system

Abstract

We provide the system. [Solution] A means of collecting information on the ownership status of agricultural machinery in the region, the work schedules of each farmer, and the registration information of volunteers, A means for generating an optimal usage plan for agricultural machinery using an AI algorithm based on the collected information, A means of notifying each farmer's terminal of the generated usage plan, Means for monitoring the real-time usage status of agricultural machinery and volunteers, A system that includes a means to update the schedule and send another notification when usage status changes.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

[0006] "Region" refers to an area within a specific geographical range where agricultural activities are carried out.

[0007] "Agricultural machinery" refers to equipment and tools used to cultivate, harvest, or manage crops.

[0008] "Ownership status" refers to information about which farmers or organizations within a region own which agricultural machinery.

[0009] A "work schedule" refers to the specific dates, times, and tasks planned for carrying out agricultural activities.

[0010] "Volunteer" refers to an individual or group that offers to provide assistance to agricultural businesses free of charge.

[0011] "Registration information" refers to data provided by volunteers, such as their contact information, available dates and times, and willingness to help.

[0012] An "AI algorithm" refers to a computational method that uses artificial intelligence technology to process data and generate an optimal usage plan for agricultural machinery.

[0013] "Optimal usage plan" refers to instructions regarding time and location generated by AI to maximize the efficiency of agricultural machinery utilization.

[0014] "Terminal" refers to information devices such as computers and smartphones used by farmers.

[0015] "Real-time" refers to a state in which data related to ongoing events is immediately processed and monitored.

[0016] "Usage situation" refers to the situation in which agricultural machinery and volunteers are actually being used.

[0017] "Usage result" refers to the data and reports obtained after using agricultural machinery.

[0018] "Report" refers to a document summarizing aggregated data and analysis results created based on usage results.

[0019] "Feedback" refers to opinions and evaluations provided by users.

Brief Explanation of Drawings

[0020] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0022] First, the terms used in the following description will be described.

[0023] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] 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).

[0027] 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."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0038] 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.

[0039] 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.

[0040] 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".

[0041] This invention is implemented as an integrated management system utilizing AI technology, with the aim of promoting the efficient use of agricultural machinery and addressing labor shortages in regional agriculture. This system primarily consists of servers, terminals, and users, with each element working in conjunction to function. A specific embodiment of this system is described below.

[0042] First, the server retrieves information from multiple databases to collect data on machine ownership, farm work schedules, and volunteer registration information, then organizes and stores this data. The collected information is analyzed by an AI algorithm and becomes the foundational data for generating the optimal machine usage schedule for each farmer. The server uses this data to optimize the placement and utilization plan of agricultural machinery, creating plans that suit multiple users (farmers).

[0043] The generated schedule is sent to the terminal via the server. The terminal can be a smartphone or computer, allowing the user to check the schedule and receive detailed information about the device being used. Based on this information, the user can adjust the schedule to suit their own convenience.

[0044] During system operation, the server monitors the real-time usage of agricultural machinery and volunteers. This is achieved by tracking the location and usage of each machine via sensors and GPS devices. The server processes this information in real time, continuously checking whether things are progressing according to schedule. Furthermore, if there are any changes in usage, the server immediately recalculates the schedule and delivers the revised plan to the terminals. This ensures that machinery is used efficiently and enables effective agricultural activities.

[0045] As a concrete example, if a farmer wants to use a tractor, they send their request to the server via a terminal. The server checks the availability of tractors in the area in real time and suggests the optimal time and location for use. If the user accepts the suggestion, the system confirms the use of that tractor from that point onward and automatically adjusts notifications to other users.

[0046] Through this process, the present invention effectively supports the efficiency of regional agriculture, reduces the cost burden on farmers, and contributes to the realization of sustainable agriculture.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server collects information from a database regarding the ownership status of agricultural machinery, the work schedules of each farmer, and volunteer registration information, and organizes and stores it as up-to-date data.

[0050] Step 2:

[0051] The server analyzes the collected data using AI algorithms to generate an optimal utilization plan for agricultural machinery. This includes an efficient schedule that takes into account the machinery's availability and the farmers' usage preferences.

[0052] Step 3:

[0053] The server sends the generated usage plan to the terminal corresponding to each farmer. The terminal displays the received information via notification or application, allowing users to check their usage schedule.

[0054] Step 4:

[0055] The user checks the notified schedule using their device and, if necessary, sends a request to change the schedule to the server via their device.

[0056] Step 5:

[0057] The server receives the change request from the user, recalculates the schedule using the AI ​​algorithm again, and resends the adjusted plan to the terminal.

[0058] Step 6:

[0059] The server monitors the usage status of each agricultural machine in real time and constantly updates the data obtained from sensors and GPS devices.

[0060] Step 7:

[0061] When the server detects a change in usage, it immediately recalculates the schedule and notifies the terminal of the latest schedule, thereby reducing waste and maintaining efficient operation.

[0062] This processing flow is designed to optimize farmers' use of machinery and also address the problem of labor shortages.

[0063] (Example 1)

[0064] 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."

[0065] Currently, it is difficult to create and adjust optimal deployment and utilization plans for agricultural machinery in order to efficiently utilize agricultural machinery in regional agriculture and to solve the problem of labor shortages at individual farms. With conventional methods, it is difficult to efficiently manage information such as the operating status of agricultural machinery and the schedules of volunteers, and to quickly adjust plans to meet the needs of each farmer, resulting in inefficient agricultural operations.

[0066] 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.

[0067] In this invention, the server includes means for collecting information on the ownership status of agricultural machinery, work plans, and supporter registration information; calculation means for generating an optimal arrangement and utilization plan for machinery based on the collected information; and means for notifying each user's computer of the generated plan. This makes it possible to efficiently create an optimal agricultural machinery utilization plan that meets the needs of each farmer and to quickly recalculate in response to change requests.

[0068] "Agricultural machinery" is a general term for devices or vehicles used to cultivate land, sow seeds, harvest, or perform related agricultural tasks.

[0069] A "work plan" is a detailed plan outlining the types of agricultural work, the schedule, and the procedures to be carried out within a specific period.

[0070] "Supporter registration information" refers to information about individuals or organizations registered to support agricultural activities, and mainly includes data on volunteers and temporary laborers.

[0071] "Optimal allocation" refers to the efficient distribution and allocation of agricultural machinery and labor in order to make the most effective use of limited resources.

[0072] "User's computer" refers to a computer or mobile terminal used by farmers or agricultural personnel, which is a device for receiving, displaying, and operating agricultural machinery usage plans.

[0073] "Calculation means" refers to a function or device for processing information and performing predetermined calculations to obtain a result.

[0074] This invention is an integrated management system utilizing AI technology, primarily aimed at the efficient use of agricultural machinery and the resolution of labor shortages in regional agriculture. This system mainly consists of servers, terminals, and users, with each element playing a specific role and working in coordination.

[0075] First, the server retrieves information on agricultural machinery ownership, work plans, and supporter registration from multiple databases using a relational database management system. This information is then organized using data cleaning and configuration techniques and stored on the server. Database software such as MySQL® is primarily used for this process.

[0076] Next, the server performs data analysis using machine learning algorithms based on the stored information to generate an optimal placement and utilization plan for agricultural machinery. Here, a machine learning framework such as TENSORFLOW® is used, and the schedule and plan are optimized through an AI model.

[0077] The generated plan is sent from the server to the terminal. The terminal is primarily a smartphone or computer owned by the user, and it provides a UI (user interface) for reviewing and adjusting the plan. Based on this information, the user can fine-tune the plan according to their own schedule and requests. The record of any adjustments is updated back on the server in real time.

[0078] This system uses sensor technology and GPS devices to monitor the on-site usage of agricultural machinery and support personnel. A server analyzes the data from these devices and recalculates the schedule in response to changes in conditions. This operation utilizes a real-time messaging platform such as Apache® Kafka. This ensures that plans are always based on the latest information and that adjustments can be made immediately as needed.

[0079] As a concrete example, a user can semi-automatically send a request to the server asking, "Is the tractor available for use from 10 AM tomorrow?" The server can then check the availability in real time and suggest the optimal usage time. This allows agricultural activities within the region to be carried out more efficiently and systematically.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server retrieves agricultural machinery ownership, work plans, and supporter registration information from multiple databases. The server uses a relational database management system such as MySQL. Input data includes each farmer's machinery ownership list, work schedule, and supporter registration data. The data is organized through a data cleaning process, removing inconsistencies and redundant information before being accurately stored on the server. The output is organized data that serves as the basis for future analysis and plan generation.

[0083] Step 2:

[0084] The server uses the data organized in Step 1 to perform data analysis using a machine learning model, such as TensorFlow. The inputs are organized machine ownership data and work schedule data. Based on this, the server performs calculations to generate an optimal allocation and utilization plan for each farmer and agricultural machinery. It uses a generative AI model to perform predictions and optimizations to improve the efficiency of the utilization schedule. The output is an optimized agricultural machinery utilization plan for each farmer.

[0085] Step 3:

[0086] The server sends the generated optimized usage plan to the terminal. The input here is the usage plan generated in step 2. The output is the schedule information displayed on the screen of the user's smartphone or computer. The terminal provides this information to the user, making it easy to check the schedule.

[0087] Step 4:

[0088] Users review the usage plan provided through their terminal and adjust it according to their own schedule. Inputs include the schedule information displayed on the terminal and the user's individual appointments. Users modify their schedules through their own actions, and these changes are sent to the server in real time. The output is the updated, adjusted usage plan, which is saved on the server and used for future work.

[0089] Step 5:

[0090] The server monitors the usage status of agricultural machinery in real time. Inputs include data from sensors and GPS devices mounted on the machinery. The server processes this data to continuously monitor usage. It also receives data through messaging systems such as Apache Kafka, helping to dynamically adjust schedules. The output provides accurate agricultural machinery management information based on the latest usage data.

[0091] (Application Example 1)

[0092] 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."

[0093] While the efficient use of machinery and equipment is required in regional agriculture and production systems, labor shortages and difficulties in proper scheduling remain challenges. Furthermore, there is a growing demand for systems that allow managers to accurately understand the real-time operating status of machinery and equipment and respond quickly accordingly.

[0094] 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.

[0095] In this invention, the server includes means for collecting information on the ownership status of agricultural machinery in the region, the work schedules of each farm operator, and the registration information of collaborators; means for generating an optimal usage plan for machinery and equipment using an artificial intelligence algorithm based on the collected information; and means for notifying each farm operator's terminal of the generated usage plan. This enables managers to efficiently operate and adjust machinery and equipment in real time based on visualized information.

[0096] "Local agricultural machinery" refers to various types of machinery and equipment used for agricultural activities in a specific region.

[0097] A "work schedule" is a plan that outlines the tasks to be performed by the farm manager and their collaborators.

[0098] "Registered information of collaborators" refers to data that includes information about individuals and organizations that cooperate in agricultural activities.

[0099] An "artificial intelligence algorithm" is a type of computer program that analyzes collected information and generates an optimal usage plan.

[0100] "Generated usage plan" refers to a plan for the use of agricultural machinery created by an artificial intelligence algorithm.

[0101] "Mechanical equipment" refers to a broad range of devices, including power machines and working machines, used in agriculture and production systems.

[0102] A "terminal" is a device used to provide users with generated usage plans and information, and includes smartphones, computers, and other similar devices.

[0103] "Real time" refers to the actual time that is currently unfolding, in other words, real time.

[0104] "Visualization" refers to displaying information and data in a way that is easy for users to understand.

[0105] One embodiment of this invention is a system that enables the efficient use of machinery and equipment in local agriculture. This system mainly consists of a server, terminals, and users, each of which works in cooperation with each other.

[0106] The server collects information on the ownership status of machinery and equipment in the region, work schedules, and registered collaborators. This includes integrating information through databases and information networks. Based on the collected information, the server uses artificial intelligence algorithms to generate an optimal usage plan for the machinery and equipment. This plan aims to maximize utilization efficiency and minimize wasted operation. Furthermore, the server monitors the operating status of the machinery and equipment in real time and recalculates and updates the schedule immediately as needed.

[0107] The terminal functions as a medium for notifying users of the generated usage plan. This includes smartphones and computers. This notification function allows users to flexibly understand and adjust the available time and location of machinery and equipment. In addition, administrators can obtain visualized information and issue quick and appropriate work instructions.

[0108] Users (farm owners and managers) can use their terminals to check schedules at their convenience and send modification requests to the server if necessary. This two-way communication ensures the maintenance of an optimal schedule.

[0109] As a concrete example, if a farm owner wants to use a specific machine, they send their desired time and location to the server from their terminal. The server analyzes the overall machine status and makes the optimal usage suggestion. If the suggestion is accepted, the adjusted schedule is automatically notified to other users.

[0110] An example of a prompt message would be something like, "Based on the current operating status of the machinery and equipment and the information of collaborators, please tell us what the next priority task should be."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server collects information from a database regarding the ownership status of local machinery and equipment, work schedules, and registration information of collaborators. Using database queries, it retrieves the necessary information and generates an integrated dataset. This dataset serves as the foundational data for future optimization calculations. The input is the database information, and the output is the integrated dataset.

[0114] Step 2:

[0115] The server uses a generated AI model based on the collected information to create an optimal usage plan for the machinery and equipment. Here, the artificial intelligence algorithm formulates the plan, taking into account parameters such as machine utilization and available time. The optimized usage schedule is output as the calculation result.

[0116] Step 3:

[0117] The server notifies each user's terminal of the generated usage plan. The schedule information is transmitted via a digital communication protocol and displayed on the terminal screen. The input is the generated usage schedule, and the output is the information displayed on the user's terminal.

[0118] Step 4:

[0119] The user checks the received schedule using their terminal and sends modification requests to the server as needed. Input is the user's change request, and output is the notification to the server based on that request. Schedule adjustments may be required based on user actions.

[0120] Step 5:

[0121] The server receives the correction request and recalculates and updates the usage plan. In this recalculation, the AI ​​algorithm reconstructs the schedule based on the newly entered information. The modified schedule is output and notified to the terminal again.

[0122] Step 6:

[0123] The system continuously monitors the real-time operating status of the machinery and equipment. The server performs anomaly detection and adjustments as needed, and keeps the schedule up-to-date. The input is sensor information from the machinery and equipment, and the output is the monitoring results.

[0124] Step 7:

[0125] The administrator will review the visualized information from the terminal and ensure the proper operation of the machinery and equipment. Specifically, this involves checking the current operating status via a video display device and determining the next action to take. This requires generating prompt messages and inputting necessary instructions into the system.

[0126] 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.

[0127] This invention provides a system using AI technology to solve challenges in local agriculture, and by combining it with an emotion engine that recognizes user emotions, it enables more flexible and effective support. Specifically, the system is built to improve the efficient use of agricultural machinery and user satisfaction through the cooperation of the server, terminal, and user elements.

[0128] First, the system uses basic data collection functions to gather and store information on local agricultural machinery ownership, each farmer's work schedule, and volunteer registration information on a server. Then, the collected data is analyzed using an AI algorithm to generate an optimized schedule for agricultural machinery use.

[0129] The generated schedule is notified from the server to each farmer's terminal, allowing users (farmers) to review and adjust the schedule to suit their own work needs. Furthermore, an emotion engine senses the user's reactions and analyzes the user's emotional data. This allows the server to dynamically adjust schedule suggestions based on the user's emotional state, thereby increasing user satisfaction.

[0130] For example, if a farmer user is dissatisfied with a proposed schedule, the emotion engine built into the terminal recognizes that emotion and analyzes the user's tone of voice and facial expressions. If the emotion engine determines that the user is "dissatisfied," the server re-evaluates the user's situation, generates alternative plans, and proposes them to the terminal again. This allows the user to use the system more comfortably and increases the flexibility of their plans.

[0131] During operation, the server monitors the usage of agricultural machinery and volunteers in real time, updating data using information from sensors. Information obtained from the emotion engine is also fed back into the system to help improve the overall service and interface. In this way, a system is provided that optimizes the user experience by recognizing emotions, contributing to increased efficiency in agricultural activities and the revitalization of local communities.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server collects information from a database regarding the ownership status of agricultural machinery within the region, the work schedules of individual farmers, and volunteer registration information, and stores it on a central server. This data is organized to ensure consistency and integrity.

[0135] Step 2:

[0136] The server analyzes the collected data using AI algorithms to generate an optimal schedule for using agricultural machinery for each farmer. This analysis is designed to maximize the efficiency of machine usage.

[0137] Step 3:

[0138] The server notifies each farmer's terminal of the generated usage schedule. The terminal then notifies the user of the arrival of the new schedule via email or application notification.

[0139] Step 4:

[0140] Users check their schedules on their devices and provide feedback on the suggested usage plans. An emotion engine analyzes the user's emotions and collects emotional data from voice input, facial expression changes, and other sources.

[0141] Step 5:

[0142] The server analyzes the user's emotional data obtained from the emotion engine and determines whether schedule adjustments are necessary based on the results. If necessary, it makes revised schedule suggestions.

[0143] Step 6:

[0144] The server monitors the real-time usage of agricultural machinery and volunteers based on information received from sensors and GPS devices, and updates the data periodically.

[0145] Step 7:

[0146] If the server detects a change in usage, it recalculates the schedule and creates a new, optimized schedule. It then notifies the device again, ensuring the user has access to the most efficient plan.

[0147] By utilizing an emotion engine, we can provide customized schedules that take user emotions into account, improving the convenience and satisfaction of farm work.

[0148] (Example 2)

[0149] 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".

[0150] In modern agriculture, the efficient allocation and use of agricultural machinery is crucial, yet optimizing this within limited resources is difficult. Furthermore, while flexible plan changes are required to meet the work needs of farmers, traditional systems have failed to adequately consider user satisfaction. This has led to problems such as decreased efficiency in agricultural activities and user dissatisfaction.

[0151] 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.

[0152] In this invention, the server includes means for collecting ownership information of agricultural machinery in the region, means for creating an efficient use plan for agricultural machinery using a generation method, and means for recognizing the emotional state of users and adjusting schedule suggestions based on emotional information. This enables optimal allocation and efficient use of agricultural machinery, as well as flexible plan adjustments that take into account the emotions of agricultural workers.

[0153] "Regional agricultural machinery" is a general term for a group of agricultural machinery and equipment used within a specific region.

[0154] "Agricultural workers' work schedules" are records of the planned work activities of people engaged in agriculture.

[0155] "Supporter registration information" refers to data that compiles contact information and basic information of individuals who are willing to help with agricultural activities.

[0156] "Generative methods" refer to algorithms and processes used to create optimal plans based on collected information.

[0157] An "efficient use plan" is a plan created to maximize the use of agricultural machinery and to avoid wasting time and resources.

[0158] "Real-time usage status" refers to information that allows for immediate understanding of the operating status of agricultural machinery and support personnel at the current time.

[0159] "Recognizing emotional states" refers to technology that detects users' emotions from their voice and facial expressions and determines their emotional state.

[0160] "Adjusting schedule proposals based on emotional information" means modifying the plan offered based on the user's emotional response to provide a more satisfying proposal.

[0161] This invention is a system designed to improve the efficiency of agricultural activities within a region and increase the satisfaction of agricultural workers. The embodiments of this invention are described in detail below.

[0162] First, the server collects information on the ownership of agricultural machinery within the region, the work schedules of agricultural workers, and the registration information of support personnel. Based on this information, the server creates an efficient usage plan for agricultural machinery using an AI-powered generation method. At this time, the server analyzes the data using machine learning algorithms to optimize resource allocation.

[0163] Next, the terminal is a device that notifies agricultural workers of the usage plan transmitted from the server. This terminal incorporates emotion recognition technology, which allows it to detect the emotions of agricultural workers. As a result, the terminal recognizes the emotional state from the user's voice tone and facial expressions and transmits that information to the server.

[0164] Agricultural workers, as users, can check their work schedules through their terminals and request revisions as needed. If they are dissatisfied with the proposed usage plan, they can express their feelings through the terminal's emotion recognition function, and the server will receive this information and readjust the plan.

[0165] For example, if a farmer feels they would like a little more free time this Thursday, they can communicate this to their device. The device detects this feeling and sends it to the server. The server receives this information, readjusts the plan, and notifies the device with a new proposal.

[0166] Examples of prompt statements are as follows:

[0167] "Please generate a usage schedule for the latest agricultural machinery."

[0168] "Please make adjustments to the plan to amplify the positive feelings of agricultural workers."

[0169] This system can improve the efficiency of agricultural activities, reduce the psychological burden on farmers, and increase regional agricultural productivity.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The server receives information on the ownership of agricultural machinery in the region, the work schedules of agricultural workers, and the registration information of support personnel. The input data consists of information obtained from each database. Upon receiving this information, the server verifies the integrity of the data, formats it appropriately, and stores it in the database.

[0173] Step 2:

[0174] The server analyzes the collected data using an AI algorithm. The input at this stage is the consistent data received in step 1. The server uses machine learning to generate the optimal schedule for using agricultural machinery. The algorithm calculates the most efficient machine allocation based on each farmer's work schedule and creates a usage plan as output.

[0175] Step 3:

[0176] The server sends the generated usage plan to each farm worker's terminal. The input is the usage plan created in step 2. This data is notified to the terminal via application or email. In practice, a notification such as "The tractor will be available from 9 AM tomorrow" is sent.

[0177] Step 4:

[0178] The device receives the usage plan, which the user then reviews. The user can submit feedback on the plan and request revisions. Input is notifications from the server, and output is user feedback and emotion data. The device has an emotion engine that recognizes the user's emotions from their voice and facial expressions and performs specific actions accordingly.

[0179] Step 5:

[0180] The terminal sends user emotion data to the server. The input is data about the user's emotional state. The server analyzes the user's feedback and determines if the usage plan needs to be readjusted. The output is the revised schedule, if necessary.

[0181] Step 6:

[0182] The server then resends the revised usage plan to the terminal for user review. In this step, the usage plan is readjusted as input and sent to the terminal. This ensures that the optimal plan is provided to meet the user's needs.

[0183] (Application Example 2)

[0184] 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".

[0185] Achieving efficient operation of machinery and equipment, as well as improving operator satisfaction, are critical challenges in industry. However, current machinery usage plans are static, making it difficult to respond flexibly to real-time changes in circumstances and operator emotions. Furthermore, because operator emotional states are not considered, dissatisfaction with the plan is likely to occur. This raises concerns about decreased work efficiency and increased mental burden on operators. There is a need to solve these problems and realize efficient and flexible planning and operation that takes operator emotions into consideration.

[0186] 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.

[0187] In this invention, the server includes means for collecting information on the ownership status of machinery and equipment in the region, the work plans of each operator, and the registration information of support staff; means for generating an optimal usage plan for the machinery and equipment based on the collected information; and means for recognizing the emotional state of the operators using an emotion engine and dynamically adjusting the schedule. This enables flexible scheduling based on the operators' emotions and efficient operation of the machinery and equipment.

[0188] "Machinery and equipment" refers to equipment used for manufacturing and processing in industry and industrial settings.

[0189] "Operator" refers to a person who operates machinery and manages the process.

[0190] A "work plan" is a guideline that outlines the schedule and procedures for tasks to be performed by operators or machinery.

[0191] "Supporters" refer to personnel or services that assist with the operation of machinery and equipment, or with the work of operators.

[0192] An "AI algorithm" is an artificial intelligence computational method used to analyze data and derive the optimal result.

[0193] An "emotion engine" is a technology that recognizes a person's emotional state and adjusts the behavior of a system based on that information.

[0194] "Real-time" refers to the ability to instantly utilize ongoing data and situations and respond without delay.

[0195] "Feedback" is the act of providing information and opinions to improve a system or process based on the results and evaluations obtained.

[0196] The system implementing this invention operates with a server, terminals, and users working in coordination. The server first collects information on the ownership status of machinery and equipment within a region, the operator's work plan, and the registration of support staff. This data is centrally collected over the network and stored in a database on the server. Using the collected data, the server generates an optimal machinery and equipment usage plan using an AI algorithm. The AI ​​algorithm is built using platforms such as TensorFlow or PyTorch.

[0197] The generated usage plan is notified to each operator's terminal via the communication network. The terminal can then check its work schedule based on this usage plan. The terminal has an emotion engine built in that analyzes the operator's voice and facial expressions to recognize their emotional state in real time. Technologies such as Microsoft® Azure® Cognitive Services are used for emotion recognition. If the operator feels dissatisfied or stressed, the emotion engine sends that information to the server.

[0198] Upon receiving the emotional data, the server re-evaluates the usage plan as needed, generates alternatives if necessary, and proposes them to the terminal again. This procedure improves operator satisfaction while maintaining planning flexibility. Furthermore, the server monitors the usage of the machine and support staff in real time and updates the situation based on sensor data.

[0199] This system takes into account the emotional state of workers within the factory, improving work efficiency and reducing the mental burden on operators. For example, if a worker feels fatigued due to monotony during assembly line work, the emotion engine analyzes their facial expression, and the server notifies the terminal to readjust the break time.

[0200] Examples of prompts for a generative AI model:

[0201] "If factory workers are unhappy with their current schedule, please tell us how to adjust it based on sentiment recognition data."

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The server collects information on the ownership status of machinery and equipment within the region, operator work plans, and support staff registration information. This information is stored in a database. Inputs are machinery and equipment ownership data, work plan data, and support staff registration information, and output is a formatted dataset. This dataset is used for subsequent processing. Specifically, it retrieves the necessary information from each device via the network.

[0205] Step 2:

[0206] The server generates an optimal usage plan for the machinery and equipment using an AI algorithm based on the collected information. The input is the dataset formatted in step 1, and the output is the optimized usage plan. Here, the AI ​​model is executed using TensorFlow or PyTorch, and the optimal schedule is calculated based on the obtained results.

[0207] Step 3:

[0208] The server notifies the operator's terminal of the generated usage plan. The input is the usage plan generated in step 2, and the output is the notification to the terminal. The server performs the operation of transmitting the plan information via network communication.

[0209] Step 4:

[0210] The device uses a built-in emotion engine to analyze the operator's voice and facial expressions to recognize their emotional state. Input is the operator's voice and facial expression data, and output is the analyzed emotional state. The emotion engine uses Microsoft Azure Cognitive Services, among other tools, to make emotional judgments.

[0211] Step 5:

[0212] The emotion engine sends the recognized emotional state to the server. The input is the emotional state data obtained in step 4, and the output is the emotional state notification to the server. Specifically, it sends the emotional data to the server using secure communication.

[0213] Step 6:

[0214] The server re-evaluates the usage plan based on the emotional state and generates alternatives as needed. The input is emotional state data and the existing usage plan, and the output is the adjusted alternative. The AI ​​model is run again to formulate a new plan under the changed conditions.

[0215] Step 7:

[0216] The server re-notifies the operator's terminal of the alternative plan. The input is the alternative plan generated in step 6, and the output is the re-notification to the terminal. The alternative plan is transmitted over the network.

[0217] Step 8:

[0218] The terminal displays the adjusted plan to the operator and provides emotional data as updated feedback. The input is the alternative plan received in step 7, and the output is the displayed new schedule and feedback information. The specific action is to show the operator the alternative plan using the display function and return the feedback to the system.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is implemented as an integrated management system utilizing AI technology, with the aim of promoting the efficient use of agricultural machinery and addressing labor shortages in regional agriculture. This system primarily consists of servers, terminals, and users, with each element working in conjunction to function. A specific embodiment of this system is described below.

[0236] First, the server retrieves information from multiple databases to collect data on machine ownership, farm work schedules, and volunteer registration information, then organizes and stores this data. The collected information is analyzed by an AI algorithm and becomes the foundational data for generating the optimal machine usage schedule for each farmer. The server uses this data to optimize the placement and utilization plan of agricultural machinery, creating plans that suit multiple users (farmers).

[0237] The generated schedule is sent to the terminal via the server. The terminal can be a smartphone or computer, allowing the user to check the schedule and receive detailed information about the device being used. Based on this information, the user can adjust the schedule to suit their own convenience.

[0238] During system operation, the server monitors the real-time usage of agricultural machinery and volunteers. This is achieved by tracking the location and usage of each machine via sensors and GPS devices. The server processes this information in real time, continuously checking whether things are progressing according to schedule. Furthermore, if there are any changes in usage, the server immediately recalculates the schedule and delivers the revised plan to the terminals. This ensures that machinery is used efficiently and enables effective agricultural activities.

[0239] As a concrete example, if a farmer wants to use a tractor, they send their request to the server via a terminal. The server checks the availability of tractors in the area in real time and suggests the optimal time and location for use. If the user accepts the suggestion, the system confirms the use of that tractor from that point onward and automatically adjusts notifications to other users.

[0240] Through this process, the present invention effectively supports the efficiency of regional agriculture, reduces the cost burden on farmers, and contributes to the realization of sustainable agriculture.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server collects information from a database regarding the ownership status of agricultural machinery, the work schedules of each farmer, and volunteer registration information, and organizes and stores it as up-to-date data.

[0244] Step 2:

[0245] The server analyzes the collected data using AI algorithms to generate an optimal utilization plan for agricultural machinery. This includes an efficient schedule that takes into account the machinery's availability and the farmers' usage preferences.

[0246] Step 3:

[0247] The server sends the generated usage plan to the terminal corresponding to each farmer. The terminal displays the received information via notification or application, allowing users to check their usage schedule.

[0248] Step 4:

[0249] The user checks the notified schedule using their device and, if necessary, sends a request to change the schedule to the server via their device.

[0250] Step 5:

[0251] The server receives the change request from the user, recalculates the schedule using the AI ​​algorithm again, and resends the adjusted plan to the terminal.

[0252] Step 6:

[0253] The server monitors the usage status of each agricultural machine in real time and constantly updates the data obtained from sensors and GPS devices.

[0254] Step 7:

[0255] When the server detects a change in usage, it immediately recalculates the schedule and notifies the terminal of the latest schedule, thereby reducing waste and maintaining efficient operation.

[0256] This processing flow is designed to optimize farmers' use of machinery and also address the problem of labor shortages.

[0257] (Example 1)

[0258] 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."

[0259] Currently, it is difficult to create and adjust optimal deployment and utilization plans for agricultural machinery in order to efficiently utilize agricultural machinery in regional agriculture and to solve the problem of labor shortages at individual farms. With conventional methods, it is difficult to efficiently manage information such as the operating status of agricultural machinery and the schedules of volunteers, and to quickly adjust plans to meet the needs of each farmer, resulting in inefficient agricultural operations.

[0260] 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.

[0261] In this invention, the server includes means for collecting information on the ownership status of agricultural machinery, work plans, and supporter registration information; calculation means for generating an optimal arrangement and utilization plan for machinery based on the collected information; and means for notifying each user's computer of the generated plan. This makes it possible to efficiently create an optimal agricultural machinery utilization plan that meets the needs of each farmer and to quickly recalculate in response to change requests.

[0262] "Agricultural machinery" is a general term for devices or vehicles used to cultivate land, sow seeds, harvest, or perform related agricultural tasks.

[0263] A "work plan" is a detailed plan outlining the types of agricultural work, the schedule, and the procedures to be carried out within a specific period.

[0264] "Supporter registration information" refers to information about individuals or organizations registered to support agricultural activities, and mainly includes data on volunteers and temporary laborers.

[0265] "Optimal allocation" refers to the efficient distribution and allocation of agricultural machinery and labor in order to make the most effective use of limited resources.

[0266] "User's computer" refers to a computer or mobile terminal used by farmers or agricultural personnel, which is a device for receiving, displaying, and operating agricultural machinery usage plans.

[0267] "Calculation means" refers to a function or device for processing information and performing predetermined calculations to obtain a result.

[0268] This invention is an integrated management system utilizing AI technology, primarily aimed at the efficient use of agricultural machinery and the resolution of labor shortages in regional agriculture. This system mainly consists of servers, terminals, and users, with each element playing a specific role and working in coordination.

[0269] First, the server retrieves information on agricultural machinery ownership, work plans, and supporter registration from multiple databases using a relational database management system. This information is then organized using data cleaning and configuration techniques and stored on the server. Database software such as MySQL is primarily used for this process.

[0270] Next, the server uses machine learning algorithms to analyze the stored information and generate an optimal placement and utilization plan for agricultural machinery. Here, machine learning frameworks such as TensorFlow are used to optimize the schedule and plan through AI models.

[0271] The generated plan is sent from the server to the terminal. The terminal is primarily a smartphone or computer owned by the user, and it provides a UI (user interface) for reviewing and adjusting the plan. Based on this information, the user can fine-tune the plan according to their own schedule and requests. The record of any adjustments is updated back on the server in real time.

[0272] This system uses sensor technology and GPS devices to monitor the on-site usage of agricultural machinery and support personnel. A server analyzes the data from these devices and recalculates the schedule in response to changes in conditions. This operation utilizes a real-time messaging platform such as Apache Kafka, ensuring that plans are always up-to-date and adjustments can be made immediately as needed.

[0273] As a concrete example, a user can semi-automatically send a request to the server asking, "Is the tractor available for use from 10 AM tomorrow?" The server can then check the availability in real time and suggest the optimal usage time. This allows agricultural activities within the region to be carried out more efficiently and systematically.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The server retrieves agricultural machinery ownership, work plans, and supporter registration information from multiple databases. The server uses a relational database management system such as MySQL. Input data includes each farmer's machinery ownership list, work schedule, and supporter registration data. The data is organized through a data cleaning process, removing inconsistencies and redundant information before being accurately stored on the server. The output is organized data that serves as the basis for future analysis and plan generation.

[0277] Step 2:

[0278] The server uses the data organized in Step 1 to perform data analysis using a machine learning model, such as TensorFlow. The inputs are organized machine ownership data and work schedule data. Based on this, the server performs calculations to generate an optimal allocation and utilization plan for each farmer and agricultural machinery. It uses a generative AI model to perform predictions and optimizations to improve the efficiency of the utilization schedule. The output is an optimized agricultural machinery utilization plan for each farmer.

[0279] Step 3:

[0280] The server sends the generated optimized usage plan to the terminal. The input here is the usage plan generated in step 2. The output is the schedule information displayed on the screen of the user's smartphone or computer. The terminal provides this information to the user, making it easy to check the schedule.

[0281] Step 4:

[0282] Users review the usage plan provided through their terminal and adjust it according to their own schedule. Inputs include the schedule information displayed on the terminal and the user's individual appointments. Users modify their schedules through their own actions, and these changes are sent to the server in real time. The output is the updated, adjusted usage plan, which is saved on the server and used for future work.

[0283] Step 5:

[0284] The server monitors the usage status of agricultural machinery in real time. The input is data from sensors and GPS devices installed on the machinery. The server processes this data to continuously grasp the usage status. It also receives data through a messaging system such as Apache Kafka and helps dynamically adjust the schedule. As output, an accurate management status of agricultural machinery based on the latest usage status can be obtained.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In regional agriculture and production systems, while efficient utilization of mechanical devices is required, labor shortages and difficulties in appropriate scheduling are issues. Also, there is an increasing demand for a system that enables managers to accurately grasp the real-time operating status of mechanical devices and respond promptly accordingly.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for collecting the ownership status of regional agricultural machinery, the work schedules of each farm operator, and the registration information of collaborators, means for generating an optimal usage plan for mechanical devices using an artificial intelligence algorithm based on the collected information, and means for notifying the generated usage plan to the terminals of each farm operator. Thereby, the manager can efficiently operate and adjust the mechanical devices in real time based on the visualized information.

[0290] "Regional agricultural machinery" refers to various mechanical devices used in agricultural activities in a specific region.

[0291] "Work schedule" refers to a plan indicating the schedule of work performed by farm operators and collaborators.

[0292] "Registered information of collaborators" refers to data that includes information about individuals and organizations that cooperate in agricultural activities.

[0293] An "artificial intelligence algorithm" is a type of computer program that analyzes collected information and generates an optimal usage plan.

[0294] "Generated usage plan" refers to a plan for the use of agricultural machinery created by an artificial intelligence algorithm.

[0295] "Mechanical equipment" refers to a broad range of devices, including power machines and working machines, used in agriculture and production systems.

[0296] A "terminal" is a device used to provide users with generated usage plans and information, and includes smartphones, computers, and other similar devices.

[0297] "Real time" refers to the actual time that is currently unfolding, in other words, real time.

[0298] "Visualization" refers to displaying information and data in a way that is easy for users to understand.

[0299] One embodiment of this invention is a system that enables the efficient use of machinery and equipment in local agriculture. This system mainly consists of a server, terminals, and users, each of which works in cooperation with each other.

[0300] The server collects information on the ownership status of machinery and equipment in the region, work schedules, and registered collaborators. This includes integrating information through databases and information networks. Based on the collected information, the server uses artificial intelligence algorithms to generate an optimal usage plan for the machinery and equipment. This plan aims to maximize utilization efficiency and minimize wasted operation. Furthermore, the server monitors the operating status of the machinery and equipment in real time and recalculates and updates the schedule immediately as needed.

[0301] The terminal functions as a medium for notifying the user of the generated usage plan. Smartphones and computers are applicable. With this notification function, the user can flexibly grasp and adjust the available time and location of the mechanical device. Also, the administrator can obtain visualized information, enabling quick and appropriate work instructions.

[0302] The user (farmer operator or administrator) can use the terminal to check the schedule according to their own convenience and, if necessary, send a modification request to the server. This two-way communication realizes the maintenance of an optimal schedule.

[0303] As a specific example, when a certain farmer operator wants to use a specific machine, they send the desired time and location from the terminal to the server. The server analyzes the overall machine situation and makes an optimal usage proposal. And if the proposal is accepted, the automatically adjusted schedule is also notified to other users.

[0304] As an example of a prompt sentence, a form such as "Please tell me the next work process to be prioritized based on the current operating status of the mechanical device and the usage information of the collaborators" can be considered.

[0305] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0306] Step 1:

[0307] The server collects the ownership status of the mechanical devices in the region, the work schedule, and the registration information of the collaborators from the database. Using a database query, it acquires the necessary information and generates an integrated dataset. This dataset functions as the basic data for future optimization calculations. The input is the information in the database, and the output is the integrated dataset.

[0308] Step 2:

[0309] The server uses a generated AI model based on the collected information to create an optimal usage plan for the machinery and equipment. Here, the artificial intelligence algorithm formulates the plan, taking into account parameters such as machine utilization and available time. The optimized usage schedule is output as the calculation result.

[0310] Step 3:

[0311] The server notifies each user's terminal of the generated usage plan. The schedule information is transmitted via a digital communication protocol and displayed on the terminal screen. The input is the generated usage schedule, and the output is the information displayed on the user's terminal.

[0312] Step 4:

[0313] The user checks the received schedule using their terminal and sends modification requests to the server as needed. Input is the user's change request, and output is the notification to the server based on that request. Schedule adjustments may be required based on user actions.

[0314] Step 5:

[0315] The server receives the correction request and recalculates and updates the usage plan. In this recalculation, the AI ​​algorithm reconstructs the schedule based on the newly entered information. The modified schedule is output and notified to the terminal again.

[0316] Step 6:

[0317] The system continuously monitors the real-time operating status of the machinery and equipment. The server performs anomaly detection and adjustments as needed, and keeps the schedule up-to-date. The input is sensor information from the machinery and equipment, and the output is the monitoring results.

[0318] Step 7:

[0319] The administrator will review the visualized information from the terminal and ensure the proper operation of the machinery and equipment. Specifically, this involves checking the current operating status via a video display device and determining the next action to take. This requires generating prompt messages and inputting necessary instructions into the system.

[0320] 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.

[0321] This invention provides a system using AI technology to solve challenges in local agriculture, and by combining it with an emotion engine that recognizes user emotions, it enables more flexible and effective support. Specifically, the system is built to improve the efficient use of agricultural machinery and user satisfaction through the cooperation of the server, terminal, and user elements.

[0322] First, the system uses basic data collection functions to gather and store information on local agricultural machinery ownership, individual farmers' work schedules, and volunteer registrations on a server. Then, the collected data is analyzed using an AI algorithm to generate an optimized schedule for agricultural machinery use.

[0323] The generated schedule is notified from the server to each farmer's terminal, allowing users (farmers) to review and adjust the schedule to suit their own work needs. Furthermore, an emotion engine senses the user's reactions and analyzes the user's emotional data. This allows the server to dynamically adjust schedule suggestions based on the user's emotional state, thereby increasing user satisfaction.

[0324] For example, if a farmer user is dissatisfied with a proposed schedule, the emotion engine built into the terminal recognizes that emotion and analyzes the user's tone of voice and facial expressions. If the emotion engine determines that the user is "dissatisfied," the server re-evaluates the user's situation, generates alternative plans, and proposes them to the terminal again. This allows the user to use the system more comfortably and increases the flexibility of their plans.

[0325] During operation, the server monitors the usage of agricultural machinery and volunteers in real time, updating data using information from sensors. Information obtained from the emotion engine is also fed back into the system to help improve the overall service and interface. In this way, a system is provided that optimizes the user experience by recognizing emotions, contributing to increased efficiency in agricultural activities and the revitalization of local communities.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The server collects information from a database regarding the ownership status of agricultural machinery within the region, the work schedules of individual farmers, and volunteer registration information, and stores it on a central server. This data is organized to ensure consistency and integrity.

[0329] Step 2:

[0330] The server analyzes the collected data using AI algorithms to generate an optimal schedule for using agricultural machinery for each farmer. This analysis is designed to maximize the efficiency of machine usage.

[0331] Step 3:

[0332] The server notifies each farmer's terminal of the generated usage schedule. The terminal then notifies the user of the arrival of the new schedule via email or application notification.

[0333] Step 4:

[0334] Users check their schedules on their devices and provide feedback on the suggested usage plans. An emotion engine analyzes the user's emotions and collects emotional data from voice input, facial expression changes, and other sources.

[0335] Step 5:

[0336] The server analyzes the user's emotional data obtained from the emotion engine and determines whether schedule adjustments are necessary based on the results. If necessary, it makes revised schedule suggestions.

[0337] Step 6:

[0338] The server monitors the real-time usage of agricultural machinery and volunteers based on information received from sensors and GPS devices, and updates the data periodically.

[0339] Step 7:

[0340] If the server detects a change in usage, it recalculates the schedule and creates a new, optimized schedule. It then notifies the device again, ensuring the user has access to the most efficient plan.

[0341] By utilizing an emotion engine, we can provide customized schedules that take user emotions into account, improving the convenience and satisfaction of farm work.

[0342] (Example 2)

[0343] 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".

[0344] In modern agriculture, the efficient allocation and use of agricultural machinery is crucial, yet optimizing this within limited resources is difficult. Furthermore, while flexible plan changes are required to meet the work needs of farmers, traditional systems have failed to adequately consider user satisfaction. This has led to problems such as decreased efficiency in agricultural activities and user dissatisfaction.

[0345] 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.

[0346] In this invention, the server includes means for collecting ownership information of agricultural machinery in the region, means for creating an efficient use plan for agricultural machinery using a generation method, and means for recognizing the emotional state of users and adjusting schedule suggestions based on emotional information. This enables optimal allocation and efficient use of agricultural machinery, as well as flexible plan adjustments that take into account the emotions of agricultural workers.

[0347] "Regional agricultural machinery" is a general term for a group of agricultural machinery and equipment used within a specific region.

[0348] "Agricultural workers' work schedules" are records of the planned work activities of people engaged in agriculture.

[0349] "Supporter registration information" refers to data that compiles contact information and basic information of individuals who are willing to help with agricultural activities.

[0350] "Generative methods" refer to algorithms and processes used to create optimal plans based on collected information.

[0351] An "efficient use plan" is a plan created to maximize the use of agricultural machinery and to avoid wasting time and resources.

[0352] "Real-time usage status" refers to information that allows for immediate understanding of the operating status of agricultural machinery and support personnel at the current time.

[0353] "Recognizing emotional states" refers to technology that detects users' emotions from their voice and facial expressions and determines their emotional state.

[0354] "Adjusting schedule proposals based on emotional information" means modifying the plan offered based on the user's emotional response to provide a more satisfying proposal.

[0355] This invention is a system designed to improve the efficiency of agricultural activities within a region and increase the satisfaction of agricultural workers. The embodiments of this invention are described in detail below.

[0356] First, the server collects information on the ownership of agricultural machinery within the region, the work schedules of agricultural workers, and the registration information of support personnel. Based on this information, the server creates an efficient usage plan for agricultural machinery using an AI-powered generation method. At this time, the server analyzes the data using machine learning algorithms to optimize resource allocation.

[0357] Next, the terminal is a device that notifies agricultural workers of the usage plan transmitted from the server. This terminal incorporates emotion recognition technology, which allows it to detect the emotions of agricultural workers. As a result, the terminal recognizes the emotional state from the user's voice tone and facial expressions and transmits that information to the server.

[0358] Agricultural workers, as users, can check their work schedules through their terminals and request revisions as needed. If they are dissatisfied with the proposed usage plan, they can express their feelings through the terminal's emotion recognition function, and the server will receive this information and readjust the plan.

[0359] For example, if a farmer feels they would like a little more free time this Thursday, they can communicate this to their device. The device detects this feeling and sends it to the server. The server receives this information, readjusts the plan, and notifies the device with a new proposal.

[0360] Examples of prompt statements are as follows:

[0361] "Please generate a usage schedule for the latest agricultural machinery."

[0362] "Please make adjustments to the plan to amplify the positive feelings of agricultural workers."

[0363] This system can improve the efficiency of agricultural activities, reduce the psychological burden on farmers, and increase regional agricultural productivity.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The server receives information on the ownership of agricultural machinery in the region, the work schedules of agricultural workers, and the registration information of support personnel. The input data consists of information obtained from each database. Upon receiving this information, the server verifies the integrity of the data, formats it appropriately, and stores it in the database.

[0367] Step 2:

[0368] The server analyzes the collected data using an AI algorithm. The input at this stage is the consistent data received in step 1. The server uses machine learning to generate the optimal schedule for using agricultural machinery. The algorithm calculates the most efficient machine allocation based on each farmer's work schedule and creates a usage plan as output.

[0369] Step 3:

[0370] The server sends the generated usage plan to each farm worker's terminal. The input is the usage plan created in step 2. This data is notified to the terminal via application or email. In practice, a notification such as "The tractor will be available from 9 AM tomorrow" is sent.

[0371] Step 4:

[0372] The device receives the usage plan, which the user then reviews. The user can submit feedback on the plan and request revisions. Input is notifications from the server, and output is user feedback and emotion data. The device has an emotion engine that recognizes the user's emotions from their voice and facial expressions and performs specific actions accordingly.

[0373] Step 5:

[0374] The terminal sends user emotion data to the server. The input is data about the user's emotional state. The server analyzes the user's feedback and determines if the usage plan needs to be readjusted. The output is the revised schedule, if necessary.

[0375] Step 6:

[0376] The server then resends the revised usage plan to the terminal for user review. In this step, the usage plan is readjusted as input and sent to the terminal. This ensures that the optimal plan is provided to meet the user's needs.

[0377] (Application Example 2)

[0378] 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."

[0379] Achieving efficient operation of machinery and equipment, as well as improving operator satisfaction, are critical challenges in industry. However, current machinery usage plans are static, making it difficult to respond flexibly to real-time changes in circumstances and operator emotions. Furthermore, because operator emotional states are not considered, dissatisfaction with the plan is likely to occur. This raises concerns about decreased work efficiency and increased mental burden on operators. There is a need to solve these problems and realize efficient and flexible planning and operation that takes operator emotions into consideration.

[0380] 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.

[0381] In this invention, the server includes means for collecting information on the ownership status of machinery and equipment in the region, the work plans of each operator, and the registration information of support staff; means for generating an optimal usage plan for the machinery and equipment based on the collected information; and means for recognizing the emotional state of the operators using an emotion engine and dynamically adjusting the schedule. This enables flexible scheduling based on the operators' emotions and efficient operation of the machinery and equipment.

[0382] "Machinery and equipment" refers to equipment used for manufacturing and processing in industry and industrial settings.

[0383] "Operator" refers to a person who operates machinery and manages the process.

[0384] A "work plan" is a guideline that outlines the schedule and procedures for tasks to be performed by operators or machinery.

[0385] "Supporters" refer to personnel or services that assist with the operation of machinery and equipment, or with the work of operators.

[0386] An "AI algorithm" is an artificial intelligence computational method used to analyze data and derive the optimal result.

[0387] An "emotion engine" is a technology that recognizes a person's emotional state and adjusts the behavior of a system based on that information.

[0388] "Real-time" refers to the ability to instantly utilize ongoing data and situations and respond without delay.

[0389] "Feedback" is the act of providing information and opinions to improve a system or process based on the results and evaluations obtained.

[0390] The system implementing this invention operates with a server, terminals, and users working in coordination. The server first collects information on the ownership status of machinery and equipment within a region, the operator's work plan, and the registration of support staff. This data is centrally collected over the network and stored in a database on the server. Using the collected data, the server generates an optimal machinery and equipment usage plan using an AI algorithm. The AI ​​algorithm is built using platforms such as TensorFlow or PyTorch.

[0391] The generated usage plan is notified to each operator's terminal via the communication network. The terminal can then check its work schedule based on this usage plan. The terminal has an emotion engine built in that analyzes the operator's voice and facial expressions to recognize their emotional state in real time. Technologies such as Microsoft Azure Cognitive Services are used for emotion recognition. If the operator feels dissatisfied or stressed, the emotion engine sends that information to the server.

[0392] Upon receiving the emotional data, the server re-evaluates the usage plan as needed, generates alternatives if necessary, and proposes them to the terminal again. This procedure improves operator satisfaction while maintaining planning flexibility. Furthermore, the server monitors the usage of the machine and support staff in real time and updates the situation based on sensor data.

[0393] This system takes into account the emotional state of workers within the factory, improving work efficiency and reducing the mental burden on operators. For example, if a worker feels fatigued due to monotony during assembly line work, the emotion engine analyzes their facial expression, and the server notifies the terminal to readjust the break time.

[0394] Examples of prompts for a generative AI model:

[0395] "If factory workers are unhappy with their current schedule, please tell us how to adjust it based on sentiment recognition data."

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The server collects information on the ownership status of machinery and equipment within the region, operator work plans, and support staff registration information. This information is stored in a database. Inputs are machinery and equipment ownership data, work plan data, and support staff registration information, and output is a formatted dataset. This dataset is used for subsequent processing. Specifically, it retrieves the necessary information from each device via the network.

[0399] Step 2:

[0400] The server generates an optimal usage plan for the machinery and equipment using an AI algorithm based on the collected information. The input is the dataset formatted in step 1, and the output is the optimized usage plan. Here, the AI ​​model is executed using TensorFlow or PyTorch, and the optimal schedule is calculated based on the obtained results.

[0401] Step 3:

[0402] The server notifies the operator's terminal of the generated usage plan. The input is the usage plan generated in step 2, and the output is the notification to the terminal. The server performs the operation of transmitting the plan information via network communication.

[0403] Step 4:

[0404] The device uses a built-in emotion engine to analyze the operator's voice and facial expressions to recognize their emotional state. Input is the operator's voice and facial expression data, and output is the analyzed emotional state. The emotion engine uses Microsoft Azure Cognitive Services, among other tools, to make emotional judgments.

[0405] Step 5:

[0406] The emotion engine sends the recognized emotional state to the server. The input is the emotional state data obtained in step 4, and the output is the emotional state notification to the server. Specifically, it sends the emotional data to the server using secure communication.

[0407] Step 6:

[0408] The server re-evaluates the usage plan based on the emotional state and generates alternatives as needed. The input is emotional state data and the existing usage plan, and the output is the adjusted alternative. The AI ​​model is run again to formulate a new plan under the changed conditions.

[0409] Step 7:

[0410] The server re-notifies the operator's terminal of the alternative plan. The input is the alternative plan generated in step 6, and the output is the re-notification to the terminal. The alternative plan is transmitted over the network.

[0411] Step 8:

[0412] The terminal displays the adjusted plan to the operator and provides emotional data as updated feedback. The input is the alternative plan received in step 7, and the output is the displayed new schedule and feedback information. The specific action is to show the operator the alternative plan using the display function and return the feedback to the system.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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".

[0429] This invention is implemented as an integrated management system utilizing AI technology, with the aim of promoting the efficient use of agricultural machinery and addressing labor shortages in regional agriculture. This system primarily consists of servers, terminals, and users, with each element working in conjunction to function. A specific embodiment of this system is described below.

[0430] First, the server retrieves information from multiple databases to collect data on machine ownership, farm work schedules, and volunteer registration information, then organizes and stores this data. The collected information is analyzed by an AI algorithm and becomes the foundational data for generating the optimal machine usage schedule for each farmer. The server uses this data to optimize the placement and utilization plan of agricultural machinery, creating plans that suit multiple users (farmers).

[0431] The generated schedule is sent to the terminal via the server. The terminal can be a smartphone or computer, allowing the user to check the schedule and receive detailed information about the device being used. Based on this information, the user can adjust the schedule to suit their own convenience.

[0432] During system operation, the server monitors the real-time usage of agricultural machinery and volunteers. This is achieved by tracking the location and usage of each machine via sensors and GPS devices. The server processes this information in real time, continuously checking whether things are progressing according to schedule. Furthermore, if there are any changes in usage, the server immediately recalculates the schedule and delivers the revised plan to the terminals. This ensures that machinery is used efficiently and enables effective agricultural activities.

[0433] As a concrete example, if a farmer wants to use a tractor, they send their request to the server via a terminal. The server checks the availability of tractors in the area in real time and suggests the optimal time and location for use. If the user accepts the suggestion, the system confirms the use of that tractor from that point onward and automatically adjusts notifications to other users.

[0434] Through this process, the present invention effectively supports the efficiency of regional agriculture, reduces the cost burden on farmers, and contributes to the realization of sustainable agriculture.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The server collects information from a database regarding the ownership status of agricultural machinery, the work schedules of each farmer, and volunteer registration information, and organizes and stores it as up-to-date data.

[0438] Step 2:

[0439] The server analyzes the collected data using AI algorithms to generate an optimal utilization plan for agricultural machinery. This includes an efficient schedule that takes into account the machinery's availability and the farmers' usage preferences.

[0440] Step 3:

[0441] The server sends the generated usage plan to the terminal corresponding to each farmer. The terminal displays the received information via notification or application, allowing users to check their usage schedule.

[0442] Step 4:

[0443] The user checks the notified schedule using their device and, if necessary, sends a request to change the schedule to the server via their device.

[0444] Step 5:

[0445] The server receives the change request from the user, recalculates the schedule using the AI ​​algorithm again, and resends the adjusted plan to the terminal.

[0446] Step 6:

[0447] The server monitors the usage status of each agricultural machine in real time and constantly updates the data obtained from sensors and GPS devices.

[0448] Step 7:

[0449] When the server detects a change in usage, it immediately recalculates the schedule and notifies the terminal of the latest schedule, thereby reducing waste and maintaining efficient operation.

[0450] This processing flow is designed to optimize farmers' use of machinery and also address the problem of labor shortages.

[0451] (Example 1)

[0452] 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."

[0453] Currently, it is difficult to create and adjust optimal deployment and utilization plans for agricultural machinery in order to efficiently utilize agricultural machinery in regional agriculture and to solve the problem of labor shortages at individual farms. With conventional methods, it is difficult to efficiently manage information such as the operating status of agricultural machinery and the schedules of volunteers, and to quickly adjust plans to meet the needs of each farmer, resulting in inefficient agricultural operations.

[0454] 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.

[0455] In this invention, the server includes means for collecting information on the ownership status of agricultural machinery, work plans, and supporter registration information; calculation means for generating an optimal arrangement and utilization plan for machinery based on the collected information; and means for notifying each user's computer of the generated plan. This makes it possible to efficiently create an optimal agricultural machinery utilization plan that meets the needs of each farmer and to quickly recalculate in response to change requests.

[0456] "Agricultural machinery" is a general term for devices or vehicles used to cultivate land, sow seeds, harvest, or perform related agricultural tasks.

[0457] A "work plan" is a detailed plan outlining the types of agricultural work, the schedule, and the procedures to be carried out within a specific period.

[0458] "Supporter registration information" refers to information about individuals or organizations registered to support agricultural activities, and mainly includes data on volunteers and temporary laborers.

[0459] "Optimal allocation" refers to the efficient distribution and allocation of agricultural machinery and labor in order to make the most effective use of limited resources.

[0460] "User's computer" refers to a computer or mobile terminal used by farmers or agricultural personnel, which is a device for receiving, displaying, and operating agricultural machinery usage plans.

[0461] "Calculation means" refers to a function or device for processing information and performing predetermined calculations to obtain a result.

[0462] This invention is an integrated management system utilizing AI technology, primarily aimed at the efficient use of agricultural machinery and the resolution of labor shortages in regional agriculture. This system mainly consists of servers, terminals, and users, with each element playing a specific role and working in coordination.

[0463] First, the server retrieves information on agricultural machinery ownership, work plans, and supporter registration from multiple databases using a relational database management system. This information is then organized using data cleaning and configuration techniques and stored on the server. Database software such as MySQL is primarily used for this process.

[0464] Next, the server uses machine learning algorithms to analyze the stored information and generate an optimal placement and utilization plan for agricultural machinery. Here, machine learning frameworks such as TensorFlow are used to optimize the schedule and plan through AI models.

[0465] The generated plan is sent from the server to the terminal. The terminal is primarily a smartphone or computer owned by the user, and it provides a UI (user interface) for reviewing and adjusting the plan. Based on this information, the user can fine-tune the plan according to their own schedule and requests. The record of any adjustments is updated back on the server in real time.

[0466] This system uses sensor technology and GPS devices to monitor the on-site usage of agricultural machinery and support personnel. A server analyzes the data from these devices and recalculates the schedule in response to changes in conditions. This operation utilizes a real-time messaging platform such as Apache Kafka, ensuring that plans are always up-to-date and adjustments can be made immediately as needed.

[0467] As a concrete example, a user can semi-automatically send a request to the server asking, "Is the tractor available for use from 10 AM tomorrow?" The server can then check the availability in real time and suggest the optimal usage time. This allows agricultural activities within the region to be carried out more efficiently and systematically.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] The server retrieves agricultural machinery ownership, work plans, and supporter registration information from multiple databases. The server uses a relational database management system such as MySQL. Input data includes each farmer's machinery ownership list, work schedule, and supporter registration data. The data is organized through a data cleaning process, removing inconsistencies and redundant information before being accurately stored on the server. The output is organized data that serves as the basis for future analysis and plan generation.

[0471] Step 2:

[0472] The server uses the data organized in Step 1 to perform data analysis using a machine learning model, such as TensorFlow. The inputs are organized machine ownership data and work schedule data. Based on this, the server performs calculations to generate an optimal allocation and utilization plan for each farmer and agricultural machinery. It uses a generative AI model to perform predictions and optimizations to improve the efficiency of the utilization schedule. The output is an optimized agricultural machinery utilization plan for each farmer.

[0473] Step 3:

[0474] The server sends the generated optimized usage plan to the terminal. The input here is the usage plan generated in step 2. The output is the schedule information displayed on the screen of the user's smartphone or computer. The terminal provides this information to the user, making it easy to check the schedule.

[0475] Step 4:

[0476] Users review the usage plan provided through their terminal and adjust it according to their own schedule. Inputs include the schedule information displayed on the terminal and the user's individual appointments. Users modify their schedules through their own actions, and these changes are sent to the server in real time. The output is the updated, adjusted usage plan, which is saved on the server and used for future work.

[0477] Step 5:

[0478] The server monitors the usage status of agricultural machinery in real time. Inputs include data from sensors and GPS devices mounted on the machinery. The server processes this data to continuously monitor usage. It also receives data through messaging systems such as Apache Kafka, helping to dynamically adjust schedules. The output provides accurate agricultural machinery management information based on the latest usage data.

[0479] (Application Example 1)

[0480] 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."

[0481] While the efficient use of machinery and equipment is required in regional agriculture and production systems, labor shortages and difficulties in proper scheduling remain challenges. Furthermore, there is a growing demand for systems that allow managers to accurately understand the real-time operating status of machinery and equipment and respond quickly accordingly.

[0482] 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.

[0483] In this invention, the server includes means for collecting information on the ownership status of agricultural machinery in the region, the work schedules of each farm operator, and the registration information of collaborators; means for generating an optimal usage plan for machinery and equipment using an artificial intelligence algorithm based on the collected information; and means for notifying each farm operator's terminal of the generated usage plan. This enables managers to efficiently operate and adjust machinery and equipment in real time based on visualized information.

[0484] "Local agricultural machinery" refers to various types of machinery and equipment used for agricultural activities in a specific region.

[0485] A "work schedule" is a plan that outlines the tasks to be performed by the farm manager and their collaborators.

[0486] "Registered information of collaborators" refers to data that includes information about individuals and organizations that cooperate in agricultural activities.

[0487] An "artificial intelligence algorithm" is a type of computer program that analyzes collected information and generates an optimal usage plan.

[0488] "Generated usage plan" refers to a plan for the use of agricultural machinery created by an artificial intelligence algorithm.

[0489] "Mechanical equipment" refers to a broad range of devices, including power machines and working machines, used in agriculture and production systems.

[0490] A "terminal" is a device used to provide users with generated usage plans and information, and includes smartphones, computers, and other similar devices.

[0491] "Real time" refers to the actual time that is currently unfolding, in other words, real time.

[0492] "Visualization" refers to displaying information and data in a way that is easy for users to understand.

[0493] One embodiment of this invention is a system that enables the efficient use of machinery and equipment in local agriculture. This system mainly consists of a server, terminals, and users, each working in cooperation with the others.

[0494] The server collects information on the ownership status of machinery and equipment in the region, work schedules, and registered collaborators. This includes integrating information through databases and information networks. Based on the collected information, the server uses artificial intelligence algorithms to generate an optimal usage plan for the machinery and equipment. This plan aims to maximize utilization efficiency and minimize wasted operation. Furthermore, the server monitors the operating status of the machinery and equipment in real time and recalculates and updates the schedule immediately as needed.

[0495] The terminal functions as a medium for notifying users of the generated usage plan. This includes smartphones and computers. This notification function allows users to flexibly understand and adjust the available time and location of machinery and equipment. In addition, administrators can obtain visualized information and issue quick and appropriate work instructions.

[0496] Users (farm owners and managers) can use their terminals to check schedules at their convenience and send modification requests to the server if necessary. This two-way communication ensures the maintenance of an optimal schedule.

[0497] As a concrete example, if a farm owner wants to use a specific machine, they send their desired time and location to the server from their terminal. The server analyzes the overall machine status and makes the optimal usage suggestion. If the suggestion is accepted, the adjusted schedule is automatically notified to other users.

[0498] An example of a prompt message would be something like, "Based on the current operating status of the machinery and equipment and the information of collaborators, please tell us what the next priority task should be."

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] The server collects information from a database regarding the ownership status of local machinery and equipment, work schedules, and registration information of collaborators. Using database queries, it retrieves the necessary information and generates an integrated dataset. This dataset serves as the foundational data for future optimization calculations. The input is the database information, and the output is the integrated dataset.

[0502] Step 2:

[0503] The server uses a generated AI model based on the collected information to create an optimal usage plan for the machinery and equipment. Here, the artificial intelligence algorithm formulates the plan, taking into account parameters such as machine utilization and available time. The optimized usage schedule is output as the calculation result.

[0504] Step 3:

[0505] The server notifies each user's terminal of the generated usage plan. The schedule information is transmitted via a digital communication protocol and displayed on the terminal screen. The input is the generated usage schedule, and the output is the information displayed on the user's terminal.

[0506] Step 4:

[0507] The user checks the received schedule using their terminal and sends modification requests to the server as needed. Input is the user's change request, and output is the notification to the server based on that request. Schedule adjustments may be required based on user actions.

[0508] Step 5:

[0509] The server receives the correction request and recalculates and updates the usage plan. In this recalculation, the AI ​​algorithm reconstructs the schedule based on the newly entered information. The modified schedule is output and notified to the terminal again.

[0510] Step 6:

[0511] The system continuously monitors the real-time operating status of the machinery and equipment. The server performs anomaly detection and adjustments as needed, and keeps the schedule up-to-date. The input is sensor information from the machinery and equipment, and the output is the monitoring results.

[0512] Step 7:

[0513] The administrator will review the visualized information from the terminal and ensure the proper operation of the machinery and equipment. Specifically, this involves checking the current operating status via a video display device and determining the next action to take. This requires generating prompt messages and inputting necessary instructions into the system.

[0514] 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.

[0515] This invention provides a system using AI technology to solve challenges in local agriculture, and by combining it with an emotion engine that recognizes user emotions, it enables more flexible and effective support. Specifically, the system is built to improve the efficient use of agricultural machinery and user satisfaction through the cooperation of the server, terminal, and user elements.

[0516] First, the system uses basic data collection functions to gather and store information on local agricultural machinery ownership, each farmer's work schedule, and volunteer registration information on a server. Then, the collected data is analyzed using an AI algorithm to generate an optimized schedule for agricultural machinery use.

[0517] The generated schedule is notified from the server to each farmer's terminal, allowing users (farmers) to review and adjust the schedule to suit their own work needs. Furthermore, an emotion engine senses the user's reactions and analyzes the user's emotional data. This allows the server to dynamically adjust schedule suggestions based on the user's emotional state, thereby increasing user satisfaction.

[0518] For example, if a farmer user is dissatisfied with a proposed schedule, the emotion engine built into the terminal recognizes that emotion and analyzes the user's tone of voice and facial expressions. If the emotion engine determines that the user is "dissatisfied," the server re-evaluates the user's situation, generates alternative plans, and proposes them to the terminal again. This allows the user to use the system more comfortably and increases the flexibility of their plans.

[0519] During operation, the server monitors the usage of agricultural machinery and volunteers in real time, updating data using information from sensors. Information obtained from the emotion engine is also fed back into the system to help improve the overall service and interface. In this way, a system is provided that optimizes the user experience by recognizing emotions, contributing to increased efficiency in agricultural activities and the revitalization of local communities.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The server collects information from a database regarding the ownership status of agricultural machinery within the region, the work schedules of individual farmers, and volunteer registration information, and stores it on a central server. This data is organized to ensure consistency and integrity.

[0523] Step 2:

[0524] The server analyzes the collected data using AI algorithms to generate an optimal schedule for using agricultural machinery for each farmer. This analysis is designed to maximize the efficiency of machine usage.

[0525] Step 3:

[0526] The server notifies each farmer's terminal of the generated usage schedule. The terminal then notifies the user of the arrival of the new schedule via email or application notification.

[0527] Step 4:

[0528] Users check their schedules on their devices and provide feedback on the suggested usage plans. An emotion engine analyzes the user's emotions and collects emotional data from voice input, facial expression changes, and other sources.

[0529] Step 5:

[0530] The server analyzes the user's emotional data obtained from the emotion engine and determines whether schedule adjustments are necessary based on the results. If necessary, it makes revised schedule suggestions.

[0531] Step 6:

[0532] The server monitors the real-time usage of agricultural machinery and volunteers based on information received from sensors and GPS devices, and updates the data periodically.

[0533] Step 7:

[0534] If the server detects a change in usage, it recalculates the schedule and creates a new, optimized schedule. It then notifies the device again, ensuring the user has access to the most efficient plan.

[0535] By utilizing an emotion engine, we can provide customized schedules that take user emotions into account, improving the convenience and satisfaction of farm work.

[0536] (Example 2)

[0537] 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."

[0538] In modern agriculture, the efficient allocation and use of agricultural machinery is crucial, yet optimizing this within limited resources is difficult. Furthermore, while flexible plan changes are required to meet the work needs of farmers, traditional systems have failed to adequately consider user satisfaction. This has led to problems such as decreased efficiency in agricultural activities and user dissatisfaction.

[0539] 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.

[0540] In this invention, the server includes means for collecting ownership information of agricultural machinery in the region, means for creating an efficient use plan for agricultural machinery using a generation method, and means for recognizing the emotional state of users and adjusting schedule suggestions based on emotional information. This enables optimal allocation and efficient use of agricultural machinery, as well as flexible plan adjustments that take into account the emotions of agricultural workers.

[0541] "Regional agricultural machinery" is a general term for a group of agricultural machinery and equipment used within a specific region.

[0542] "Agricultural workers' work schedules" are records of the planned work activities of people engaged in agriculture.

[0543] "Supporter registration information" refers to data that compiles contact information and basic information of individuals who are willing to help with agricultural activities.

[0544] "Generative methods" refer to algorithms and processes used to create optimal plans based on collected information.

[0545] An "efficient use plan" is a plan created to maximize the use of agricultural machinery and to avoid wasting time and resources.

[0546] "Real-time usage status" refers to information that allows for immediate understanding of the operating status of agricultural machinery and support personnel at the current time.

[0547] "Recognizing emotional states" refers to technology that detects users' emotions from their voice and facial expressions and determines their emotional state.

[0548] "Adjusting schedule proposals based on emotional information" means modifying the plan offered based on the user's emotional response to provide a more satisfying proposal.

[0549] This invention is a system designed to improve the efficiency of agricultural activities within a region and increase the satisfaction of agricultural workers. The embodiments of this invention are described in detail below.

[0550] First, the server collects information on the ownership of agricultural machinery within the region, the work schedules of agricultural workers, and the registration information of support personnel. Based on this information, the server creates an efficient usage plan for agricultural machinery using an AI-powered generation method. At this time, the server analyzes the data using machine learning algorithms to optimize resource allocation.

[0551] Next, the terminal is a device that notifies agricultural workers of the usage plan transmitted from the server. This terminal incorporates emotion recognition technology, which allows it to detect the emotions of agricultural workers. As a result, the terminal recognizes the emotional state from the user's voice tone and facial expressions and transmits that information to the server.

[0552] Agricultural workers, as users, can check their work schedules through their terminals and request revisions as needed. If they are dissatisfied with the proposed usage plan, they can express their feelings through the terminal's emotion recognition function, and the server will receive this information and readjust the plan.

[0553] For example, if a farmer feels they would like a little more free time this Thursday, they can communicate this to their device. The device detects this feeling and sends it to the server. The server receives this information, readjusts the plan, and notifies the device with a new proposal.

[0554] Examples of prompt statements are as follows:

[0555] "Please generate a usage schedule for the latest agricultural machinery."

[0556] "Please make adjustments to the plan to amplify the positive feelings of agricultural workers."

[0557] This system can improve the efficiency of agricultural activities, reduce the psychological burden on farmers, and increase regional agricultural productivity.

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The server receives information on the ownership of agricultural machinery in the region, the work schedules of agricultural workers, and the registration information of support personnel. The input data consists of information obtained from each database. Upon receiving this information, the server verifies the integrity of the data, formats it appropriately, and stores it in the database.

[0561] Step 2:

[0562] The server analyzes the collected data using an AI algorithm. The input at this stage is the consistent data received in step 1. The server uses machine learning to generate the optimal schedule for using agricultural machinery. The algorithm calculates the most efficient machine allocation based on each farmer's work schedule and creates a usage plan as output.

[0563] Step 3:

[0564] The server sends the generated usage plan to each farm worker's terminal. The input is the usage plan created in step 2. This data is notified to the terminal via application or email. In practice, a notification such as "The tractor will be available from 9 AM tomorrow" is sent.

[0565] Step 4:

[0566] The device receives the usage plan, which the user then reviews. The user can submit feedback on the plan and request revisions. Input is notifications from the server, and output is user feedback and emotion data. The device has an emotion engine that recognizes the user's emotions from their voice and facial expressions and performs specific actions accordingly.

[0567] Step 5:

[0568] The terminal sends user emotion data to the server. The input is data about the user's emotional state. The server analyzes the user's feedback and determines if the usage plan needs to be readjusted. The output is the revised schedule, if necessary.

[0569] Step 6:

[0570] The server then resends the revised usage plan to the terminal for user review. In this step, the usage plan is readjusted as input and sent to the terminal. This ensures that the optimal plan is provided to meet the user's needs.

[0571] (Application Example 2)

[0572] 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."

[0573] Achieving efficient operation of machinery and equipment, as well as improving operator satisfaction, are critical challenges in industry. However, current machinery usage plans are static, making it difficult to respond flexibly to real-time changes in circumstances and operator emotions. Furthermore, because operator emotional states are not considered, dissatisfaction with the plan is likely to occur. This raises concerns about decreased work efficiency and increased mental burden on operators. There is a need to solve these problems and realize efficient and flexible planning and operation that takes operator emotions into consideration.

[0574] 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.

[0575] In this invention, the server includes means for collecting information on the ownership status of machinery and equipment in the region, the work plans of each operator, and the registration information of support staff; means for generating an optimal usage plan for the machinery and equipment based on the collected information; and means for recognizing the emotional state of the operators using an emotion engine and dynamically adjusting the schedule. This enables flexible scheduling based on the operators' emotions and efficient operation of the machinery and equipment.

[0576] "Machinery and equipment" refers to equipment used for manufacturing and processing in industry and industrial settings.

[0577] "Operator" refers to a person who operates machinery and manages the process.

[0578] A "work plan" is a guideline that outlines the schedule and procedures for tasks to be performed by operators or machinery.

[0579] "Supporters" refer to personnel or services that assist with the operation of machinery and equipment, or with the work of operators.

[0580] An "AI algorithm" is an artificial intelligence computational method used to analyze data and derive the optimal result.

[0581] An "emotion engine" is a technology that recognizes a person's emotional state and adjusts the behavior of a system based on that information.

[0582] "Real-time" refers to the ability to instantly utilize ongoing data and situations and respond without delay.

[0583] "Feedback" is the act of providing information and opinions to improve a system or process based on the results and evaluations obtained.

[0584] The system implementing this invention operates with a server, terminals, and users working in coordination. The server first collects information on the ownership status of machinery and equipment within a region, the operator's work plan, and the registration of support staff. This data is centrally collected over the network and stored in a database on the server. Using the collected data, the server generates an optimal machinery and equipment usage plan using an AI algorithm. The AI ​​algorithm is built using platforms such as TensorFlow or PyTorch.

[0585] The generated usage plan is notified to each operator's terminal via the communication network. The terminal can then check its work schedule based on this usage plan. The terminal has an emotion engine built in that analyzes the operator's voice and facial expressions to recognize their emotional state in real time. Technologies such as Microsoft Azure Cognitive Services are used for emotion recognition. If the operator feels dissatisfied or stressed, the emotion engine sends that information to the server.

[0586] Upon receiving the emotional data, the server re-evaluates the usage plan as needed, generates alternatives if necessary, and proposes them to the terminal again. This procedure improves operator satisfaction while maintaining planning flexibility. Furthermore, the server monitors the usage of the machine and support staff in real time and updates the situation based on sensor data.

[0587] This system takes into account the emotional state of workers within the factory, improving work efficiency and reducing the mental burden on operators. For example, if a worker feels fatigued due to monotony during assembly line work, the emotion engine analyzes their facial expression, and the server notifies the terminal to readjust the break time.

[0588] Examples of prompts for a generative AI model:

[0589] "If factory workers are unhappy with their current schedule, please tell us how to adjust it based on sentiment recognition data."

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The server collects information on the ownership status of machinery and equipment within the region, operator work plans, and support staff registration information. This information is stored in a database. Inputs are machinery and equipment ownership data, work plan data, and support staff registration information, and output is a formatted dataset. This dataset is used for subsequent processing. Specifically, it retrieves the necessary information from each device via the network.

[0593] Step 2:

[0594] The server generates an optimal usage plan for the machinery and equipment using an AI algorithm based on the collected information. The input is the dataset formatted in step 1, and the output is the optimized usage plan. Here, the AI ​​model is executed using TensorFlow or PyTorch, and the optimal schedule is calculated based on the obtained results.

[0595] Step 3:

[0596] The server notifies the operator's terminal of the generated usage plan. The input is the usage plan generated in step 2, and the output is the notification to the terminal. The server performs the operation of transmitting the plan information via network communication.

[0597] Step 4:

[0598] The device uses a built-in emotion engine to analyze the operator's voice and facial expressions to recognize their emotional state. Input is the operator's voice and facial expression data, and output is the analyzed emotional state. The emotion engine uses Microsoft Azure Cognitive Services, among other tools, to make emotional judgments.

[0599] Step 5:

[0600] The emotion engine sends the recognized emotional state to the server. The input is the emotional state data obtained in step 4, and the output is the emotional state notification to the server. Specifically, it sends the emotional data to the server using secure communication.

[0601] Step 6:

[0602] The server re-evaluates the usage plan based on the emotional state and generates alternatives as needed. The input is emotional state data and the existing usage plan, and the output is the adjusted alternative. The AI ​​model is run again to formulate a new plan under the changed conditions.

[0603] Step 7:

[0604] The server re-notifies the operator's terminal of the alternative plan. The input is the alternative plan generated in step 6, and the output is the re-notification to the terminal. The alternative plan is transmitted over the network.

[0605] Step 8:

[0606] The terminal displays the adjusted plan to the operator and provides emotional data as updated feedback. The input is the alternative plan received in step 7, and the output is the displayed new schedule and feedback information. The specific action is to show the operator the alternative plan using the display function and return the feedback to the system.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] [Fourth Embodiment]

[0611] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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".

[0624] This invention is implemented as an integrated management system utilizing AI technology, with the aim of promoting the efficient use of agricultural machinery and addressing labor shortages in regional agriculture. This system primarily consists of servers, terminals, and users, with each element working in conjunction to function. A specific embodiment of this system is described below.

[0625] First, the server retrieves information from multiple databases to collect data on machine ownership, farm work schedules, and volunteer registration information, then organizes and stores this data. The collected information is analyzed by an AI algorithm and becomes the foundational data for generating the optimal machine usage schedule for each farmer. The server uses this data to optimize the placement and utilization plan of agricultural machinery, creating plans that suit multiple users (farmers).

[0626] The generated schedule is sent to the terminal via the server. The terminal can be a smartphone or computer, allowing the user to check the schedule and receive detailed information about the device being used. Based on this information, the user can adjust the schedule to suit their own convenience.

[0627] During system operation, the server monitors the real-time usage of agricultural machinery and volunteers. This is achieved by tracking the location and usage of each machine via sensors and GPS devices. The server processes this information in real time, continuously checking whether things are progressing according to schedule. Furthermore, if there are any changes in usage, the server immediately recalculates the schedule and delivers the revised plan to the terminals. This ensures that machinery is used efficiently and enables effective agricultural activities.

[0628] As a concrete example, if a farmer wants to use a tractor, they send their request to the server via a terminal. The server checks the availability of tractors in the area in real time and suggests the optimal time and location for use. If the user accepts the suggestion, the system confirms the use of that tractor from that point onward and automatically adjusts notifications to other users.

[0629] Through this process, the present invention effectively supports the efficiency of regional agriculture, reduces the cost burden on farmers, and contributes to the realization of sustainable agriculture.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] The server collects information from a database regarding the ownership status of agricultural machinery, the work schedules of each farmer, and volunteer registration information, and organizes and stores it as up-to-date data.

[0633] Step 2:

[0634] The server analyzes the collected data using AI algorithms to generate an optimal utilization plan for agricultural machinery. This includes an efficient schedule that takes into account the machinery's availability and the farmers' usage preferences.

[0635] Step 3:

[0636] The server sends the generated usage plan to the terminal corresponding to each farmer. The terminal displays the received information via notification or application, allowing users to check their usage schedule.

[0637] Step 4:

[0638] The user checks the notified schedule using their device and, if necessary, sends a request to change the schedule to the server via their device.

[0639] Step 5:

[0640] The server receives the change request from the user, recalculates the schedule using the AI ​​algorithm again, and resends the adjusted plan to the terminal.

[0641] Step 6:

[0642] The server monitors the usage status of each agricultural machine in real time and constantly updates the data obtained from sensors and GPS devices.

[0643] Step 7:

[0644] When the server detects a change in usage, it immediately recalculates the schedule and notifies the terminal of the latest schedule, thereby reducing waste and maintaining efficient operation.

[0645] This processing flow is designed to optimize farmers' use of machinery and also address the problem of labor shortages.

[0646] (Example 1)

[0647] 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".

[0648] Currently, it is difficult to create and adjust optimal deployment and utilization plans for agricultural machinery in order to efficiently utilize agricultural machinery in regional agriculture and to solve the problem of labor shortages at individual farms. With conventional methods, it is difficult to efficiently manage information such as the operating status of agricultural machinery and the schedules of volunteers, and to quickly adjust plans to meet the needs of each farmer, resulting in inefficient agricultural operations.

[0649] 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.

[0650] In this invention, the server includes means for collecting information on the ownership status of agricultural machinery, work plans, and supporter registration information; calculation means for generating an optimal arrangement and utilization plan for machinery based on the collected information; and means for notifying each user's computer of the generated plan. This makes it possible to efficiently create an optimal agricultural machinery utilization plan that meets the needs of each farmer and to quickly recalculate in response to change requests.

[0651] "Agricultural machinery" is a general term for devices or vehicles used to cultivate land, sow seeds, harvest, or perform related agricultural tasks.

[0652] A "work plan" is a detailed plan outlining the types of agricultural work, the schedule, and the procedures to be carried out within a specific period.

[0653] "Supporter registration information" refers to information about individuals or organizations registered to support agricultural activities, and mainly includes data on volunteers and temporary laborers.

[0654] "Optimal allocation" refers to the efficient distribution and allocation of agricultural machinery and labor in order to make the most effective use of limited resources.

[0655] "User's computer" refers to a computer or mobile terminal used by farmers or agricultural personnel, which is a device for receiving, displaying, and operating agricultural machinery usage plans.

[0656] "Calculation means" refers to a function or device for processing information and performing predetermined calculations to obtain a result.

[0657] This invention is an integrated management system utilizing AI technology, primarily aimed at the efficient use of agricultural machinery and the resolution of labor shortages in regional agriculture. This system mainly consists of servers, terminals, and users, with each element playing a specific role and working in coordination.

[0658] First, the server retrieves information on agricultural machinery ownership, work plans, and supporter registration from multiple databases using a relational database management system. This information is then organized using data cleaning and configuration techniques and stored on the server. Database software such as MySQL is primarily used for this process.

[0659] Next, the server uses machine learning algorithms to analyze the stored information and generate an optimal placement and utilization plan for agricultural machinery. Here, machine learning frameworks such as TensorFlow are used to optimize the schedule and plan through AI models.

[0660] The generated plan is sent from the server to the terminal. The terminal is primarily a smartphone or computer owned by the user, and it provides a UI (user interface) for reviewing and adjusting the plan. Based on this information, the user can fine-tune the plan according to their own schedule and requests. The record of any adjustments is updated back on the server in real time.

[0661] This system uses sensor technology and GPS devices to monitor the on-site usage of agricultural machinery and support personnel. A server analyzes the data from these devices and recalculates the schedule in response to changes in conditions. This operation utilizes a real-time messaging platform such as Apache Kafka, ensuring that plans are always up-to-date and adjustments can be made immediately as needed.

[0662] As a concrete example, a user can semi-automatically send a request to the server asking, "Is the tractor available for use from 10 AM tomorrow?" The server can then check the availability in real time and suggest the optimal usage time. This allows agricultural activities within the region to be carried out more efficiently and systematically.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The server retrieves agricultural machinery ownership, work plans, and supporter registration information from multiple databases. The server uses a relational database management system such as MySQL. Input data includes each farmer's machinery ownership list, work schedule, and supporter registration data. The data is organized through a data cleaning process, removing inconsistencies and redundant information before being accurately stored on the server. The output is organized data that serves as the basis for future analysis and plan generation.

[0666] Step 2:

[0667] The server uses the data organized in Step 1 to perform data analysis using a machine learning model, such as TensorFlow. The inputs are organized machine ownership data and work schedule data. Based on this, the server performs calculations to generate an optimal allocation and utilization plan for each farmer and agricultural machinery. It uses a generative AI model to perform predictions and optimizations to improve the efficiency of the utilization schedule. The output is an optimized agricultural machinery utilization plan for each farmer.

[0668] Step 3:

[0669] The server sends the generated optimized usage plan to the terminal. The input here is the usage plan generated in step 2. The output is the schedule information displayed on the screen of the user's smartphone or computer. The terminal provides this information to the user, making it easy to check the schedule.

[0670] Step 4:

[0671] Users review the usage plan provided through their terminal and adjust it according to their own schedule. Inputs include the schedule information displayed on the terminal and the user's individual appointments. Users modify their schedules through their own actions, and these changes are sent to the server in real time. The output is the updated, adjusted usage plan, which is saved on the server and used for future work.

[0672] Step 5:

[0673] The server monitors the usage status of agricultural machinery in real time. Inputs include data from sensors and GPS devices mounted on the machinery. The server processes this data to continuously monitor usage. It also receives data through messaging systems such as Apache Kafka, helping to dynamically adjust schedules. The output provides accurate agricultural machinery management information based on the latest usage data.

[0674] (Application Example 1)

[0675] 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".

[0676] While the efficient use of machinery and equipment is required in regional agriculture and production systems, labor shortages and difficulties in proper scheduling remain challenges. Furthermore, there is a growing demand for systems that allow managers to accurately understand the real-time operating status of machinery and equipment and respond quickly accordingly.

[0677] 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.

[0678] In this invention, the server includes means for collecting information on the ownership status of agricultural machinery in the region, the work schedules of each farm operator, and the registration information of collaborators; means for generating an optimal usage plan for machinery and equipment using an artificial intelligence algorithm based on the collected information; and means for notifying each farm operator's terminal of the generated usage plan. This enables managers to efficiently operate and adjust machinery and equipment in real time based on visualized information.

[0679] "Local agricultural machinery" refers to various types of machinery and equipment used for agricultural activities in a specific region.

[0680] A "work schedule" is a plan that outlines the tasks to be performed by the farm manager and their collaborators.

[0681] "Registered information of collaborators" refers to data that includes information about individuals and organizations that cooperate in agricultural activities.

[0682] An "artificial intelligence algorithm" is a type of computer program that analyzes collected information and generates an optimal usage plan.

[0683] "Generated usage plan" refers to a plan for the use of agricultural machinery created by an artificial intelligence algorithm.

[0684] "Mechanical equipment" refers to a broad range of devices, including power machines and working machines, used in agriculture and production systems.

[0685] A "terminal" is a device used to provide users with generated usage plans and information, and includes smartphones, computers, and other similar devices.

[0686] "Real time" refers to the actual time that is currently unfolding, in other words, real time.

[0687] "Visualization" refers to displaying information and data in a way that is easy for users to understand.

[0688] One embodiment of this invention is a system that enables the efficient use of machinery and equipment in local agriculture. This system mainly consists of a server, terminals, and users, each working in cooperation with the others.

[0689] The server collects information on the ownership status of machinery and equipment in the region, work schedules, and registered collaborators. This includes integrating information through databases and information networks. Based on the collected information, the server uses artificial intelligence algorithms to generate an optimal usage plan for the machinery and equipment. This plan aims to maximize utilization efficiency and minimize wasted operation. Furthermore, the server monitors the operating status of the machinery and equipment in real time and recalculates and updates the schedule immediately as needed.

[0690] The terminal functions as a medium for notifying users of the generated usage plan. This includes smartphones and computers. This notification function allows users to flexibly understand and adjust the available time and location of machinery and equipment. In addition, administrators can obtain visualized information and issue quick and appropriate work instructions.

[0691] Users (farm owners and managers) can use their terminals to check schedules at their convenience and send modification requests to the server if necessary. This two-way communication ensures the maintenance of an optimal schedule.

[0692] As a concrete example, if a farm owner wants to use a specific machine, they send their desired time and location to the server from their terminal. The server analyzes the overall machine status and makes the optimal usage suggestion. If the suggestion is accepted, the adjusted schedule is automatically notified to other users.

[0693] An example of a prompt message would be something like, "Based on the current operating status of the machinery and equipment and the information of collaborators, please tell us what the next priority task should be."

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] The server collects information from a database regarding the ownership status of local machinery and equipment, work schedules, and registration information of collaborators. Using database queries, it retrieves the necessary information and generates an integrated dataset. This dataset serves as the foundational data for future optimization calculations. The input is the database information, and the output is the integrated dataset.

[0697] Step 2:

[0698] The server uses a generated AI model based on the collected information to create an optimal usage plan for the machinery and equipment. Here, the artificial intelligence algorithm formulates the plan, taking into account parameters such as machine utilization and available time. The optimized usage schedule is output as the calculation result.

[0699] Step 3:

[0700] The server notifies each user's terminal of the generated usage plan. The schedule information is transmitted via a digital communication protocol and displayed on the terminal screen. The input is the generated usage schedule, and the output is the information displayed on the user's terminal.

[0701] Step 4:

[0702] The user checks the received schedule using their terminal and sends modification requests to the server as needed. Input is the user's change request, and output is the notification to the server based on that request. Schedule adjustments may be required based on user actions.

[0703] Step 5:

[0704] The server receives the correction request and recalculates and updates the usage plan. In this recalculation, the AI ​​algorithm reconstructs the schedule based on the newly entered information. The modified schedule is output and notified to the terminal again.

[0705] Step 6:

[0706] The system continuously monitors the real-time operating status of the machinery and equipment. The server performs anomaly detection and adjustments as needed, and keeps the schedule up-to-date. The input is sensor information from the machinery and equipment, and the output is the monitoring results.

[0707] Step 7:

[0708] The administrator will review the visualized information from the terminal and ensure the proper operation of the machinery and equipment. Specifically, this involves checking the current operating status via a video display device and determining the next action to take. This requires generating prompt messages and inputting necessary instructions into the system.

[0709] 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.

[0710] This invention provides a system using AI technology to solve challenges in local agriculture, and by combining it with an emotion engine that recognizes user emotions, it enables more flexible and effective support. Specifically, the system is built to improve the efficient use of agricultural machinery and user satisfaction through the cooperation of the server, terminal, and user elements.

[0711] First, the system uses basic data collection functions to gather and store information on local agricultural machinery ownership, each farmer's work schedule, and volunteer registration information on a server. Then, the collected data is analyzed using an AI algorithm to generate an optimized schedule for agricultural machinery use.

[0712] The generated schedule is notified from the server to each farmer's terminal, allowing users (farmers) to review and adjust the schedule to suit their own work needs. Furthermore, an emotion engine senses the user's reactions and analyzes the user's emotional data. This allows the server to dynamically adjust schedule suggestions based on the user's emotional state, thereby increasing user satisfaction.

[0713] For example, if a farmer user is dissatisfied with a proposed schedule, the emotion engine built into the terminal recognizes that emotion and analyzes the user's tone of voice and facial expressions. If the emotion engine determines that the user is "dissatisfied," the server re-evaluates the user's situation, generates alternative plans, and proposes them to the terminal again. This allows the user to use the system more comfortably and increases the flexibility of their plans.

[0714] During operation, the server monitors the usage of agricultural machinery and volunteers in real time, updating data using information from sensors. Information obtained from the emotion engine is also fed back into the system to help improve the overall service and interface. In this way, a system is provided that optimizes the user experience by recognizing emotions, contributing to increased efficiency in agricultural activities and the revitalization of local communities.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The server collects information from a database regarding the ownership status of agricultural machinery within the region, the work schedules of individual farmers, and volunteer registration information, and stores it on a central server. This data is organized to ensure consistency and integrity.

[0718] Step 2:

[0719] The server analyzes the collected data using AI algorithms to generate an optimal schedule for using agricultural machinery for each farmer. This analysis is designed to maximize the efficiency of machine usage.

[0720] Step 3:

[0721] The server notifies each farmer's terminal of the generated usage schedule. The terminal then notifies the user of the arrival of the new schedule via email or application notification.

[0722] Step 4:

[0723] Users check their schedules on their devices and provide feedback on the suggested usage plans. An emotion engine analyzes the user's emotions and collects emotional data from voice input, facial expression changes, and other sources.

[0724] Step 5:

[0725] The server analyzes the user's emotional data obtained from the emotion engine and determines whether schedule adjustments are necessary based on the results. If necessary, it makes revised schedule suggestions.

[0726] Step 6:

[0727] The server monitors the real-time usage of agricultural machinery and volunteers based on information received from sensors and GPS devices, and updates the data periodically.

[0728] Step 7:

[0729] If the server detects a change in usage, it recalculates the schedule and creates a new, optimized schedule. It then notifies the device again, ensuring the user has access to the most efficient plan.

[0730] By utilizing an emotion engine, we can provide customized schedules that take user emotions into account, improving the convenience and satisfaction of farm work.

[0731] (Example 2)

[0732] 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".

[0733] In modern agriculture, the efficient allocation and use of agricultural machinery is crucial, yet optimizing this within limited resources is difficult. Furthermore, while flexible plan changes are required to meet the work needs of farmers, traditional systems have failed to adequately consider user satisfaction. This has led to problems such as decreased efficiency in agricultural activities and user dissatisfaction.

[0734] 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.

[0735] In this invention, the server includes means for collecting ownership information of agricultural machinery in the region, means for creating an efficient use plan for agricultural machinery using a generation method, and means for recognizing the emotional state of users and adjusting schedule suggestions based on emotional information. This enables optimal allocation and efficient use of agricultural machinery, as well as flexible plan adjustments that take into account the emotions of agricultural workers.

[0736] "Regional agricultural machinery" is a general term for a group of agricultural machinery and equipment used within a specific region.

[0737] "Agricultural workers' work schedules" are records of the planned work activities of people engaged in agriculture.

[0738] "Supporter registration information" refers to data that compiles contact information and basic information of individuals who are willing to help with agricultural activities.

[0739] "Generative methods" refer to algorithms and processes used to create optimal plans based on collected information.

[0740] An "efficient use plan" is a plan created to maximize the use of agricultural machinery and to avoid wasting time and resources.

[0741] "Real-time usage status" refers to information that allows for immediate understanding of the operating status of agricultural machinery and support personnel at the current time.

[0742] "Recognizing emotional states" refers to technology that detects users' emotions from their voice and facial expressions and determines their emotional state.

[0743] "Adjusting schedule proposals based on emotional information" means modifying the plan offered based on the user's emotional response to provide a more satisfying proposal.

[0744] This invention is a system designed to improve the efficiency of agricultural activities within a region and increase the satisfaction of agricultural workers. The embodiments of this invention are described in detail below.

[0745] First, the server collects information on the ownership of agricultural machinery within the region, the work schedules of agricultural workers, and the registration information of support personnel. Based on this information, the server creates an efficient usage plan for agricultural machinery using an AI-powered generation method. At this time, the server analyzes the data using machine learning algorithms to optimize resource allocation.

[0746] Next, the terminal is a device that notifies agricultural workers of the usage plan transmitted from the server. This terminal incorporates emotion recognition technology, which allows it to detect the emotions of agricultural workers. As a result, the terminal recognizes the emotional state from the user's voice tone and facial expressions and transmits that information to the server.

[0747] Agricultural workers, as users, can check their work schedules through their terminals and request revisions as needed. If they are dissatisfied with the proposed usage plan, they can express their feelings through the terminal's emotion recognition function, and the server will receive this information and readjust the plan.

[0748] For example, if a farmer feels they would like a little more free time this Thursday, they can communicate this to their device. The device detects this feeling and sends it to the server. The server receives this information, readjusts the plan, and notifies the device with a new proposal.

[0749] Examples of prompt statements are as follows:

[0750] "Please generate a usage schedule for the latest agricultural machinery."

[0751] "Please make adjustments to the plan to amplify the positive feelings of agricultural workers."

[0752] This system can improve the efficiency of agricultural activities, reduce the psychological burden on farmers, and increase regional agricultural productivity.

[0753] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0754] Step 1:

[0755] The server receives information on the ownership of agricultural machinery in the region, the work schedules of agricultural workers, and the registration information of support personnel. The input data consists of information obtained from each database. Upon receiving this information, the server verifies the integrity of the data, formats it appropriately, and stores it in the database.

[0756] Step 2:

[0757] The server analyzes the collected data using an AI algorithm. The input at this stage is the consistent data received in step 1. The server uses machine learning to generate the optimal schedule for using agricultural machinery. The algorithm calculates the most efficient machine allocation based on each farmer's work schedule and creates a usage plan as output.

[0758] Step 3:

[0759] The server sends the generated usage plan to each farm worker's terminal. The input is the usage plan created in step 2. This data is notified to the terminal via application or email. In practice, a notification such as "The tractor will be available from 9 AM tomorrow" is sent.

[0760] Step 4:

[0761] The device receives the usage plan, which the user then reviews. The user can submit feedback on the plan and request revisions. Input is notifications from the server, and output is user feedback and emotion data. The device has an emotion engine that recognizes the user's emotions from their voice and facial expressions and performs specific actions accordingly.

[0762] Step 5:

[0763] The terminal sends user emotion data to the server. The input is data about the user's emotional state. The server analyzes the user's feedback and determines if the usage plan needs to be readjusted. The output is the revised schedule, if necessary.

[0764] Step 6:

[0765] The server then resends the revised usage plan to the terminal for user review. In this step, the usage plan is readjusted as input and sent to the terminal. This ensures that the optimal plan is provided to meet the user's needs.

[0766] (Application Example 2)

[0767] 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".

[0768] Achieving efficient operation of machinery and equipment, as well as improving operator satisfaction, are critical challenges in industry. However, current machinery usage plans are static, making it difficult to respond flexibly to real-time changes in circumstances and operator emotions. Furthermore, because operator emotional states are not considered, dissatisfaction with the plan is likely to occur. This raises concerns about decreased work efficiency and increased mental burden on operators. There is a need to solve these problems and realize efficient and flexible planning and operation that takes operator emotions into consideration.

[0769] 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.

[0770] In this invention, the server includes means for collecting information on the ownership status of machinery and equipment in the region, the work plans of each operator, and the registration information of support staff; means for generating an optimal usage plan for the machinery and equipment based on the collected information; and means for recognizing the emotional state of the operators using an emotion engine and dynamically adjusting the schedule. This enables flexible scheduling based on the operators' emotions and efficient operation of the machinery and equipment.

[0771] "Machinery and equipment" refers to equipment used for manufacturing and processing in industry and industrial settings.

[0772] "Operator" refers to a person who operates machinery and manages the process.

[0773] A "work plan" is a guideline that outlines the schedule and procedures for tasks to be performed by operators or machinery.

[0774] "Supporters" refer to personnel or services that assist with the operation of machinery and equipment, or with the work of operators.

[0775] An "AI algorithm" is an artificial intelligence computational method used to analyze data and derive the optimal result.

[0776] An "emotion engine" is a technology that recognizes a person's emotional state and adjusts the behavior of a system based on that information.

[0777] "Real-time" refers to the ability to instantly utilize ongoing data and situations and respond without delay.

[0778] "Feedback" is the act of providing information and opinions to improve a system or process based on the results and evaluations obtained.

[0779] The system implementing this invention operates with a server, terminals, and users working in coordination. The server first collects information on the ownership status of machinery and equipment within a region, the operator's work plan, and the registration of support staff. This data is centrally collected over the network and stored in a database on the server. Using the collected data, the server generates an optimal machinery and equipment usage plan using an AI algorithm. The AI ​​algorithm is built using platforms such as TensorFlow or PyTorch.

[0780] The generated usage plan is notified to each operator's terminal via the communication network. The terminal can then check its work schedule based on this usage plan. The terminal has an emotion engine built in that analyzes the operator's voice and facial expressions to recognize their emotional state in real time. Technologies such as Microsoft Azure Cognitive Services are used for emotion recognition. If the operator feels dissatisfied or stressed, the emotion engine sends that information to the server.

[0781] Upon receiving the emotional data, the server re-evaluates the usage plan as needed, generates alternatives if necessary, and proposes them to the terminal again. This procedure improves operator satisfaction while maintaining planning flexibility. Furthermore, the server monitors the usage of the machine and support staff in real time and updates the situation based on sensor data.

[0782] This system takes into account the emotional state of workers within the factory, improving work efficiency and reducing the mental burden on operators. For example, if a worker feels fatigued due to monotony during assembly line work, the emotion engine analyzes their facial expression, and the server notifies the terminal to readjust the break time.

[0783] Examples of prompts for a generative AI model:

[0784] "If factory workers are unhappy with their current schedule, please tell us how to adjust it based on sentiment recognition data."

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The server collects information on the ownership status of machinery and equipment within the region, operator work plans, and support staff registration information. This information is stored in a database. Inputs are machinery and equipment ownership data, work plan data, and support staff registration information, and output is a formatted dataset. This dataset is used for subsequent processing. Specifically, it retrieves the necessary information from each device via the network.

[0788] Step 2:

[0789] The server generates an optimal usage plan for the machinery and equipment using an AI algorithm based on the collected information. The input is the dataset formatted in step 1, and the output is the optimized usage plan. Here, the AI ​​model is executed using TensorFlow or PyTorch, and the optimal schedule is calculated based on the obtained results.

[0790] Step 3:

[0791] The server notifies the operator's terminal of the generated usage plan. The input is the usage plan generated in step 2, and the output is the notification to the terminal. The server performs the operation of transmitting the plan information via network communication.

[0792] Step 4:

[0793] The device uses a built-in emotion engine to analyze the operator's voice and facial expressions to recognize their emotional state. Input is the operator's voice and facial expression data, and output is the analyzed emotional state. The emotion engine uses Microsoft Azure Cognitive Services, among other tools, to make emotional judgments.

[0794] Step 5:

[0795] The emotion engine sends the recognized emotional state to the server. The input is the emotional state data obtained in step 4, and the output is the emotional state notification to the server. Specifically, it sends the emotional data to the server using secure communication.

[0796] Step 6:

[0797] The server re-evaluates the usage plan based on the emotional state and generates alternatives as needed. The input is emotional state data and the existing usage plan, and the output is the adjusted alternative. The AI ​​model is run again to formulate a new plan under the changed conditions.

[0798] Step 7:

[0799] The server re-notifies the operator's terminal of the alternative plan. The input is the alternative plan generated in step 6, and the output is the re-notification to the terminal. The alternative plan is transmitted over the network.

[0800] Step 8:

[0801] The terminal displays the adjusted plan to the operator and provides emotional data as updated feedback. The input is the alternative plan received in step 7, and the output is the displayed new schedule and feedback information. The specific action is to show the operator the alternative plan using the display function and return the feedback to the system.

[0802] 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.

[0803] 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 those described above. 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 shown 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.

[0804] 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 robot 414.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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 this memory.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A means of collecting information on the ownership status of agricultural machinery in the region, the work schedules of each farmer, and the registration information of volunteers,

[0826] A means for generating an optimal usage plan for agricultural machinery using an AI algorithm based on the collected information,

[0827] A means of notifying each farmer's terminal of the generated usage plan,

[0828] Means for monitoring the real-time usage status of agricultural machinery and volunteers,

[0829] A system that includes a means to update the schedule and send another notification when usage status changes.

[0830] (Claim 2)

[0831] The system according to claim 1, comprising means for receiving schedule confirmation and revision requests from farmers and performing recalculations.

[0832] (Claim 3)

[0833] The system according to claim 1, comprising means for aggregating usage data and generating reports, and means for improving the overall efficiency of the system by utilizing user feedback.

[0834] "Example 1"

[0835] (Claim 1)

[0836] A means of collecting information on the ownership status of agricultural machinery, work plans, and supporter registration information,

[0837] Based on the collected information, a calculation means for generating an optimal machine placement and utilization plan,

[0838] A means of notifying each user's computer of the generated plan,

[0839] Monitoring means for understanding the usage status of machines and supporters,

[0840] A system that includes means to revise the plan and re-notify users when circumstances change.

[0841] (Claim 2)

[0842] The system according to claim 1, which receives plan confirmation and change requests from users and performs recalculations.

[0843] (Claim 3)

[0844] The system according to claim 1, comprising means for aggregating usage results and creating a report, and means for improving the overall efficiency of the system by incorporating user feedback.

[0845] "Application Example 1"

[0846] (Claim 1)

[0847] A means of collecting information on the ownership status of agricultural machinery in the region, the work schedules of each farm owner, and the registration information of collaborators,

[0848] A means for generating an optimal usage plan for machinery and equipment using an artificial intelligence algorithm based on the collected information,

[0849] A means of notifying each farm manager's terminal of the generated usage plan,

[0850] Means for monitoring the real-time usage status of machinery and equipment and collaborators,

[0851] A means to update the schedule and notify again when usage status changes,

[0852] A means for monitoring the operating status of machinery and equipment in a production system and generating an efficient task plan,

[0853] A means of providing administrators with a video display device to visualize planned tasks,

[0854] A system that includes means for adjusting the operation of machinery and equipment based on shortages of specific materials.

[0855] (Claim 2)

[0856] The system according to claim 1, comprising means for receiving schedule confirmation and revision requests from farm managers and performing recalculations.

[0857] (Claim 3)

[0858] The system according to claim 1, comprising means for aggregating information on usage results and generating a report, and means for improving the overall efficiency of the system by utilizing user feedback.

[0859] "Example 2 of combining an emotion engine"

[0860] (Claim 1)

[0861] A means of collecting information on the ownership of agricultural machinery in the region, the work schedules of agricultural workers, and the registration information of supporters,

[0862] A means for creating an efficient usage plan for agricultural machinery using a generation method based on the collected information,

[0863] Means for transmitting the generated usage plan to each agricultural worker's device,

[0864] A means of tracking the current usage status of agricultural machinery and supporters,

[0865] A means to revise the schedule and resend it in response to changing circumstances,

[0866] A system that recognizes the emotional state of a user and includes means for adjusting schedule suggestions based on emotional information.

[0867] (Claim 2)

[0868] The system according to claim 1, comprising means for receiving requests for plan confirmation and revision from agricultural workers and for re-evaluating the plan.

[0869] (Claim 3)

[0870] The system according to claim 1, comprising means for accumulating data on usage results and generating information, and means for improving the overall performance of the system by utilizing user feedback.

[0871] "Application example 2 when combining with an emotional engine"

[0872] (Claim 1)

[0873] A means of collecting information on the ownership status of machinery and equipment in the region, the work plans of each operator, and the registration information of support personnel,

[0874] A means for generating an optimal usage plan for machinery and equipment using an AI algorithm based on the collected information,

[0875] Means for notifying each operator's device of the generated usage plan,

[0876] Means for monitoring the real-time usage status of machinery and equipment and support personnel,

[0877] A means of updating the plan and notifying again when usage changes,

[0878] A means of flexibly adjusting the schedule using an emotion engine that recognizes the emotional state of the operator,

[0879] A system that includes means for analyzing visual and audio input and suggesting options based on emotions.

[0880] (Claim 2)

[0881] The system according to claim 1, comprising means for receiving requests for plan confirmation or modification from the operator and performing recalculations, and means for presenting alternative plans according to the emotional state.

[0882] (Claim 3)

[0883] The system according to claim 1, comprising means for aggregating data on usage results and generating reports, and means for improving the overall efficiency of the system by utilizing operator feedback and sentiment data. [Explanation of Symbols]

[0884] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information on the ownership status of agricultural machinery in the region, the work schedules of each farmer, and the registration information of volunteers, A means for generating an optimal usage plan for agricultural machinery using an AI algorithm based on the collected information, A means of notifying each farmer's terminal of the generated usage plan, Means for monitoring the real-time usage status of agricultural machinery and volunteers, A system that includes a means to update the schedule and send another notification when usage status changes.

2. The system according to claim 1, further comprising means for receiving schedule confirmation and revision requests from farmers and performing recalculations.

3. The system according to claim 1, comprising means for aggregating usage data and generating reports, and means for improving the overall efficiency of the system by utilizing user feedback.

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