if the user is experiencing stress, the work speed can be adjusted to reduce the stress. Furthermore, if the user expresses satisfaction, the system may suggest ways to further improve efficiency.

[0112] Users can access farm data and their own sentiment analysis results through a dedicated application. This allows users to select optimal production strategies and understand how their emotional state impacts their production activities. This comprehensive management system aims not only to improve the quality and efficiency of agricultural production but also to enhance users' mental well-being.

[0113] The following describes the processing flow.

[0114] Step 1:

[0115] The server collects environmental information such as temperature, humidity, and sunshine duration in real time through electronic components (sensors) placed on the farm and stores it in a database.

[0116] Step 2:

[0117] The server applies artificial intelligence algorithms to analyze the collected environmental information. This allows it to calculate the current state of the crops and the optimal cultivation procedures. For example, it can determine the timing of watering and the appropriate amount of fertilizer.

[0118] Step 3:

[0119] The emotion engine uses camera and voice input to analyze the user's facial expressions and voice, collecting emotional data. This data is used to understand the user's psychological state.

[0120] Step 4:

[0121] The server comprehensively evaluates the analyzed environmental information and emotional data to generate optimal instructions for agricultural activities. If the emotional data is negative, the server adjusts the work to alleviate the user's stress.

[0122] Step 5:

[0123] The terminal transmits instructions received from the server to agricultural robots and drones, causing them to perform specific agricultural tasks. These tasks are carried out accurately and efficiently based on the analysis results.

[0124] Step 6:

[0125] Users can monitor the progress of their work and the results of sentiment analysis in real time through an interface provided via their terminal. Furthermore, users can customize their agricultural activities by making manual adjustments based on the system's suggestions as needed.

[0126] (Example 2)

[0127] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0128] Conventional agricultural management systems have limitations in optimizing operations based on the analysis of environmental information, and furthermore, they do not adjust operations according to the user's emotional state, making it difficult to achieve efficient and user-friendly agricultural management.

[0129] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0130] In this invention, the server includes a sensor device for acquiring environmental data, an artificial intelligence device for analyzing the environmental data acquired from the sensor device, a control device for controlling a work device based on the analysis results of the artificial intelligence device, an emotion recognition device for analyzing the user's emotional state, and a control device for adjusting the work device based on the analysis results of the emotion recognition device. This makes it possible to optimize agricultural processes by considering both environmental information and the user's emotional state.

[0131] "Environmental data" refers to natural conditions in agriculture, such as temperature, humidity, and the amount of sunlight, and is information acquired by sensor devices.

[0132] A "sensor device" is a device installed to acquire environmental data, such as temperature, humidity, and sunlight.

[0133] An "artificial intelligence device" refers to a system that analyzes acquired environmental data and generates instructions necessary for optimizing work.

[0134] "Working equipment" refers to a machine or robot that performs agricultural processes based on instructions from an artificial intelligence device.

[0135] A "control device" is a device that operates work equipment and executes instructions from artificial intelligence devices or emotion recognition devices.

[0136] An "emotion recognition device" is a device used to analyze a user's emotional state, determining stress levels and feelings of security by analyzing facial expressions and voice.

[0137] The system according to this invention aims to achieve efficient agricultural management by collecting and analyzing data related to the agricultural environment. The system includes the following main components.

[0138] First, the server acquires environmental data using sensor devices installed on the farm. The sensor devices measure environmental information such as temperature, humidity, and sunlight duration in real time and transmit it to a database. The artificial intelligence system within the server analyzes this data in the background and generates instructions to optimize agricultural processes.

[0139] Next, the server monitors the user's condition using an emotion recognition device. It analyzes the user's facial expressions and voice using cameras and voice input devices to detect stress levels and satisfaction. This analysis is then used to adjust the operation of the work equipment.

[0140] For example, if the server determines that the user's stress level is high, the control unit will reduce the operating speed of the work device to alleviate the user's burden. Furthermore, if the user's satisfaction level is high, the system can suggest and implement even more efficient work methods.

[0141] Users can check the environmental conditions of their farm and the results of their own emotional analysis through a dedicated application. By using this application, users can understand the impact of their emotions on agricultural processes and select the optimal production strategies.

[0142] Examples of prompt statements include the following:

[0143] "Suggest the best course of action to take when a user is experiencing stress during farm work."

[0144] "Please tell me how to increase production efficiency when users express satisfaction."

[0145] This system aims to achieve both improved work efficiency and increased user satisfaction by comprehensively utilizing environmental information and the user's emotional state.

[0146] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0147] Step 1:

[0148] The server collects environmental data from sensor devices installed on the farm. Inputs include temperature, humidity, and daylight hours, and the sensor devices acquire this data in real time. The server receives this data and stores it in a database, allowing for a clear understanding of the farm's current state.

[0149] Step 2:

[0150] The server inputs environmental data stored in the database into the artificial intelligence (AI) device for analysis. The input is the environmental data obtained in step 1. The AI ​​device analyzes this data to calculate irrigation schedules and optimal timing for fertilizer application. As output, specific instructions for optimizing agricultural processes are generated.

[0151] Step 3:

[0152] The server analyzes the user's emotional state using an emotion recognition device. Input consists of the user's facial expressions and voice, collected from a camera and voice input device. Based on this data, the emotion engine evaluates the user's stress levels and sense of security. The output provides information about the user's current emotional state.

[0153] Step 4:

[0154] The server integrates the analysis results from step 2 and the emotion analysis results from step 3, and operates a control device that issues instructions to the work equipment. Based on the analysis results and emotional state, the control device gives appropriate instructions to the work equipment. For example, if it is determined that the user is feeling stressed, an instruction to reduce the work speed will be issued. This provides the user with optimal working conditions.

[0155] Step 5:

[0156] Users can check the status of their farm and their own sentiment analysis results through a dedicated application. Input consists of output data from steps 1 through 4. Based on this information, users can select optimal production strategies and understand the impact of their emotional state on agricultural processes. This enables efficient and user-friendly agricultural management.

[0157] (Application Example 2)

[0158] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0159] In agriculture, precise and flexible work control is required, taking into account not only appropriate management based on environmental information but also the psychological state of the workers. However, conventional systems only perform mechanical tasks that rely on environmental information and lack consideration for the user's emotions, making it difficult to maximize production efficiency while reducing user stress.

[0160] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0161] In this invention, the server includes intelligent modeling means for analyzing data obtained from a device for acquiring environmental information, means for performing operations using an automated machine based on the analysis results of the intelligent model, and management means for performing emotion analysis and adjusting operations based on the results while considering the user's emotional state. This makes it possible to increase the efficiency of agricultural operations while simultaneously reducing stress according to the user's emotional state.

[0162] "Environmental information" refers to data that indicates natural conditions in a specific space, such as temperature, humidity, and sunlight.

[0163] A "device" is a collection of hardware components that have the function of acquiring environmental information.

[0164] An "intelligent model" is an artificial intelligence algorithm that analyzes acquired environmental information and makes appropriate decisions.

[0165] An "automated machine" is a device that automatically performs physical tasks based on instructions from an intelligent model.

[0166] "Emotional analysis" is a process that determines a user's psychological state based on their facial expressions, voice, and other factors.

[0167] A "management system" is a control system that appropriately adjusts the operation of automated machines based on the results of emotion analysis.

[0168] "Agricultural operations" refers to the entire range of physical and administrative tasks associated with agricultural activities.

[0169] To realize this invention, a comprehensive system is needed for acquiring, analyzing, and managing environmental information in agricultural facilities within a smart city. The specific steps are as follows:

[0170] The server continuously collects data using devices to acquire environmental information such as temperature, humidity, and sunlight. This data is analyzed by a cloud-based intelligent model to derive the optimal conditions and actions necessary for agricultural operations. This analysis utilizes a generative AI model employing adaptive learning algorithms. This enables dynamic optimization for various environmental conditions.

[0171] The terminal is equipped with a sensor that analyzes the user's emotions in real time. Using a camera and voice input function, it determines the user's emotions through an emotion analysis process based on their facial expressions and voice. Based on this, it optimizes the work environment by adjusting the operating speed of agricultural equipment, for example, to reduce stress, according to the user's emotional state.

[0172] Users can view the analysis results and emotional feedback of the intelligent model through a dedicated mobile application. This application provides an interactive interface that allows for the selection of measures and manual adjustment of environmental controls.

[0173] As a concrete example, in a certain urban agricultural facility, if particularly strong sunlight is predicted on a given day, the irrigation system will automatically activate to manage humidity and temperature for that day. Furthermore, if users exhibit stress reactions during that time, the work speed will be reduced and the lighting environment will be adjusted.

[0174] An example of a prompt for a generative AI model is: "Based on the latest agricultural data, if user emotional feedback indicates high satisfaction, what efficiency improvements would you suggest next?" Using this prompt, artificial intelligence can suggest specific improvements to enhance the user experience.

[0175] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0176] Step 1:

[0177] The server receives data such as temperature, humidity, and sunlight from devices that acquire environmental information. Based on this input data, the server preprocesses it by saving it to a database and preparing it as input for the next intelligent model.

[0178] Step 2:

[0179] The server runs a cloud-based generative AI model using environmental data stored in a database. A data analysis engine analyzes this environmental information and outputs optimal agricultural operation schedules and adjustment suggestions. This output is used as operational instructions for automated machinery.

[0180] Step 3:

[0181] The device uses a camera and microphone to capture the user's facial expressions and voice. Using this emotional data as input, the device's emotion analysis engine performs a process to determine the user's psychological state and outputs the user's emotional state.

[0182] Step 4:

[0183] The server integrates the analysis results of the generated AI model with the user's emotional state obtained from the terminal. Based on this, it outputs instructions to adjust the operating speed and work methods of the automated machine. Specific adjustments include increasing or decreasing the work speed and changing environmental settings.

[0184] Step 5:

[0185] Users view analysis results and sentiment feedback from the server using an application on their device. Furthermore, users can review suggested tasks and manually adjust actions as needed. This optimizes the user experience.

[0186] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0187] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0188] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0189] [Second Embodiment]

[0190] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0191] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0192] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0193] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0194] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0195] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0196] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0197] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0198] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0199] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0200] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0201] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0202] The system in this invention is an integrated system that enables efficient management of the entire agricultural environment. It solves various challenges in conventional agricultural production through the acquisition of environmental information, data analysis, and automation of physical tasks.

[0203] At the heart of this system is a server, which aggregates environmental information from multiple electronic components (sensors). The server receives this data in real time and stores it in a database. Next, the server uses its built-in artificial intelligence algorithms to analyze the accumulated data and calculate the optimal environmental conditions for crop growth. For example, the server calculates the timing of irrigation and the appropriate amount of fertilizer based on fluctuations in temperature and humidity.

[0204] Meanwhile, the terminal receives instructions from the server and transmits them to local machinery (agricultural robots and drones). Specifically, the terminal has the function of sending specific operation commands to individual machinery based on the server's analysis results. The terminal also monitors the status of the machinery and the progress of the work, and feeds this information back to the server, thereby improving the efficiency of the entire system.

[0205] Users operate this system via smart devices. Using the application interface, users can view real-time farm monitoring data. This data, including temperature, humidity, and sunshine duration, is displayed in graphs, allowing users to understand the farm's conditions at a glance. Furthermore, users can manually modify work instructions as needed. As a result, users have flexible control over the timing and settings of their work.

[0206] In this way, the system of the present invention can improve the accuracy of agricultural work while increasing agricultural productivity and reducing the burden on human labor. For example, it can respond immediately to changes in weather and supply the water and nutrients that plants need in a timely manner, and concrete attempts can be made to maximize yields.

[0207] The following describes the processing flow.

[0208] Step 1:

[0209] The server acquires environmental information such as temperature, humidity, and sunshine duration in real time from various sensors placed throughout the farm. The collected data is immediately stored in a database.

[0210] Step 2:

[0211] The server uses collected environmental information to apply artificial intelligence algorithms and perform analysis. This allows it to understand the current state of crops and predicted weather conditions. Specifically, optimization calculations are performed based on this data to determine the required amount and timing of irrigation and fertilization.

[0212] Step 3:

[0213] Based on the results obtained from the analysis, the server generates specific work instructions for agricultural robots and drones. For example, this might include a command such as "Start irrigation in the designated area at 8:00 AM."

[0214] Step 4:

[0215] The terminal receives work instructions sent from the server. Upon receiving the instructions, the terminal sends control signals to the robots and drones. These control signals ensure that each machine performs its operations precisely.

[0216] Step 5:

[0217] The terminal monitors the operation of agricultural robots and drones in real time. It acquires additional data from sensors during operation and immediately reports any abnormalities to the server.

[0218] Step 6:

[0219] Users can monitor farm conditions in real time using an operating application via a terminal. Monitoring results are displayed to the user in graphs and numerical data. Users can also manually set or adjust work instructions as needed.

[0220] (Example 1)

[0221] Next, we will describe Example 1. 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."

[0222] In modern agriculture, it is essential to respond immediately to changes in environmental conditions and perform tasks at the appropriate time. However, current technology makes the process of collecting and analyzing environmental data and feeding that back into physical work inefficient, resulting in a heavy human burden and making it difficult to maximize productivity. Therefore, an integrated system is needed that efficiently acquires and analyzes environmental information and automates the execution of tasks.

[0223] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0224] In this invention, the server includes an information acquisition means for acquiring environmental data, an intelligent analysis means for analyzing the environmental data acquired from the information acquisition means, and a work mechanism means for executing work actions based on the analysis results of the intelligent analysis means. This enables rapid and efficient agricultural work in response to changes in environmental conditions.

[0225] "Environmental data" refers to information about the natural environment, such as temperature, humidity, soil moisture, and sunlight, which is important for agriculture and other activities.

[0226] "Information acquisition means" refers to the functions and operations performed by devices and sensors placed to collect environmental data.

[0227] "Intelligent analysis means" refers to artificial intelligence algorithms and data processing technologies used to process and analyze acquired environmental data.

[0228] "Work actions" refer to specific physical tasks and operations performed based on the results of environmental data analysis.

[0229] "Work mechanism means" refers to the devices and equipment used to actually perform physical work based on the results of the analysis.

[0230] "Feedback means" refers to a function that monitors the operating status and results of the work mechanism and transmits the information based on that to the server or other system components.

[0231] "Terminal operation means" refers to the interface or functions that allow a user to monitor the system status using a terminal and make adjustments or operations as needed.

[0232] To implement this invention, an integrated agricultural management system is required. This system has a set of functions including the acquisition, analysis, and feedback of environmental data, and the automation of agricultural operations.

[0233] The server uses a wide variety of sensor devices to acquire environmental data. This includes general-purpose environmental sensors for measuring temperature and humidity, and the data is aggregated using wireless communication technology. The acquired data is stored in a database system such as MySQL.

[0234] The server also incorporates artificial intelligence algorithms such as TensorFlow to analyze the collected data. Specifically, it calculates optimal crop growth conditions based on environmental data and predicts future environmental changes using predictive models. Based on these analysis results, the optimal irrigation schedule and fertilizer distribution are determined.

[0235] The terminal uses autonomous agricultural robots and unmanned aerial vehicles to perform physical agricultural tasks based on instructions received from the server. The terminal sends necessary action commands to these machines and feeds back progress to the server. This progress information is used for subsequent data analysis.

[0236] Users can use smart devices to monitor the overall system status and take action as needed. The application interface graphically displays real-time data such as temperature, humidity, and sunlight to support user decision-making. Users can also manually modify work instructions through the application, enabling flexible management.

[0237] One concrete example is a system that can respond immediately to sudden changes in weather and supply crops with the water they need at the appropriate time. An example of a prompt message would be, "Please suggest the optimal amount of fertilizer to apply based on this week's weather data."

[0238] This system makes it possible to improve agricultural productivity and reduce the burden on human resources.

[0239] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0240] Step 1:

[0241] The server receives data from a group of sensors to collect environmental information. Inputs include temperature, humidity, soil moisture, and sunlight. This data is transmitted to the server using wireless technology. After receiving this raw data, the server stores it in a MySQL database. The output is time-series data stored in the database. Storing the data in this format allows for efficient use in subsequent analysis steps.

[0242] Step 2:

[0243] The server uses artificial intelligence algorithms such as TensorFlow to analyze environmental data stored in the database. Historical environmental data and current real-time data are used as input. The server analyzes this data to calculate irrigation timing and fertilizer application amounts in order to formulate an optimal farming plan. The output is an optimized farming schedule based on the analysis results. This result is sent to the terminal and used for the next physical task.

[0244] Step 3:

[0245] The terminal sends operational instructions to machinery based on analysis results received from the server. For example, it commands irrigation equipment to water at specified times and drones to spread fertilizer over a specific area. The input is operational instruction data from the server, and the output is the execution of the actual physical work. This automates and efficiently carries out planned agricultural tasks.

[0246] Step 4:

[0247] The terminal monitors the progress of the physical work being performed and sends real-time feedback to the server. The terminal receives status information from the machinery and the status of work completion as input. As output, the progress of the work and the actual data necessary for the next plan are returned to the server. This feedback loop enables more precise instructions in future work plans.

[0248] Step 5:

[0249] Users use smart devices to understand the overall system status and take action as needed. Through the application interface, users receive monitoring data and work schedules as input. Output includes graphs and dashboards that visualize environmental changes and work progress. Based on this information, users can achieve optimal farm management by making manual adjustments as required.

[0250] (Application Example 1)

[0251] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0252] In urban agricultural environments, significant fluctuations in environmental conditions make efficient production management difficult. Furthermore, conventional systems lack sufficient real-time monitoring and adjustment capabilities, leading to increased human workload. This hinders the realization of sustainable urban agriculture.

[0253] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0254] In this invention, the server includes means for a device for collecting environmental data, means for an information processing device for analyzing the environmental data acquired from the device, means for an operating device for executing work procedures based on the analysis results of the information processing device, and means for a display device that uses the analysis results of the information processing device to present information to the user in real time and allow instructions to be changed. This enables real-time, efficient environmental adjustment and production management in agricultural activities in urban environments.

[0255] "Environmental data" refers to information related to weather conditions such as temperature, humidity, and light levels in urban areas and agricultural land, as well as land-related information.

[0256] A "device" is an electronic device, such as a sensor or measuring instrument, used to collect environmental data.

[0257] An "information processing device" is a device consisting of a computer and software that analyzes collected environmental data and extracts necessary information.

[0258] An "operating device" is a machine or equipment used to execute specific agricultural tasks or production management procedures based on the results analyzed by an information processing device.

[0259] A "display device" is a device that presents information to the user in real time and provides an interface for changing instructions as needed.

[0260] The system for realizing this invention provides efficient environmental management in urban agriculture. A server collects environmental data through multiple devices. These devices are used to measure weather conditions such as temperature, humidity, and light intensity using sensors.

[0261] The server analyzes environmental data acquired using an information processing device. This analysis utilizes a database management system and computer software to execute AI algorithms. As a result of the analysis, the ideal environmental conditions for crop growth are calculated, and the necessary agricultural activities are identified.

[0262] The operating system automatically executes work procedures based on instructions from the server. This system often includes irrigation systems, fertilizers, and other agricultural machinery. This allows for environmental adjustments without human intervention.

[0263] Users can remotely check the system status using smartphones or display devices. Simultaneously, users can modify instructions based on information provided by the server via the display device. This functionality requires application software for smart devices, allowing users to monitor farm conditions in real time and control the operation of the machinery.

[0264] As a concrete example, if a user is managing a rooftop garden in an urban environment, the application can detect temperature increases and, based on the AI's analysis, suggest appropriate irrigation to the user. The user can then use their smartphone to take action based on this suggestion.

[0265] An example of a prompt would be: "Please provide the best suggestions for creating environmental conditions conducive to plant growth in an urban farm. Clearly indicate any missing elements or points to be aware of."

[0266] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0267] Step 1:

[0268] The server collects weather data such as temperature, humidity, and light intensity from environmental sensors. The signals transmitted from the sensors are received by a receiver and processed as digital data. This serves as the input, and the collected information is stored in the database as environmental data.

[0269] Step 2:

[0270] The server analyzes accumulated environmental data using an information processing device. The input environmental data is analyzed using an AI algorithm to calculate the optimal environmental conditions necessary for crop growth. The output is generated as recommendations for necessary agricultural activities.

[0271] Step 3:

[0272] The server sends instructions to the operating devices based on the analysis results. Specifically, it commands irrigation equipment and fertilizer application equipment on when and how they should operate. The input is the analysis results from the previous step, and the output is the operation command.

[0273] Step 4:

[0274] Users check the system status via smart devices. Output information from the server is displayed on the interface, allowing users to make decisions based on this information. Operating status of the operating equipment and environmental monitoring data are provided as input.

[0275] Step 5:

[0276] The user modifies the instructions via the display device as needed. The server receives the new instructions from the user and resends them to the operating device. Here, the user's operation becomes the new input, and the updated operation command is output.

[0277] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0278] The system according to this invention achieves more advanced agricultural management by collecting and analyzing data related to the agricultural environment and further taking into account the user's emotions. First, the server receives general environmental information from electronic components. This includes temperature, humidity, and sunshine hours. The server stores this data in a database and performs analysis using artificial intelligence. Through this analysis, appropriate irrigation schedules and fertilizer application timings can be determined.

[0279] Furthermore, this system can identify user emotions by incorporating an emotion engine. The emotion engine analyzes the user's facial expressions and voice through cameras and voice input devices to recognize emotional states such as stress and a sense of security. This information is used to consider the psychological impact on the user's productive activities.

[0280] Specifically, the terminal controls the agricultural robot based on analysis results from the server, while also incorporating user feedback obtained from the emotion engine. For example, if the user is experiencing stress, the work speed can be adjusted to reduce the stress. Furthermore, if the user expresses satisfaction, the system may suggest ways to further improve efficiency.

[0281] Users can view farm data and their own sentiment analysis results through a dedicated application. This enables users to select optimal production measures and understand how their emotional state affects production activities. This comprehensive management system aims not only to improve the quality and efficiency of agricultural production but also to enhance users' mental satisfaction.

[0282] The following explains the processing flow.

[0283] Step 1:

[0284] The server collects environmental information such as temperature, humidity, and sunlight hours in real time through electronic components (sensors) placed on the farm and stores it in the database.

[0285] Step 2:

[0286] The server applies an artificial intelligence algorithm to analyze the collected environmental information. This calculates the current state of the crops and the optimal cultivation procedures. For example, it determines the timing of watering and the appropriate amount of fertilizer.

[0287] Step 3:

[0288] The emotion engine analyzes the user's expressions and voice using a camera and voice input to collect emotion data. This data is used to understand the user's psychological state.

[0289] Step 4:

[0290] The server comprehensively evaluates the analyzed environmental information and emotion data and generates optimal instructions for agricultural activities. If the emotion data is negative, the server makes work adjustments to relieve the user's stress.

[0291] Step 5:

[0292] The terminal transmits instructions received from the server to agricultural robots and drones, causing them to perform specific agricultural tasks. These tasks are carried out accurately and efficiently based on the analysis results.

[0293] Step 6:

[0294] Users can monitor the progress of their work and the results of sentiment analysis in real time through an interface provided via their terminal. Furthermore, users can customize their agricultural activities by making manual adjustments based on the system's suggestions as needed.

[0295] (Example 2)

[0296] Next, we will describe Example 2. 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".

[0297] Conventional agricultural management systems have limitations in optimizing operations based on the analysis of environmental information, and furthermore, they do not adjust operations according to the user's emotional state, making it difficult to achieve efficient and user-friendly agricultural management.

[0298] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0299] In this invention, the server includes a sensor device for acquiring environmental data, an artificial intelligence device for analyzing the environmental data acquired from the sensor device, a control device for controlling a work device based on the analysis results of the artificial intelligence device, an emotion recognition device for analyzing the user's emotional state, and a control device for adjusting the work device based on the analysis results of the emotion recognition device. This makes it possible to optimize agricultural processes by considering both environmental information and the user's emotional state.

[0300] "Environmental data" refers to natural conditions in agriculture, such as temperature, humidity, and the amount of sunlight, and is information acquired by sensor devices.

[0301] The "sensor device" is a device installed to acquire environmental data, which measures things such as temperature, humidity, and sunlight.

[0302] The "artificial intelligence device" refers to a system for analyzing the acquired environmental data and generating instructions necessary for optimizing work.

[0303] The "working device" refers to a machine or robot that executes agricultural processes based on instructions from an artificial intelligence device.

[0304] The "control device" is a device that operates a working device and executes instructions from an artificial intelligence device or an emotion recognition device.

[0305] The "emotion recognition device" is a device for analyzing the emotional state of a user, which analyzes expressions and voices to judge stress and a sense of security.

[0306] The system according to this invention collects data related to the agricultural environment and analyzes it to achieve efficient agricultural management. The system includes the following main components.

[0307] First, the server uses sensor devices installed on the farm to acquire environmental data. The sensor devices measure environmental information such as temperature, humidity, and sunlight time in real time and transmit it to the database. The artificial intelligence device in the server analyzes these data in the background and generates instructions for optimizing agricultural processes.

[0308] Next, the server monitors the user's situation using an emotion recognition device. Using a camera and a voice input device, it analyzes the user's expression and voice to sense stress and satisfaction. This analysis result is used to adjust the operation of the working device.

[0309] For example, if the server determines that the user's stress level is high, the control unit will reduce the operating speed of the work device to alleviate the user's burden. Furthermore, if the user's satisfaction level is high, the system can suggest and implement even more efficient work methods.

[0310] Users can check the environmental conditions of their farm and the results of their own emotional analysis through a dedicated application. By using this application, users can understand the impact of their emotions on agricultural processes and select the optimal production strategies.

[0311] Examples of prompt statements include the following:

[0312] "Suggest the best course of action to take when a user is experiencing stress during farm work."

[0313] "Please tell me how to increase production efficiency when users express satisfaction."

[0314] This system aims to achieve both improved work efficiency and increased user satisfaction by comprehensively utilizing environmental information and the user's emotional state.

[0315] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0316] Step 1:

[0317] The server collects environmental data from sensor devices installed on the farm. Inputs include temperature, humidity, and daylight hours, and the sensor devices acquire this data in real time. The server receives this data and stores it in a database, allowing for a clear understanding of the farm's current state.

[0318] Step 2:

[0319] The server inputs environmental data stored in the database into the artificial intelligence (AI) device for analysis. The input is the environmental data obtained in step 1. The AI ​​device analyzes this data to calculate irrigation schedules and optimal timing for fertilizer application. As output, specific instructions for optimizing agricultural processes are generated.

[0320] Step 3:

[0321] The server analyzes the user's emotional state using an emotion recognition device. Input consists of the user's facial expressions and voice, collected from a camera and voice input device. Based on this data, the emotion engine evaluates the user's stress levels and sense of security. The output provides information about the user's current emotional state.

[0322] Step 4:

[0323] The server integrates the analysis results from step 2 and the emotion analysis results from step 3, and operates a control device that issues instructions to the work equipment. Based on the analysis results and emotional state, the control device gives appropriate instructions to the work equipment. For example, if it is determined that the user is feeling stressed, an instruction to reduce the work speed will be issued. This provides the user with optimal working conditions.

[0324] Step 5:

[0325] Users can check the status of their farm and their own sentiment analysis results through a dedicated application. Input consists of output data from steps 1 through 4. Based on this information, users can select optimal production strategies and understand the impact of their emotional state on agricultural processes. This enables efficient and user-friendly agricultural management.

[0326] (Application Example 2)

[0327] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0328] In agriculture, precise and flexible work control is required, taking into account not only appropriate management based on environmental information but also the psychological state of the workers. However, conventional systems only perform mechanical tasks that rely on environmental information and lack consideration for the user's emotions, making it difficult to maximize production efficiency while reducing user stress.

[0329] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0330] In this invention, the server includes intelligent modeling means for analyzing data obtained from a device for acquiring environmental information, means for performing operations using an automated machine based on the analysis results of the intelligent model, and management means for performing emotion analysis and adjusting operations based on the results while considering the user's emotional state. This makes it possible to increase the efficiency of agricultural operations while simultaneously reducing stress according to the user's emotional state.

[0331] "Environmental information" refers to data that indicates natural conditions in a specific space, such as temperature, humidity, and sunlight.

[0332] A "device" is a collection of hardware components that have the function of acquiring environmental information.

[0333] An "intelligent model" is an artificial intelligence algorithm that analyzes acquired environmental information and makes appropriate decisions.

[0334] An "automated machine" is a device that automatically performs physical tasks based on instructions from an intelligent model.

[0335] "Emotional analysis" is a process that determines a user's psychological state based on their facial expressions, voice, and other factors.

[0336] A "management system" is a control system that appropriately adjusts the operation of automated machines based on the results of emotion analysis.

[0337] "Agricultural operations" refers to the entire range of physical and administrative tasks associated with agricultural activities.

[0338] To realize this invention, a comprehensive system is needed for acquiring, analyzing, and managing environmental information in agricultural facilities within a smart city. The specific steps are as follows:

[0339] The server continuously collects data using devices to acquire environmental information such as temperature, humidity, and sunlight. This data is analyzed by a cloud-based intelligent model to derive the optimal conditions and actions necessary for agricultural operations. This analysis utilizes a generative AI model employing adaptive learning algorithms. This enables dynamic optimization for various environmental conditions.

[0340] The terminal is equipped with a sensor that analyzes the user's emotions in real time. Using a camera and voice input function, it determines the user's emotions through an emotion analysis process based on their facial expressions and voice. Based on this, it optimizes the work environment by adjusting the operating speed of agricultural equipment, for example, to reduce stress, according to the user's emotional state.

[0341] Users can view the analysis results and emotional feedback of the intelligent model through a dedicated mobile application. This application provides an interactive interface that allows for the selection of measures and manual adjustment of environmental controls.

[0342] As a concrete example, in a certain urban agricultural facility, if particularly strong sunlight is predicted on a given day, the irrigation system will automatically activate to manage humidity and temperature for that day. Furthermore, if users exhibit stress reactions during that time, the work speed will be reduced and the lighting environment will be adjusted.

[0343] An example of a prompt for a generative AI model is: "Based on the latest agricultural data, if user emotional feedback indicates high satisfaction, what efficiency improvements would you suggest next?" Using this prompt, artificial intelligence can suggest specific improvements to enhance the user experience.

[0344] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0345] Step 1:

[0346] The server receives data such as temperature, humidity, and sunlight from devices that acquire environmental information. Based on this input data, the server preprocesses it by saving it to a database and preparing it as input for the next intelligent model.

[0347] Step 2:

[0348] The server runs a cloud-based generative AI model using environmental data stored in a database. A data analysis engine analyzes this environmental information and outputs optimal agricultural operation schedules and adjustment suggestions. This output is used as operational instructions for automated machinery.

[0349] Step 3:

[0350] The device uses a camera and microphone to capture the user's facial expressions and voice. Using this emotional data as input, the device's emotion analysis engine performs a process to determine the user's psychological state and outputs the user's emotional state.

[0351] Step 4:

[0352] The server integrates the analysis results of the generated AI model with the user's emotional state obtained from the terminal. Based on this, it outputs instructions to adjust the operating speed and work methods of the automated machine. Specific adjustments include increasing or decreasing the work speed and changing environmental settings.

[0353] Step 5:

[0354] Users view analysis results and sentiment feedback from the server using an application on their device. Furthermore, users can review suggested tasks and manually adjust actions as needed. This optimizes the user experience.

[0355] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0356] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0357] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0358] [Third Embodiment]

[0359] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0360] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0361] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0362] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0363] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0364] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0365] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0366] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0367] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0368] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0369] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0370] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0371] The system in this invention is an integrated system that enables efficient management of the entire agricultural environment. It solves various challenges in conventional agricultural production through the acquisition of environmental information, data analysis, and automation of physical tasks.

[0372] At the heart of this system is a server, which aggregates environmental information from multiple electronic components (sensors). The server receives this data in real time and stores it in a database. Next, the server uses its built-in artificial intelligence algorithms to analyze the accumulated data and calculate the optimal environmental conditions for crop growth. For example, the server calculates the timing of irrigation and the appropriate amount of fertilizer based on fluctuations in temperature and humidity.

[0373] Meanwhile, the terminal receives instructions from the server and transmits them to local machinery (agricultural robots and drones). Specifically, the terminal has the function of sending specific operation commands to individual machinery based on the server's analysis results. The terminal also monitors the status of the machinery and the progress of the work, and feeds this information back to the server, thereby improving the efficiency of the entire system.

[0374] Users operate this system via smart devices. Using the application interface, users can view real-time farm monitoring data. This data, including temperature, humidity, and sunshine duration, is displayed in graphs, allowing users to understand the farm's conditions at a glance. Furthermore, users can manually modify work instructions as needed. As a result, users have flexible control over the timing and settings of their work.

[0375] In this way, the system of the present invention can improve the accuracy of agricultural work while increasing agricultural productivity and reducing the burden on human labor. For example, it can respond immediately to changes in weather and supply the water and nutrients that plants need in a timely manner, and concrete attempts can be made to maximize yields.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] The server acquires environmental information such as temperature, humidity, and sunshine duration in real time from various sensors placed throughout the farm. The collected data is immediately stored in a database.

[0379] Step 2:

[0380] The server uses collected environmental information to apply artificial intelligence algorithms and perform analysis. This allows it to understand the current state of crops and predicted weather conditions. Specifically, optimization calculations are performed based on this data to determine the required amount and timing of irrigation and fertilization.

[0381] Step 3:

[0382] Based on the results obtained from the analysis, the server generates specific work instructions for agricultural robots and drones. For example, this might include a command such as "Start irrigation in the designated area at 8:00 AM."

[0383] Step 4:

[0384] The terminal receives work instructions sent from the server. Upon receiving the instructions, the terminal sends control signals to the robots and drones. These control signals ensure that each machine performs its operations precisely.

[0385] Step 5:

[0386] The terminal monitors the operation of agricultural robots and drones in real time. It acquires additional data from sensors during operation and immediately reports any abnormalities to the server.

[0387] Step 6:

[0388] Users can monitor farm conditions in real time using an operating application via a terminal. Monitoring results are displayed to the user in graphs and numerical data. Users can also manually set or adjust work instructions as needed.

[0389] (Example 1)

[0390] Next, we will describe Example 1. 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."

[0391] In modern agriculture, it is essential to respond immediately to changes in environmental conditions and perform tasks at the appropriate time. However, current technology makes the process of collecting and analyzing environmental data and feeding that back into physical work inefficient, resulting in a heavy human burden and making it difficult to maximize productivity. Therefore, an integrated system is needed that efficiently acquires and analyzes environmental information and automates the execution of tasks.

[0392] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0393] In this invention, the server includes an information acquisition means for acquiring environmental data, an intelligent analysis means for analyzing the environmental data acquired from the information acquisition means, and a work mechanism means for executing work actions based on the analysis results of the intelligent analysis means. This enables rapid and efficient agricultural work in response to changes in environmental conditions.

[0394] "Environmental data" refers to information about the natural environment, such as temperature, humidity, soil moisture, and sunlight, which is important for agriculture and other activities.

[0395] "Information acquisition means" refers to the functions and operations performed by devices and sensors placed to collect environmental data.

[0396] "Intelligent analysis means" refers to artificial intelligence algorithms and data processing technologies used to process and analyze acquired environmental data.

[0397] "Work actions" refer to specific physical tasks and operations performed based on the results of environmental data analysis.

[0398] "Work mechanism means" refers to the devices and equipment used to actually perform physical work based on the results of the analysis.

[0399] "Feedback means" refers to a function that monitors the operating status and results of the work mechanism and transmits the information based on that to the server or other system components.

[0400] "Terminal operation means" refers to the interface or functions that allow a user to monitor the system status using a terminal and make adjustments or operations as needed.

[0401] To implement this invention, an integrated agricultural management system is required. This system has a set of functions including the acquisition, analysis, and feedback of environmental data, and the automation of agricultural operations.

[0402] The server uses a wide variety of sensor devices to acquire environmental data. This includes general-purpose environmental sensors for measuring temperature and humidity, and the data is aggregated using wireless communication technology. The acquired data is stored in a database system such as MySQL.

[0403] The server also incorporates artificial intelligence algorithms such as TensorFlow to analyze the collected data. Specifically, it calculates optimal crop growth conditions based on environmental data and predicts future environmental changes using predictive models. Based on these analysis results, the optimal irrigation schedule and fertilizer distribution are determined.

[0404] The terminal uses autonomous agricultural robots and unmanned aerial vehicles to perform physical agricultural tasks based on instructions received from the server. The terminal sends necessary action commands to these machines and feeds back progress to the server. This progress information is used for subsequent data analysis.

[0405] Users can use smart devices to monitor the overall system status and take action as needed. The application interface graphically displays real-time data such as temperature, humidity, and sunlight to support user decision-making. Users can also manually modify work instructions through the application, enabling flexible management.

[0406] One concrete example is a system that can respond immediately to sudden changes in weather and supply crops with the water they need at the appropriate time. An example of a prompt message would be, "Please suggest the optimal amount of fertilizer to apply based on this week's weather data."

[0407] This system makes it possible to improve agricultural productivity and reduce the burden on human resources.

[0408] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0409] Step 1:

[0410] The server receives data from a group of sensors to collect environmental information. Inputs include temperature, humidity, soil moisture, and sunlight. This data is transmitted to the server using wireless technology. After receiving this raw data, the server stores it in a MySQL database. The output is time-series data stored in the database. Storing the data in this format allows for efficient use in subsequent analysis steps.

[0411] Step 2:

[0412] The server uses artificial intelligence algorithms such as TensorFlow to analyze environmental data stored in the database. Historical environmental data and current real-time data are used as input. The server analyzes this data to calculate irrigation timing and fertilizer application amounts in order to formulate an optimal farming plan. The output is an optimized farming schedule based on the analysis results. This result is sent to the terminal and used for the next physical task.

[0413] Step 3:

[0414] The terminal sends operational instructions to machinery based on analysis results received from the server. For example, it commands irrigation equipment to water at specified times and drones to spread fertilizer over a specific area. The input is operational instruction data from the server, and the output is the execution of the actual physical work. This automates and efficiently carries out planned agricultural tasks.

[0415] Step 4:

[0416] The terminal monitors the progress of the physical work being performed and sends real-time feedback to the server. The terminal receives status information from the machinery and the status of work completion as input. As output, the progress of the work and the actual data necessary for the next plan are returned to the server. This feedback loop enables more precise instructions in future work plans.

[0417] Step 5:

[0418] Users use smart devices to understand the overall system status and take action as needed. Through the application interface, users receive monitoring data and work schedules as input. Output includes graphs and dashboards that visualize environmental changes and work progress. Based on this information, users can achieve optimal farm management by making manual adjustments as required.

[0419] (Application Example 1)

[0420] Next, we will explain Application Example 1. In the following explanation, 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."

[0421] In urban agricultural environments, significant fluctuations in environmental conditions make efficient production management difficult. Furthermore, conventional systems lack sufficient real-time monitoring and adjustment capabilities, leading to increased human workload. This hinders the realization of sustainable urban agriculture.

[0422] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0423] In this invention, the server includes means for a device for collecting environmental data, means for an information processing device for analyzing the environmental data acquired from the device, means for an operating device for executing work procedures based on the analysis results of the information processing device, and means for a display device that uses the analysis results of the information processing device to present information to the user in real time and allow instructions to be changed. This enables real-time, efficient environmental adjustment and production management in agricultural activities in urban environments.

[0424] "Environmental data" refers to information related to weather conditions such as temperature, humidity, and light levels in urban areas and agricultural land, as well as land-related information.

[0425] A "device" is an electronic device, such as a sensor or measuring instrument, used to collect environmental data.

[0426] An "information processing device" is a device consisting of a computer and software that analyzes collected environmental data and extracts necessary information.

[0427] An "operating device" is a machine or equipment used to execute specific agricultural tasks or production management procedures based on the results analyzed by an information processing device.

[0428] A "display device" is a device that presents information to the user in real time and provides an interface for changing instructions as needed.

[0429] The system for realizing this invention provides efficient environmental management in urban agriculture. A server collects environmental data through multiple devices. These devices are used to measure weather conditions such as temperature, humidity, and light intensity using sensors.

[0430] The server analyzes environmental data acquired using an information processing device. This analysis utilizes a database management system and computer software to execute AI algorithms. As a result of the analysis, the ideal environmental conditions for crop growth are calculated, and the necessary agricultural activities are identified.

[0431] The operating system automatically executes work procedures based on instructions from the server. This system often includes irrigation systems, fertilizers, and other agricultural machinery. This allows for environmental adjustments without human intervention.

[0432] Users can remotely check the system status using smartphones or display devices. Simultaneously, users can modify instructions based on information provided by the server via the display device. This functionality requires application software for smart devices, allowing users to monitor farm conditions in real time and control the operation of the machinery.

[0433] As a concrete example, if a user is managing a rooftop garden in an urban environment, the application can detect temperature increases and, based on the AI's analysis, suggest appropriate irrigation to the user. The user can then use their smartphone to take action based on this suggestion.

[0434] An example of a prompt would be: "Please provide the best suggestions for creating environmental conditions conducive to plant growth in an urban farm. Clearly indicate any missing elements or points to be aware of."

[0435] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0436] Step 1:

[0437] The server collects weather data such as temperature, humidity, and light intensity from environmental sensors. The signals transmitted from the sensors are received by a receiver and processed as digital data. This serves as the input, and the collected information is stored in the database as environmental data.

[0438] Step 2:

[0439] The server analyzes accumulated environmental data using an information processing device. The input environmental data is analyzed using an AI algorithm to calculate the optimal environmental conditions necessary for crop growth. The output is generated as recommendations for necessary agricultural activities.

[0440] Step 3:

[0441] The server sends instructions to the operating devices based on the analysis results. Specifically, it commands irrigation equipment and fertilizer application equipment on when and how they should operate. The input is the analysis results from the previous step, and the output is the operation command.

[0442] Step 4:

[0443] Users check the system status via smart devices. Output information from the server is displayed on the interface, allowing users to make decisions based on this information. Operating status of the operating equipment and environmental monitoring data are provided as input.

[0444] Step 5:

[0445] The user modifies the instructions via the display device as needed. The server receives the new instructions from the user and resends them to the operating device. Here, the user's operation becomes the new input, and the updated operation command is output.

[0446] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0447] The system according to this invention achieves more advanced agricultural management by collecting and analyzing data related to the agricultural environment and further taking into account the user's emotions. First, the server receives general environmental information from electronic components. This includes temperature, humidity, and sunshine hours. The server stores this data in a database and performs analysis using artificial intelligence. Through this analysis, appropriate irrigation schedules and fertilizer application timings can be determined.

[0448] Furthermore, this system can identify user emotions by incorporating an emotion engine. The emotion engine analyzes the user's facial expressions and voice through cameras and voice input devices to recognize emotional states such as stress and a sense of security. This information is used to consider the psychological impact on the user's productive activities.

[0449] Specifically, the terminal controls the agricultural robot based on analysis results from the server, while also incorporating user feedback obtained from the emotion engine. For example, if the user is experiencing stress, the work speed can be adjusted to reduce the stress. Furthermore, if the user expresses satisfaction, the system may suggest ways to further improve efficiency.

[0450] Users can access farm data and their own sentiment analysis results through a dedicated application. This allows users to select optimal production strategies and understand how their emotional state impacts their production activities. This comprehensive management system aims not only to improve the quality and efficiency of agricultural production but also to enhance users' mental well-being.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The server collects environmental information such as temperature, humidity, and sunshine duration in real time through electronic components (sensors) placed on the farm and stores it in a database.

[0454] Step 2:

[0455] The server applies artificial intelligence algorithms to analyze the collected environmental information. This allows it to calculate the current state of the crops and the optimal cultivation procedures. For example, it can determine the timing of watering and the appropriate amount of fertilizer.

[0456] Step 3:

[0457] The emotion engine uses camera and voice input to analyze the user's facial expressions and voice, collecting emotional data. This data is used to understand the user's psychological state.

[0458] Step 4:

[0459] The server comprehensively evaluates the analyzed environmental information and emotional data to generate optimal instructions for agricultural activities. If the emotional data is negative, the server adjusts the work to alleviate the user's stress.

[0460] Step 5:

[0461] The terminal transmits instructions received from the server to agricultural robots and drones, causing them to perform specific agricultural tasks. These tasks are carried out accurately and efficiently based on the analysis results.

[0462] Step 6:

[0463] Users can monitor the progress of their work and the results of sentiment analysis in real time through an interface provided via their terminal. Furthermore, users can customize their agricultural activities by making manual adjustments based on the system's suggestions as needed.

[0464] (Example 2)

[0465] Next, we will describe Example 2. 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."

[0466] Conventional agricultural management systems have limitations in optimizing operations based on the analysis of environmental information, and furthermore, they do not adjust operations according to the user's emotional state, making it difficult to achieve efficient and user-friendly agricultural management.

[0467] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0468] In this invention, the server includes a sensor device for acquiring environmental data, an artificial intelligence device for analyzing the environmental data acquired from the sensor device, a control device for controlling a work device based on the analysis results of the artificial intelligence device, an emotion recognition device for analyzing the user's emotional state, and a control device for adjusting the work device based on the analysis results of the emotion recognition device. This makes it possible to optimize agricultural processes by considering both environmental information and the user's emotional state.

[0469] "Environmental data" refers to natural conditions in agriculture, such as temperature, humidity, and the amount of sunlight, and is information acquired by sensor devices.

[0470] A "sensor device" is a device installed to acquire environmental data, such as temperature, humidity, and sunlight.

[0471] An "artificial intelligence device" refers to a system that analyzes acquired environmental data and generates instructions necessary for optimizing work.

[0472] "Working equipment" refers to a machine or robot that performs agricultural processes based on instructions from an artificial intelligence device.

[0473] A "control device" is a device that operates work equipment and executes instructions from artificial intelligence devices or emotion recognition devices.

[0474] An "emotion recognition device" is a device used to analyze a user's emotional state, determining stress levels and feelings of security by analyzing facial expressions and voice.

[0475] The system according to this invention aims to achieve efficient agricultural management by collecting and analyzing data related to the agricultural environment. The system includes the following main components.

[0476] First, the server acquires environmental data using sensor devices installed on the farm. The sensor devices measure environmental information such as temperature, humidity, and sunlight duration in real time and transmit it to a database. The artificial intelligence system within the server analyzes this data in the background and generates instructions to optimize agricultural processes.

[0477] Next, the server monitors the user's condition using an emotion recognition device. It analyzes the user's facial expressions and voice using cameras and voice input devices to detect stress levels and satisfaction. This analysis is then used to adjust the operation of the work equipment.

[0478] For example, if the server determines that the user's stress level is high, the control unit will reduce the operating speed of the work device to alleviate the user's burden. Furthermore, if the user's satisfaction level is high, the system can suggest and implement even more efficient work methods.

[0479] Users can check the environmental conditions of their farm and the results of their own emotional analysis through a dedicated application. By using this application, users can understand the impact of their emotions on agricultural processes and select the optimal production strategies.

[0480] Examples of prompt statements include the following:

[0481] "Suggest the best course of action to take when a user is experiencing stress during farm work."

[0482] "Please tell me how to increase production efficiency when users express satisfaction."

[0483] This system aims to achieve both improved work efficiency and increased user satisfaction by comprehensively utilizing environmental information and the user's emotional state.

[0484] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0485] Step 1:

[0486] The server collects environmental data from sensor devices installed on the farm. Inputs include temperature, humidity, and daylight hours, and the sensor devices acquire this data in real time. The server receives this data and stores it in a database, allowing for a clear understanding of the farm's current state.

[0487] Step 2:

[0488] The server inputs environmental data stored in the database into the artificial intelligence (AI) device for analysis. The input is the environmental data obtained in step 1. The AI ​​device analyzes this data to calculate irrigation schedules and optimal timing for fertilizer application. As output, specific instructions for optimizing agricultural processes are generated.

[0489] Step 3:

[0490] The server analyzes the user's emotional state using an emotion recognition device. Input consists of the user's facial expressions and voice, collected from a camera and voice input device. Based on this data, the emotion engine evaluates the user's stress levels and sense of security. The output provides information about the user's current emotional state.

[0491] Step 4:

[0492] The server integrates the analysis results from step 2 and the emotion analysis results from step 3, and operates a control device that issues instructions to the work equipment. Based on the analysis results and emotional state, the control device gives appropriate instructions to the work equipment. For example, if it is determined that the user is feeling stressed, an instruction to reduce the work speed will be issued. This provides the user with optimal working conditions.

[0493] Step 5:

[0494] Users can check the status of their farm and their own sentiment analysis results through a dedicated application. Input consists of output data from steps 1 through 4. Based on this information, users can select optimal production strategies and understand the impact of their emotional state on agricultural processes. This enables efficient and user-friendly agricultural management.

[0495] (Application Example 2)

[0496] Next, we will explain application example 2. In the following explanation, 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."

[0497] In agriculture, precise and flexible work control is required, taking into account not only appropriate management based on environmental information but also the psychological state of the workers. However, conventional systems only perform mechanical tasks that rely on environmental information and lack consideration for the user's emotions, making it difficult to maximize production efficiency while reducing user stress.

[0498] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0499] In this invention, the server includes intelligent modeling means for analyzing data obtained from a device for acquiring environmental information, means for performing operations using an automated machine based on the analysis results of the intelligent model, and management means for performing emotion analysis and adjusting operations based on the results while considering the user's emotional state. This makes it possible to increase the efficiency of agricultural operations while simultaneously reducing stress according to the user's emotional state.

[0500] "Environmental information" refers to data that indicates natural conditions in a specific space, such as temperature, humidity, and sunlight.

[0501] A "device" is a collection of hardware components that have the function of acquiring environmental information.

[0502] An "intelligent model" is an artificial intelligence algorithm that analyzes acquired environmental information and makes appropriate decisions.

[0503] An "automated machine" is a device that automatically performs physical tasks based on instructions from an intelligent model.

[0504] "Emotional analysis" is a process that determines a user's psychological state based on their facial expressions, voice, and other factors.

[0505] A "management system" is a control system that appropriately adjusts the operation of automated machines based on the results of emotion analysis.

[0506] "Agricultural operations" refers to the entire range of physical and administrative tasks associated with agricultural activities.

[0507] To realize this invention, a comprehensive system is needed for acquiring, analyzing, and managing environmental information in agricultural facilities within a smart city. The specific steps are as follows:

[0508] The server continuously collects data using devices to acquire environmental information such as temperature, humidity, and sunlight. This data is analyzed by a cloud-based intelligent model to derive the optimal conditions and actions necessary for agricultural operations. This analysis utilizes a generative AI model employing adaptive learning algorithms. This enables dynamic optimization for various environmental conditions.

[0509] The terminal is equipped with a sensor that analyzes the user's emotions in real time. Using a camera and voice input function, it determines the user's emotions through an emotion analysis process based on their facial expressions and voice. Based on this, it optimizes the work environment by adjusting the operating speed of agricultural equipment, for example, to reduce stress, according to the user's emotional state.

[0510] Users can view the analysis results and emotional feedback of the intelligent model through a dedicated mobile application. This application provides an interactive interface that allows for the selection of measures and manual adjustment of environmental controls.

[0511] As a concrete example, in a certain urban agricultural facility, if particularly strong sunlight is predicted on a given day, the irrigation system will automatically activate to manage humidity and temperature for that day. Furthermore, if users exhibit stress reactions during that time, the work speed will be reduced and the lighting environment will be adjusted.

[0512] An example of a prompt for a generative AI model is: "Based on the latest agricultural data, if user emotional feedback indicates high satisfaction, what efficiency improvements would you suggest next?" Using this prompt, artificial intelligence can suggest specific improvements to enhance the user experience.

[0513] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0514] Step 1:

[0515] The server receives data such as temperature, humidity, and sunlight from devices that acquire environmental information. Based on this input data, the server preprocesses it by saving it to a database and preparing it as input for the next intelligent model.

[0516] Step 2:

[0517] The server runs a cloud-based generative AI model using environmental data stored in a database. A data analysis engine analyzes this environmental information and outputs optimal agricultural operation schedules and adjustment suggestions. This output is used as operational instructions for automated machinery.

[0518] Step 3:

[0519] The device uses a camera and microphone to capture the user's facial expressions and voice. Using this emotional data as input, the device's emotion analysis engine performs a process to determine the user's psychological state and outputs the user's emotional state.

[0520] Step 4:

[0521] The server integrates the analysis results of the generated AI model with the user's emotional state obtained from the terminal. Based on this, it outputs instructions to adjust the operating speed and work methods of the automated machine. Specific adjustments include increasing or decreasing the work speed and changing environmental settings.

[0522] Step 5:

[0523] Users view analysis results and sentiment feedback from the server using an application on their device. Furthermore, users can review suggested tasks and manually adjust actions as needed. This optimizes the user experience.

[0524] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0525] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0526] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0527] [Fourth Embodiment]

[0528] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0529] As shown in Figure 7, the 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.

[0530] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0531] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0532] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0533] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0534] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0535] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0536] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0537] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0538] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0539] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0540] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0541] The system in this invention is an integrated system that enables efficient management of the entire agricultural environment. It solves various challenges in conventional agricultural production through the acquisition of environmental information, data analysis, and automation of physical tasks.

[0542] At the heart of this system is a server, which aggregates environmental information from multiple electronic components (sensors). The server receives this data in real time and stores it in a database. Next, the server uses its built-in artificial intelligence algorithms to analyze the accumulated data and calculate the optimal environmental conditions for crop growth. For example, the server calculates the timing of irrigation and the appropriate amount of fertilizer based on fluctuations in temperature and humidity.

[0543] Meanwhile, the terminal receives instructions from the server and transmits them to local machinery (agricultural robots and drones). Specifically, the terminal has the function of sending specific operation commands to individual machinery based on the server's analysis results. The terminal also monitors the status of the machinery and the progress of the work, and feeds this information back to the server, thereby improving the efficiency of the entire system.

[0544] Users operate this system via smart devices. Using the application interface, users can view real-time farm monitoring data. This data, including temperature, humidity, and sunshine duration, is displayed in graphs, allowing users to understand the farm's conditions at a glance. Furthermore, users can manually modify work instructions as needed. As a result, users have flexible control over the timing and settings of their work.

[0545] In this way, the system of the present invention can improve the accuracy of agricultural work while increasing agricultural productivity and reducing the burden on human labor. For example, it can respond immediately to changes in weather and supply the water and nutrients that plants need in a timely manner, and concrete attempts can be made to maximize yields.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] The server acquires environmental information such as temperature, humidity, and sunshine duration in real time from various sensors placed throughout the farm. The collected data is immediately stored in a database.

[0549] Step 2:

[0550] The server uses collected environmental information to apply artificial intelligence algorithms and perform analysis. This allows it to understand the current state of crops and predicted weather conditions. Specifically, optimization calculations are performed based on this data to determine the required amount and timing of irrigation and fertilization.

[0551] Step 3:

[0552] Based on the results obtained from the analysis, the server generates specific work instructions for agricultural robots and drones. For example, this might include a command such as "Start irrigation in the designated area at 8:00 AM."

[0553] Step 4:

[0554] The terminal receives work instructions sent from the server. Upon receiving the instructions, the terminal sends control signals to the robots and drones. These control signals ensure that each machine performs its operations precisely.

[0555] Step 5:

[0556] The terminal monitors the operation of agricultural robots and drones in real time. It acquires additional data from sensors during operation and immediately reports any abnormalities to the server.

[0557] Step 6:

[0558] Users can monitor farm conditions in real time using an operating application via a terminal. Monitoring results are displayed to the user in graphs and numerical data. Users can also manually set or adjust work instructions as needed.

[0559] (Example 1)

[0560] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0561] In modern agriculture, it is essential to respond immediately to changes in environmental conditions and perform tasks at the appropriate time. However, current technology makes the process of collecting and analyzing environmental data and feeding that back into physical work inefficient, resulting in a heavy human burden and making it difficult to maximize productivity. Therefore, an integrated system is needed that efficiently acquires and analyzes environmental information and automates the execution of tasks.

[0562] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0563] In this invention, the server includes an information acquisition means for acquiring environmental data, an intelligent analysis means for analyzing the environmental data acquired from the information acquisition means, and a work mechanism means for executing work actions based on the analysis results of the intelligent analysis means. This enables rapid and efficient agricultural work in response to changes in environmental conditions.

[0564] "Environmental data" refers to information about the natural environment, such as temperature, humidity, soil moisture, and sunlight, which is important for agriculture and other activities.

[0565] "Information acquisition means" refers to the functions and operations performed by devices and sensors placed to collect environmental data.

[0566] "Intelligent analysis means" refers to artificial intelligence algorithms and data processing technologies used to process and analyze acquired environmental data.

[0567] "Work actions" refer to specific physical tasks and operations performed based on the results of environmental data analysis.

[0568] "Work mechanism means" refers to the devices and equipment used to actually perform physical work based on the results of the analysis.

[0569] "Feedback means" refers to a function that monitors the operating status and results of the work mechanism and transmits the information based on that to the server or other system components.

[0570] "Terminal operation means" refers to the interface or functions that allow a user to monitor the system status using a terminal and make adjustments or operations as needed.

[0571] To implement this invention, an integrated agricultural management system is required. This system has a set of functions including the acquisition, analysis, and feedback of environmental data, and the automation of agricultural operations.

[0572] The server uses a wide variety of sensor devices to acquire environmental data. This includes general-purpose environmental sensors for measuring temperature and humidity, and the data is aggregated using wireless communication technology. The acquired data is stored in a database system such as MySQL.

[0573] The server also incorporates artificial intelligence algorithms such as TensorFlow to analyze the collected data. Specifically, it calculates optimal crop growth conditions based on environmental data and predicts future environmental changes using predictive models. Based on these analysis results, the optimal irrigation schedule and fertilizer distribution are determined.

[0574] The terminal uses autonomous agricultural robots and unmanned aerial vehicles to perform physical agricultural tasks based on instructions received from the server. The terminal sends necessary action commands to these machines and feeds back progress to the server. This progress information is used for subsequent data analysis.

[0575] Users can use smart devices to monitor the overall system status and take action as needed. The application interface graphically displays real-time data such as temperature, humidity, and sunlight to support user decision-making. Users can also manually modify work instructions through the application, enabling flexible management.

[0576] One concrete example is a system that can respond immediately to sudden changes in weather and supply crops with the water they need at the appropriate time. An example of a prompt message would be, "Please suggest the optimal amount of fertilizer to apply based on this week's weather data."

[0577] This system makes it possible to improve agricultural productivity and reduce the burden on human resources.

[0578] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0579] Step 1:

[0580] The server receives data from a group of sensors to collect environmental information. Inputs include temperature, humidity, soil moisture, and sunlight. This data is transmitted to the server using wireless technology. After receiving this raw data, the server stores it in a MySQL database. The output is time-series data stored in the database. Storing the data in this format allows for efficient use in subsequent analysis steps.

[0581] Step 2:

[0582] The server uses artificial intelligence algorithms such as TensorFlow to analyze environmental data stored in the database. Historical environmental data and current real-time data are used as input. The server analyzes this data to calculate irrigation timing and fertilizer application amounts in order to formulate an optimal farming plan. The output is an optimized farming schedule based on the analysis results. This result is sent to the terminal and used for the next physical task.

[0583] Step 3:

[0584] The terminal sends operational instructions to machinery based on analysis results received from the server. For example, it commands irrigation equipment to water at specified times and drones to spread fertilizer over a specific area. The input is operational instruction data from the server, and the output is the execution of the actual physical work. This automates and efficiently carries out planned agricultural tasks.

[0585] Step 4:

[0586] The terminal monitors the progress of the physical work being performed and sends real-time feedback to the server. The terminal receives status information from the machinery and the status of work completion as input. As output, the progress of the work and the actual data necessary for the next plan are returned to the server. This feedback loop enables more precise instructions in future work plans.

[0587] Step 5:

[0588] Users use smart devices to understand the overall system status and take action as needed. Through the application interface, users receive monitoring data and work schedules as input. Output includes graphs and dashboards that visualize environmental changes and work progress. Based on this information, users can achieve optimal farm management by making manual adjustments as required.

[0589] (Application Example 1)

[0590] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0591] In urban agricultural environments, significant fluctuations in environmental conditions make efficient production management difficult. Furthermore, conventional systems lack sufficient real-time monitoring and adjustment capabilities, leading to increased human workload. This hinders the realization of sustainable urban agriculture.

[0592] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0593] In this invention, the server includes means for a device for collecting environmental data, means for an information processing device for analyzing the environmental data acquired from the device, means for an operating device for executing work procedures based on the analysis results of the information processing device, and means for a display device that uses the analysis results of the information processing device to present information to the user in real time and allow instructions to be changed. This enables real-time, efficient environmental adjustment and production management in agricultural activities in urban environments.

[0594] "Environmental data" refers to information related to weather conditions such as temperature, humidity, and light levels in urban areas and agricultural land, as well as land-related information.

[0595] A "device" is an electronic device, such as a sensor or measuring instrument, used to collect environmental data.

[0596] An "information processing device" is a device consisting of a computer and software that analyzes collected environmental data and extracts necessary information.

[0597] An "operating device" is a machine or equipment used to execute specific agricultural tasks or production management procedures based on the results analyzed by an information processing device.

[0598] A "display device" is a device that presents information to the user in real time and provides an interface for changing instructions as needed.

[0599] The system for realizing this invention provides efficient environmental management in urban agriculture. A server collects environmental data through multiple devices. These devices are used to measure weather conditions such as temperature, humidity, and light intensity using sensors.

[0600] The server analyzes environmental data acquired using an information processing device. This analysis utilizes a database management system and computer software to execute AI algorithms. As a result of the analysis, the ideal environmental conditions for crop growth are calculated, and the necessary agricultural activities are identified.

[0601] The operating system automatically executes work procedures based on instructions from the server. This system often includes irrigation systems, fertilizers, and other agricultural machinery. This allows for environmental adjustments without human intervention.

[0602] Users can remotely check the system status using smartphones or display devices. Simultaneously, users can modify instructions based on information provided by the server via the display device. This functionality requires application software for smart devices, allowing users to monitor farm conditions in real time and control the operation of the machinery.

[0603] As a concrete example, if a user is managing a rooftop garden in an urban environment, the application can detect temperature increases and, based on the AI's analysis, suggest appropriate irrigation to the user. The user can then use their smartphone to take action based on this suggestion.

[0604] An example of a prompt would be: "Please provide the best suggestions for creating environmental conditions conducive to plant growth in an urban farm. Clearly indicate any missing elements or points to be aware of."

[0605] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0606] Step 1:

[0607] The server collects weather data such as temperature, humidity, and light intensity from environmental sensors. The signals transmitted from the sensors are received by a receiver and processed as digital data. This serves as the input, and the collected information is stored in the database as environmental data.

[0608] Step 2:

[0609] The server analyzes accumulated environmental data using an information processing device. The input environmental data is analyzed using an AI algorithm to calculate the optimal environmental conditions necessary for crop growth. The output is generated as recommendations for necessary agricultural activities.

[0610] Step 3:

[0611] The server sends instructions to the operating devices based on the analysis results. Specifically, it commands irrigation equipment and fertilizer application equipment on when and how they should operate. The input is the analysis results from the previous step, and the output is the operation command.

[0612] Step 4:

[0613] Users check the system status via smart devices. Output information from the server is displayed on the interface, allowing users to make decisions based on this information. Operating status of the operating equipment and environmental monitoring data are provided as input.

[0614] Step 5:

[0615] The user modifies the instructions via the display device as needed. The server receives the new instructions from the user and resends them to the operating device. Here, the user's operation becomes the new input, and the updated operation command is output.

[0616] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0617] The system according to this invention achieves more advanced agricultural management by collecting and analyzing data related to the agricultural environment and further taking into account the user's emotions. First, the server receives general environmental information from electronic components. This includes temperature, humidity, and sunshine hours. The server stores this data in a database and performs analysis using artificial intelligence. Through this analysis, appropriate irrigation schedules and fertilizer application timings can be determined.

[0618] Furthermore, this system can identify user emotions by incorporating an emotion engine. The emotion engine analyzes the user's facial expressions and voice through cameras and voice input devices to recognize emotional states such as stress and a sense of security. This information is used to consider the psychological impact on the user's productive activities.

[0619] Specifically, the terminal controls the agricultural robot based on analysis results from the server, while also incorporating user feedback obtained from the emotion engine. For example, if the user is experiencing stress, the work speed can be adjusted to reduce the stress. Furthermore, if the user expresses satisfaction, the system may suggest ways to further improve efficiency.

[0620] Users can access farm data and their own sentiment analysis results through a dedicated application. This allows users to select optimal production strategies and understand how their emotional state impacts their production activities. This comprehensive management system aims not only to improve the quality and efficiency of agricultural production but also to enhance users' mental well-being.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The server collects environmental information such as temperature, humidity, and sunshine duration in real time through electronic components (sensors) placed on the farm and stores it in a database.

[0624] Step 2:

[0625] The server applies artificial intelligence algorithms to analyze the collected environmental information. This allows it to calculate the current state of the crops and the optimal cultivation procedures. For example, it can determine the timing of watering and the appropriate amount of fertilizer.

[0626] Step 3:

[0627] The emotion engine uses camera and voice input to analyze the user's facial expressions and voice, collecting emotional data. This data is used to understand the user's psychological state.

[0628] Step 4:

[0629] The server comprehensively evaluates the analyzed environmental information and emotional data to generate optimal instructions for agricultural activities. If the emotional data is negative, the server adjusts the work to alleviate the user's stress.

[0630] Step 5:

[0631] The terminal transmits instructions received from the server to agricultural robots and drones, causing them to perform specific agricultural tasks. These tasks are carried out accurately and efficiently based on the analysis results.

[0632] Step 6:

[0633] Users can monitor the progress of their work and the results of sentiment analysis in real time through an interface provided via their terminal. Furthermore, users can customize their agricultural activities by making manual adjustments based on the system's suggestions as needed.

[0634] (Example 2)

[0635] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0636] Conventional agricultural management systems have limitations in optimizing operations based on the analysis of environmental information, and furthermore, they do not adjust operations according to the user's emotional state, making it difficult to achieve efficient and user-friendly agricultural management.

[0637] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0638] In this invention, the server includes a sensor device for acquiring environmental data, an artificial intelligence device for analyzing the environmental data acquired from the sensor device, a control device for controlling a work device based on the analysis results of the artificial intelligence device, an emotion recognition device for analyzing the user's emotional state, and a control device for adjusting the work device based on the analysis results of the emotion recognition device. This makes it possible to optimize agricultural processes by considering both environmental information and the user's emotional state.

[0639] "Environmental data" refers to natural conditions in agriculture, such as temperature, humidity, and the amount of sunlight, and is information acquired by sensor devices.

[0640] A "sensor device" is a device installed to acquire environmental data, such as temperature, humidity, and sunlight.

[0641] An "artificial intelligence device" refers to a system that analyzes acquired environmental data and generates instructions necessary for optimizing work.

[0642] "Working equipment" refers to a machine or robot that performs agricultural processes based on instructions from an artificial intelligence device.

[0643] A "control device" is a device that operates work equipment and executes instructions from artificial intelligence devices or emotion recognition devices.

[0644] An "emotion recognition device" is a device used to analyze a user's emotional state, determining stress levels and feelings of security by analyzing facial expressions and voice.

[0645] The system according to this invention aims to achieve efficient agricultural management by collecting and analyzing data related to the agricultural environment. The system includes the following main components.

[0646] First, the server acquires environmental data using sensor devices installed on the farm. The sensor devices measure environmental information such as temperature, humidity, and sunlight duration in real time and transmit it to a database. The artificial intelligence system within the server analyzes this data in the background and generates instructions to optimize agricultural processes.

[0647] Next, the server monitors the user's condition using an emotion recognition device. It analyzes the user's facial expressions and voice using cameras and voice input devices to detect stress levels and satisfaction. This analysis is then used to adjust the operation of the work equipment.

[0648] For example, if the server determines that the user's stress level is high, the control unit will reduce the operating speed of the work device to alleviate the user's burden. Furthermore, if the user's satisfaction level is high, the system can suggest and implement even more efficient work methods.

[0649] Users can check the environmental conditions of their farm and the results of their own emotional analysis through a dedicated application. By using this application, users can understand the impact of their emotions on agricultural processes and select the optimal production strategies.

[0650] Examples of prompt statements include the following:

[0651] "Suggest the best course of action to take when a user is experiencing stress during farm work."

[0652] "Please tell me how to increase production efficiency when users express satisfaction."

[0653] This system aims to achieve both improved work efficiency and increased user satisfaction by comprehensively utilizing environmental information and the user's emotional state.

[0654] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0655] Step 1:

[0656] The server collects environmental data from sensor devices installed on the farm. Inputs include temperature, humidity, and daylight hours, and the sensor devices acquire this data in real time. The server receives this data and stores it in a database, allowing for a clear understanding of the farm's current state.

[0657] Step 2:

[0658] The server inputs environmental data stored in the database into the artificial intelligence (AI) device for analysis. The input is the environmental data obtained in step 1. The AI ​​device analyzes this data to calculate irrigation schedules and optimal timing for fertilizer application. As output, specific instructions for optimizing agricultural processes are generated.

[0659] Step 3:

[0660] The server analyzes the user's emotional state using an emotion recognition device. Input consists of the user's facial expressions and voice, collected from a camera and voice input device. Based on this data, the emotion engine evaluates the user's stress levels and sense of security. The output provides information about the user's current emotional state.

[0661] Step 4:

[0662] The server integrates the analysis results from step 2 and the emotion analysis results from step 3, and operates a control device that issues instructions to the work equipment. Based on the analysis results and emotional state, the control device gives appropriate instructions to the work equipment. For example, if it is determined that the user is feeling stressed, an instruction to reduce the work speed will be issued. This provides the user with optimal working conditions.

[0663] Step 5:

[0664] Users can check the status of their farm and their own sentiment analysis results through a dedicated application. Input consists of output data from steps 1 through 4. Based on this information, users can select optimal production strategies and understand the impact of their emotional state on agricultural processes. This enables efficient and user-friendly agricultural management.

[0665] (Application Example 2)

[0666] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] In agriculture, precise and flexible work control is required, taking into account not only appropriate management based on environmental information but also the psychological state of the workers. However, conventional systems only perform mechanical tasks that rely on environmental information and lack consideration for the user's emotions, making it difficult to maximize production efficiency while reducing user stress.

[0668] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0669] In this invention, the server includes intelligent modeling means for analyzing data obtained from a device for acquiring environmental information, means for performing operations using an automated machine based on the analysis results of the intelligent model, and management means for performing emotion analysis and adjusting operations based on the results while considering the user's emotional state. This makes it possible to increase the efficiency of agricultural operations while simultaneously reducing stress according to the user's emotional state.

[0670] "Environmental information" refers to data that indicates natural conditions in a specific space, such as temperature, humidity, and sunlight.

[0671] A "device" is a collection of hardware components that have the function of acquiring environmental information.

[0672] An "intelligent model" is an artificial intelligence algorithm that analyzes acquired environmental information and makes appropriate decisions.

[0673] An "automated machine" is a device that automatically performs physical tasks based on instructions from an intelligent model.

[0674] "Emotional analysis" is a process that determines a user's psychological state based on their facial expressions, voice, and other factors.

[0675] A "management system" is a control system that appropriately adjusts the operation of automated machines based on the results of emotion analysis.

[0676] "Agricultural operations" refers to the entire range of physical and administrative tasks associated with agricultural activities.

[0677] To realize this invention, a comprehensive system is needed for acquiring, analyzing, and managing environmental information in agricultural facilities within a smart city. The specific steps are as follows:

[0678] The server continuously collects data using devices to acquire environmental information such as temperature, humidity, and sunlight. This data is analyzed by a cloud-based intelligent model to derive the optimal conditions and actions necessary for agricultural operations. This analysis utilizes a generative AI model employing adaptive learning algorithms. This enables dynamic optimization for various environmental conditions.

[0679] The terminal is equipped with a sensor that analyzes the user's emotions in real time. Using a camera and voice input function, it determines the user's emotions through an emotion analysis process based on their facial expressions and voice. Based on this, it optimizes the work environment by adjusting the operating speed of agricultural equipment, for example, to reduce stress, according to the user's emotional state.

[0680] Users can view the analysis results and emotional feedback of the intelligent model through a dedicated mobile application. This application provides an interactive interface that allows for the selection of measures and manual adjustment of environmental controls.

[0681] As a concrete example, in a certain urban agricultural facility, if particularly strong sunlight is predicted on a given day, the irrigation system will automatically activate to manage humidity and temperature for that day. Furthermore, if users exhibit stress reactions during that time, the work speed will be reduced and the lighting environment will be adjusted.

[0682] An example of a prompt for a generative AI model is: "Based on the latest agricultural data, if user emotional feedback indicates high satisfaction, what efficiency improvements would you suggest next?" Using this prompt, artificial intelligence can suggest specific improvements to enhance the user experience.

[0683] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0684] Step 1:

[0685] The server receives data such as temperature, humidity, and sunlight from devices that acquire environmental information. Based on this input data, the server preprocesses it by saving it to a database and preparing it as input for the next intelligent model.

[0686] Step 2:

[0687] The server runs a cloud-based generative AI model using environmental data stored in a database. A data analysis engine analyzes this environmental information and outputs optimal agricultural operation schedules and adjustment suggestions. This output is used as operational instructions for automated machinery.

[0688] Step 3:

[0689] The device uses a camera and microphone to capture the user's facial expressions and voice. Using this emotional data as input, the device's emotion analysis engine performs a process to determine the user's psychological state and outputs the user's emotional state.

[0690] Step 4:

[0691] The server integrates the analysis results of the generated AI model with the user's emotional state obtained from the terminal. Based on this, it outputs instructions to adjust the operating speed and work methods of the automated machine. Specific adjustments include increasing or decreasing the work speed and changing environmental settings.

[0692] Step 5:

[0693] Users view analysis results and sentiment feedback from the server using an application on their device. Furthermore, users can review suggested tasks and manually adjust actions as needed. This optimizes the user experience.

[0694] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0695] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0696] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0697] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0698] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0699] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0700] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0701] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0702] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0703] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0704] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0705] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0706] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0707] 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.

[0708] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0709] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0710] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0711] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0712] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0713] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0714] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0715] The following is further disclosed regarding the embodiments described above.

[0716] (Claim 1)

[0717] Electronic components for acquiring environmental information,

[0718] An artificial intelligence that analyzes environmental information obtained from the aforementioned electronic components,

[0719] A mechanical device that performs physical work based on the analysis results of the aforementioned artificial intelligence,

[0720] A system that includes this.

[0721] (Claim 2)

[0722] The system according to claim 1, wherein the physical work includes agricultural activities.

[0723] (Claim 3)

[0724] The system according to claim 1, wherein the environmental information includes temperature, humidity, and sunshine duration.

[0725] "Example 1"

[0726] (Claim 1)

[0727] Information acquisition means for obtaining environmental data,

[0728] An intelligent analysis means for analyzing environmental data acquired from the aforementioned information acquisition means,

[0729] A work mechanism means that performs work actions based on the analysis results of the intelligent analysis means,

[0730] A feedback means that monitors the state of the aforementioned work mechanism means and provides feedback on the monitoring results,

[0731] A terminal operation means for visualizing and adjusting the operation of the feedback means and the work mechanism means,

[0732] A system that includes this.

[0733] (Claim 2)

[0734] The system according to claim 1, wherein the aforementioned work activity includes agricultural work.

[0735] (Claim 3)

[0736] The system according to claim 1, in which the environmental data includes weather conditions.

[0737] "Application Example 1"

[0738] (Claim 1)

[0739] A device for collecting environmental data,

[0740] An information processing device that analyzes environmental data acquired from the aforementioned device,

[0741] An operating device that executes a work procedure based on the analysis results of the aforementioned information processing device,

[0742] A display device that uses the analysis results of the aforementioned information processing device to present information to the user in real time and allows the user to change instructions,

[0743] A system that includes this.

[0744] (Claim 2)

[0745] The system according to claim 1, wherein the operating device includes production activities.

[0746] (Claim 3)

[0747] The system according to claim 1, wherein the environmental data includes weather conditions and light intensity.

[0748] "Example 2 of combining an emotion engine"

[0749] (Claim 1)

[0750] Sensor devices for acquiring environmental data,

[0751] An artificial intelligence device that analyzes environmental data acquired from the aforementioned sensor device,

[0752] A control device for controlling a work device based on the analysis results of the artificial intelligence device,

[0753] An emotion recognition device for analyzing the user's emotional state,

[0754] A control device for adjusting the work device based on the analysis results of the emotion recognition device,

[0755] A system that includes this.

[0756] (Claim 2)

[0757] The system according to claim 1, wherein the aforementioned work includes an agricultural process.

[0758] (Claim 3)

[0759] The system according to claim 1, wherein the environmental data includes temperature, humidity, and the duration of sunlight.

[0760] "Application example 2 when combining with an emotional engine"

[0761] (Claim 1)

[0762] A device for acquiring environmental information,

[0763] An intelligent model that analyzes environmental information acquired from the aforementioned device,

[0764] An automated machine that performs operations based on the analysis results of the aforementioned intelligent model,

[0765] Processes and methods for performing emotion analysis,

[0766] A management means that adjusts the operation considering the user's emotional state based on the results of the aforementioned emotion analysis,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, wherein the aforementioned operation includes agricultural operations and is performed according to the emotional state of the user.

[0770] (Claim 3)

[0771] The system according to claim 1, wherein the environmental information includes temperature, humidity, and sunlight, and the user's emotional information includes facial expressions and voice. [Explanation of symbols]

[0772] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for collecting environmental data, An information processing device that analyzes environmental data acquired from the aforementioned device, An operating device that executes a work procedure based on the analysis results of the aforementioned information processing device, A display device that uses the analysis results of the aforementioned information processing device to present information to the user in real time and allows the user to change instructions, A system that includes this.

2. The system according to claim 1, wherein the operating device includes production activities.

3. The system according to claim 1, wherein the environmental data includes weather conditions and light intensity.

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