system

Abstract

We provide the system. [Solution] Sensor devices for acquiring environmental information, A computing device that includes a generative model for analyzing data from the aforementioned sensor device and generating a farm work flow, An execution device for performing farm work based on the work flow generated by the aforementioned computing device, A communication device for transmitting information between the aforementioned computing device and execution device at high speed, 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 method for controlling a persona chatbot, which is performed by at least one processor, the method 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] In the agricultural field, the shortage of labor and the decline in the efficiency of agricultural operations have become serious problems. In particular, due to the aging of the population, the number of agricultural workers has decreased, making it difficult to secure the labor force. As a result, the production efficiency has decreased, and there is a risk that a stable food supply will become difficult. Conventional agricultural management methods are difficult to respond immediately, and rapid decision-making in response to changes in the farm environment is required. Under such circumstances, a system that can autonomously and efficiently manage farms is needed.

Means for Solving the Problems

[0005] To solve these problems, the present invention provides a system comprising a sensor device for acquiring environmental information, a computing device including a generative model for analyzing data and generating farm work flows, an execution device for performing farm work based on the generated work flows, and a communication device for high-speed transmission of information between the computing device and the execution device. This system enables users to make immediate decisions by dynamically allocating multiple tasks and reporting the situation using natural language, thereby realizing efficient and autonomous farm management.

[0006] "Environmental information" refers to data related to the environment necessary for agricultural management, such as farm temperature, humidity, soil nutrient status, and vegetation conditions.

[0007] A "sensor device" is a device used to acquire physical environmental information from a farm, and it is responsible for transmitting this information to a computing device.

[0008] A "generative model" refers to a program that has an algorithm for dynamically generating farm work flows based on acquired environmental information.

[0009] A "computational device" refers to a system that includes hardware for analyzing data received from sensor devices and determining the farm's workflow using a generative model.

[0010] A "workflow" refers to a series of tasks necessary for farm management, and is a plan dynamically constructed by a generative model.

[0011] An "execution device" refers to a machine or robot that specifically executes the workflow generated by a computing device.

[0012] "Communication equipment" refers to equipment and protocols that enable the rapid and reliable transmission of information between a computing device and an execution device.

[0013] "User" refers to an individual or person with a role responsible for operating and supervising the system. [Brief explanation of the drawing]

[0014] [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiments for Carrying out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a 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.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] As shown in Figure 2, in the data processing device 12, 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention relates to a system designed to achieve autonomous and efficient farm management in the agricultural sector. Specific embodiments of the invention are as follows:

[0036] This system consists primarily of multiple sensor devices placed throughout the farm and a computing device for processing the environmental information obtained from these sensors. The computing device is equipped with a generative model, which analyzes the environmental information in real time and dynamically generates the workflow within the farm.

[0037] The server collects environmental information transmitted from sensor devices placed on the farm, such as temperature, humidity, and soil nutrient status. The server quickly processes this information and uses a generative model to plan the workflow necessary for farm management. In doing so, it also takes historical data and predicted weather information into consideration, enabling more accurate planning.

[0038] Based on the generated workflow, the server assigns specific tasks to multiple execution devices, or robots, deployed throughout the farm. Examples of such instructions include, "Since rain is predicted for tomorrow, complete watering today."

[0039] The terminal receives these instructions and operates autonomously within the farm, performing its assigned tasks. For example, if the terminal is a watering robot, it will spray the required amount of water in the designated area. Progress and completion information of the work are sent to the server in real time, and the next work instructions are generated.

[0040] Furthermore, the system of the present invention has the function of reporting the farm status to the user in natural language using a generative model. This allows the user to understand the farm status in real time and to give the system correction instructions or new instructions as needed.

[0041] Thus, the present invention achieves increased efficiency and autonomy in farm management by constructing a system consisting of a sensor device, a computing device, an execution device, and a communication device. This system supports farmers in effectively carrying out agricultural work with a small number of people and contributes to a stable food supply.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects environmental information in real time from sensor devices installed on the farm. This information includes temperature, humidity, and soil nutrient status. The server also obtains weather forecast data via the internet.

[0045] Step 2:

[0046] The server analyzes collected environmental information and weather forecast data. Using a generative model, it uses this data to predict crop conditions and growth, and identify necessary agricultural tasks. For example, if soil moisture is insufficient, it will determine that watering is necessary.

[0047] Step 3:

[0048] Based on the analysis results, the server generates a specific workflow and assigns tasks to each execution device. During this process, it considers the terminal's location and operating status to determine the most efficient route and procedure.

[0049] Step 4:

[0050] The terminal performs designated farm tasks based on work instructions received from the server. For example, it might move to a designated area and spray the required amount of water. The progress of the work is constantly fed back to the server.

[0051] Step 5:

[0052] Once the task is complete, the terminal sends a completion report to the server. The server re-evaluates the newly updated environment information and, if necessary, generates the next workflow.

[0053] Step 6:

[0054] The server reports the latest farm status and work progress to the user in natural language. The user can then use this information to give further instructions or make adjustments, enabling immediate response and decision-making.

[0055] (Example 1)

[0056] 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."

[0057] While there is a need to improve efficiency and autonomy in farm management, conventional methods present challenges in achieving optimal overall management because data collection and analysis from sensors, as well as the execution of tasks, are carried out individually. Furthermore, real-time situation monitoring and dynamic task allocation are difficult, leading to decreased work efficiency.

[0058] 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.

[0059] In this invention, the server includes sensor means for acquiring environmental data, computation means including a generative model, and execution means for performing tasks. This enables real-time, efficient analysis of environmental information, generation of optimal work plans, and dynamic execution of tasks.

[0060] "Environmental data" refers to data that indicates external environmental conditions in an agricultural field, such as temperature, humidity, and soil nutrient status.

[0061] "Sensing means" refers to devices and equipment installed on farms to acquire environmental data, and includes thermometers, hygrometers, soil sensors, etc.

[0062] A "generative model" is an algorithm or program that analyzes collected data to generate an efficient work plan.

[0063] "Computational means" refers to devices such as computers and servers that execute generative models and perform data analysis and generate work plans.

[0064] "Execution means" refers to devices and equipment that perform specific agricultural tasks based on work plans generated by calculation means, and includes robots and work machines.

[0065] "Communication means" refers to devices and technologies for rapidly transmitting data between computing means and execution means, and includes wireless communication and network connections.

[0066] A "distribution means" is a function that instructs the execution device to perform specific tasks based on the generated work plan.

[0067] "Natural language" refers to the language that humans use on a daily basis, and the language that computational systems use to report analysis results in an easily understandable way.

[0068] The present invention is a system that enables autonomous and efficient work execution in farm management. The system consists of multiple sensor means, computing means, and execution means.

[0069] The server collects environmental data such as temperature, humidity, and soil nutrient status through sensor devices placed throughout the farm. These sensors include various thermometers, hygrometers, and soil sensors. The server centrally manages the data from the sensors and analyzes it using a generative AI model. This generative model takes historical data and weather forecasts into consideration and is used to develop optimal work plans for farming.

[0070] As a concrete example, the server generates a task that prioritizes watering immediately if the soil moisture falls below a certain standard. This plan, along with the work schedule, is assigned to the execution device via the distribution device.

[0071] The terminals receive specific work tasks from the server and autonomously perform tasks in their assigned areas. For example, a watering robot follows instructions from the server and sprays the necessary amount of water in areas with low humidity and dry conditions. The progress of the terminals and information on the completion of tasks are fed back to the server in real time.

[0072] Users can understand farm conditions in real time, reported in natural language by a generative model. For example, information such as "Watering is complete in Area A" or "Fertilization will be postponed due to rainfall forecast" is provided. Based on this information, users can give new instructions to the system.

[0073] An example of a prompt message is, "What is the highest priority task based on tomorrow's weather forecast?"

[0074] This invention enables efficient and effective farm management even with a small number of people, thereby supporting a stable food supply.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server collects environmental data from sensor devices placed on the farm. These sensors acquire data such as temperature, humidity, and soil nutrient levels, and transmit it to the server. Based on this input data, the server organizes and stores this information. Specifically, it periodically checks the sensor data and records it in a database.

[0078] Step 2:

[0079] The server performs real-time analysis using a generative AI model based on collected environmental data. It combines the environmental data as input with historical data and weather forecast data to process the data and generate an optimal work plan. As output, it generates specific tasks, such as "prioritize watering in area A" if humidity is low. In terms of specific actions, the AI ​​model detects anomalies and predicted fluctuations and formulates instructions accordingly.

[0080] Step 3:

[0081] The server sends the generated work plan to the execution device via the distribution device. Based on this output task, specific work instructions are sent to the terminal. As input, the current status of each execution device is considered, and a plan is developed to ensure efficient operation. As specific actions, the task schedule is adjusted, optimized instructions are sent to the execution device, and confirmation is performed.

[0082] Step 4:

[0083] The terminal autonomously performs tasks within the farm according to the instructions it receives. In the case of a watering robot, it starts watering based on the input task, "sprinkle a specified amount of water in area A." Output includes information on the progress and completion of the work. Specifically, it adjusts its movement path and selects appropriate tools to carry out the work efficiently.

[0084] Step 5:

[0085] The server collects work progress information sent from terminals and analyzes the data through a generated AI model. It visualizes overall progress and generates the next work instructions as needed. Specifically, this includes reviewing real-time data and revising the plan as appropriate.

[0086] Step 6:

[0087] Users monitor real-time work status provided by the server and input new instructions as needed. For example, they can issue instructions to respond to sudden weather changes. Specifically, they might input prompts such as "Delay fertilization in preparation for tomorrow's rain forecast," which are then reflected in the system.

[0088] (Application Example 1)

[0089] 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."

[0090] Modern industrial production demands the creation of efficient and flexible workflows. However, the real-time monitoring of each process and the difficulty in dynamically assigning tasks limit productivity improvements. Furthermore, particularly in manufacturing environments, there are many situations requiring rapid responses, and human resources alone are insufficient to address these challenges.

[0091] 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.

[0092] In this invention, the server includes data collection means, information processing means, autonomous work means, user interface means, and information exchange means. This enables real-time monitoring of the situation at each process in the factory, automatic generation of optimal work plans using generative models, and dynamic task assignment. Furthermore, instructions and immediate notifications using a natural language interface enable rapid response on site.

[0093] "Environmental information" refers to physical or chemical data obtained from various sensors involved in the production process.

[0094] "Data collection means" refers to a device or system for acquiring and aggregating environmental information from sensors installed within a factory.

[0095] "Information processing means" refers to a device or system that uses collected data for analysis and constructs an optimal work plan through a generative model.

[0096] A "generative model" is an artificial intelligence model used to generate the optimal workflow using collected environmental information as input.

[0097] An "autonomous work device" is a device or robot that automatically performs various tasks within a factory based on a work flow constructed using a generative model.

[0098] A "user interface" is an interface that allows a human user to interact with a system and obtain information or give instructions.

[0099] An "information exchange means" is a communication system for transmitting data quickly and efficiently between an information processing means and an autonomous work means.

[0100] "Natural language" refers to the language that humans use on a daily basis, and is used for users to interact with systems intuitively.

[0101] To implement this invention, the server, terminals, and users each play their respective roles and operate the entire system. First, the server collects data from various environmental sensors within the factory. Specifically, IoT devices such as Raspberry Pi and Arduino are used for this purpose. This data includes environmental information such as temperature, humidity, and vibration, and is transmitted to the server via MQTT or HTTP protocols.

[0102] The server executes information processing programs developed in Python or R and analyzes collected data using generative AI models such as TENSORFLOW® and PyTorch. This model generates the optimal production flow in real time and constructs work instructions. An example of a specific prompt message would be in the format of "Humidity sensor data: threshold exceeded, predicted weather: rain, what is the recommended work?".

[0103] Furthermore, the generated work instructions are transmitted to autonomous work devices such as factory robots and work terminals. Based on these instructions, the robots dynamically perform tasks. This enables specific actions, such as automatically activating the ventilation system when excessive humidity is detected.

[0104] Users receive natural language reports from the system through a smartphone application or web interface. This user interface, developed with Flutter® and React Native, offers intuitive operation. This allows users to send new instructions to the system as needed, enabling real-time changes and optimizations.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server collects data from various sensors within the factory. This includes obtaining environmental information such as temperature, humidity, and vibration via Raspberry Pi or Arduino. It receives data transmitted from each sensor device using MQTT or HTTP protocols as input, and temporarily stores this data on the server. The output is an integrated dataset for analysis.

[0108] Step 2:

[0109] The server inputs the collected data into a generative AI model and begins analysis in real time. The input includes pre-stored historical data, as well as external predicted weather information. The generative AI model uses TensorFlow and PyTorch to generate the optimal workflow based on the data. The output consists of prompt messages such as "Humidity sensor data: threshold exceeded, predicted weather: rain, what is the recommended action?" and corresponding specific action instructions.

[0110] Step 3:

[0111] The server transmits the generated work instructions to the appropriate autonomous work terminal. This involves information exchange via communication with the work terminal. The input is a work plan based on the output of the generative model, and the output is specific task information in a format understandable to the terminal. The terminal receives this information and performs the corresponding physical task (e.g., activating a ventilation system or operating equipment).

[0112] Step 4:

[0113] Users receive reports from the server in natural language via a smartphone application or web interface. Inputs are work progress and alert information sent from the server. Outputs are reports in a user-friendly text format. This allows users to understand the real-time status of the factory and send new instructions to the system as needed.

[0114] 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.

[0115] This invention relates to a system for achieving efficient and autonomous farm management in agriculture, and in particular, by taking user emotions into consideration, it enables interactive and flexible farm operation. Embodiments of this system are described below.

[0116] This system primarily consists of a sensor device, a computing device, an execution device, and a communication device. A key feature of this invention is that the computing device is equipped with an emotion recognition engine.

[0117] The server first collects environmental information in real time from sensor devices placed on the farm. This includes basic environmental data such as temperature, humidity, and soil condition. Furthermore, the server improves forecast accuracy by obtaining weather forecast data from external sources.

[0118] Based on the collected data, a generative model on the server analyzes the farm's situation in detail and generates an appropriate workflow. During this process, an emotion engine considers the user's emotional state and adjusts the workflow content and reporting tone accordingly. For example, if a user is feeling stressed, more considerate language can be used when explaining work instructions.

[0119] The generated workflow is transmitted from the server to each execution device on the farm. The terminals (robots and automated devices) faithfully execute the instructed tasks and provide feedback on progress and completion status to the server. For example, a terminal instructed to "complete watering within the next two hours" will proceed with the task while choosing the most efficient route.

[0120] Users gain real-time insights into their farm's status through reports from the server. These reports are customized by the emotion engine based on the user's state. For example, if a user is satisfied, the report will include positive feedback about the results achieved. In this way, the emotion engine also supports appropriate responses based on the user's emotions.

[0121] Thus, the system of the present invention enhances the efficiency and flexibility of farm operations by integrating the collection and analysis of environmental data and the execution of work instructions, while also recognizing the user's emotions and responding appropriately.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server collects environmental information in real time from sensor devices placed on the farm. This information includes temperature, humidity, and soil nutrient status. In addition, the server obtains external weather forecast data via the internet.

[0125] Step 2:

[0126] The server analyzes environmental information and weather forecast data it has collected. Here, a generative model is used to analyze the data and check the growth status of crops and the necessary farming tasks. For example, if soil dryness is observed, regular watering is instructed.

[0127] Step 3:

[0128] The emotion engine on the server analyzes the user's emotional state using emotion recognition technology. Based on the voice and input the user makes through the interface, it determines the user's current emotions.

[0129] Step 4:

[0130] The server-generated workflow is adjusted based on the results of the emotion engine. If the user is experiencing stress, the work instructions can be adjusted to be more relaxing and include positive language.

[0131] Step 5:

[0132] The server transmits the coordinated workflow to the execution device. The terminal receives these instructions and operates autonomously within the farm, performing the specified task. For example, it might water a designated area with the appropriate amount of water.

[0133] Step 6:

[0134] After the terminal completes a task, it feeds back completion information to the server. This feedback allows the server to update its information for issuing the next task instruction.

[0135] Step 7:

[0136] The server uses an emotion engine to report the latest farm status and work progress to the user in natural language. This report is tailored to the user's emotional state and presented in a more considerate manner. For example, it may include positive feedback on achievements.

[0137] (Example 2)

[0138] 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".

[0139] In modern agriculture, there is a need to create work plans that respond quickly to changes in environmental conditions and to carry out work autonomously. However, current systems have challenges in considering user emotional states during interaction and in efficiently allocating tasks autonomously. This leads to problems such as decreased efficiency in farm management and reduced user satisfaction.

[0140] 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.

[0141] In this invention, the server includes means for using a detection device to acquire environmental data, means for analyzing the information to generate a work plan and considering the user's emotional state using an emotion engine, and implementation devices for carrying out farm work and providing progress feedback. This enables rapid response to environmental conditions and flexible farm management that takes user emotions into consideration.

[0142] A "detection device" refers to sensors or devices used to acquire environmental data, specifically devices that collect information such as temperature, humidity, and soil moisture in real time.

[0143] A "processing unit" is a computer system that analyzes acquired data and generates a work plan while taking into account the user's emotional state.

[0144] A "generative model" is a software algorithm that operates within a computing unit and automatically creates an optimal work plan based on collected data.

[0145] The "emotion engine" is a special component that analyzes the user's emotional state and adjusts work plans and reports based on that analysis.

[0146] An "implementation device" is an autonomous device or robot that performs actual farm work based on a generated work plan and provides feedback on the results.

[0147] A "transmission device" is a communication infrastructure for rapidly sending and receiving data between a computing device and an execution device.

[0148] This invention is a system for efficiently and autonomously performing tasks in farm management, and in particular, by taking user emotions into consideration, it enables highly convenient operation.

[0149] The server acquires environmental data in real time using multiple sensing devices placed on the farm. These devices include temperature sensors, humidity sensors, and soil moisture sensors, allowing for a detailed understanding of the farm's conditions. The server also acquires weather forecast data from external services to supplement the environmental information.

[0150] The acquired data is analyzed by a computing unit located within the server. This computing unit is equipped with a generative model that integrates historical data and predictive information to automatically generate an optimal work plan. This work plan is then adjusted according to the user's needs and circumstances by an emotion engine specifically designed to analyze the user's emotional state.

[0151] The generated work plan is sent to a terminal (implementation device). The terminal autonomously executes tasks on the farm based on the work instructions. For example, if it receives the instruction "Complete watering by 2 PM," the terminal will select the most efficient route and complete the task. In this process, the terminal feeds back the progress of the work to the server to ensure proper execution of the task.

[0152] Users can stay informed about the farm's status in real time through reports from the server. The emotion engine customizes the reports the user receives, adjusting the tone and content according to the user's emotional state. For example, if the user is stressed, the report can be softened.

[0153] For example, when a user is feeling stressed, the system can report that "Today's work is progressing smoothly and on schedule." An example of a prompt message would be "Generate an encouraging report for the user."

[0154] Thus, the system of the present invention integrates data collection, analysis, execution, and reporting to users, and particularly enhances user interaction through an emotion engine, thereby supporting the efficient operation of farms.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The server receives environmental data from the detection device. Input data includes temperature, humidity, and soil moisture information. This data is collected in real time and stored in a database. An organized environmental dataset is generated as output. This data is used as preparation for subsequent analysis.

[0158] Step 2:

[0159] The server retrieves weather forecast data from external services. The input is weather information via an API, obtaining weather data such as temperature forecasts, precipitation forecasts, and wind speed. This data is integrated with collected environmental data. The output is an integrated environmental and weather dataset, enabling more accurate situational analysis.

[0160] Step 3:

[0161] The server's computing units analyze the integrated dataset using corresponding generative AI models. The previously integrated environmental and weather data are used as input, and data calculations evaluate the current state of the farm and the necessary work. The output is the generation of an optimal work plan. Here, the generative AI model analyzes data patterns and provides insights to determine the necessary work.

[0162] Step 4:

[0163] An emotion engine integrated into the computing unit adjusts the work plan considering the user's emotional state. Inputs include the previously created work plan and the user's emotional state obtained through the user interface. The output is the adjusted work plan, taking emotions into consideration. Specifically, if the user experiences stress, considerations such as reducing the workload are taken.

[0164] Step 5:

[0165] The terminal receives the adjusted work plan and begins autonomous work on the farm. The input to the terminal includes the work plan created in the previous step. The output is the progress of the specified task. For example, the terminal moves to a designated area to water it and reports its progress to the server each time it completes an activity.

[0166] Step 6:

[0167] The user receives reports from the server and checks the progress of the farm. The user is provided with a refined report from the server as input. The output is an improved understanding for the user and further interaction with the system based on feedback. Specific actions include the user reading the report and deciding on their next course of action.

[0168] (Application Example 2)

[0169] 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".

[0170] In recent years, while automation in factories has advanced, it has become recognized that the emotional state of human workers significantly impacts productivity and work efficiency. However, conventional automation systems have difficulty adjusting work schedules and managing workloads while considering workers' emotions, making it challenging to maintain a suitable working environment. Therefore, there is a need for a system that can simultaneously improve productivity and worker satisfaction by understanding workers' emotional states and flexibly adjusting work content.

[0171] 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.

[0172] In this invention, the server includes a measurement means for acquiring environmental information, a calculation means for generating a work schedule, and an emotion recognition means for evaluating the emotional state of the worker and adjusting the work schedule. This makes it possible to adjust the work schedule and optimize the reporting content according to the emotional state of the worker.

[0173] A "measuring device" is a device that acquires information such as the temperature and humidity of the environment and the operating status of the equipment.

[0174] The "computational means" is a processor that generates a work schedule based on acquired environmental information and the emotional state of the workers.

[0175] "Execution means" refers to a device or system that performs work based on a schedule generated by a calculation means.

[0176] "Communication means" refers to interfaces or protocols for high-speed data transmission between computing means and execution means.

[0177] An "emotion recognition tool" is a module that analyzes data such as a worker's facial expressions and voice to evaluate their psychological state.

[0178] The system based on this invention consists of various measuring means, calculation means, execution means, communication means, and emotion recognition means. Specific embodiments thereof are described below.

[0179] 1. Measurement means

[0180] The server uses a Raspberry Pi to measure temperature and humidity inside the factory. This sensor data is used to monitor environmental conditions.

[0181] 2. Means of calculation

[0182] The server runs an emotion recognition model using a Python program and the TensorFlow library. This model uses facial expression data captured by a camera to evaluate the emotional state of employees. The computational system has the ability to generate an optimal work schedule based on this data and adjust its content according to the psychological state of the workers.

[0183] 3. Emotion recognition means

[0184] As an emotion recognition tool, the server performs image processing and analyzes facial expressions captured by the camera. This analysis evaluates the worker's stress and satisfaction levels, and the results are fed back into adjusting the work schedule.

[0185] 4. Execution Methods

[0186] The terminal receives the generated work schedule and performs the designated tasks at the appropriate times. This may include self-driving robots such as Roomba.

[0187] 5. Means of communication

[0188] The MQTT protocol is used for communication, enabling high-speed, real-time data exchange between the server and the terminal.

[0189] When the server receives data from a temperature sensor, it can take control actions such as instructing the cooling system to operate in response to temperature fluctuations. Furthermore, if it determines that a worker is fatigued, it can send a gentle instruction such as "Let's take a short break," thereby simultaneously improving worker productivity and providing psychological support. An example of this prompt might be: "Based on the current work situation and emotional analysis, create appropriate instructions for the worker. Worker A is fatigued."

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server uses a Raspberry Pi to collect environmental data from temperature and humidity sensors within the factory. It receives numerical data transmitted from the sensors as input and monitors this data in real time to understand environmental changes.

[0193] Step 2:

[0194] The server uses a camera sensor to capture the worker's facial expressions and acquires the image data. The image data obtained from the camera is passed to an emotion recognition model using TensorFlow for emotion analysis. The output is quantified as the worker's emotional state (e.g., stress, satisfaction).

[0195] Step 3:

[0196] The server generates a work schedule using computational methods based on collected environmental and emotional data. It provides prompts based on environmental information and emotional states as input to an AI model, which then outputs work instructions that take into account the psychological burden on the worker.

[0197] Step 4:

[0198] The server sends the generated work schedule and instructions to the terminal using the MQTT protocol. It receives the work schedule as input and runs to provide appropriate instructions to the terminal as output.

[0199] Step 5:

[0200] The terminal controls self-propelled robots and other automated devices as means of execution, according to the received work schedule. It receives instructions from the server as input, performs the work, and provides feedback to the server as output regarding the progress and completion status of the work.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This invention relates to a system designed to achieve autonomous and efficient farm management in the agricultural sector. Specific embodiments of the invention are as follows:

[0218] This system consists primarily of multiple sensor devices placed throughout the farm and a computing device for processing the environmental information obtained from these sensors. The computing device is equipped with a generative model, which analyzes the environmental information in real time and dynamically generates the workflow within the farm.

[0219] The server collects environmental information transmitted from sensor devices placed on the farm, such as temperature, humidity, and soil nutrient status. The server quickly processes this information and uses a generative model to plan the workflow necessary for farm management. In doing so, it also takes historical data and predicted weather information into consideration, enabling more accurate planning.

[0220] Based on the generated workflow, the server assigns specific tasks to multiple execution devices, or robots, deployed throughout the farm. Examples of such instructions include, "Since rain is predicted for tomorrow, complete watering today."

[0221] The terminal receives these instructions and operates autonomously within the farm, performing its assigned tasks. For example, if the terminal is a watering robot, it will spray the required amount of water in the designated area. Progress and completion information of the work are sent to the server in real time, and the next work instructions are generated.

[0222] Furthermore, the system of the present invention has the function of reporting the farm status to the user in natural language using a generative model. This allows the user to understand the farm status in real time and to give the system correction instructions or new instructions as needed.

[0223] Thus, the present invention achieves increased efficiency and autonomy in farm management by constructing a system consisting of a sensor device, a computing device, an execution device, and a communication device. This system supports farmers in effectively carrying out agricultural work with a small number of people and contributes to a stable food supply.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The server collects environmental information in real time from sensor devices installed on the farm. This information includes temperature, humidity, and soil nutrient status. The server also obtains weather forecast data via the internet.

[0227] Step 2:

[0228] The server analyzes collected environmental information and weather forecast data. Using a generative model, it uses this data to predict crop conditions and growth, and identify necessary agricultural tasks. For example, if soil moisture is insufficient, it will determine that watering is necessary.

[0229] Step 3:

[0230] Based on the analysis results, the server generates a specific workflow and assigns tasks to each execution device. During this process, it considers the terminal's location and operating status to determine the most efficient route and procedure.

[0231] Step 4:

[0232] The terminal performs designated farm tasks based on work instructions received from the server. For example, it might move to a designated area and spray the required amount of water. The progress of the work is constantly fed back to the server.

[0233] Step 5:

[0234] Once the task is complete, the terminal sends a completion report to the server. The server re-evaluates the newly updated environment information and, if necessary, generates the next workflow.

[0235] Step 6:

[0236] The server reports the latest farm status and work progress to the user in natural language. The user can then use this information to give further instructions or make adjustments, enabling immediate response and decision-making.

[0237] (Example 1)

[0238] 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."

[0239] While there is a need to improve efficiency and autonomy in farm management, conventional methods present challenges in achieving optimal overall management because data collection and analysis from sensors, as well as the execution of tasks, are carried out individually. Furthermore, real-time situation monitoring and dynamic task allocation are difficult, leading to decreased work efficiency.

[0240] 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.

[0241] In this invention, the server includes sensor means for acquiring environmental data, computation means including a generative model, and execution means for performing tasks. This enables real-time, efficient analysis of environmental information, generation of optimal work plans, and dynamic execution of tasks.

[0242] "Environmental data" refers to data that indicates external environmental conditions in an agricultural field, such as temperature, humidity, and soil nutrient status.

[0243] "Sensing means" refers to devices and equipment installed on farms to acquire environmental data, and includes thermometers, hygrometers, soil sensors, etc.

[0244] A "generative model" is an algorithm or program that analyzes collected data to generate an efficient work plan.

[0245] "Computational means" refers to devices such as computers and servers that execute generative models and perform data analysis and generate work plans.

[0246] "Execution means" refers to devices and equipment that perform specific agricultural tasks based on work plans generated by calculation means, and includes robots and work machines.

[0247] "Communication means" refers to devices and technologies for rapidly transmitting data between computing means and execution means, and includes wireless communication and network connections.

[0248] A "distribution means" is a function that instructs the execution device to perform specific tasks based on the generated work plan.

[0249] "Natural language" refers to the language that humans use on a daily basis, and the language that computational systems use to report analysis results in an easily understandable way.

[0250] The present invention is a system that enables autonomous and efficient work execution in farm management. The system consists of multiple sensor means, computing means, and execution means.

[0251] The server collects environmental data such as temperature, humidity, and soil nutrient status through sensor devices placed throughout the farm. These sensors include various thermometers, hygrometers, and soil sensors. The server centrally manages the data from the sensors and analyzes it using a generative AI model. This generative model takes historical data and weather forecasts into consideration and is used to develop optimal work plans for farming.

[0252] As a concrete example, the server generates a task that prioritizes watering immediately if the soil moisture falls below a certain standard. This plan, along with the work schedule, is assigned to the execution device via the distribution device.

[0253] The terminals receive specific work tasks from the server and autonomously perform tasks in their assigned areas. For example, a watering robot follows instructions from the server and sprays the necessary amount of water in areas with low humidity and dry conditions. The progress of the terminals and information on the completion of tasks are fed back to the server in real time.

[0254] Users can understand farm conditions in real time, reported in natural language by a generative model. For example, information such as "Watering is complete in Area A" or "Fertilization will be postponed due to rainfall forecast" is provided. Based on this information, users can give new instructions to the system.

[0255] An example of a prompt message is, "What is the highest priority task based on tomorrow's weather forecast?"

[0256] This invention enables efficient and effective farm management even with a small number of people, thereby supporting a stable food supply.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The server collects environmental data from sensor devices placed on the farm. These sensors acquire data such as temperature, humidity, and soil nutrient levels, and transmit it to the server. Based on this input data, the server organizes and stores this information. Specifically, it periodically checks the sensor data and records it in a database.

[0260] Step 2:

[0261] The server performs real-time analysis using a generative AI model based on collected environmental data. It combines the environmental data as input with historical data and weather forecast data to process the data and generate an optimal work plan. As output, it generates specific tasks, such as "prioritize watering in area A" if humidity is low. In terms of specific actions, the AI ​​model detects anomalies and predicted fluctuations and formulates instructions accordingly.

[0262] Step 3:

[0263] The server sends the generated work plan to the execution device via the distribution device. Based on this output task, specific work instructions are sent to the terminal. As input, the current status of each execution device is considered, and a plan is developed to ensure efficient operation. As specific actions, the task schedule is adjusted, optimized instructions are sent to the execution device, and confirmation is performed.

[0264] Step 4:

[0265] The terminal autonomously performs tasks within the farm according to the instructions it receives. In the case of a watering robot, it starts watering based on the input task, "sprinkle a specified amount of water in area A." Output includes information on the progress and completion of the work. Specifically, it adjusts its movement path and selects appropriate tools to carry out the work efficiently.

[0266] Step 5:

[0267] The server collects work progress information sent from terminals and analyzes the data through a generated AI model. It visualizes overall progress and generates the next work instructions as needed. Specifically, this includes reviewing real-time data and revising the plan as appropriate.

[0268] Step 6:

[0269] Users monitor real-time work status provided by the server and input new instructions as needed. For example, they can issue instructions to respond to sudden weather changes. Specifically, they might input prompts such as "Delay fertilization in preparation for tomorrow's rain forecast," which are then reflected in the system.

[0270] (Application Example 1)

[0271] 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."

[0272] Modern industrial production demands the creation of efficient and flexible workflows. However, the real-time monitoring of each process and the difficulty in dynamically assigning tasks limit productivity improvements. Furthermore, particularly in manufacturing environments, there are many situations requiring rapid responses, and human resources alone are insufficient to address these challenges.

[0273] 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.

[0274] In this invention, the server includes data collection means, information processing means, autonomous work means, user interface means, and information exchange means. This enables real-time monitoring of the situation at each process in the factory, automatic generation of optimal work plans using generative models, and dynamic task assignment. Furthermore, instructions and immediate notifications using a natural language interface enable rapid response on site.

[0275] "Environmental information" refers to physical or chemical data obtained from various sensors involved in the production process.

[0276] "Data collection means" refers to a device or system for acquiring and aggregating environmental information from sensors installed within a factory.

[0277] "Information processing means" refers to a device or system that uses collected data for analysis and constructs an optimal work plan through a generative model.

[0278] A "generative model" is an artificial intelligence model used to generate the optimal workflow using collected environmental information as input.

[0279] An "autonomous work device" is a device or robot that automatically performs various tasks within a factory based on a work flow constructed using a generative model.

[0280] "User interface means" refers to an interface for a human user to interact with a system, obtain information, or give instructions.

[0281] "Information exchange means" refers to a communication system for efficiently transmitting data between information processing means and autonomous work means at high speed.

[0282] "Natural language" refers to the language that humans use in daily life and is used for users to intuitively interact with the system.

[0283] To implement this invention, a server, a terminal, and a user each play their respective roles to operate the entire system. First, the server aggregates data from each environmental sensor in the factory. As specific hardware, IoT devices such as Raspberry Pi and Arduino are used. This data includes environmental information such as temperature, humidity, and vibration and is sent to the server via MQTT or HTTP protocols.

[0284] The server executes an information processing program developed in Python or R and analyzes the collected data using a generative AI model such as TensorFlow or PyTorch. This model generates an optimal production flow in real time and constructs work instructions. As an example of a specific prompt sentence, a format such as "Humidity sensor data: Threshold exceeded, Predicted weather: Rain, Recommended work content?" can be considered.

[0285] Furthermore, the generated work instructions are sent to factory robots or work terminals, which are autonomous work means. Based on this instruction, the robot dynamically executes the task. This enables specific actions such as automatically starting the ventilation system when excessive humidity is detected.

[0286] The user receives natural language reports from the system through a smartphone application or a web interface. This user interface is developed with Flutter or React Native and enables intuitive operations. As a result, the user can send new instructions to the system as needed and make changes and optimizations in real time.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server collects data from each sensor in the factory. At this time, environmental information such as temperature, humidity, and vibration is obtained via Raspberry Pi or Arduino. As input, it receives data transmitted from each sensor device using the MQTT or HTTP protocol and temporarily stores this on the server. The output is an integrated dataset for use in analysis.

[0290] Step 2:

[0291] The server inputs the collected data into the generated AI model and starts analysis in real time. The input includes past data accumulated in advance and combines external predicted weather information. The generated AI model uses TensorFlow or PyTorch and generates an optimal workflow based on the data. The output is a prompt sentence such as "Humidity sensor data: exceeding the threshold, predicted weather: rain, what are the recommended work contents?" and the corresponding specific work instructions.

[0292] Step 3:

[0293] The server transmits the generated work instructions to the appropriate autonomous work terminal. This involves information exchange via communication with the work terminal. The input is a work plan based on the output of the generative model, and the output is specific task information in a format understandable to the terminal. The terminal receives this information and performs the corresponding physical task (e.g., activating a ventilation system or operating equipment).

[0294] Step 4:

[0295] Users receive reports from the server in natural language via a smartphone application or web interface. Inputs are work progress and alert information sent from the server. Outputs are reports in a user-friendly text format. This allows users to understand the real-time status of the factory and send new instructions to the system as needed.

[0296] 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.

[0297] This invention relates to a system for achieving efficient and autonomous farm management in agriculture, and in particular, by taking user emotions into consideration, it enables interactive and flexible farm operation. Embodiments of this system are described below.

[0298] This system primarily consists of a sensor device, a computing device, an execution device, and a communication device. A key feature of this invention is that the computing device is equipped with an emotion recognition engine.

[0299] The server first collects environmental information in real time from sensor devices placed on the farm. This includes basic environmental data such as temperature, humidity, and soil condition. Furthermore, the server improves forecast accuracy by obtaining weather forecast data from external sources.

[0300] Based on the obtained data, the generation model in the server analyzes the situation of the farm in detail and generates an appropriate work flow. At this time, the emotion engine takes into account the user's emotional state and adjusts the content of the work flow and the tone of the report. For example, when the user is feeling stressed, a more considerate expression can be chosen when explaining the work instructions.

[0301] The generated work flow is transmitted from the server to each execution device in the farm. The terminals (robots and automatic devices) faithfully execute the instructed work and feedback the progress and completion status to the server. For example, a terminal that has received an instruction to "complete watering within the next two hours" proceeds with the work while choosing an efficient route.

[0302] The user grasps the situation of the farm in real time through the report from the server. This report is customized according to the user's state by the emotion engine. As a specific example, when the user is satisfied, a report including positive feedback on the achieved results is made. In this way, the emotion engine also supports appropriate responses according to the user's emotions.

[0303] In this way, the system of the present invention integrates the collection, analysis, and execution of work instructions of environmental data, recognizes the user's emotions, and makes appropriate responses, thereby improving the efficiency and flexibility of farm operation.

[0304] The following describes the processing flow.

[0305] Step 1:

[0306] The server collects environmental information in real time from the sensor devices arranged on the farm. This information includes temperature, humidity, soil nutrient status, etc. In addition, the server obtains external weather forecast data via the Internet.

[0307] Step 2:

[0308] The server analyzes environmental information and weather forecast data it has collected. Here, a generative model is used to analyze the data and check the growth status of crops and the necessary farming tasks. For example, if soil dryness is observed, regular watering is instructed.

[0309] Step 3:

[0310] The emotion engine on the server analyzes the user's emotional state using emotion recognition technology. Based on the voice and input the user makes through the interface, it determines the user's current emotions.

[0311] Step 4:

[0312] The server-generated workflow is adjusted based on the results of the emotion engine. If the user is experiencing stress, the work instructions can be adjusted to be more relaxing and include positive language.

[0313] Step 5:

[0314] The server transmits the coordinated workflow to the execution device. The terminal receives these instructions and operates autonomously within the farm, performing the specified task. For example, it might water a designated area with the appropriate amount of water.

[0315] Step 6:

[0316] After the terminal completes a task, it feeds back completion information to the server. This feedback allows the server to update its information for issuing the next task instruction.

[0317] Step 7:

[0318] The server uses an emotion engine to report the latest farm status and work progress to the user in natural language. This report is tailored to the user's emotional state and presented in a more considerate manner. For example, it may include positive feedback on achievements.

[0319] (Example 2)

[0320] 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".

[0321] In modern agriculture, there is a need to create work plans that respond quickly to changes in environmental conditions and to carry out work autonomously. However, current systems have challenges in considering user emotional states during interaction and in efficiently allocating tasks autonomously. This leads to problems such as decreased efficiency in farm management and reduced user satisfaction.

[0322] 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.

[0323] In this invention, the server includes means for using a detection device to acquire environmental data, means for analyzing the information to generate a work plan and considering the user's emotional state using an emotion engine, and implementation devices for carrying out farm work and providing progress feedback. This enables rapid response to environmental conditions and flexible farm management that takes user emotions into consideration.

[0324] A "detection device" refers to sensors or devices used to acquire environmental data, specifically devices that collect information such as temperature, humidity, and soil moisture in real time.

[0325] A "processing unit" is a computer system that analyzes acquired data and generates a work plan while taking into account the user's emotional state.

[0326] A "generative model" is a software algorithm that operates within a computing unit and automatically creates an optimal work plan based on collected data.

[0327] The "emotion engine" is a special component that analyzes the user's emotional state and adjusts work plans and reports based on that analysis.

[0328] An "implementation device" is an autonomous device or robot that performs actual farm work based on a generated work plan and provides feedback on the results.

[0329] A "transmission device" is a communication infrastructure for rapidly sending and receiving data between a computing device and an execution device.

[0330] This invention is a system for efficiently and autonomously performing tasks in farm management, and in particular, by taking user emotions into consideration, it enables highly convenient operation.

[0331] The server acquires environmental data in real time using multiple sensing devices placed on the farm. These devices include temperature sensors, humidity sensors, and soil moisture sensors, allowing for a detailed understanding of the farm's conditions. The server also acquires weather forecast data from external services to supplement the environmental information.

[0332] The acquired data is analyzed by a computing unit located within the server. This computing unit is equipped with a generative model that integrates historical data and predictive information to automatically generate an optimal work plan. This work plan is then adjusted according to the user's needs and circumstances by an emotion engine specifically designed to analyze the user's emotional state.

[0333] The generated work plan is sent to a terminal (implementation device). The terminal autonomously executes tasks on the farm based on the work instructions. For example, if it receives the instruction "Complete watering by 2 PM," the terminal will select the most efficient route and complete the task. In this process, the terminal feeds back the progress of the work to the server to ensure proper execution of the task.

[0334] Users can stay informed about the farm's status in real time through reports from the server. The emotion engine customizes the reports the user receives, adjusting the tone and content according to the user's emotional state. For example, if the user is stressed, the report can be softened.

[0335] For example, when a user is feeling stressed, the system can report that "Today's work is progressing smoothly and on schedule." An example of a prompt message would be "Generate an encouraging report for the user."

[0336] Thus, the system of the present invention integrates data collection, analysis, execution, and reporting to users, and particularly enhances user interaction through an emotion engine, thereby supporting the efficient operation of farms.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The server receives environmental data from the detection device. Input data includes temperature, humidity, and soil moisture information. This data is collected in real time and stored in a database. An organized environmental dataset is generated as output. This data is used as preparation for subsequent analysis.

[0340] Step 2:

[0341] The server retrieves weather forecast data from external services. The input is weather information via an API, obtaining weather data such as temperature forecasts, precipitation forecasts, and wind speed. This data is integrated with collected environmental data. The output is an integrated environmental and weather dataset, enabling more accurate situational analysis.

[0342] Step 3:

[0343] The server's computing units analyze the integrated dataset using corresponding generative AI models. The previously integrated environmental and weather data are used as input, and data calculations evaluate the current state of the farm and the necessary work. The output is the generation of an optimal work plan. Here, the generative AI model analyzes data patterns and provides insights to determine the necessary work.

[0344] Step 4:

[0345] An emotion engine integrated into the computing unit adjusts the work plan considering the user's emotional state. Inputs include the previously created work plan and the user's emotional state obtained through the user interface. The output is the adjusted work plan, taking emotions into consideration. Specifically, if the user experiences stress, considerations such as reducing the workload are taken.

[0346] Step 5:

[0347] The terminal receives the adjusted work plan and begins autonomous work on the farm. The input to the terminal includes the work plan created in the previous step. The output is the progress of the specified task. For example, the terminal moves to a designated area to water it and reports its progress to the server each time it completes an activity.

[0348] Step 6:

[0349] The user receives reports from the server and checks the progress of the farm. The user is provided with a refined report from the server as input. The output is an improved understanding for the user and further interaction with the system based on feedback. Specific actions include the user reading the report and deciding on their next course of action.

[0350] (Application Example 2)

[0351] 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."

[0352] In recent years, while automation in factories has advanced, it has become recognized that the emotional state of human workers significantly impacts productivity and work efficiency. However, conventional automation systems have difficulty adjusting work schedules and managing workloads while considering workers' emotions, making it challenging to maintain a suitable working environment. Therefore, there is a need for a system that can simultaneously improve productivity and worker satisfaction by understanding workers' emotional states and flexibly adjusting work content.

[0353] 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.

[0354] In this invention, the server includes a measurement means for acquiring environmental information, a calculation means for generating a work schedule, and an emotion recognition means for evaluating the emotional state of the worker and adjusting the work schedule. This makes it possible to adjust the work schedule and optimize the reporting content according to the emotional state of the worker.

[0355] A "measuring device" is a device that acquires information such as the temperature and humidity of the environment and the operating status of the equipment.

[0356] The "computational means" is a processor that generates a work schedule based on acquired environmental information and the emotional state of the workers.

[0357] "Execution means" refers to a device or system that performs work based on a schedule generated by a calculation means.

[0358] "Communication means" refers to interfaces or protocols for high-speed data transmission between computing means and execution means.

[0359] An "emotion recognition tool" is a module that analyzes data such as a worker's facial expressions and voice to evaluate their psychological state.

[0360] The system based on this invention consists of various measuring means, calculation means, execution means, communication means, and emotion recognition means. Specific embodiments thereof are described below.

[0361] 1. Measurement means

[0362] The server uses a Raspberry Pi to measure temperature and humidity inside the factory. This sensor data is used to monitor environmental conditions.

[0363] 2. Means of calculation

[0364] The server runs an emotion recognition model using a Python program and the TensorFlow library. This model uses facial expression data captured by a camera to evaluate the emotional state of employees. The computational system has the ability to generate an optimal work schedule based on this data and adjust its content according to the psychological state of the workers.

[0365] 3. Emotion recognition means

[0366] As an emotion recognition tool, the server performs image processing and analyzes facial expressions captured by the camera. This analysis evaluates the worker's stress and satisfaction levels, and the results are fed back into adjusting the work schedule.

[0367] 4. Execution Methods

[0368] The terminal receives the generated work schedule and performs the designated tasks at the appropriate times. This may include self-driving robots such as Roomba.

[0369] 5. Means of communication

[0370] The MQTT protocol is used for communication, enabling high-speed, real-time data exchange between the server and the terminal.

[0371] When the server receives data from a temperature sensor, it can take control actions such as instructing the cooling system to operate in response to temperature fluctuations. Furthermore, if it determines that a worker is fatigued, it can send a gentle instruction such as "Let's take a short break," thereby simultaneously improving worker productivity and providing psychological support. An example of this prompt might be: "Based on the current work situation and emotional analysis, create appropriate instructions for the worker. Worker A is fatigued."

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The server uses a Raspberry Pi to collect environmental data from temperature and humidity sensors within the factory. It receives numerical data transmitted from the sensors as input and monitors this data in real time to understand environmental changes.

[0375] Step 2:

[0376] The server uses a camera sensor to capture the worker's facial expressions and acquires the image data. The image data obtained from the camera is passed to an emotion recognition model using TensorFlow for emotion analysis. The output is quantified as the worker's emotional state (e.g., stress, satisfaction).

[0377] Step 3:

[0378] The server generates a work schedule using computational methods based on collected environmental and emotional data. It provides prompts based on environmental information and emotional states as input to an AI model, which then outputs work instructions that take into account the psychological burden on the worker.

[0379] Step 4:

[0380] The server sends the generated work schedule and instructions to the terminal using the MQTT protocol. It receives the work schedule as input and runs to provide appropriate instructions to the terminal as output.

[0381] Step 5:

[0382] The terminal controls self-propelled robots and other automated devices as means of execution, according to the received work schedule. It receives instructions from the server as input, performs the work, and provides feedback to the server as output regarding the progress and completion status of the work.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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".

[0399] This invention relates to a system designed to achieve autonomous and efficient farm management in the agricultural sector. Specific embodiments of the invention are as follows:

[0400] This system consists primarily of multiple sensor devices placed throughout the farm and a computing device for processing the environmental information obtained from these sensors. The computing device is equipped with a generative model, which analyzes the environmental information in real time and dynamically generates the workflow within the farm.

[0401] The server collects environmental information transmitted from sensor devices placed on the farm, such as temperature, humidity, and soil nutrient status. The server quickly processes this information and uses a generative model to plan the workflow necessary for farm management. In doing so, it also takes historical data and predicted weather information into consideration, enabling more accurate planning.

[0402] Based on the generated workflow, the server assigns specific tasks to multiple execution devices, or robots, deployed throughout the farm. Examples of such instructions include, "Since rain is predicted for tomorrow, complete watering today."

[0403] The terminal receives these instructions and operates autonomously within the farm, performing its assigned tasks. For example, if the terminal is a watering robot, it will spray the required amount of water in the designated area. Progress and completion information of the work are sent to the server in real time, and the next work instructions are generated.

[0404] Furthermore, the system of the present invention has the function of reporting the farm status to the user in natural language using a generative model. This allows the user to understand the farm status in real time and to give the system correction instructions or new instructions as needed.

[0405] Thus, the present invention achieves increased efficiency and autonomy in farm management by constructing a system consisting of a sensor device, a computing device, an execution device, and a communication device. This system supports farmers in effectively carrying out agricultural work with a small number of people and contributes to a stable food supply.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The server collects environmental information in real time from sensor devices installed on the farm. This information includes temperature, humidity, and soil nutrient status. The server also obtains weather forecast data via the internet.

[0409] Step 2:

[0410] The server analyzes collected environmental information and weather forecast data. Using a generative model, it uses this data to predict crop conditions and growth, and identify necessary agricultural tasks. For example, if soil moisture is insufficient, it will determine that watering is necessary.

[0411] Step 3:

[0412] Based on the analysis results, the server generates a specific workflow and assigns tasks to each execution device. During this process, it considers the terminal's location and operating status to determine the most efficient route and procedure.

[0413] Step 4:

[0414] The terminal performs designated farm tasks based on work instructions received from the server. For example, it might move to a designated area and spray the required amount of water. The progress of the work is constantly fed back to the server.

[0415] Step 5:

[0416] Once the task is complete, the terminal sends a completion report to the server. The server re-evaluates the newly updated environment information and, if necessary, generates the next workflow.

[0417] Step 6:

[0418] The server reports the latest farm status and work progress to the user in natural language. The user can then use this information to give further instructions or make adjustments, enabling immediate response and decision-making.

[0419] (Example 1)

[0420] 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."

[0421] While there is a need to improve efficiency and autonomy in farm management, conventional methods present challenges in achieving optimal overall management because data collection and analysis from sensors, as well as the execution of tasks, are carried out individually. Furthermore, real-time situation monitoring and dynamic task allocation are difficult, leading to decreased work efficiency.

[0422] 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.

[0423] In this invention, the server includes sensor means for acquiring environmental data, computation means including a generative model, and execution means for performing tasks. This enables real-time, efficient analysis of environmental information, generation of optimal work plans, and dynamic execution of tasks.

[0424] "Environmental data" refers to data that indicates external environmental conditions in an agricultural field, such as temperature, humidity, and soil nutrient status.

[0425] "Sensing means" refers to devices and equipment installed on farms to acquire environmental data, and includes thermometers, hygrometers, soil sensors, etc.

[0426] A "generative model" is an algorithm or program that analyzes collected data to generate an efficient work plan.

[0427] "Computational means" refers to devices such as computers and servers that execute generative models and perform data analysis and generate work plans.

[0428] "Execution means" refers to devices and equipment that perform specific agricultural tasks based on work plans generated by calculation means, and includes robots and work machines.

[0429] "Communication means" refers to devices and technologies for rapidly transmitting data between computing means and execution means, and includes wireless communication and network connections.

[0430] A "distribution means" is a function that instructs the execution device to perform specific tasks based on the generated work plan.

[0431] "Natural language" refers to the language that humans use on a daily basis, and the language that computational systems use to report analysis results in an easily understandable way.

[0432] The present invention is a system that enables autonomous and efficient work execution in farm management. The system consists of multiple sensor means, computing means, and execution means.

[0433] The server collects environmental data such as temperature, humidity, and soil nutrient status through sensor devices placed throughout the farm. These sensors include various thermometers, hygrometers, and soil sensors. The server centrally manages the data from the sensors and analyzes it using a generative AI model. This generative model takes historical data and weather forecasts into consideration and is used to develop optimal work plans for farming.

[0434] As a concrete example, the server generates a task that prioritizes watering immediately if the soil moisture falls below a certain standard. This plan, along with the work schedule, is assigned to the execution device via the distribution device.

[0435] The terminals receive specific work tasks from the server and autonomously perform tasks in their assigned areas. For example, a watering robot follows instructions from the server and sprays the necessary amount of water in areas with low humidity and dry conditions. The progress of the terminals and information on the completion of tasks are fed back to the server in real time.

[0436] Users can understand farm conditions in real time, reported in natural language by a generative model. For example, information such as "Watering is complete in Area A" or "Fertilization will be postponed due to rainfall forecast" is provided. Based on this information, users can give new instructions to the system.

[0437] An example of a prompt message is, "What is the highest priority task based on tomorrow's weather forecast?"

[0438] This invention enables efficient and effective farm management even with a small number of people, thereby supporting a stable food supply.

[0439] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0440] Step 1:

[0441] The server collects environmental data from sensor devices placed on the farm. These sensors acquire data such as temperature, humidity, and soil nutrient levels, and transmit it to the server. Based on this input data, the server organizes and stores this information. Specifically, it periodically checks the sensor data and records it in a database.

[0442] Step 2:

[0443] The server performs real-time analysis using a generative AI model based on collected environmental data. It combines the environmental data as input with historical data and weather forecast data to process the data and generate an optimal work plan. As output, it generates specific tasks, such as "prioritize watering in area A" if humidity is low. In terms of specific actions, the AI ​​model detects anomalies and predicted fluctuations and formulates instructions accordingly.

[0444] Step 3:

[0445] The server sends the generated work plan to the execution device via the distribution device. Based on this output task, specific work instructions are sent to the terminal. As input, the current status of each execution device is considered, and a plan is developed to ensure efficient operation. As specific actions, the task schedule is adjusted, optimized instructions are sent to the execution device, and confirmation is performed.

[0446] Step 4:

[0447] The terminal autonomously performs tasks within the farm according to the instructions it receives. In the case of a watering robot, it starts watering based on the input task, "sprinkle a specified amount of water in area A." Output includes information on the progress and completion of the work. Specifically, it adjusts its movement path and selects appropriate tools to carry out the work efficiently.

[0448] Step 5:

[0449] The server collects work progress information sent from terminals and analyzes the data through a generated AI model. It visualizes overall progress and generates the next work instructions as needed. Specifically, this includes reviewing real-time data and revising the plan as appropriate.

[0450] Step 6:

[0451] Users monitor real-time work status provided by the server and input new instructions as needed. For example, they can issue instructions to respond to sudden weather changes. Specifically, they might input prompts such as "Delay fertilization in preparation for tomorrow's rain forecast," which are then reflected in the system.

[0452] (Application Example 1)

[0453] 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."

[0454] Modern industrial production demands the creation of efficient and flexible workflows. However, the real-time monitoring of each process and the difficulty in dynamically assigning tasks limit productivity improvements. Furthermore, particularly in manufacturing environments, there are many situations requiring rapid responses, and human resources alone are insufficient to address these challenges.

[0455] 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.

[0456] In this invention, the server includes data collection means, information processing means, autonomous work means, user interface means, and information exchange means. This enables real-time monitoring of the situation at each process in the factory, automatic generation of optimal work plans using generative models, and dynamic task assignment. Furthermore, instructions and immediate notifications using a natural language interface enable rapid response on site.

[0457] "Environmental information" refers to physical or chemical data obtained from various sensors involved in the production process.

[0458] "Data collection means" refers to a device or system for acquiring and aggregating environmental information from sensors installed within a factory.

[0459] "Information processing means" refers to a device or system that uses collected data for analysis and constructs an optimal work plan through a generative model.

[0460] A "generative model" is an artificial intelligence model used to generate the optimal workflow using collected environmental information as input.

[0461] An "autonomous work device" is a device or robot that automatically performs various tasks within a factory based on a work flow constructed using a generative model.

[0462] A "user interface" is an interface that allows a human user to interact with a system and obtain information or give instructions.

[0463] An "information exchange means" is a communication system for transmitting data quickly and efficiently between an information processing means and an autonomous work means.

[0464] "Natural language" refers to the language that humans use on a daily basis, and is used for users to interact with systems intuitively.

[0465] To implement this invention, the server, terminals, and users each play their respective roles and operate the entire system. First, the server collects data from various environmental sensors within the factory. Specifically, IoT devices such as Raspberry Pi and Arduino are used for this purpose. This data includes environmental information such as temperature, humidity, and vibration, and is transmitted to the server via MQTT or HTTP protocols.

[0466] The server executes information processing programs developed in Python or R and analyzes collected data using generative AI models such as TensorFlow and PyTorch. This model generates the optimal production flow in real time and constructs work instructions. An example of a specific prompt message would be in the format of "Humidity sensor data: threshold exceeded, predicted weather: rain, what is the recommended work?".

[0467] Furthermore, the generated work instructions are transmitted to autonomous work devices such as factory robots and work terminals. Based on these instructions, the robots dynamically perform tasks. This enables specific actions, such as automatically activating the ventilation system when excessive humidity is detected.

[0468] Users receive natural language reports from the system through a smartphone application or web interface. This user interface, developed with Flutter and React Native, is intuitive to use. This allows users to send new instructions to the system as needed, enabling real-time changes and optimizations.

[0469] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0470] Step 1:

[0471] The server collects data from various sensors within the factory. This includes obtaining environmental information such as temperature, humidity, and vibration via Raspberry Pi or Arduino. It receives data transmitted from each sensor device using MQTT or HTTP protocols as input, and temporarily stores this data on the server. The output is an integrated dataset for analysis.

[0472] Step 2:

[0473] The server inputs the collected data into a generative AI model and begins analysis in real time. The input includes pre-stored historical data, as well as external predicted weather information. The generative AI model uses TensorFlow and PyTorch to generate the optimal workflow based on the data. The output consists of prompt messages such as "Humidity sensor data: threshold exceeded, predicted weather: rain, what is the recommended action?" and corresponding specific action instructions.

[0474] Step 3:

[0475] The server transmits the generated work instructions to the appropriate autonomous work terminal. This involves information exchange via communication with the work terminal. The input is a work plan based on the output of the generative model, and the output is specific task information in a format understandable to the terminal. The terminal receives this information and performs the corresponding physical task (e.g., activating a ventilation system or operating equipment).

[0476] Step 4:

[0477] Users receive reports from the server in natural language via a smartphone application or web interface. Inputs are work progress and alert information sent from the server. Outputs are reports in a user-friendly text format. This allows users to understand the real-time status of the factory and send new instructions to the system as needed.

[0478] 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.

[0479] This invention relates to a system for achieving efficient and autonomous farm management in agriculture, and in particular, by taking user emotions into consideration, it enables interactive and flexible farm operation. Embodiments of this system are described below.

[0480] This system primarily consists of a sensor device, a computing device, an execution device, and a communication device. A key feature of this invention is that the computing device is equipped with an emotion recognition engine.

[0481] The server first collects environmental information in real time from sensor devices placed on the farm. This includes basic environmental data such as temperature, humidity, and soil condition. Furthermore, the server improves forecast accuracy by obtaining weather forecast data from external sources.

[0482] Based on the collected data, a generative model on the server analyzes the farm's situation in detail and generates an appropriate workflow. During this process, an emotion engine considers the user's emotional state and adjusts the workflow content and reporting tone accordingly. For example, if a user is feeling stressed, more considerate language can be used when explaining work instructions.

[0483] The generated workflow is transmitted from the server to each execution device on the farm. The terminals (robots and automated devices) faithfully execute the instructed tasks and provide feedback on progress and completion status to the server. For example, a terminal instructed to "complete watering within the next two hours" will proceed with the task while choosing the most efficient route.

[0484] Users gain real-time insights into their farm's status through reports from the server. These reports are customized by the emotion engine based on the user's state. For example, if a user is satisfied, the report will include positive feedback about the results achieved. In this way, the emotion engine also supports appropriate responses based on the user's emotions.

[0485] Thus, the system of the present invention enhances the efficiency and flexibility of farm operations by integrating the collection and analysis of environmental data and the execution of work instructions, while also recognizing the user's emotions and responding appropriately.

[0486] The following describes the processing flow.

[0487] Step 1:

[0488] The server collects environmental information in real time from sensor devices placed on the farm. This information includes temperature, humidity, and soil nutrient status. In addition, the server obtains external weather forecast data via the internet.

[0489] Step 2:

[0490] The server analyzes environmental information and weather forecast data it has collected. Here, a generative model is used to analyze the data and check the growth status of crops and the necessary farming tasks. For example, if soil dryness is observed, regular watering is instructed.

[0491] Step 3:

[0492] The emotion engine on the server analyzes the user's emotional state using emotion recognition technology. Based on the voice and input the user makes through the interface, it determines the user's current emotions.

[0493] Step 4:

[0494] The server-generated workflow is adjusted based on the results of the emotion engine. If the user is experiencing stress, the work instructions can be adjusted to be more relaxing and include positive language.

[0495] Step 5:

[0496] The server transmits the coordinated workflow to the execution device. The terminal receives these instructions and operates autonomously within the farm, performing the specified task. For example, it might water a designated area with the appropriate amount of water.

[0497] Step 6:

[0498] After the terminal completes a task, it feeds back completion information to the server. This feedback allows the server to update its information for issuing the next task instruction.

[0499] Step 7:

[0500] The server uses an emotion engine to report the latest farm status and work progress to the user in natural language. This report is tailored to the user's emotional state and presented in a more considerate manner. For example, it may include positive feedback on achievements.

[0501] (Example 2)

[0502] 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."

[0503] In modern agriculture, there is a need to create work plans that respond quickly to changes in environmental conditions and to carry out work autonomously. However, current systems have challenges in considering user emotional states during interaction and in efficiently allocating tasks autonomously. This leads to problems such as decreased efficiency in farm management and reduced user satisfaction.

[0504] 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.

[0505] In this invention, the server includes means for using a detection device to acquire environmental data, means for analyzing the information to generate a work plan and considering the user's emotional state using an emotion engine, and implementation devices for carrying out farm work and providing progress feedback. This enables rapid response to environmental conditions and flexible farm management that takes user emotions into consideration.

[0506] A "detection device" refers to sensors or devices used to acquire environmental data, specifically devices that collect information such as temperature, humidity, and soil moisture in real time.

[0507] A "processing unit" is a computer system that analyzes acquired data and generates a work plan while taking into account the user's emotional state.

[0508] A "generative model" is a software algorithm that operates within a computing unit and automatically creates an optimal work plan based on collected data.

[0509] The "emotion engine" is a special component that analyzes the user's emotional state and adjusts work plans and reports based on that analysis.

[0510] An "implementation device" is an autonomous device or robot that performs actual farm work based on a generated work plan and provides feedback on the results.

[0511] A "transmission device" is a communication infrastructure for rapidly sending and receiving data between a computing device and an execution device.

[0512] This invention is a system for efficiently and autonomously performing tasks in farm management, and in particular, by taking user emotions into consideration, it enables highly convenient operation.

[0513] The server acquires environmental data in real time using multiple sensing devices placed on the farm. These devices include temperature sensors, humidity sensors, and soil moisture sensors, allowing for a detailed understanding of the farm's conditions. The server also acquires weather forecast data from external services to supplement the environmental information.

[0514] The acquired data is analyzed by a computing unit located within the server. This computing unit is equipped with a generative model that integrates historical data and predictive information to automatically generate an optimal work plan. This work plan is then adjusted according to the user's needs and circumstances by an emotion engine specifically designed to analyze the user's emotional state.

[0515] The generated work plan is sent to a terminal (implementation device). The terminal autonomously executes tasks on the farm based on the work instructions. For example, if it receives the instruction "Complete watering by 2 PM," the terminal will select the most efficient route and complete the task. In this process, the terminal feeds back the progress of the work to the server to ensure proper execution of the task.

[0516] Users can stay informed about the farm's status in real time through reports from the server. The emotion engine customizes the reports the user receives, adjusting the tone and content according to the user's emotional state. For example, if the user is stressed, the report can be softened.

[0517] For example, when a user is feeling stressed, the system can report that "Today's work is progressing smoothly and on schedule." An example of a prompt message would be "Generate an encouraging report for the user."

[0518] Thus, the system of the present invention integrates data collection, analysis, execution, and reporting to users, and particularly enhances user interaction through an emotion engine, thereby supporting the efficient operation of farms.

[0519] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0520] Step 1:

[0521] The server receives environmental data from the detection device. Input data includes temperature, humidity, and soil moisture information. This data is collected in real time and stored in a database. An organized environmental dataset is generated as output. This data is used as preparation for subsequent analysis.

[0522] Step 2:

[0523] The server retrieves weather forecast data from external services. The input is weather information via an API, obtaining weather data such as temperature forecasts, precipitation forecasts, and wind speed. This data is integrated with collected environmental data. The output is an integrated environmental and weather dataset, enabling more accurate situational analysis.

[0524] Step 3:

[0525] The server's computing units analyze the integrated dataset using corresponding generative AI models. The previously integrated environmental and weather data are used as input, and data calculations evaluate the current state of the farm and the necessary work. The output is the generation of an optimal work plan. Here, the generative AI model analyzes data patterns and provides insights to determine the necessary work.

[0526] Step 4:

[0527] An emotion engine integrated into the computing unit adjusts the work plan considering the user's emotional state. Inputs include the previously created work plan and the user's emotional state obtained through the user interface. The output is the adjusted work plan, taking emotions into consideration. Specifically, if the user experiences stress, considerations such as reducing the workload are taken.

[0528] Step 5:

[0529] The terminal receives the adjusted work plan and begins autonomous work on the farm. The input to the terminal includes the work plan created in the previous step. The output is the progress of the specified task. For example, the terminal moves to a designated area to water it and reports its progress to the server each time it completes an activity.

[0530] Step 6:

[0531] The user receives reports from the server and checks the progress of the farm. The user is provided with a refined report from the server as input. The output is an improved understanding for the user and further interaction with the system based on feedback. Specific actions include the user reading the report and deciding on their next course of action.

[0532] (Application Example 2)

[0533] 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."

[0534] In recent years, while automation in factories has advanced, it has become recognized that the emotional state of human workers significantly impacts productivity and work efficiency. However, conventional automation systems have difficulty adjusting work schedules and managing workloads while considering workers' emotions, making it challenging to maintain a suitable working environment. Therefore, there is a need for a system that can simultaneously improve productivity and worker satisfaction by understanding workers' emotional states and flexibly adjusting work content.

[0535] 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.

[0536] In this invention, the server includes a measurement means for acquiring environmental information, a calculation means for generating a work schedule, and an emotion recognition means for evaluating the emotional state of the worker and adjusting the work schedule. This makes it possible to adjust the work schedule and optimize the reporting content according to the emotional state of the worker.

[0537] A "measuring device" is a device that acquires information such as the temperature and humidity of the environment and the operating status of the equipment.

[0538] The "computational means" is a processor that generates a work schedule based on acquired environmental information and the emotional state of the workers.

[0539] "Execution means" refers to a device or system that performs work based on a schedule generated by a calculation means.

[0540] "Communication means" refers to interfaces or protocols for high-speed data transmission between computing means and execution means.

[0541] An "emotion recognition tool" is a module that analyzes data such as a worker's facial expressions and voice to evaluate their psychological state.

[0542] The system based on this invention consists of various measuring means, calculation means, execution means, communication means, and emotion recognition means. Specific embodiments thereof are described below.

[0543] 1. Measurement means

[0544] The server uses a Raspberry Pi to measure temperature and humidity inside the factory. This sensor data is used to monitor environmental conditions.

[0545] 2. Means of calculation

[0546] The server runs an emotion recognition model using a Python program and the TensorFlow library. This model uses facial expression data captured by a camera to evaluate the emotional state of employees. The computational system has the ability to generate an optimal work schedule based on this data and adjust its content according to the psychological state of the workers.

[0547] 3. Emotion recognition means

[0548] As an emotion recognition tool, the server performs image processing and analyzes facial expressions captured by the camera. This analysis evaluates the worker's stress and satisfaction levels, and the results are fed back into adjusting the work schedule.

[0549] 4. Execution Methods

[0550] The terminal receives the generated work schedule and performs the designated tasks at the appropriate times. This may include self-driving robots such as Roomba.

[0551] 5. Means of communication

[0552] The MQTT protocol is used for communication, enabling high-speed, real-time data exchange between the server and the terminal.

[0553] When the server receives data from a temperature sensor, it can take control actions such as instructing the cooling system to operate in response to temperature fluctuations. Furthermore, if it determines that a worker is fatigued, it can send a gentle instruction such as "Let's take a short break," thereby simultaneously improving worker productivity and providing psychological support. An example of this prompt might be: "Based on the current work situation and emotional analysis, create appropriate instructions for the worker. Worker A is fatigued."

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] The server uses a Raspberry Pi to collect environmental data from temperature and humidity sensors within the factory. It receives numerical data transmitted from the sensors as input and monitors this data in real time to understand environmental changes.

[0557] Step 2:

[0558] The server uses a camera sensor to capture the worker's facial expressions and acquires the image data. The image data obtained from the camera is passed to an emotion recognition model using TensorFlow for emotion analysis. The output is quantified as the worker's emotional state (e.g., stress, satisfaction).

[0559] Step 3:

[0560] The server generates a work schedule using computational methods based on collected environmental and emotional data. It provides prompts based on environmental information and emotional states as input to an AI model, which then outputs work instructions that take into account the psychological burden on the worker.

[0561] Step 4:

[0562] The server sends the generated work schedule and instructions to the terminal using the MQTT protocol. It receives the work schedule as input and runs to provide appropriate instructions to the terminal as output.

[0563] Step 5:

[0564] The terminal controls self-propelled robots and other automated devices as means of execution, according to the received work schedule. It receives instructions from the server as input, performs the work, and provides feedback to the server as output regarding the progress and completion status of the work.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] [Fourth Embodiment]

[0569] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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".

[0582] This invention relates to a system designed to achieve autonomous and efficient farm management in the agricultural sector. Specific embodiments of the invention are as follows:

[0583] This system consists primarily of multiple sensor devices placed throughout the farm and a computing device for processing the environmental information obtained from these sensors. The computing device is equipped with a generative model, which analyzes the environmental information in real time and dynamically generates the workflow within the farm.

[0584] The server collects environmental information transmitted from sensor devices placed on the farm, such as temperature, humidity, and soil nutrient status. The server quickly processes this information and uses a generative model to plan the workflow necessary for farm management. In doing so, it also takes historical data and predicted weather information into consideration, enabling more accurate planning.

[0585] Based on the generated workflow, the server assigns specific tasks to multiple execution devices, or robots, deployed throughout the farm. Examples of such instructions include, "Since rain is predicted for tomorrow, complete watering today."

[0586] The terminal receives these instructions and operates autonomously within the farm, performing its assigned tasks. For example, if the terminal is a watering robot, it will spray the required amount of water in the designated area. Progress and completion information of the work are sent to the server in real time, and the next work instructions are generated.

[0587] Furthermore, the system of the present invention has the function of reporting the farm status to the user in natural language using a generative model. This allows the user to understand the farm status in real time and to give the system correction instructions or new instructions as needed.

[0588] Thus, the present invention achieves increased efficiency and autonomy in farm management by constructing a system consisting of a sensor device, a computing device, an execution device, and a communication device. This system supports farmers in effectively carrying out agricultural work with a small number of people and contributes to a stable food supply.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The server collects environmental information in real time from sensor devices installed on the farm. This information includes temperature, humidity, and soil nutrient status. The server also obtains weather forecast data via the internet.

[0592] Step 2:

[0593] The server analyzes collected environmental information and weather forecast data. Using a generative model, it uses this data to predict crop conditions and growth, and identify necessary agricultural tasks. For example, if soil moisture is insufficient, it will determine that watering is necessary.

[0594] Step 3:

[0595] Based on the analysis results, the server generates a specific workflow and assigns tasks to each execution device. During this process, it considers the terminal's location and operating status to determine the most efficient route and procedure.

[0596] Step 4:

[0597] The terminal performs designated farm tasks based on work instructions received from the server. For example, it might move to a designated area and spray the required amount of water. The progress of the work is constantly fed back to the server.

[0598] Step 5:

[0599] Once the task is complete, the terminal sends a completion report to the server. The server re-evaluates the newly updated environment information and, if necessary, generates the next workflow.

[0600] Step 6:

[0601] The server reports the latest farm status and work progress to the user in natural language. The user can then use this information to give further instructions or make adjustments, enabling immediate response and decision-making.

[0602] (Example 1)

[0603] 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".

[0604] While there is a need to improve efficiency and autonomy in farm management, conventional methods present challenges in achieving optimal overall management because data collection and analysis from sensors, as well as the execution of tasks, are carried out individually. Furthermore, real-time situation monitoring and dynamic task allocation are difficult, leading to decreased work efficiency.

[0605] 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.

[0606] In this invention, the server includes sensor means for acquiring environmental data, computation means including a generative model, and execution means for performing tasks. This enables real-time, efficient analysis of environmental information, generation of optimal work plans, and dynamic execution of tasks.

[0607] "Environmental data" refers to data that indicates external environmental conditions in an agricultural field, such as temperature, humidity, and soil nutrient status.

[0608] "Sensing means" refers to devices and equipment installed on farms to acquire environmental data, and includes thermometers, hygrometers, soil sensors, etc.

[0609] A "generative model" is an algorithm or program that analyzes collected data to generate an efficient work plan.

[0610] "Computational means" refers to devices such as computers and servers that execute generative models and perform data analysis and generate work plans.

[0611] "Execution means" refers to devices and equipment that perform specific agricultural tasks based on work plans generated by calculation means, and includes robots and work machines.

[0612] "Communication means" refers to devices and technologies for rapidly transmitting data between computing means and execution means, and includes wireless communication and network connections.

[0613] A "distribution means" is a function that instructs the execution device to perform specific tasks based on the generated work plan.

[0614] "Natural language" refers to the language that humans use on a daily basis, and the language that computational systems use to report analysis results in an easily understandable way.

[0615] The present invention is a system that enables autonomous and efficient work execution in farm management. The system consists of multiple sensor means, computing means, and execution means.

[0616] The server collects environmental data such as temperature, humidity, and soil nutrient status through sensor devices placed throughout the farm. These sensors include various thermometers, hygrometers, and soil sensors. The server centrally manages the data from the sensors and analyzes it using a generative AI model. This generative model takes historical data and weather forecasts into consideration and is used to develop optimal work plans for farming.

[0617] As a concrete example, the server generates a task that prioritizes watering immediately if the soil moisture falls below a certain standard. This plan, along with the work schedule, is assigned to the execution device via the distribution device.

[0618] The terminals receive specific work tasks from the server and autonomously perform tasks in their assigned areas. For example, a watering robot follows instructions from the server and sprays the necessary amount of water in areas with low humidity and dry conditions. The progress of the terminals and information on the completion of tasks are fed back to the server in real time.

[0619] Users can understand farm conditions in real time, reported in natural language by a generative model. For example, information such as "Watering is complete in Area A" or "Fertilization will be postponed due to rainfall forecast" is provided. Based on this information, users can give new instructions to the system.

[0620] An example of a prompt message is, "What is the highest priority task based on tomorrow's weather forecast?"

[0621] This invention enables efficient and effective farm management even with a small number of people, thereby supporting a stable food supply.

[0622] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0623] Step 1:

[0624] The server collects environmental data from sensor devices placed on the farm. These sensors acquire data such as temperature, humidity, and soil nutrient levels, and transmit it to the server. Based on this input data, the server organizes and stores this information. Specifically, it periodically checks the sensor data and records it in a database.

[0625] Step 2:

[0626] The server performs real-time analysis using a generative AI model based on collected environmental data. It combines the environmental data as input with historical data and weather forecast data to process the data and generate an optimal work plan. As output, it generates specific tasks, such as "prioritize watering in area A" if humidity is low. In terms of specific actions, the AI ​​model detects anomalies and predicted fluctuations and formulates instructions accordingly.

[0627] Step 3:

[0628] The server sends the generated work plan to the execution device via the distribution device. Based on this output task, specific work instructions are sent to the terminal. As input, the current status of each execution device is considered, and a plan is developed to ensure efficient operation. As specific actions, the task schedule is adjusted, optimized instructions are sent to the execution device, and confirmation is performed.

[0629] Step 4:

[0630] The terminal autonomously performs tasks within the farm according to the instructions it receives. In the case of a watering robot, it starts watering based on the input task, "sprinkle a specified amount of water in area A." Output includes information on the progress and completion of the work. Specifically, it adjusts its movement path and selects appropriate tools to carry out the work efficiently.

[0631] Step 5:

[0632] The server collects work progress information sent from terminals and analyzes the data through a generated AI model. It visualizes overall progress and generates the next work instructions as needed. Specifically, this includes reviewing real-time data and revising the plan as appropriate.

[0633] Step 6:

[0634] Users monitor real-time work status provided by the server and input new instructions as needed. For example, they can issue instructions to respond to sudden weather changes. Specifically, they might input prompts such as "Delay fertilization in preparation for tomorrow's rain forecast," which are then reflected in the system.

[0635] (Application Example 1)

[0636] 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".

[0637] Modern industrial production demands the creation of efficient and flexible workflows. However, the real-time monitoring of each process and the difficulty in dynamically assigning tasks limit productivity improvements. Furthermore, particularly in manufacturing environments, there are many situations requiring rapid responses, and human resources alone are insufficient to address these challenges.

[0638] 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.

[0639] In this invention, the server includes data collection means, information processing means, autonomous work means, user interface means, and information exchange means. This enables real-time monitoring of the situation at each process in the factory, automatic generation of optimal work plans using generative models, and dynamic task assignment. Furthermore, instructions and immediate notifications using a natural language interface enable rapid response on site.

[0640] "Environmental information" refers to physical or chemical data obtained from various sensors involved in the production process.

[0641] "Data collection means" refers to a device or system for acquiring and aggregating environmental information from sensors installed within a factory.

[0642] "Information processing means" refers to a device or system that uses collected data for analysis and constructs an optimal work plan through a generative model.

[0643] A "generative model" is an artificial intelligence model used to generate the optimal workflow using collected environmental information as input.

[0644] An "autonomous work device" is a device or robot that automatically performs various tasks within a factory based on a work flow constructed using a generative model.

[0645] A "user interface" is an interface that allows a human user to interact with a system and obtain information or give instructions.

[0646] An "information exchange means" is a communication system for transmitting data quickly and efficiently between an information processing means and an autonomous work means.

[0647] "Natural language" refers to the language that humans use on a daily basis, and is used for users to interact with systems intuitively.

[0648] To implement this invention, the server, terminals, and users each play their respective roles and operate the entire system. First, the server collects data from various environmental sensors within the factory. Specifically, IoT devices such as Raspberry Pi and Arduino are used for this purpose. This data includes environmental information such as temperature, humidity, and vibration, and is transmitted to the server via MQTT or HTTP protocols.

[0649] The server executes information processing programs developed in Python or R and analyzes collected data using generative AI models such as TensorFlow and PyTorch. This model generates the optimal production flow in real time and constructs work instructions. An example of a specific prompt message would be in the format of "Humidity sensor data: threshold exceeded, predicted weather: rain, what is the recommended work?".

[0650] Furthermore, the generated work instructions are transmitted to autonomous work devices such as factory robots and work terminals. Based on these instructions, the robots dynamically perform tasks. This enables specific actions, such as automatically activating the ventilation system when excessive humidity is detected.

[0651] Users receive natural language reports from the system through a smartphone application or web interface. This user interface, developed with Flutter and React Native, is intuitive to use. This allows users to send new instructions to the system as needed, enabling real-time changes and optimizations.

[0652] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0653] Step 1:

[0654] The server collects data from various sensors within the factory. This includes obtaining environmental information such as temperature, humidity, and vibration via Raspberry Pi or Arduino. It receives data transmitted from each sensor device using MQTT or HTTP protocols as input, and temporarily stores this data on the server. The output is an integrated dataset for analysis.

[0655] Step 2:

[0656] The server inputs the collected data into a generative AI model and begins analysis in real time. The input includes pre-stored historical data, as well as external predicted weather information. The generative AI model uses TensorFlow and PyTorch to generate the optimal workflow based on the data. The output consists of prompt messages such as "Humidity sensor data: threshold exceeded, predicted weather: rain, what is the recommended action?" and corresponding specific action instructions.

[0657] Step 3:

[0658] The server transmits the generated work instructions to the appropriate autonomous work terminal. This involves information exchange via communication with the work terminal. The input is a work plan based on the output of the generative model, and the output is specific task information in a format understandable to the terminal. The terminal receives this information and performs the corresponding physical task (e.g., activating a ventilation system or operating equipment).

[0659] Step 4:

[0660] Users receive reports from the server in natural language via a smartphone application or web interface. Inputs are work progress and alert information sent from the server. Outputs are reports in a user-friendly text format. This allows users to understand the real-time status of the factory and send new instructions to the system as needed.

[0661] 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.

[0662] This invention relates to a system for achieving efficient and autonomous farm management in agriculture, and in particular, by taking user emotions into consideration, it enables interactive and flexible farm operation. Embodiments of this system are described below.

[0663] This system primarily consists of a sensor device, a computing device, an execution device, and a communication device. A key feature of this invention is that the computing device is equipped with an emotion recognition engine.

[0664] The server first collects environmental information in real time from sensor devices placed on the farm. This includes basic environmental data such as temperature, humidity, and soil condition. Furthermore, the server improves forecast accuracy by obtaining weather forecast data from external sources.

[0665] Based on the collected data, a generative model on the server analyzes the farm's situation in detail and generates an appropriate workflow. During this process, an emotion engine considers the user's emotional state and adjusts the workflow content and reporting tone accordingly. For example, if a user is feeling stressed, more considerate language can be used when explaining work instructions.

[0666] The generated workflow is transmitted from the server to each execution device on the farm. The terminals (robots and automated devices) faithfully execute the instructed tasks and provide feedback on progress and completion status to the server. For example, a terminal instructed to "complete watering within the next two hours" will proceed with the task while choosing the most efficient route.

[0667] Users gain real-time insights into their farm's status through reports from the server. These reports are customized by the emotion engine based on the user's state. For example, if a user is satisfied, the report will include positive feedback about the results achieved. In this way, the emotion engine also supports appropriate responses based on the user's emotions.

[0668] Thus, the system of the present invention enhances the efficiency and flexibility of farm operations by integrating the collection and analysis of environmental data and the execution of work instructions, while also recognizing the user's emotions and responding appropriately.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The server collects environmental information in real time from sensor devices placed on the farm. This information includes temperature, humidity, and soil nutrient status. In addition, the server obtains external weather forecast data via the internet.

[0672] Step 2:

[0673] The server analyzes environmental information and weather forecast data it has collected. Here, a generative model is used to analyze the data and check the growth status of crops and the necessary farming tasks. For example, if soil dryness is observed, regular watering is instructed.

[0674] Step 3:

[0675] The emotion engine on the server analyzes the user's emotional state using emotion recognition technology. Based on the voice and input the user makes through the interface, it determines the user's current emotions.

[0676] Step 4:

[0677] The server-generated workflow is adjusted based on the results of the emotion engine. If the user is experiencing stress, the work instructions can be adjusted to be more relaxing and include positive language.

[0678] Step 5:

[0679] The server transmits the coordinated workflow to the execution device. The terminal receives these instructions and operates autonomously within the farm, performing the specified task. For example, it might water a designated area with the appropriate amount of water.

[0680] Step 6:

[0681] After the terminal completes a task, it feeds back completion information to the server. This feedback allows the server to update its information for issuing the next task instruction.

[0682] Step 7:

[0683] The server uses an emotion engine to report the latest farm status and work progress to the user in natural language. This report is tailored to the user's emotional state and presented in a more considerate manner. For example, it may include positive feedback on achievements.

[0684] (Example 2)

[0685] 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".

[0686] In modern agriculture, there is a need to create work plans that respond quickly to changes in environmental conditions and to carry out work autonomously. However, current systems have challenges in considering user emotional states during interaction and in efficiently allocating tasks autonomously. This leads to problems such as decreased efficiency in farm management and reduced user satisfaction.

[0687] 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.

[0688] In this invention, the server includes means for using a detection device to acquire environmental data, means for analyzing the information to generate a work plan and considering the user's emotional state using an emotion engine, and implementation devices for carrying out farm work and providing progress feedback. This enables rapid response to environmental conditions and flexible farm management that takes user emotions into consideration.

[0689] A "detection device" refers to sensors or devices used to acquire environmental data, specifically devices that collect information such as temperature, humidity, and soil moisture in real time.

[0690] A "processing unit" is a computer system that analyzes acquired data and generates a work plan while taking into account the user's emotional state.

[0691] A "generative model" is a software algorithm that operates within a computing unit and automatically creates an optimal work plan based on collected data.

[0692] The "emotion engine" is a special component that analyzes the user's emotional state and adjusts work plans and reports based on that analysis.

[0693] An "implementation device" is an autonomous device or robot that performs actual farm work based on a generated work plan and provides feedback on the results.

[0694] A "transmission device" is a communication infrastructure for rapidly sending and receiving data between a computing device and an execution device.

[0695] This invention is a system for efficiently and autonomously performing tasks in farm management, and in particular, by taking user emotions into consideration, it enables highly convenient operation.

[0696] The server acquires environmental data in real time using multiple sensing devices placed on the farm. These devices include temperature sensors, humidity sensors, and soil moisture sensors, allowing for a detailed understanding of the farm's conditions. The server also acquires weather forecast data from external services to supplement the environmental information.

[0697] The acquired data is analyzed by a computing unit located within the server. This computing unit is equipped with a generative model that integrates historical data and predictive information to automatically generate an optimal work plan. This work plan is then adjusted according to the user's needs and circumstances by an emotion engine specifically designed to analyze the user's emotional state.

[0698] The generated work plan is sent to a terminal (implementation device). The terminal autonomously executes tasks on the farm based on the work instructions. For example, if it receives the instruction "Complete watering by 2 PM," the terminal will select the most efficient route and complete the task. In this process, the terminal feeds back the progress of the work to the server to ensure proper execution of the task.

[0699] Users can stay informed about the farm's status in real time through reports from the server. The emotion engine customizes the reports the user receives, adjusting the tone and content according to the user's emotional state. For example, if the user is stressed, the report can be softened.

[0700] For example, when a user is feeling stressed, the system can report that "Today's work is progressing smoothly and on schedule." An example of a prompt message would be "Generate an encouraging report for the user."

[0701] Thus, the system of the present invention integrates data collection, analysis, execution, and reporting to users, and particularly enhances user interaction through an emotion engine, thereby supporting the efficient operation of farms.

[0702] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0703] Step 1:

[0704] The server receives environmental data from the detection device. Input data includes temperature, humidity, and soil moisture information. This data is collected in real time and stored in a database. An organized environmental dataset is generated as output. This data is used as preparation for subsequent analysis.

[0705] Step 2:

[0706] The server retrieves weather forecast data from external services. The input is weather information via an API, obtaining weather data such as temperature forecasts, precipitation forecasts, and wind speed. This data is integrated with collected environmental data. The output is an integrated environmental and weather dataset, enabling more accurate situational analysis.

[0707] Step 3:

[0708] The server's computing units analyze the integrated dataset using corresponding generative AI models. The previously integrated environmental and weather data are used as input, and data calculations evaluate the current state of the farm and the necessary work. The output is the generation of an optimal work plan. Here, the generative AI model analyzes data patterns and provides insights to determine the necessary work.

[0709] Step 4:

[0710] An emotion engine integrated into the computing unit adjusts the work plan considering the user's emotional state. Inputs include the previously created work plan and the user's emotional state obtained through the user interface. The output is the adjusted work plan, taking emotions into consideration. Specifically, if the user experiences stress, considerations such as reducing the workload are taken.

[0711] Step 5:

[0712] The terminal receives the adjusted work plan and begins autonomous work on the farm. The input to the terminal includes the work plan created in the previous step. The output is the progress of the specified task. For example, the terminal moves to a designated area to water it and reports its progress to the server each time it completes an activity.

[0713] Step 6:

[0714] The user receives reports from the server and checks the progress of the farm. The user is provided with a refined report from the server as input. The output is an improved understanding for the user and further interaction with the system based on feedback. Specific actions include the user reading the report and deciding on their next course of action.

[0715] (Application Example 2)

[0716] 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".

[0717] In recent years, while automation in factories has advanced, it has become recognized that the emotional state of human workers significantly impacts productivity and work efficiency. However, conventional automation systems have difficulty adjusting work schedules and managing workloads while considering workers' emotions, making it challenging to maintain a suitable working environment. Therefore, there is a need for a system that can simultaneously improve productivity and worker satisfaction by understanding workers' emotional states and flexibly adjusting work content.

[0718] 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.

[0719] In this invention, the server includes a measurement means for acquiring environmental information, a calculation means for generating a work schedule, and an emotion recognition means for evaluating the emotional state of the worker and adjusting the work schedule. This makes it possible to adjust the work schedule and optimize the reporting content according to the emotional state of the worker.

[0720] A "measuring device" is a device that acquires information such as the temperature and humidity of the environment and the operating status of the equipment.

[0721] The "computational means" is a processor that generates a work schedule based on acquired environmental information and the emotional state of the workers.

[0722] "Execution means" refers to a device or system that performs work based on a schedule generated by a calculation means.

[0723] "Communication means" refers to interfaces or protocols for high-speed data transmission between computing means and execution means.

[0724] An "emotion recognition tool" is a module that analyzes data such as a worker's facial expressions and voice to evaluate their psychological state.

[0725] The system based on this invention consists of various measuring means, calculation means, execution means, communication means, and emotion recognition means. Specific embodiments thereof are described below.

[0726] 1. Measurement means

[0727] The server uses a Raspberry Pi to measure temperature and humidity inside the factory. This sensor data is used to monitor environmental conditions.

[0728] 2. Means of calculation

[0729] The server runs an emotion recognition model using a Python program and the TensorFlow library. This model uses facial expression data captured by a camera to evaluate the emotional state of employees. The computational system has the ability to generate an optimal work schedule based on this data and adjust its content according to the psychological state of the workers.

[0730] 3. Emotion recognition means

[0731] As an emotion recognition tool, the server performs image processing and analyzes facial expressions captured by the camera. This analysis evaluates the worker's stress and satisfaction levels, and the results are fed back into adjusting the work schedule.

[0732] 4. Execution Methods

[0733] The terminal receives the generated work schedule and performs the designated tasks at the appropriate times. This may include self-driving robots such as Roomba.

[0734] 5. Means of communication

[0735] The MQTT protocol is used for communication, enabling high-speed, real-time data exchange between the server and the terminal.

[0736] When the server receives data from a temperature sensor, it can take control actions such as instructing the cooling system to operate in response to temperature fluctuations. Furthermore, if it determines that a worker is fatigued, it can send a gentle instruction such as "Let's take a short break," thereby simultaneously improving worker productivity and providing psychological support. An example of this prompt might be: "Based on the current work situation and emotional analysis, create appropriate instructions for the worker. Worker A is fatigued."

[0737] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0738] Step 1:

[0739] The server uses a Raspberry Pi to collect environmental data from temperature and humidity sensors within the factory. It receives numerical data transmitted from the sensors as input and monitors this data in real time to understand environmental changes.

[0740] Step 2:

[0741] The server uses a camera sensor to capture the worker's facial expressions and acquires the image data. The image data obtained from the camera is passed to an emotion recognition model using TensorFlow for emotion analysis. The output is quantified as the worker's emotional state (e.g., stress, satisfaction).

[0742] Step 3:

[0743] The server generates a work schedule using computational methods based on collected environmental and emotional data. It provides prompts based on environmental information and emotional states as input to an AI model, which then outputs work instructions that take into account the psychological burden on the worker.

[0744] Step 4:

[0745] The server sends the generated work schedule and instructions to the terminal using the MQTT protocol. It receives the work schedule as input and runs to provide appropriate instructions to the terminal as output.

[0746] Step 5:

[0747] The terminal controls self-propelled robots and other automated devices as means of execution, according to the received work schedule. It receives instructions from the server as input, performs the work, and provides feedback to the server as output regarding the progress and completion status of the work.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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."

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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 as being incorporated by reference.

[0769] The following is further disclosed regarding the embodiments described above.

[0770] (Claim 1)

[0771] Sensor devices for acquiring environmental information,

[0772] A computing device that includes a generative model for analyzing data from the aforementioned sensor device and generating a farm work flow,

[0773] An execution device for performing farm work based on the work flow generated by the aforementioned computing device,

[0774] A communication device for transmitting information between the aforementioned computing device and execution device at high speed,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, characterized in that the generative model has a function to report to the user using natural language.

[0778] (Claim 3)

[0779] The system according to claim 1, characterized in that the execution device has a function for dynamically allocating multiple tasks within the farm.

[0780] "Example 1"

[0781] (Claim 1)

[0782] Sensor means for acquiring environmental data,

[0783] A calculation means including a generation model for analyzing information from the aforementioned sensor means and generating a work plan,

[0784] Based on the work plan generated by the calculation means, an execution means for carrying out the work,

[0785] A communication means for rapidly transmitting data between the calculation means and the execution means,

[0786] A distribution method that distributes tasks based on the generated work plan,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, characterized in that the generative model has a function to report the situation to the user using natural language.

[0790] (Claim 3)

[0791] The system according to claim 1, characterized in that the execution means has a function for dynamically allocating multiple tasks.

[0792] "Application Example 1"

[0793] (Claim 1)

[0794] Data collection means for obtaining environmental information,

[0795] Information processing means including a generative model for analyzing information from the data collection means and generating a work plan for the production process,

[0796] An autonomous work means for executing a production process based on a work plan generated by the aforementioned information processing means,

[0797] Information exchange means for transmitting information between the information processing means and the autonomous work means at high speed,

[0798] A user interface means for monitoring the situation in real time and sending instructions,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, characterized in that the generative model has the function of providing reports and instructions using natural language and immediately notifying the progress status within the factory.

[0802] (Claim 3)

[0803] The system according to claim 1, characterized in that the autonomous work means has a function to dynamically allocate multiple processes within the production process.

[0804] "Example 2 of combining an emotion engine"

[0805] (Claim 1)

[0806] A detection device for acquiring environmental data,

[0807] A computing device including a generative model for analyzing information from the aforementioned detection device and generating a work plan, the computing device comprising an emotion engine that takes into account the user's emotional state,

[0808] An implementation device for carrying out agricultural work based on the work plan generated by the aforementioned computing device,

[0809] A transmission device for rapidly transmitting data between the aforementioned computing device and the implementation device,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, characterized in that the generative model provides a function to report to the user using natural language processing and adjusts the content of the report according to the user's emotional state.

[0813] (Claim 3)

[0814] The system according to claim 1, characterized in that the implementing device has a function to autonomously allocate multiple tasks within the farm and to feed back the progress to the computing device.

[0815] "Application example 2 when combining with an emotional engine"

[0816] (Claim 1)

[0817] Measurement means for acquiring environmental information,

[0818] A calculation means for analyzing data from the aforementioned measurement means and generating a work schedule,

[0819] An execution means for performing work based on the work schedule generated by the calculation means,

[0820] A communication means for transmitting information between the calculation means and the execution means at high speed,

[0821] An emotion recognition tool for evaluating the emotional state of workers and adjusting work schedules,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, characterized in that the calculation means has a function to report to the worker using natural language and adjust the content of the report according to the worker's emotional state.

[0825] (Claim 3)

[0826] The system according to claim 1, characterized in that the execution means has a function to dynamically assign multiple tasks and adjust the workload according to the emotional state of the worker. [Explanation of Symbols]

[0827] 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. Sensor devices for acquiring environmental information, A computing device that includes a generative model for analyzing data from the aforementioned sensor device and generating a farm work flow, An execution device for performing farm work based on the work flow generated by the aforementioned computing device, A communication device for transmitting information between the aforementioned computing device and execution device at high speed, A system that includes this.

2. The system according to claim 1, characterized in that the generative model has a function to report to the user using natural language.

3. The system according to claim 1, characterized in that the execution device has a function for dynamically allocating multiple tasks within the farm.

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