system

Abstract

We provide the system. [Solution] A data collection means that acquires data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land, Data preprocessing means for preprocessing data collected by the data collection means and converting it into an analyzable format, An analysis means analyzes the data obtained by the data preprocessing means and performs an analysis to predict the health status and growth of crops, Based on the analysis results obtained by the aforementioned analysis means, a schedule generation means generates an optimal schedule for agricultural work, A notification means that notifies agricultural workers of the schedule generated by the schedule generation means and instructs the work device to execute it, In collaboration with the national government, local governments, and staffing agencies, we have developed a human resource matching system that matches aspiring agricultural workers with agricultural organizations. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the domestic food production volume has been on a decreasing trend, and the main factors include the aging and decrease of agricultural workers. In addition, the introduction of the latest technologies and equipment is essential for improving agricultural productivity, but in order to efficiently operate them, it is necessary to cultivate and secure skilled personnel. Furthermore, a system is required to effectively attract those who wish to engage in agriculture from urban areas to the agricultural field to compensate for the shortage of personnel. In such a current situation, there is an urgent need for a system that can achieve efficient and sustainable agricultural production.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that analyzes growth status and abnormalities based on data on crop condition, soil conditions, and weather conditions acquired from information collection devices installed in agricultural land, and generates an optimal work schedule. Furthermore, machine learning is used in the analysis to achieve highly accurate anomaly detection. In addition, by collaborating with the national government, local governments, and staffing agencies, it provides a means to effectively guide urban labor into the agricultural sector and compensate for labor shortages. In this way, the aim is to realize efficient and sustainable food production and prevent a decline in domestic food production.

[0006] An "information gathering device" is a device installed on agricultural land to acquire data such as crop conditions, soil conditions, and weather conditions.

[0007] A "data collection means" is a component of a system that has the function of importing data obtained from an information collection device into a server.

[0008] "Data preprocessing means" refers to processes such as noise reduction and outlier filtering performed to convert collected raw data into an analyzable format.

[0009] "Analysis means" refers to a function that uses machine learning models and other analysis techniques to predict the health and growth of crops based on data obtained by data preprocessing means.

[0010] "Schedule generation means" refers to algorithms and systems that automatically generate the optimal schedule for agricultural work based on the results obtained by the analysis means.

[0011] A "notification means" is a communication function that transmits the generated work schedule to agricultural workers and instructs the work equipment to execute it.

[0012] "Talent matching methods" refer to processes and systems that work in cooperation with the national government, local governments, and staffing agencies to appropriately connect urban labor with agricultural organizations.

[0013] A "machine learning model" is an artificial intelligence technology used for data analysis, referring to an algorithm that recognizes patterns from data and classifies and predicts the state of crops. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

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

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system aimed at improving agricultural efficiency and realizing a sustainable production system. This system has the functions of data collection, analysis, schedule generation, notification, and personnel matching. Specific embodiments of each function are described below.

[0036] Data collection

[0037] The server acquires real-time data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land. This data is automatically recorded periodically and stored on the server. Examples include humidity and temperature data from soil sensors, and crop image data captured by cameras.

[0038] Data preprocessing and analysis

[0039] The server preprocesses the collected raw data, removing noise and filtering outliers to convert it into an analyzable format. Next, the preprocessed data is fed into a machine learning model to predict crop health and growth. For example, it can analyze abnormalities in leaf color and shape from crop images to enable early detection of pests and diseases.

[0040] Schedule generation and notifications

[0041] The server automatically generates an optimal farming schedule based on the analysis results. The generated schedule is notified to the user, and execution instructions are also sent to the work equipment. For example, it takes the weather forecast for the following week into consideration to determine the optimal days for harvesting and fertilizing.

[0042] Robot control

[0043] The terminal controls agricultural robots based on schedules sent from the server. This ensures that planned tasks are carried out efficiently and accurately. For example, the robots can automatically move around a field and remove weeds in a designated area.

[0044] Talent matching

[0045] The server works in cooperation with national and local governments, as well as staffing agencies, to match urban labor with agricultural organizations. If a user is an urban resident seeking to work in agriculture, that information is provided to agricultural corporations, enabling specific placements to address labor shortages.

[0046] This system is expected to improve the efficiency of agricultural production, increase labor mobility, and enable sustainable food production.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server will begin periodically acquiring data from information gathering devices installed on the agricultural land. This includes acquiring humidity and temperature data from soil sensors and taking images of crops with cameras.

[0050] Step 2:

[0051] The server preprocesses the collected raw data. This process removes noise and filters outliers. The image data is then converted into an analyzable format, with particularly important features extracted.

[0052] Step 3:

[0053] The server uses pre-processed data to perform analysis using machine learning models. Specifically, it analyzes crop health and predicts growth, and identifies the cause of any abnormalities detected.

[0054] Step 4:

[0055] The server generates an optimal farming schedule based on the analysis results. This process takes into account crop conditions and weather data to determine the necessary tasks and their timing.

[0056] Step 5:

[0057] The server notifies the user of the generated schedule. The user receives the work schedule via smartphone or computer. Necessary instructions are also sent to the work equipment.

[0058] Step 6:

[0059] The terminal controls the agricultural robot based on instructions received from the server. The robot performs the planned tasks, for example, automating harvesting within a specified area.

[0060] Step 7:

[0061] The server initiates the personnel matching process. It collects personnel needs from agricultural organizations and matches them with information on urban farmers seeking employment, thereby securing the necessary workforce.

[0062] Through these steps, this system efficiently and effectively supports agricultural operations and promotes improved efficiency in food production.

[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] Modern agriculture demands improved production efficiency, greater precision in operations, and sustainable management, while simultaneously facing challenges such as labor shortages and unpredictable weather conditions. Conventional methods have made it difficult to efficiently create optimal timetables for farm work and to detect plant health conditions early. Furthermore, mechanisms for flexibly utilizing urban labor in the agricultural sector are not yet fully developed. The objective of this invention is to solve these problems.

[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 information gathering means for acquiring information from data collection devices installed in agricultural areas, information preprocessing means for converting the information into an analyzable form, and analysis means for analyzing the state of plants using an artificial intelligence model. This makes it possible to create an optimal timetable for agricultural work, to detect abnormal conditions in plants early, and to efficiently utilize urban labor in agriculture.

[0068] An "information gathering device" is a mechanical device installed in an agricultural area to acquire information related to the condition of plants, soil conditions, and weather conditions.

[0069] "Information gathering means" refers to a method or process for collecting information obtained from an information gathering device.

[0070] "Information preprocessing means" refers to a method or technique for preprocessing collected raw data, removing noise, and converting it into a format that can be analyzed.

[0071] "Analysis method" refers to an analytical process that uses artificial intelligence models to predict the health and growth of plants.

[0072] "Timetable generation means" refers to a method or means for creating an optimal time schedule for agricultural work based on analysis results.

[0073] "Notification means" refers to a procedure or system for informing agricultural workers of the generated time schedule and instructing the work equipment to carry it out.

[0074] "Labor force adjustment measures" refer to means of appropriately matching urban labor to agricultural organizations in cooperation with the national government, local administrative agencies, and labor dispatch agencies.

[0075] An "artificial intelligence model" is a set of algorithms and models designed for computer-based data analysis and prediction.

[0076] This invention provides a system for the efficient management of agricultural areas. This system consists of an information gathering device, a server, and terminals, each functioning in coordination with the others.

[0077] The server receives data in real time from information gathering devices installed in the agricultural area. These devices include soil sensors, weather sensors, and high-resolution cameras, which provide information on plant conditions, soil conditions, and weather conditions. The server stores this information in a database, which forms the basis for continuous monitoring and analysis.

[0078] The server then preprocesses the collected data. It performs noise reduction and outlier filtering, and converts the data into a standardized format. This preprocessed data is suitable for analysis. Generative AI models are used for the analysis. For example, if plant images are provided, analyzing the leaf color and shape can help detect plant abnormalities early.

[0079] Based on the analysis results, the server creates an optimal farming timetable. It can adjust the timing of fertilization and irrigation, taking weather forecast data into consideration. This timetable is automatically notified to the user, and specific instructions for operating agricultural robots are created.

[0080] The terminal controls agricultural robots based on a timetable received from the server. The robots perform tasks such as removing weeds in designated areas and harvesting crops at the appropriate time. This leads to increased efficiency and precision in agricultural work.

[0081] Furthermore, the server has the function of matching urban labor with agricultural organizations in cooperation with national and local government agencies and labor dispatch agencies. This can alleviate the shortage of agricultural labor and enable flexible responses to seasonal peak seasons.

[0082] As a concrete example, the following is an example of a prompt message for analyzing the health status of a plant.

[0083] "Based on current weather conditions and soil data, please generate a fertilization schedule for tomatoes this month."

[0084] By using such a system, it is possible to contribute to improving the efficiency of agricultural production and building a sustainable production system.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server acquires data in real time from information gathering devices. It receives data related to plant conditions, soil conditions, and weather conditions as input. This data is provided by soil sensors, weather sensors, and high-resolution cameras. The server records this raw data in a database and stores it as foundational data. For example, temperature, humidity, sunlight, and plant images are recorded every hour.

[0088] Step 2:

[0089] The server preprocesses the collected data. It takes previously collected raw data as input. This data is subjected to a denoising algorithm, and outliers are filtered out. The output is data formatted into a unified format. Specifically, sensor data with extreme values ​​is smoothed, and image data is normalized.

[0090] Step 3:

[0091] The server inputs pre-processed data into a generating AI model and performs analysis. The input is pre-processed data, and the analysis predicts the health and growth of the crops. The output is a list of whether or not abnormalities are present and the growth predictions. Specifically, the AI ​​can scan plant images and detect leaf lesions.

[0092] Step 4:

[0093] The server generates an optimal farming timetable based on the analysis results. The inputs used are the analysis results and weather forecast data. The output is a specific work schedule for tasks such as fertilization, irrigation, and harvesting. For example, the program automatically determines the appropriate irrigation days by considering a week's weather forecast.

[0094] Step 5:

[0095] The server notifies the user of the generated schedule and sends execution instructions to the work equipment. The generated schedule is used as input. The output consists of the notification sent to the user and control commands for the work equipment. For example, the user checks the next day's work plan using a smartphone app, and the agricultural robot is provided with confirmed route information.

[0096] Step 6:

[0097] The server collaborates with national and local government agencies and labor placement agencies to match workers. It receives information on urban farmers as input and outputs a list of suitable candidates for each agricultural organization. In particular, it can match experienced workers with farms that require short-term labor.

[0098] (Application Example 1)

[0099] 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."

[0100] As agricultural efficiency and sustainable production systems are increasingly demanded, accurate data collection, real-time information provision, and proper schedule management are crucial. However, conventional technologies remain insufficient to address challenges such as efficient management of individual agricultural projects and appropriate matching of urban labor.

[0101] 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.

[0102] In this invention, the server includes means for acquiring data on crop status, soil conditions, and weather conditions from an information collection device installed on an agricultural site; means for preprocessing the data collected by the data collection means and converting it into an analyzable format; and means for analyzing the data obtained by the data preprocessing means to predict crop health and growth. This enables real-time agricultural management based on accurate data, efficient labor matching, and intuitive information provision through a visualization device.

[0103] An "information gathering device" is a device installed on agricultural land that acquires data on crop conditions, soil conditions, and weather conditions.

[0104] "Data preprocessing means" refers to means of converting raw data collected by an information collection device into an analyzable format.

[0105] "Analysis means" refers to methods for predicting crop health and growth based on pre-processed data.

[0106] A "schedule generation method" is a means of generating an optimal farm work schedule based on the analysis results.

[0107] A "notification means" is a means of notifying the user of the generated schedule and instructing the work device to execute it.

[0108] A "talent matching method" is a means of matching individuals who wish to work in agriculture with agricultural organizations, in cooperation with the national and local governments and labor organizations.

[0109] A "visualization device" is a device that visually displays analysis results and generated schedules.

[0110] A "mobile information terminal" refers to a portable information processing device such as a smartphone, which is used to run applications that support agricultural management.

[0111] This invention's implementation system achieves agricultural efficiency through the cooperation of three entities: a server, a terminal, and a user.

[0112] The server collects data in real time from information gathering devices installed on agricultural land. These devices provide data related to crop conditions, soil conditions, and weather conditions. The server preprocesses this data, removing noise and filtering outliers to convert it into an analyzable format. Furthermore, it uses a generative AI model based on the preprocessed data to predict crop health and growth. The server then leverages machine learning models to generate an optimal farming schedule and notifies users and work equipment.

[0113] The terminal functions as a visualization device, displaying schedules and analysis results provided by the server to the user in real time. The terminal can include mobile information devices such as smartphones and head-mounted displays. This allows users to intuitively obtain information and give instructions for agricultural work.

[0114] As a concrete example, users can use their mobile devices to monitor crops in their fields in real time and receive immediate notifications when abnormalities are detected. Furthermore, they can rationally plan the harvest date for the following week based on weather forecasts. By utilizing this system, the efficiency of agricultural production can be maximized, and the effective use of environmental resources can be achieved.

[0115] Examples of prompts include, "Based on crop health data, please show the best way to generate a farming schedule for the next week," and "Manage multiple agricultural projects within the city and aggregate and display data in real time." Through these prompts, the system provides guidance for optimal farming management.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server receives data on crop conditions, soil conditions, and weather conditions as input from information gathering devices. Specifically, it collects data in real time from sensors and cameras and transmits that information to the server. The output is a collection of raw data.

[0119] Step 2:

[0120] The server preprocesses the acquired raw data. Specifically, it performs data noise reduction, outlier filtering, and scaling. The output of this processing is clean data suitable for analysis.

[0121] Step 3:

[0122] The server inputs pre-processed data into a generating AI model to predict crop health and growth. At this stage, data calculations are used to detect crop anomalies and predict growth patterns. The output is a set of analysis results.

[0123] Step 4:

[0124] The server generates an optimal farming schedule based on the analysis results. Here, weather forecast data and analysis results are combined, and machine learning algorithms are used to create an efficient farming plan. The output is an optimized schedule.

[0125] Step 5:

[0126] The terminal displays the schedule and analysis results generated using a visualization device to the user. Specifically, the schedule and detailed data analysis results are visually displayed on the screen of a smartphone or head-mounted display. The output is the displayed information.

[0127] Step 6:

[0128] The user issues instructions to agricultural robots and work equipment based on the displayed schedule. Agricultural work begins by inputting instructions into the device via voice input or tap controls. The output is a set of instructions for the agricultural equipment.

[0129] Step 7:

[0130] When a user needs labor, the server collaborates with national and local governments and labor agencies to match them with suitable personnel. This process processes personnel information data and arranges the most suitable candidates. The output is data on the matched workers.

[0131] 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.

[0132] This invention combines a system designed to improve agricultural efficiency with an emotion engine that recognizes user emotions. This system includes functions for data collection, data preprocessing, analysis, schedule generation, notification, and personnel matching, and improves the user experience through feedback from the emotion engine.

[0133] Data Acquisition and Preprocessing

[0134] The server acquires data on crop conditions, soil conditions, and weather conditions through information gathering devices placed on agricultural land. The collected data is denoised and outlier-processed, and then converted into an analyzable format.

[0135] Data analysis and schedule generation

[0136] The server supplies pre-processed data to a machine learning model to predict crop growth and assess their health. Based on the analysis results, it generates an optimal farming schedule to maximize work efficiency.

[0137] Notifications and emotional feedback

[0138] When the server notifies the user of the schedule it has created, it uses an emotion engine to recognize the user's emotional state. The emotion engine evaluates emotions from voice and nonverbal behavior and provides feedback based on that. For example, if the user is feeling stressed, the system will send an encouraging message in a gentle tone.

[0139] Robot control

[0140] The terminal controls the farm robots according to schedules and instructions generated by the server. The robots effectively perform farm work in various areas of the field based on the specified schedule.

[0141] Talent matching and mental support

[0142] The server has the function of matching local agricultural organizations with urban labor, enabling efficient personnel allocation. In addition, the emotion engine provides regular mental health advice to support the motivation of agricultural workers.

[0143] This system aims to simultaneously improve productivity and reduce the psychological burden on farmers by combining data-driven agricultural management with emotion-based feedback.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The server periodically acquires data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land. This data is provided as physical measurements from sensors and camera images.

[0147] Step 2:

[0148] The server performs data preprocessing on the acquired data. Specifically, it removes noise from the data and filters out unwanted outliers. It also extracts necessary features from the image data.

[0149] Step 3:

[0150] The server performs analysis using pre-processed data. Image data of crops is input into a machine learning model to detect their growth status and any abnormalities. For example, the health of the leaves can be used to assess the presence or absence of disease.

[0151] Step 4:

[0152] The server generates an optimal farming schedule based on the analysis results. This includes adjusting the work schedule to take weather forecast data into account. A schedule is created that includes specific work plans such as harvesting and fertilizing.

[0153] Step 5:

[0154] Following schedule generation, the server activates the emotion engine and initiates a feedback process to recognize the user's emotional state. It analyzes voice and nonverbal behavioral data to evaluate the user's emotional state.

[0155] Step 6:

[0156] The server takes the assessed emotional state into consideration and adjusts the content of notifications sent to the user. For example, if the user is feeling stressed, it may send a message encouraging them to relax.

[0157] Step 7:

[0158] The terminal controls the farm robots based on instructions from the server. The robots move and perform tasks according to the generated schedule to carry out specific work.

[0159] Step 8:

[0160] The server works in cooperation with national and local governments and staffing agencies to match urban labor with agricultural organizations. In this process, it uses an emotional engine to support placement, taking into account the motivation and suitability of prospective farmers.

[0161] In this way, this system improves agricultural productivity and worker satisfaction through advanced data processing and emotional feedback functions combined with an emotion engine.

[0162] (Example 2)

[0163] 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".

[0164] There are challenges in agriculture, including improving labor efficiency and reducing the psychological burden on agricultural workers. In today's agricultural environment, it is necessary to efficiently collect and analyze large amounts of information, such as plant conditions and weather conditions, and to take necessary measures. Furthermore, ensuring the mental well-being of agricultural workers and appropriately matching labor resources is also crucial.

[0165] 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.

[0166] In this invention, the server includes information gathering means, initial processing means, and analysis means. This enables efficient information gathering and data analysis in agriculture, and through the generation of optimal farm work schedules and psychological support for workers, it becomes possible to improve the efficiency of agriculture and reduce the psychological burden on workers.

[0167] "Information gathering means" refers to devices and processes for acquiring information on plant conditions, soil conditions, and weather conditions using sensors and measuring devices installed in agricultural areas.

[0168] "Initial processing means" refers to devices and algorithms that perform preprocessing, such as noise reduction and outlier processing, to convert information obtained by information gathering means into an analyzable format.

[0169] "Analysis means" refers to devices and processes that use machine learning models and analytical algorithms to predict the health and growth of plants using initially processed information.

[0170] The "schedule generation means" refers to a device and algorithm for generating the optimal schedule for agricultural work based on the analysis results obtained from the analysis means.

[0171] "Notification means" refers to devices and processes for notifying agricultural workers of the generated schedule and instructing work equipment to carry it out.

[0172] "Emotion recognition means" refers to a device and algorithm for recognizing the emotional state of a worker by analyzing their voice and nonverbal behavior, and for providing feedback that corresponds to that emotion.

[0173] A "labor force matching system" refers to a mechanism and process that works in cooperation with national and local governments and labor supply organizations to effectively match aspiring agricultural workers with agricultural organizations.

[0174] This invention is a system designed to improve agricultural efficiency, integrating functions such as information gathering, initial data processing, analysis, schedule generation, notification, emotional feedback, and labor force matching.

[0175] In information gathering, the server utilizes information gathering devices such as sensors and drones placed in agricultural areas. This allows it to acquire weather information such as plant growth status, soil moisture, temperature, and precipitation. The data transmitted from the sensors is stored in the server's central database in real time.

[0176] During initial data processing, the server uses a Python-based data cleansing script to remove noise from the collected data, process outliers, and format the data into a clean format suitable for analysis. Libraries such as NumPy and Pandas can be used for this process.

[0177] During the data analysis phase, the server uses the initially processed data to input into a machine learning model to predict plant growth and assess their health. This analysis utilizes analytical tools such as Scikit-learn and TENSORFLOW®, which can improve the accuracy of crop predictions. For example, it can predict how certain weather patterns will affect crop growth.

[0178] Regarding schedule generation, the server generates the optimal schedule for farm work based on the analysis results and plans a schedule that maximizes work efficiency. This uses Python scripts and optimization algorithms.

[0179] As a notification and emotional feedback function, the server evaluates the user's emotional state from voice and nonverbal data through an emotion engine and provides appropriate feedback. For example, if the user is feeling stressed, the server can generate a friendly message as a prompt from the generated AI model, such as, "How is your farm work going today? Let's take a short break to relieve stress."

[0180] In labor matching, the server efficiently connects local agricultural organizations with urban labor. This utilizes database management and Python data processing techniques. Based on skills and experience, the server automatically suggests appropriate labor assignments, promoting efficient utilization of workers.

[0181] Thus, the present invention is a system that utilizes agricultural data and combines it with feedback based on the emotions of the workers to improve productivity and reduce the mental burden on workers.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] Information gathering

[0185] The server acquires data on plant conditions, soil conditions, and weather conditions from sensors and drones installed in agricultural areas. Numerical data transmitted in real time from the sensors (e.g., temperature, humidity, soil pH) is recorded in a central database. Input is raw data from various sensors, and output is a set of initial state information stored in the database.

[0186] Step 2:

[0187] Data Initialization

[0188] The server processes the acquired raw data using a Python script. Specifically, it uses libraries such as NumPy and Pandas to remove noise and correct or delete outliers. The input is raw data, and the output is a clean dataset. This improves data quality and increases the accuracy of the analysis.

[0189] Step 3:

[0190] Data Analysis

[0191] The server performs analysis using machine learning models based on clean data. It runs growth prediction models and health assessment models using Scikit-learn and TensorFlow. The input is a pre-processed dataset, and the output is the growth prediction and health assessment results for individual plants. This provides crucial information for future farming plans.

[0192] Step 4:

[0193] Schedule generation

[0194] The server generates an optimized farming schedule based on the analysis results. It uses a Python optimization algorithm to create a farming schedule that takes weather forecast information into account. The inputs are the analysis results and weather forecast data, and the output is a specific schedule plan for the next farming operation.

[0195] Step 5:

[0196] Notifications and emotional feedback

[0197] The server notifies the user of the generated schedule and analyzes the user's emotional tendencies using an emotion engine. It evaluates voice and nonverbal data and provides emotionally appropriate feedback. The input is the response data collected from the user, and the output is the corresponding emotional feedback message. For example, if the user is feeling tired, the system will send a message such as, "Take a break and don't push yourself too hard today."

[0198] Step 6:

[0199] Robot control

[0200] The terminal controls agricultural robots based on schedule information. It instructs the robots to sow seeds, water, and harvest via a Raspberry Pi. The input is work instruction data sent from the server, and the output is the actual work action performed by the robot.

[0201] Step 7:

[0202] Labor matching

[0203] The server uses a database to allocate labor based on skills and experience. A Python script collaborates with national and local governments and labor organizations to select suitable workers and notify agricultural organizations. The input is worker skill data, and the output is a list of optimal workers.

[0204] (Application Example 2)

[0205] 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".

[0206] In the agricultural sector, efficient farming practices are key to increasing productivity. However, particularly in agriculture near urban areas, the shortage of farm workers and the need to consider their mental health are significant challenges. Furthermore, the stress levels and job satisfaction of farm workers directly impact productivity. Therefore, it is necessary to streamline work schedules, optimize personnel allocation, and consider the emotional state of farm workers to reduce their psychological burden.

[0207] 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.

[0208] In this invention, the server includes data collection means for acquiring data from information gathering devices installed on agricultural land, schedule generation means for analyzing the data and generating an optimal work schedule, and emotion recognition means for recognizing the user's emotions from voice and nonverbal actions. This enables efficient agricultural work while providing appropriate feedback according to the emotional state of agricultural workers.

[0209] An "information gathering device" is a device installed on agricultural land to acquire data on crop conditions, soil conditions, and weather conditions.

[0210] A "data collection means" is a means that has the function of collecting data acquired from an information collection device.

[0211] "Data preprocessing means" refers to means for processing collected data to convert it into an analyzable format.

[0212] "Analysis means" refers to methods for analyzing pre-processed data to predict crop health and growth.

[0213] The "schedule generation means" is a means for generating an optimal schedule for agricultural work based on the analysis results obtained by the analysis means.

[0214] A "notification means" is a means that has the function of notifying agricultural workers of the generated schedule and instructing them to carry out the work.

[0215] "Personnel matching methods" refer to means of matching individuals who wish to work in agriculture with agricultural organizations, in cooperation with local organizations and labor dispatch agencies.

[0216] An "emotion recognition means" is a means of recognizing the user's emotional state from their voice and nonverbal actions, and providing feedback appropriate to that state.

[0217] A "control means" is a means of giving instructions to an agricultural work management support system to control its actions.

[0218] The system of the present invention is realized by having information gathering devices placed in agricultural land and interacting via a network, with the server, terminals, and users each playing specific roles. The server acquires data such as crop conditions, soil conditions, and weather conditions from the information gathering devices. In this way, various environmental data of the agricultural land is collected.

[0219] Next, the data is preprocessed on the server to remove noise and convert it into a format suitable for analysis. The preprocessed data is then analyzed using machine learning models (e.g., TensorFlow or scikit-learn) to help predict crop growth and assess their health.

[0220] Based on the analysis results, the server generates an optimal farming schedule and notifies the terminal. In this process, the notification utilizes emotion recognition technology (e.g., Google® Cloud Speech-to-Text API and Emotion Analysis API) to consider the user's emotional state and provide appropriate feedback. For example, if the emotion engine determines that the user is stressed, the system will provide advice in a calm tone.

[0221] The server also collaborates with local organizations to provide a personnel matching function, enabling the efficient allocation of agricultural labor. The terminals control agricultural robots based on the generated schedule and perform the actual farm work.

[0222] For example, if it rains continuously in a certain area, the server uses that data to evaluate the impact on crop growth, predict the next sunny period, and notifies farmers of their work schedule via their devices. At this time, the emotion engine might determine that the user is tired and send a message such as, "Take a break and resume work at the next opportune moment."

[0223] An example of a prompt might be: "Design an application that generates a work schedule that takes into account the current emotional state of agricultural workers. Include a feature that reads their emotions and provides appropriate feedback."

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] The server acquires data from information collection devices installed on agricultural land. The input consists of raw data on crop conditions, soil conditions, and weather conditions. After receiving this data, the server performs noise reduction processing and converts it into a format suitable for subsequent analysis. The output is a clean dataset.

[0227] Step 2:

[0228] The server feeds preprocessed data into a machine learning model and performs data analysis. The input is a clean dataset. Specifically, it uses scikit-learn and TensorFlow to predict crop growth and assess their health. The output generated as a result of the analysis is predicted data regarding the state of the crops.

[0229] Step 3:

[0230] The server generates an optimal schedule for agricultural work based on predictive data obtained through analysis. The input is predictive data. A generating AI model is used to create the schedule, setting work times and priorities. The output is a specific work schedule.

[0231] Step 4:

[0232] The server notifies the terminal of the generated schedule. During this process, the server evaluates the user's emotional state using emotion recognition technology. The input consists of the work schedule and the user's voice and non-verbal data. Using an emotion recognition API, the server adjusts the feedback message according to the user's emotions and notifies them as output.

[0233] Step 5:

[0234] The terminal controls the agricultural robot according to the received schedule. The input is the work schedule. Specifically, it instructs the robot to start work in each area, automating the task. In this process, the robot operates according to the set schedule and generates the results of the work execution as output.

[0235] Step 6:

[0236] The server collaborates with local organizations and labor placement agencies to provide a talent matching function. Inputs include registration information of agricultural workers and demand data from agricultural organizations. A matching algorithm is used to select the most suitable personnel, and the matching results are presented as output.

[0237] Step 7:

[0238] Users engage in farm work based on system feedback. Input to the user consists of feedback messages and schedule information. Users implement methods to improve work efficiency while taking stress-reducing advice into consideration. Output is work progress and results.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] [Second Embodiment]

[0243] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0244] 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.

[0245] 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).

[0246] 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.

[0247] 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.

[0248] 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).

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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".

[0255] This invention is a system aimed at improving agricultural efficiency and realizing a sustainable production system. This system has the functions of data collection, analysis, schedule generation, notification, and personnel matching. Specific embodiments of each function are described below.

[0256] Data collection

[0257] The server acquires real-time data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land. This data is automatically recorded periodically and stored on the server. Examples include humidity and temperature data from soil sensors, and crop image data captured by cameras.

[0258] Data preprocessing and analysis

[0259] The server preprocesses the collected raw data, removing noise and filtering outliers to convert it into an analyzable format. Next, the preprocessed data is fed into a machine learning model to predict crop health and growth. For example, it can analyze abnormalities in leaf color and shape from crop images to enable early detection of pests and diseases.

[0260] Schedule generation and notifications

[0261] The server automatically generates an optimal farming schedule based on the analysis results. The generated schedule is notified to the user, and execution instructions are also sent to the work equipment. For example, it takes the weather forecast for the following week into consideration to determine the optimal days for harvesting and fertilizing.

[0262] Robot control

[0263] The terminal controls agricultural robots based on schedules sent from the server. This ensures that planned tasks are carried out efficiently and accurately. For example, the robots can automatically move around a field and remove weeds in a designated area.

[0264] Talent matching

[0265] The server works in cooperation with national and local governments, as well as staffing agencies, to match urban labor with agricultural organizations. If a user is an urban resident seeking to work in agriculture, that information is provided to agricultural corporations, enabling specific placements to address labor shortages.

[0266] This system is expected to improve the efficiency of agricultural production, increase labor mobility, and enable sustainable food production.

[0267] The following describes the processing flow.

[0268] Step 1:

[0269] The server will begin periodically acquiring data from information gathering devices installed on the agricultural land. This includes acquiring humidity and temperature data from soil sensors and taking images of crops with cameras.

[0270] Step 2:

[0271] The server preprocesses the collected raw data. This process removes noise and filters outliers. The image data is then converted into an analyzable format, with particularly important features extracted.

[0272] Step 3:

[0273] The server uses pre-processed data to perform analysis using machine learning models. Specifically, it analyzes crop health and predicts growth, and identifies the cause of any abnormalities detected.

[0274] Step 4:

[0275] The server generates an optimal farming schedule based on the analysis results. This process takes into account crop conditions and weather data to determine the necessary tasks and their timing.

[0276] Step 5:

[0277] The server notifies the user of the generated schedule. The user receives the work schedule via smartphone or computer. Necessary instructions are also sent to the work equipment.

[0278] Step 6:

[0279] Based on the instructions received from the server, the terminal controls the agricultural work robot. The robot performs the planned work, for example, automating the harvesting work within the specified range.

[0280] Step 7:

[0281] The server starts the process of personnel matching. By collecting the personnel needs from agricultural organizations and matching them with the information of those who wish to engage in farming in urban areas, the necessary labor force is ensured.

[0282] Through these steps, this system efficiently and effectively supports agricultural operations and promotes the improvement of food production efficiency.

[0283] (Example 1)

[0284] Next, 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".

[0285] In modern agriculture, while there are demands for improving production efficiency, the accuracy of work, and achieving sustainable operations, it is necessary to cope with a shortage of personnel and uncertain weather conditions. With conventional methods, it has been difficult to efficiently create an optimal work schedule for agricultural work and detect the early health status of plants. Furthermore, a mechanism for flexibly utilizing the labor force in urban areas in the agricultural field has not been fully established. It is an object of the present invention to solve these problems.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0287] In this invention, the server includes information gathering means for acquiring information from data collection devices installed in agricultural areas, information preprocessing means for converting the information into an analyzable form, and analysis means for analyzing the state of plants using an artificial intelligence model. This makes it possible to create an optimal timetable for agricultural work, to detect abnormal conditions in plants early, and to efficiently utilize urban labor in agriculture.

[0288] An "information gathering device" is a mechanical device installed in an agricultural area to acquire information related to the condition of plants, soil conditions, and weather conditions.

[0289] "Information gathering means" refers to a method or process for collecting information obtained from an information gathering device.

[0290] "Information preprocessing means" refers to a method or technique for preprocessing collected raw data, removing noise, and converting it into a format that can be analyzed.

[0291] "Analysis method" refers to an analytical process that uses artificial intelligence models to predict the health and growth of plants.

[0292] "Timetable generation means" refers to a method or means for creating an optimal time schedule for agricultural work based on analysis results.

[0293] "Notification means" refers to a procedure or system for informing agricultural workers of the generated time schedule and instructing the work equipment to carry it out.

[0294] "Labor force adjustment measures" refer to means of appropriately matching urban labor to agricultural organizations in cooperation with the national government, local administrative agencies, and labor dispatch agencies.

[0295] An "artificial intelligence model" is a set of algorithms and models designed for computer-based data analysis and prediction.

[0296] This invention provides a system for the efficient management of agricultural areas. This system consists of an information gathering device, a server, and terminals, each functioning in coordination with the others.

[0297] The server receives data in real time from information gathering devices installed in the agricultural area. These devices include soil sensors, weather sensors, and high-resolution cameras, which provide information on plant conditions, soil conditions, and weather conditions. The server stores this information in a database, which forms the basis for continuous monitoring and analysis.

[0298] The server then preprocesses the collected data. It performs noise reduction and outlier filtering, and converts the data into a standardized format. This preprocessed data is suitable for analysis. Generative AI models are used for the analysis. For example, if plant images are provided, analyzing the leaf color and shape can help detect plant abnormalities early.

[0299] Based on the analysis results, the server creates an optimal farming timetable. It can adjust the timing of fertilization and irrigation, taking weather forecast data into consideration. This timetable is automatically notified to the user, and specific instructions for operating agricultural robots are created.

[0300] The terminal controls agricultural robots based on a timetable received from the server. The robots perform tasks such as removing weeds in designated areas and harvesting crops at the appropriate time. This leads to increased efficiency and precision in agricultural work.

[0301] Furthermore, the server has the function of matching urban labor with agricultural organizations in cooperation with national and local government agencies and labor dispatch agencies. This can alleviate the shortage of agricultural labor and enable flexible responses to seasonal peak seasons.

[0302] As a concrete example, the following is an example of a prompt message for analyzing the health status of a plant.

[0303] "Generate the fertilization schedule for tomatoes this month based on the current weather conditions and soil data."

[0304] By using such a system, it is possible to contribute to the improvement of agricultural production efficiency and the construction of a sustainable production system.

[0305] The flow of the specific process in Example 1 will be described using FIG. 11.

[0306] Step 1:

[0307] The server acquires data in real time from the information collection device. As input, it receives data related to the plant's condition, soil conditions, and weather conditions. This data is provided by soil sensors, weather sensors, and high-resolution cameras. The server records this raw data in a database and accumulates it as basic data. For example, temperature, humidity, sunlight amount, plant images, etc. are recorded every hour.

[0308] Step 2:

[0309] The server preprocesses the collected data. As input, it acquires the previously collected raw data. This data is subjected to a noise removal algorithm, and outliers are filtered. As output, data formatted in a unified format is obtained. Specifically, sensor data with extreme values is smoothed, and image data is normalized.

[0310] Step 3:

[0311] The server inputs the preprocessed data into the generated AI model and performs analysis. The input is the preprocessed data, and through analysis, the health status and growth prediction of the crop are carried out. As output, a list of the presence or absence of abnormalities and growth predictions is obtained. As a specific operation, the AI can scan plant images and detect leaf lesions.

[0312] Step 4:

[0313] The server generates an optimal farming timetable based on the analysis results. The inputs used are the analysis results and weather forecast data. The output is a specific work schedule for tasks such as fertilization, irrigation, and harvesting. For example, the program automatically determines the appropriate irrigation days by considering a week's weather forecast.

[0314] Step 5:

[0315] The server notifies the user of the generated schedule and sends execution instructions to the work equipment. The generated schedule is used as input. The output consists of the notification sent to the user and control commands for the work equipment. For example, the user checks the next day's work plan using a smartphone app, and the agricultural robot is provided with confirmed route information.

[0316] Step 6:

[0317] The server collaborates with national and local government agencies and labor placement agencies to match workers. It receives information on urban farmers as input and outputs a list of suitable candidates for each agricultural organization. In particular, it can match experienced workers with farms that require short-term labor.

[0318] (Application Example 1)

[0319] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0320] As agricultural efficiency and sustainable production systems are increasingly demanded, accurate data collection, real-time information provision, and proper schedule management are crucial. However, conventional technologies remain insufficient to address challenges such as efficient management of individual agricultural projects and appropriate matching of urban labor.

[0321] 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.

[0322] In this invention, the server includes means for acquiring data on crop status, soil conditions, and weather conditions from an information collection device installed on an agricultural site; means for preprocessing the data collected by the data collection means and converting it into an analyzable format; and means for analyzing the data obtained by the data preprocessing means to predict crop health and growth. This enables real-time agricultural management based on accurate data, efficient labor matching, and intuitive information provision through a visualization device.

[0323] An "information gathering device" is a device installed on agricultural land that acquires data on crop conditions, soil conditions, and weather conditions.

[0324] "Data preprocessing means" refers to means of converting raw data collected by an information collection device into an analyzable format.

[0325] "Analysis means" refers to methods for predicting crop health and growth based on pre-processed data.

[0326] A "schedule generation method" is a means of generating an optimal farm work schedule based on the analysis results.

[0327] A "notification means" is a means of notifying the user of the generated schedule and instructing the work device to execute it.

[0328] A "talent matching method" is a means of matching individuals who wish to work in agriculture with agricultural organizations, in cooperation with the national and local governments and labor organizations.

[0329] A "visualization device" is a device that visually displays analysis results and generated schedules.

[0330] A "mobile information terminal" refers to a portable information processing device such as a smartphone, which is used to run applications that support agricultural management.

[0331] This invention's implementation system achieves agricultural efficiency through the cooperation of three entities: a server, a terminal, and a user.

[0332] The server collects data in real time from information gathering devices installed on agricultural land. These devices provide data related to crop conditions, soil conditions, and weather conditions. The server preprocesses this data, removing noise and filtering outliers to convert it into an analyzable format. Furthermore, it uses a generative AI model based on the preprocessed data to predict crop health and growth. The server then leverages machine learning models to generate an optimal farming schedule and notifies users and work equipment.

[0333] The terminal functions as a visualization device, displaying schedules and analysis results provided by the server to the user in real time. The terminal can include mobile information devices such as smartphones and head-mounted displays. This allows users to intuitively obtain information and give instructions for agricultural work.

[0334] As a concrete example, users can use their mobile devices to monitor crops in their fields in real time and receive immediate notifications when abnormalities are detected. Furthermore, they can rationally plan the harvest date for the following week based on weather forecasts. By utilizing this system, the efficiency of agricultural production can be maximized, and the effective use of environmental resources can be achieved.

[0335] Examples of prompts include, "Based on crop health data, please show the best way to generate a farming schedule for the next week," and "Manage multiple agricultural projects within the city and aggregate and display data in real time." Through these prompts, the system provides guidance for optimal farming management.

[0336] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0337] Step 1:

[0338] The server receives data on crop conditions, soil conditions, and weather conditions as input from information gathering devices. Specifically, it collects data in real time from sensors and cameras and transmits that information to the server. The output is a collection of raw data.

[0339] Step 2:

[0340] The server preprocesses the acquired raw data. Specifically, it performs data noise reduction, outlier filtering, and scaling. The output of this processing is clean data suitable for analysis.

[0341] Step 3:

[0342] The server inputs pre-processed data into a generating AI model to predict crop health and growth. At this stage, data calculations are used to detect crop anomalies and predict growth patterns. The output is a set of analysis results.

[0343] Step 4:

[0344] The server generates an optimal farming schedule based on the analysis results. Here, weather forecast data and analysis results are combined, and machine learning algorithms are used to create an efficient farming plan. The output is an optimized schedule.

[0345] Step 5:

[0346] The terminal displays the schedule and analysis results generated using a visualization device to the user. Specifically, the schedule and detailed data analysis results are visually displayed on the screen of a smartphone or head-mounted display. The output is the displayed information.

[0347] Step 6:

[0348] The user issues instructions to agricultural robots and work equipment based on the displayed schedule. Agricultural work begins by inputting instructions into the device via voice input or tap controls. The output is a set of instructions for the agricultural equipment.

[0349] Step 7:

[0350] When a user needs labor, the server collaborates with national and local governments and labor agencies to match them with suitable personnel. This process processes personnel information data and arranges the most suitable candidates. The output is data on the matched workers.

[0351] 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.

[0352] This invention combines a system designed to improve agricultural efficiency with an emotion engine that recognizes user emotions. This system includes functions for data collection, data preprocessing, analysis, schedule generation, notification, and personnel matching, and improves the user experience through feedback from the emotion engine.

[0353] Data Acquisition and Preprocessing

[0354] The server acquires data on crop conditions, soil conditions, and weather conditions through information gathering devices placed on agricultural land. The collected data is denoised and outlier-processed, and then converted into an analyzable format.

[0355] Data analysis and schedule generation

[0356] The server supplies pre-processed data to a machine learning model to predict crop growth and assess their health. Based on the analysis results, it generates an optimal farming schedule to maximize work efficiency.

[0357] Notifications and emotional feedback

[0358] When the server notifies the user of the schedule it has created, it uses an emotion engine to recognize the user's emotional state. The emotion engine evaluates emotions from voice and nonverbal behavior and provides feedback based on that. For example, if the user is feeling stressed, the system will send an encouraging message in a gentle tone.

[0359] Robot control

[0360] The terminal controls the farm robots according to schedules and instructions generated by the server. The robots effectively perform farm work in various areas of the field based on the specified schedule.

[0361] Talent matching and mental support

[0362] The server has the function of matching local agricultural organizations with urban labor, enabling efficient personnel allocation. In addition, the emotion engine provides regular mental health advice to support the motivation of agricultural workers.

[0363] This system aims to simultaneously improve productivity and reduce the psychological burden on farmers by combining data-driven agricultural management with emotion-based feedback.

[0364] The following describes the processing flow.

[0365] Step 1:

[0366] The server periodically acquires data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land. This data is provided as physical measurements from sensors and camera images.

[0367] Step 2:

[0368] The server performs data preprocessing on the acquired data. Specifically, it removes noise from the data and filters out unwanted outliers. It also extracts necessary features from the image data.

[0369] Step 3:

[0370] The server performs analysis using pre-processed data. Image data of crops is input into a machine learning model to detect their growth status and any abnormalities. For example, the health of the leaves can be used to assess the presence or absence of disease.

[0371] Step 4:

[0372] The server generates an optimal farming schedule based on the analysis results. This includes adjusting the work schedule to take weather forecast data into account. A schedule is created that includes specific work plans such as harvesting and fertilizing.

[0373] Step 5:

[0374] Following schedule generation, the server activates the emotion engine and initiates a feedback process to recognize the user's emotional state. It analyzes voice and nonverbal behavioral data to evaluate the user's emotional state.

[0375] Step 6:

[0376] The server takes the assessed emotional state into consideration and adjusts the content of notifications sent to the user. For example, if the user is feeling stressed, it may send a message encouraging them to relax.

[0377] Step 7:

[0378] The terminal controls the farm robots based on instructions from the server. The robots move and perform tasks according to the generated schedule to carry out specific work.

[0379] Step 8:

[0380] The server works in cooperation with national and local governments and staffing agencies to match urban labor with agricultural organizations. In this process, it uses an emotional engine to support placement, taking into account the motivation and suitability of prospective farmers.

[0381] In this way, this system improves agricultural productivity and worker satisfaction through advanced data processing and emotional feedback functions combined with an emotion engine.

[0382] (Example 2)

[0383] 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".

[0384] There are challenges in agriculture, including improving labor efficiency and reducing the psychological burden on agricultural workers. In today's agricultural environment, it is necessary to efficiently collect and analyze large amounts of information, such as plant conditions and weather conditions, and to take necessary measures. Furthermore, ensuring the mental well-being of agricultural workers and appropriately matching labor resources is also crucial.

[0385] 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.

[0386] In this invention, the server includes information gathering means, initial processing means, and analysis means. This enables efficient information gathering and data analysis in agriculture, and through the generation of optimal farm work schedules and psychological support for workers, it becomes possible to improve the efficiency of agriculture and reduce the psychological burden on workers.

[0387] "Information gathering means" refers to devices and processes for acquiring information on plant conditions, soil conditions, and weather conditions using sensors and measuring devices installed in agricultural areas.

[0388] "Initial processing means" refers to devices and algorithms that perform preprocessing, such as noise reduction and outlier processing, to convert information obtained by information gathering means into an analyzable format.

[0389] "Analysis means" refers to devices and processes that use machine learning models and analytical algorithms to predict the health and growth of plants using initially processed information.

[0390] The "schedule generation means" refers to a device and algorithm for generating the optimal schedule for agricultural work based on the analysis results obtained from the analysis means.

[0391] "Notification means" refers to devices and processes for notifying agricultural workers of the generated schedule and instructing work equipment to carry it out.

[0392] "Emotion recognition means" refers to a device and algorithm for recognizing the emotional state of a worker by analyzing their voice and nonverbal behavior, and for providing feedback that corresponds to that emotion.

[0393] A "labor force matching system" refers to a mechanism and process that works in cooperation with national and local governments and labor supply organizations to effectively match aspiring agricultural workers with agricultural organizations.

[0394] This invention is a system designed to improve agricultural efficiency, integrating functions such as information gathering, initial data processing, analysis, schedule generation, notification, emotional feedback, and labor force matching.

[0395] In information gathering, the server utilizes information gathering devices such as sensors and drones placed in agricultural areas. This allows it to acquire weather information such as plant growth status, soil moisture, temperature, and precipitation. The data transmitted from the sensors is stored in the server's central database in real time.

[0396] During initial data processing, the server uses a Python-based data cleansing script to remove noise from the collected data, process outliers, and format the data into a clean format suitable for analysis. Libraries such as NumPy and Pandas can be used for this process.

[0397] During the data analysis phase, the server uses the initially processed data to input into a machine learning model to predict plant growth and assess their health. This analysis utilizes analytical tools such as Scikit-learn and TensorFlow, which can improve the accuracy of crop predictions. For example, it can predict how certain weather patterns will affect crop growth.

[0398] Regarding schedule generation, the server generates the optimal schedule for farm work based on the analysis results and plans a schedule that maximizes work efficiency. This uses Python scripts and optimization algorithms.

[0399] As a notification and emotional feedback function, the server evaluates the user's emotional state from voice and nonverbal data through an emotion engine and provides appropriate feedback. For example, if the user is feeling stressed, the server can generate a friendly message as a prompt from the generated AI model, such as, "How is your farm work going today? Let's take a short break to relieve stress."

[0400] In labor matching, the server efficiently connects local agricultural organizations with urban labor. This utilizes database management and Python data processing techniques. Based on skills and experience, the server automatically suggests appropriate labor assignments, promoting efficient utilization of workers.

[0401] Thus, the present invention is a system that utilizes agricultural data and combines it with feedback based on the emotions of the workers to improve productivity and reduce the mental burden on workers.

[0402] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0403] Step 1:

[0404] Information gathering

[0405] The server acquires data on plant conditions, soil conditions, and weather conditions from sensors and drones installed in agricultural areas. Numerical data transmitted in real time from the sensors (e.g., temperature, humidity, soil pH) is recorded in a central database. Input is raw data from various sensors, and output is a set of initial state information stored in the database.

[0406] Step 2:

[0407] Data Initialization

[0408] The server processes the acquired raw data using a Python script. Specifically, it uses libraries such as NumPy and Pandas to remove noise and correct or delete outliers. The input is raw data, and the output is a clean dataset. This improves data quality and increases the accuracy of the analysis.

[0409] Step 3:

[0410] Data Analysis

[0411] The server performs analysis using machine learning models based on clean data. It runs growth prediction models and health assessment models using Scikit-learn and TensorFlow. The input is a pre-processed dataset, and the output is the growth prediction and health assessment results for individual plants. This provides crucial information for future farming plans.

[0412] Step 4:

[0413] Schedule generation

[0414] The server generates an optimized farming schedule based on the analysis results. It uses a Python optimization algorithm to create a farming schedule that takes weather forecast information into account. The inputs are the analysis results and weather forecast data, and the output is a specific schedule plan for the next farming operation.

[0415] Step 5:

[0416] Notifications and emotional feedback

[0417] The server notifies the user of the generated schedule and analyzes the user's emotional tendencies using an emotion engine. It evaluates voice and nonverbal data and provides emotionally appropriate feedback. The input is the response data collected from the user, and the output is the corresponding emotional feedback message. For example, if the user is feeling tired, the system will send a message such as, "Take a break and don't push yourself too hard today."

[0418] Step 6:

[0419] Robot control

[0420] The terminal controls agricultural robots based on schedule information. It instructs the robots to sow seeds, water, and harvest via a Raspberry Pi. The input is work instruction data sent from the server, and the output is the actual work action performed by the robot.

[0421] Step 7:

[0422] Labor matching

[0423] The server uses a database to allocate labor based on skills and experience. A Python script collaborates with national and local governments and labor organizations to select suitable workers and notify agricultural organizations. The input is worker skill data, and the output is a list of optimal workers.

[0424] (Application Example 2)

[0425] 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."

[0426] In the agricultural sector, efficient farming practices are key to increasing productivity. However, particularly in agriculture near urban areas, the shortage of farm workers and the need to consider their mental health are significant challenges. Furthermore, the stress levels and job satisfaction of farm workers directly impact productivity. Therefore, it is necessary to streamline work schedules, optimize personnel allocation, and consider the emotional state of farm workers to reduce their psychological burden.

[0427] 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.

[0428] In this invention, the server includes data collection means for acquiring data from information gathering devices installed on agricultural land, schedule generation means for analyzing the data and generating an optimal work schedule, and emotion recognition means for recognizing the user's emotions from voice and nonverbal actions. This enables efficient agricultural work while providing appropriate feedback according to the emotional state of agricultural workers.

[0429] An "information gathering device" is a device installed on agricultural land to acquire data on crop conditions, soil conditions, and weather conditions.

[0430] A "data collection means" is a means that has the function of collecting data acquired from an information collection device.

[0431] "Data preprocessing means" refers to means for processing collected data to convert it into an analyzable format.

[0432] "Analysis means" refers to methods for analyzing pre-processed data to predict crop health and growth.

[0433] The "schedule generation means" is a means for generating an optimal schedule for agricultural work based on the analysis results obtained by the analysis means.

[0434] A "notification means" is a means that has the function of notifying agricultural workers of the generated schedule and instructing them to carry out the work.

[0435] "Personnel matching methods" refer to means of matching individuals who wish to work in agriculture with agricultural organizations, in cooperation with local organizations and labor dispatch agencies.

[0436] An "emotion recognition means" is a means of recognizing the user's emotional state from their voice and nonverbal actions, and providing feedback appropriate to that state.

[0437] A "control means" is a means of giving instructions to an agricultural work management support system to control its actions.

[0438] The system of the present invention is realized by having information gathering devices placed in agricultural land and interacting via a network, with the server, terminals, and users each playing specific roles. The server acquires data such as crop conditions, soil conditions, and weather conditions from the information gathering devices. In this way, various environmental data of the agricultural land is collected.

[0439] Next, the data is preprocessed on the server to remove noise and convert it into a format suitable for analysis. The preprocessed data is then analyzed using machine learning models (e.g., TensorFlow or scikit-learn) to help predict crop growth and assess their health.

[0440] Based on the analysis results, the server generates an optimal farming schedule and notifies the terminal. In this process, the notification utilizes emotion recognition technology (e.g., Google Cloud Speech-to-Text API and Emotion Analysis API) to consider the user's emotional state and provide appropriate feedback. For example, if the emotion engine determines that the user is stressed, the system will provide advice in a calm tone.

[0441] The server also collaborates with local organizations to provide a personnel matching function, enabling the efficient allocation of agricultural labor. The terminals control agricultural robots based on the generated schedule and perform the actual farm work.

[0442] For example, if it rains continuously in a certain area, the server uses that data to evaluate the impact on crop growth, predict the next sunny period, and notifies farmers of their work schedule via their devices. At this time, the emotion engine might determine that the user is tired and send a message such as, "Take a break and resume work at the next opportune moment."

[0443] An example of a prompt might be: "Design an application that generates a work schedule that takes into account the current emotional state of agricultural workers. Include a feature that reads their emotions and provides appropriate feedback."

[0444] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0445] Step 1:

[0446] The server acquires data from information collection devices installed on agricultural land. The input consists of raw data on crop conditions, soil conditions, and weather conditions. After receiving this data, the server performs noise reduction processing and converts it into a format suitable for subsequent analysis. The output is a clean dataset.

[0447] Step 2:

[0448] The server feeds preprocessed data into a machine learning model and performs data analysis. The input is a clean dataset. Specifically, it uses scikit-learn and TensorFlow to predict crop growth and assess their health. The output generated as a result of the analysis is predicted data regarding the state of the crops.

[0449] Step 3:

[0450] The server generates an optimal schedule for agricultural work based on predictive data obtained through analysis. The input is predictive data. A generating AI model is used to create the schedule, setting work times and priorities. The output is a specific work schedule.

[0451] Step 4:

[0452] The server notifies the terminal of the generated schedule. During this process, the server evaluates the user's emotional state using emotion recognition technology. The input consists of the work schedule and the user's voice and non-verbal data. Using an emotion recognition API, the server adjusts the feedback message according to the user's emotions and notifies them as output.

[0453] Step 5:

[0454] The terminal controls the agricultural robot according to the received schedule. The input is the work schedule. Specifically, it instructs the robot to start work in each area, automating the task. In this process, the robot operates according to the set schedule and generates the results of the work execution as output.

[0455] Step 6:

[0456] The server collaborates with local organizations and labor placement agencies to provide a talent matching function. Inputs include registration information of agricultural workers and demand data from agricultural organizations. A matching algorithm is used to select the most suitable personnel, and the matching results are presented as output.

[0457] Step 7:

[0458] Users engage in farm work based on system feedback. Input to the user consists of feedback messages and schedule information. Users implement methods to improve work efficiency while taking stress-reducing advice into consideration. Output is work progress and results.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] [Third Embodiment]

[0463] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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).

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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".

[0475] This invention is a system aimed at improving agricultural efficiency and realizing a sustainable production system. This system has the functions of data collection, analysis, schedule generation, notification, and personnel matching. Specific embodiments of each function are described below.

[0476] Data collection

[0477] The server acquires real-time data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land. This data is automatically recorded periodically and stored on the server. Examples include humidity and temperature data from soil sensors, and crop image data captured by cameras.

[0478] Data preprocessing and analysis

[0479] The server preprocesses the collected raw data, removing noise and filtering outliers to convert it into an analyzable format. Next, the preprocessed data is fed into a machine learning model to predict crop health and growth. For example, it can analyze abnormalities in leaf color and shape from crop images to enable early detection of pests and diseases.

[0480] Schedule generation and notifications

[0481] The server automatically generates an optimal farming schedule based on the analysis results. The generated schedule is notified to the user, and execution instructions are also sent to the work equipment. For example, it takes the weather forecast for the following week into consideration to determine the optimal days for harvesting and fertilizing.

[0482] Robot control

[0483] The terminal controls agricultural robots based on schedules sent from the server. This ensures that planned tasks are carried out efficiently and accurately. For example, the robots can automatically move around a field and remove weeds in a designated area.

[0484] Talent matching

[0485] The server works in cooperation with national and local governments, as well as staffing agencies, to match urban labor with agricultural organizations. If a user is an urban resident seeking to work in agriculture, that information is provided to agricultural corporations, enabling specific placements to address labor shortages.

[0486] This system is expected to improve the efficiency of agricultural production, increase labor mobility, and enable sustainable food production.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The server will begin periodically acquiring data from information gathering devices installed on the agricultural land. This includes acquiring humidity and temperature data from soil sensors and taking images of crops with cameras.

[0490] Step 2:

[0491] The server preprocesses the collected raw data. This process removes noise and filters outliers. The image data is then converted into an analyzable format, with particularly important features extracted.

[0492] Step 3:

[0493] The server uses pre-processed data to perform analysis using machine learning models. Specifically, it analyzes crop health and predicts growth, and identifies the cause of any abnormalities detected.

[0494] Step 4:

[0495] The server generates an optimal farming schedule based on the analysis results. This process takes into account crop conditions and weather data to determine the necessary tasks and their timing.

[0496] Step 5:

[0497] The server notifies the user of the generated schedule. The user receives the work schedule via smartphone or computer. Necessary instructions are also sent to the work equipment.

[0498] Step 6:

[0499] The terminal controls the agricultural robot based on instructions received from the server. The robot performs the planned tasks, for example, automating harvesting within a specified area.

[0500] Step 7:

[0501] The server initiates the personnel matching process. It collects personnel needs from agricultural organizations and matches them with information on urban farmers seeking employment, thereby securing the necessary workforce.

[0502] Through these steps, this system efficiently and effectively supports agricultural operations and promotes improved efficiency in food production.

[0503] (Example 1)

[0504] 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."

[0505] Modern agriculture demands improved production efficiency, greater precision in operations, and sustainable management, while simultaneously facing challenges such as labor shortages and unpredictable weather conditions. Conventional methods have made it difficult to efficiently create optimal timetables for farm work and to detect plant health conditions early. Furthermore, mechanisms for flexibly utilizing urban labor in the agricultural sector are not yet fully developed. The objective of this invention is to solve these problems.

[0506] 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.

[0507] In this invention, the server includes information gathering means for acquiring information from data collection devices installed in agricultural areas, information preprocessing means for converting the information into an analyzable form, and analysis means for analyzing the state of plants using an artificial intelligence model. This makes it possible to create an optimal timetable for agricultural work, to detect abnormal conditions in plants early, and to efficiently utilize urban labor in agriculture.

[0508] An "information gathering device" is a mechanical device installed in an agricultural area to acquire information related to the condition of plants, soil conditions, and weather conditions.

[0509] "Information gathering means" refers to a method or process for collecting information obtained from an information gathering device.

[0510] "Information preprocessing means" refers to a method or technique for preprocessing collected raw data, removing noise, and converting it into a format that can be analyzed.

[0511] "Analysis method" refers to an analytical process that uses artificial intelligence models to predict the health and growth of plants.

[0512] "Timetable generation means" refers to a method or means for creating an optimal time schedule for agricultural work based on analysis results.

[0513] "Notification means" refers to a procedure or system for informing agricultural workers of the generated time schedule and instructing the work equipment to carry it out.

[0514] "Labor force adjustment measures" refer to means of appropriately matching urban labor to agricultural organizations in cooperation with the national government, local administrative agencies, and labor dispatch agencies.

[0515] An "artificial intelligence model" is a set of algorithms and models designed for computer-based data analysis and prediction.

[0516] This invention provides a system for the efficient management of agricultural areas. This system consists of an information gathering device, a server, and terminals, each functioning in coordination with the others.

[0517] The server receives data in real time from information gathering devices installed in the agricultural area. These devices include soil sensors, weather sensors, and high-resolution cameras, which provide information on plant conditions, soil conditions, and weather conditions. The server stores this information in a database, which forms the basis for continuous monitoring and analysis.

[0518] The server then preprocesses the collected data. It performs noise reduction and outlier filtering, and converts the data into a standardized format. This preprocessed data is suitable for analysis. Generative AI models are used for the analysis. For example, if plant images are provided, analyzing the leaf color and shape can help detect plant abnormalities early.

[0519] Based on the analysis results, the server creates an optimal farming timetable. It can adjust the timing of fertilization and irrigation, taking weather forecast data into consideration. This timetable is automatically notified to the user, and specific instructions for operating agricultural robots are created.

[0520] The terminal controls agricultural robots based on a timetable received from the server. The robots perform tasks such as removing weeds in designated areas and harvesting crops at the appropriate time. This leads to increased efficiency and precision in agricultural work.

[0521] Furthermore, the server has the function of matching urban labor with agricultural organizations in cooperation with national and local government agencies and labor dispatch agencies. This can alleviate the shortage of agricultural labor and enable flexible responses to seasonal peak seasons.

[0522] As a concrete example, the following is an example of a prompt message for analyzing the health status of a plant.

[0523] "Based on current weather conditions and soil data, please generate a fertilization schedule for tomatoes this month."

[0524] By using such a system, it is possible to contribute to improving the efficiency of agricultural production and building a sustainable production system.

[0525] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0526] Step 1:

[0527] The server acquires data in real time from information gathering devices. It receives data related to plant conditions, soil conditions, and weather conditions as input. This data is provided by soil sensors, weather sensors, and high-resolution cameras. The server records this raw data in a database and stores it as foundational data. For example, temperature, humidity, sunlight, and plant images are recorded every hour.

[0528] Step 2:

[0529] The server preprocesses the collected data. It takes previously collected raw data as input. This data is subjected to a denoising algorithm, and outliers are filtered out. The output is data formatted into a unified format. Specifically, sensor data with extreme values ​​is smoothed, and image data is normalized.

[0530] Step 3:

[0531] The server inputs pre-processed data into a generating AI model and performs analysis. The input is pre-processed data, and the analysis predicts the health and growth of the crops. The output is a list of whether or not abnormalities are present and the growth predictions. Specifically, the AI ​​can scan plant images and detect leaf lesions.

[0532] Step 4:

[0533] The server generates an optimal farming timetable based on the analysis results. The inputs used are the analysis results and weather forecast data. The output is a specific work schedule for tasks such as fertilization, irrigation, and harvesting. For example, the program automatically determines the appropriate irrigation days by considering a week's weather forecast.

[0534] Step 5:

[0535] The server notifies the user of the generated schedule and sends execution instructions to the work equipment. The generated schedule is used as input. The output consists of the notification sent to the user and control commands for the work equipment. For example, the user checks the next day's work plan using a smartphone app, and the agricultural robot is provided with confirmed route information.

[0536] Step 6:

[0537] The server collaborates with national and local government agencies and labor placement agencies to match workers. It receives information on urban farmers as input and outputs a list of suitable candidates for each agricultural organization. In particular, it can match experienced workers with farms that require short-term labor.

[0538] (Application Example 1)

[0539] 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."

[0540] As agricultural efficiency and sustainable production systems are increasingly demanded, accurate data collection, real-time information provision, and proper schedule management are crucial. However, conventional technologies remain insufficient to address challenges such as efficient management of individual agricultural projects and appropriate matching of urban labor.

[0541] 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.

[0542] In this invention, the server includes means for acquiring data on crop status, soil conditions, and weather conditions from an information collection device installed on an agricultural site; means for preprocessing the data collected by the data collection means and converting it into an analyzable format; and means for analyzing the data obtained by the data preprocessing means to predict crop health and growth. This enables real-time agricultural management based on accurate data, efficient labor matching, and intuitive information provision through a visualization device.

[0543] An "information gathering device" is a device installed on agricultural land that acquires data on crop conditions, soil conditions, and weather conditions.

[0544] "Data preprocessing means" refers to means of converting raw data collected by an information collection device into an analyzable format.

[0545] "Analysis means" refers to methods for predicting crop health and growth based on pre-processed data.

[0546] A "schedule generation method" is a means of generating an optimal farm work schedule based on the analysis results.

[0547] A "notification means" is a means of notifying the user of the generated schedule and instructing the work device to execute it.

[0548] A "talent matching method" is a means of matching individuals who wish to work in agriculture with agricultural organizations, in cooperation with the national and local governments and labor organizations.

[0549] A "visualization device" is a device that visually displays analysis results and generated schedules.

[0550] A "mobile information terminal" refers to a portable information processing device such as a smartphone, which is used to run applications that support agricultural management.

[0551] This invention's implementation system achieves agricultural efficiency through the cooperation of three entities: a server, a terminal, and a user.

[0552] The server collects data in real time from information gathering devices installed on agricultural land. These devices provide data related to crop conditions, soil conditions, and weather conditions. The server preprocesses this data, removing noise and filtering outliers to convert it into an analyzable format. Furthermore, it uses a generative AI model based on the preprocessed data to predict crop health and growth. The server then leverages machine learning models to generate an optimal farming schedule and notifies users and work equipment.

[0553] The terminal functions as a visualization device, displaying schedules and analysis results provided by the server to the user in real time. The terminal can include mobile information devices such as smartphones and head-mounted displays. This allows users to intuitively obtain information and give instructions for agricultural work.

[0554] As a concrete example, users can use their mobile devices to monitor crops in their fields in real time and receive immediate notifications when abnormalities are detected. Furthermore, they can rationally plan the harvest date for the following week based on weather forecasts. By utilizing this system, the efficiency of agricultural production can be maximized, and the effective use of environmental resources can be achieved.

[0555] Examples of prompts include, "Based on crop health data, please show the best way to generate a farming schedule for the next week," and "Manage multiple agricultural projects within the city and aggregate and display data in real time." Through these prompts, the system provides guidance for optimal farming management.

[0556] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0557] Step 1:

[0558] The server receives data on crop conditions, soil conditions, and weather conditions as input from information gathering devices. Specifically, it collects data in real time from sensors and cameras and transmits that information to the server. The output is a collection of raw data.

[0559] Step 2:

[0560] The server preprocesses the acquired raw data. Specifically, it performs data noise reduction, outlier filtering, and scaling. The output of this processing is clean data suitable for analysis.

[0561] Step 3:

[0562] The server inputs pre-processed data into a generating AI model to predict crop health and growth. At this stage, data calculations are used to detect crop anomalies and predict growth patterns. The output is a set of analysis results.

[0563] Step 4:

[0564] The server generates an optimal farming schedule based on the analysis results. Here, weather forecast data and analysis results are combined, and machine learning algorithms are used to create an efficient farming plan. The output is an optimized schedule.

[0565] Step 5:

[0566] The terminal displays the schedule and analysis results generated using a visualization device to the user. Specifically, the schedule and detailed data analysis results are visually displayed on the screen of a smartphone or head-mounted display. The output is the displayed information.

[0567] Step 6:

[0568] The user issues instructions to agricultural robots and work equipment based on the displayed schedule. Agricultural work begins by inputting instructions into the device via voice input or tap controls. The output is a set of instructions for the agricultural equipment.

[0569] Step 7:

[0570] When a user needs labor, the server collaborates with national and local governments and labor agencies to match them with suitable personnel. This process processes personnel information data and arranges the most suitable candidates. The output is data on the matched workers.

[0571] 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.

[0572] This invention combines a system designed to improve agricultural efficiency with an emotion engine that recognizes user emotions. This system includes functions for data collection, data preprocessing, analysis, schedule generation, notification, and personnel matching, and improves the user experience through feedback from the emotion engine.

[0573] Data Acquisition and Preprocessing

[0574] The server acquires data on crop conditions, soil conditions, and weather conditions through information gathering devices placed on agricultural land. The collected data is denoised and outlier-processed, and then converted into an analyzable format.

[0575] Data analysis and schedule generation

[0576] The server supplies pre-processed data to a machine learning model to predict crop growth and assess their health. Based on the analysis results, it generates an optimal farming schedule to maximize work efficiency.

[0577] Notifications and emotional feedback

[0578] When the server notifies the user of the schedule it has created, it uses an emotion engine to recognize the user's emotional state. The emotion engine evaluates emotions from voice and nonverbal behavior and provides feedback based on that. For example, if the user is feeling stressed, the system will send an encouraging message in a gentle tone.

[0579] Robot control

[0580] The terminal controls the farm robots according to schedules and instructions generated by the server. The robots effectively perform farm work in various areas of the field based on the specified schedule.

[0581] Talent matching and mental support

[0582] The server has the function of matching local agricultural organizations with urban labor, enabling efficient personnel allocation. In addition, the emotion engine provides regular mental health advice to support the motivation of agricultural workers.

[0583] This system aims to simultaneously improve productivity and reduce the psychological burden on farmers by combining data-driven agricultural management with emotion-based feedback.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The server periodically acquires data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land. This data is provided as physical measurements from sensors and camera images.

[0587] Step 2:

[0588] The server performs data preprocessing on the acquired data. Specifically, it removes noise from the data and filters out unwanted outliers. It also extracts necessary features from the image data.

[0589] Step 3:

[0590] The server performs analysis using pre-processed data. Image data of crops is input into a machine learning model to detect their growth status and any abnormalities. For example, the health of the leaves can be used to assess the presence or absence of disease.

[0591] Step 4:

[0592] The server generates an optimal farming schedule based on the analysis results. This includes adjusting the work schedule to take weather forecast data into account. A schedule is created that includes specific work plans such as harvesting and fertilizing.

[0593] Step 5:

[0594] Following schedule generation, the server activates the emotion engine and initiates a feedback process to recognize the user's emotional state. It analyzes voice and nonverbal behavioral data to evaluate the user's emotional state.

[0595] Step 6:

[0596] The server takes the assessed emotional state into consideration and adjusts the content of notifications sent to the user. For example, if the user is feeling stressed, it may send a message encouraging them to relax.

[0597] Step 7:

[0598] The terminal controls the farm robots based on instructions from the server. The robots move and perform tasks according to the generated schedule to carry out specific work.

[0599] Step 8:

[0600] The server works in cooperation with national and local governments and staffing agencies to match urban labor with agricultural organizations. In this process, it uses an emotional engine to support placement, taking into account the motivation and suitability of prospective farmers.

[0601] In this way, this system improves agricultural productivity and worker satisfaction through advanced data processing and emotional feedback functions combined with an emotion engine.

[0602] (Example 2)

[0603] 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."

[0604] There are challenges in agriculture, including improving labor efficiency and reducing the psychological burden on agricultural workers. In today's agricultural environment, it is necessary to efficiently collect and analyze large amounts of information, such as plant conditions and weather conditions, and to take necessary measures. Furthermore, ensuring the mental well-being of agricultural workers and appropriately matching labor resources is also crucial.

[0605] 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.

[0606] In this invention, the server includes information gathering means, initial processing means, and analysis means. This enables efficient information gathering and data analysis in agriculture, and through the generation of optimal farm work schedules and psychological support for workers, it becomes possible to improve the efficiency of agriculture and reduce the psychological burden on workers.

[0607] "Information gathering means" refers to devices and processes for acquiring information on plant conditions, soil conditions, and weather conditions using sensors and measuring devices installed in agricultural areas.

[0608] "Initial processing means" refers to devices and algorithms that perform preprocessing, such as noise reduction and outlier processing, to convert information obtained by information gathering means into an analyzable format.

[0609] "Analysis means" refers to devices and processes that use machine learning models and analytical algorithms to predict the health and growth of plants using initially processed information.

[0610] The "schedule generation means" refers to a device and algorithm for generating the optimal schedule for agricultural work based on the analysis results obtained from the analysis means.

[0611] "Notification means" refers to devices and processes for notifying agricultural workers of the generated schedule and instructing work equipment to carry it out.

[0612] "Emotion recognition means" refers to a device and algorithm for recognizing the emotional state of a worker by analyzing their voice and nonverbal behavior, and for providing feedback that corresponds to that emotion.

[0613] A "labor force matching system" refers to a mechanism and process that works in cooperation with national and local governments and labor supply organizations to effectively match aspiring agricultural workers with agricultural organizations.

[0614] This invention is a system designed to improve agricultural efficiency, integrating functions such as information gathering, initial data processing, analysis, schedule generation, notification, emotional feedback, and labor force matching.

[0615] In information gathering, the server utilizes information gathering devices such as sensors and drones placed in agricultural areas. This allows it to acquire weather information such as plant growth status, soil moisture, temperature, and precipitation. The data transmitted from the sensors is stored in the server's central database in real time.

[0616] During initial data processing, the server uses a Python-based data cleansing script to remove noise from the collected data, process outliers, and format the data into a clean format suitable for analysis. Libraries such as NumPy and Pandas can be used for this process.

[0617] During the data analysis phase, the server uses the initially processed data to input into a machine learning model to predict plant growth and assess their health. This analysis utilizes analytical tools such as Scikit-learn and TensorFlow, which can improve the accuracy of crop predictions. For example, it can predict how certain weather patterns will affect crop growth.

[0618] Regarding schedule generation, the server generates the optimal schedule for farm work based on the analysis results and plans a schedule that maximizes work efficiency. This uses Python scripts and optimization algorithms.

[0619] As a notification and emotional feedback function, the server evaluates the user's emotional state from voice and nonverbal data through an emotion engine and provides appropriate feedback. For example, if the user is feeling stressed, the server can generate a friendly message as a prompt from the generated AI model, such as, "How is your farm work going today? Let's take a short break to relieve stress."

[0620] In labor matching, the server efficiently connects local agricultural organizations with urban labor. This utilizes database management and Python data processing techniques. Based on skills and experience, the server automatically suggests appropriate labor assignments, promoting efficient utilization of workers.

[0621] Thus, the present invention is a system that utilizes agricultural data and combines it with feedback based on the emotions of the workers to improve productivity and reduce the mental burden on workers.

[0622] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0623] Step 1:

[0624] Information gathering

[0625] The server acquires data on plant conditions, soil conditions, and weather conditions from sensors and drones installed in agricultural areas. Numerical data transmitted in real time from the sensors (e.g., temperature, humidity, soil pH) is recorded in a central database. Input is raw data from various sensors, and output is a set of initial state information stored in the database.

[0626] Step 2:

[0627] Data Initialization

[0628] The server processes the acquired raw data using a Python script. Specifically, it uses libraries such as NumPy and Pandas to remove noise and correct or delete outliers. The input is raw data, and the output is a clean dataset. This improves data quality and increases the accuracy of the analysis.

[0629] Step 3:

[0630] Data Analysis

[0631] The server performs analysis using machine learning models based on clean data. It runs growth prediction models and health assessment models using Scikit-learn and TensorFlow. The input is a pre-processed dataset, and the output is the growth prediction and health assessment results for individual plants. This provides crucial information for future farming plans.

[0632] Step 4:

[0633] Schedule generation

[0634] The server generates an optimized farming schedule based on the analysis results. It uses a Python optimization algorithm to create a farming schedule that takes weather forecast information into account. The inputs are the analysis results and weather forecast data, and the output is a specific schedule plan for the next farming operation.

[0635] Step 5:

[0636] Notifications and emotional feedback

[0637] The server notifies the user of the generated schedule and analyzes the user's emotional tendencies using an emotion engine. It evaluates voice and nonverbal data and provides emotionally appropriate feedback. The input is the response data collected from the user, and the output is the corresponding emotional feedback message. For example, if the user is feeling tired, the system will send a message such as, "Take a break and don't push yourself too hard today."

[0638] Step 6:

[0639] Robot control

[0640] The terminal controls agricultural robots based on schedule information. It instructs the robots to sow seeds, water, and harvest via a Raspberry Pi. The input is work instruction data sent from the server, and the output is the actual work action performed by the robot.

[0641] Step 7:

[0642] Labor matching

[0643] The server uses a database to allocate labor based on skills and experience. A Python script collaborates with national and local governments and labor organizations to select suitable workers and notify agricultural organizations. The input is worker skill data, and the output is a list of optimal workers.

[0644] (Application Example 2)

[0645] 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."

[0646] In the agricultural sector, efficient farming practices are key to increasing productivity. However, particularly in agriculture near urban areas, the shortage of farm workers and the need to consider their mental health are significant challenges. Furthermore, the stress levels and job satisfaction of farm workers directly impact productivity. Therefore, it is necessary to streamline work schedules, optimize personnel allocation, and consider the emotional state of farm workers to reduce their psychological burden.

[0647] 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.

[0648] In this invention, the server includes data collection means for acquiring data from information gathering devices installed on agricultural land, schedule generation means for analyzing the data and generating an optimal work schedule, and emotion recognition means for recognizing the user's emotions from voice and nonverbal actions. This enables efficient agricultural work while providing appropriate feedback according to the emotional state of agricultural workers.

[0649] An "information gathering device" is a device installed on agricultural land to acquire data on crop conditions, soil conditions, and weather conditions.

[0650] A "data collection means" is a means that has the function of collecting data acquired from an information collection device.

[0651] "Data preprocessing means" refers to means for processing collected data to convert it into an analyzable format.

[0652] "Analysis means" refers to methods for analyzing pre-processed data to predict crop health and growth.

[0653] The "schedule generation means" is a means for generating an optimal schedule for agricultural work based on the analysis results obtained by the analysis means.

[0654] A "notification means" is a means that has the function of notifying agricultural workers of the generated schedule and instructing them to carry out the work.

[0655] "Personnel matching methods" refer to means of matching individuals who wish to work in agriculture with agricultural organizations, in cooperation with local organizations and labor dispatch agencies.

[0656] An "emotion recognition means" is a means of recognizing the user's emotional state from their voice and nonverbal actions, and providing feedback appropriate to that state.

[0657] A "control means" is a means of giving instructions to an agricultural work management support system to control its actions.

[0658] The system of the present invention is realized by having information gathering devices placed in agricultural land and interacting via a network, with the server, terminals, and users each playing specific roles. The server acquires data such as crop conditions, soil conditions, and weather conditions from the information gathering devices. In this way, various environmental data of the agricultural land is collected.

[0659] Next, the data is preprocessed on the server to remove noise and convert it into a format suitable for analysis. The preprocessed data is then analyzed using machine learning models (e.g., TensorFlow or scikit-learn) to help predict crop growth and assess their health.

[0660] Based on the analysis results, the server generates an optimal farming schedule and notifies the terminal. In this process, the notification utilizes emotion recognition technology (e.g., Google Cloud Speech-to-Text API and Emotion Analysis API) to consider the user's emotional state and provide appropriate feedback. For example, if the emotion engine determines that the user is stressed, the system will provide advice in a calm tone.

[0661] The server also collaborates with local organizations to provide a personnel matching function, enabling the efficient allocation of agricultural labor. The terminals control agricultural robots based on the generated schedule and perform the actual farm work.

[0662] For example, if it rains continuously in a certain area, the server uses that data to evaluate the impact on crop growth, predict the next sunny period, and notifies farmers of their work schedule via their devices. At this time, the emotion engine might determine that the user is tired and send a message such as, "Take a break and resume work at the next opportune moment."

[0663] An example of a prompt might be: "Design an application that generates a work schedule that takes into account the current emotional state of agricultural workers. Include a feature that reads their emotions and provides appropriate feedback."

[0664] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0665] Step 1:

[0666] The server acquires data from information collection devices installed on agricultural land. The input consists of raw data on crop conditions, soil conditions, and weather conditions. After receiving this data, the server performs noise reduction processing and converts it into a format suitable for subsequent analysis. The output is a clean dataset.

[0667] Step 2:

[0668] The server feeds preprocessed data into a machine learning model and performs data analysis. The input is a clean dataset. Specifically, it uses scikit-learn and TensorFlow to predict crop growth and assess their health. The output generated as a result of the analysis is predicted data regarding the state of the crops.

[0669] Step 3:

[0670] The server generates an optimal schedule for agricultural work based on predictive data obtained through analysis. The input is predictive data. A generating AI model is used to create the schedule, setting work times and priorities. The output is a specific work schedule.

[0671] Step 4:

[0672] The server notifies the terminal of the generated schedule. During this process, the server evaluates the user's emotional state using emotion recognition technology. The input consists of the work schedule and the user's voice and non-verbal data. Using an emotion recognition API, the server adjusts the feedback message according to the user's emotions and notifies them as output.

[0673] Step 5:

[0674] The terminal controls the agricultural robot according to the received schedule. The input is the work schedule. Specifically, it instructs the robot to start work in each area, automating the task. In this process, the robot operates according to the set schedule and generates the results of the work execution as output.

[0675] Step 6:

[0676] The server collaborates with local organizations and labor placement agencies to provide a talent matching function. Inputs include registration information of agricultural workers and demand data from agricultural organizations. A matching algorithm is used to select the most suitable personnel, and the matching results are presented as output.

[0677] Step 7:

[0678] Users engage in farm work based on system feedback. Input to the user consists of feedback messages and schedule information. Users implement methods to improve work efficiency while taking stress-reducing advice into consideration. Output is work progress and results.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] [Fourth Embodiment]

[0683] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0684] 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.

[0685] 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).

[0686] 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.

[0687] 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.

[0688] 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).

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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".

[0696] This invention is a system aimed at improving agricultural efficiency and realizing a sustainable production system. This system has the functions of data collection, analysis, schedule generation, notification, and personnel matching. Specific embodiments of each function are described below.

[0697] Data collection

[0698] The server acquires real-time data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land. This data is automatically recorded periodically and stored on the server. Examples include humidity and temperature data from soil sensors, and crop image data captured by cameras.

[0699] Data preprocessing and analysis

[0700] The server preprocesses the collected raw data, removing noise and filtering outliers to convert it into an analyzable format. Next, the preprocessed data is fed into a machine learning model to predict crop health and growth. For example, it can analyze abnormalities in leaf color and shape from crop images to enable early detection of pests and diseases.

[0701] Schedule generation and notifications

[0702] The server automatically generates an optimal farming schedule based on the analysis results. The generated schedule is notified to the user, and execution instructions are also sent to the work equipment. For example, it takes the weather forecast for the following week into consideration to determine the optimal days for harvesting and fertilizing.

[0703] Robot control

[0704] The terminal controls agricultural robots based on schedules sent from the server. This ensures that planned tasks are carried out efficiently and accurately. For example, the robots can automatically move around a field and remove weeds in a designated area.

[0705] Talent matching

[0706] The server works in cooperation with national and local governments, as well as staffing agencies, to match urban labor with agricultural organizations. If a user is an urban resident seeking to work in agriculture, that information is provided to agricultural corporations, enabling specific placements to address labor shortages.

[0707] This system is expected to improve the efficiency of agricultural production, increase labor mobility, and enable sustainable food production.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The server will begin periodically acquiring data from information gathering devices installed on the agricultural land. This includes acquiring humidity and temperature data from soil sensors and taking images of crops with cameras.

[0711] Step 2:

[0712] The server preprocesses the collected raw data. This process removes noise and filters outliers. The image data is then converted into an analyzable format, with particularly important features extracted.

[0713] Step 3:

[0714] The server uses pre-processed data to perform analysis using machine learning models. Specifically, it analyzes crop health and predicts growth, and identifies the cause of any abnormalities detected.

[0715] Step 4:

[0716] The server generates an optimal farming schedule based on the analysis results. This process takes into account crop conditions and weather data to determine the necessary tasks and their timing.

[0717] Step 5:

[0718] The server notifies the user of the generated schedule. The user receives the work schedule via smartphone or computer. Necessary instructions are also sent to the work equipment.

[0719] Step 6:

[0720] The terminal controls the agricultural robot based on instructions received from the server. The robot performs the planned tasks, for example, automating harvesting within a specified area.

[0721] Step 7:

[0722] The server initiates the personnel matching process. It collects personnel needs from agricultural organizations and matches them with information on urban farmers seeking employment, thereby securing the necessary workforce.

[0723] Through these steps, this system efficiently and effectively supports agricultural operations and promotes improved efficiency in food production.

[0724] (Example 1)

[0725] 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".

[0726] Modern agriculture demands improved production efficiency, greater precision in operations, and sustainable management, while simultaneously facing challenges such as labor shortages and unpredictable weather conditions. Conventional methods have made it difficult to efficiently create optimal timetables for farm work and to detect plant health conditions early. Furthermore, mechanisms for flexibly utilizing urban labor in the agricultural sector are not yet fully developed. The objective of this invention is to solve these problems.

[0727] 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.

[0728] In this invention, the server includes information gathering means for acquiring information from data collection devices installed in agricultural areas, information preprocessing means for converting the information into an analyzable form, and analysis means for analyzing the state of plants using an artificial intelligence model. This makes it possible to create an optimal timetable for agricultural work, to detect abnormal conditions in plants early, and to efficiently utilize urban labor in agriculture.

[0729] An "information gathering device" is a mechanical device installed in an agricultural area to acquire information related to the condition of plants, soil conditions, and weather conditions.

[0730] "Information gathering means" refers to a method or process for collecting information obtained from an information gathering device.

[0731] "Information preprocessing means" refers to a method or technique for preprocessing collected raw data, removing noise, and converting it into a format that can be analyzed.

[0732] "Analysis method" refers to an analytical process that uses artificial intelligence models to predict the health and growth of plants.

[0733] "Timetable generation means" refers to a method or means for creating an optimal time schedule for agricultural work based on analysis results.

[0734] "Notification means" refers to a procedure or system for informing agricultural workers of the generated time schedule and instructing the work equipment to carry it out.

[0735] "Labor force adjustment measures" refer to means of appropriately matching urban labor to agricultural organizations in cooperation with the national government, local administrative agencies, and labor dispatch agencies.

[0736] An "artificial intelligence model" is a set of algorithms and models designed for computer-based data analysis and prediction.

[0737] This invention provides a system for the efficient management of agricultural areas. This system consists of an information gathering device, a server, and terminals, each functioning in coordination with the others.

[0738] The server receives data in real time from information gathering devices installed in the agricultural area. These devices include soil sensors, weather sensors, and high-resolution cameras, which provide information on plant conditions, soil conditions, and weather conditions. The server stores this information in a database, which forms the basis for continuous monitoring and analysis.

[0739] The server then preprocesses the collected data. It performs noise reduction and outlier filtering, and converts the data into a standardized format. This preprocessed data is suitable for analysis. Generative AI models are used for the analysis. For example, if plant images are provided, analyzing the leaf color and shape can help detect plant abnormalities early.

[0740] Based on the analysis results, the server creates an optimal farming timetable. It can adjust the timing of fertilization and irrigation, taking weather forecast data into consideration. This timetable is automatically notified to the user, and specific instructions for operating agricultural robots are created.

[0741] The terminal controls agricultural robots based on a timetable received from the server. The robots perform tasks such as removing weeds in designated areas and harvesting crops at the appropriate time. This leads to increased efficiency and precision in agricultural work.

[0742] Furthermore, the server has the function of matching urban labor with agricultural organizations in cooperation with national and local government agencies and labor dispatch agencies. This can alleviate the shortage of agricultural labor and enable flexible responses to seasonal peak seasons.

[0743] As a concrete example, the following is an example of a prompt message for analyzing the health status of a plant.

[0744] "Based on current weather conditions and soil data, please generate a fertilization schedule for tomatoes this month."

[0745] By using such a system, it is possible to contribute to improving the efficiency of agricultural production and building a sustainable production system.

[0746] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0747] Step 1:

[0748] The server acquires data in real time from information gathering devices. It receives data related to plant conditions, soil conditions, and weather conditions as input. This data is provided by soil sensors, weather sensors, and high-resolution cameras. The server records this raw data in a database and stores it as foundational data. For example, temperature, humidity, sunlight, and plant images are recorded every hour.

[0749] Step 2:

[0750] The server preprocesses the collected data. It takes previously collected raw data as input. This data is subjected to a denoising algorithm, and outliers are filtered out. The output is data formatted into a unified format. Specifically, sensor data with extreme values ​​is smoothed, and image data is normalized.

[0751] Step 3:

[0752] The server inputs pre-processed data into a generating AI model and performs analysis. The input is pre-processed data, and the analysis predicts the health and growth of the crops. The output is a list of whether or not abnormalities are present and the growth predictions. Specifically, the AI ​​can scan plant images and detect leaf lesions.

[0753] Step 4:

[0754] The server generates an optimal farming timetable based on the analysis results. The inputs used are the analysis results and weather forecast data. The output is a specific work schedule for tasks such as fertilization, irrigation, and harvesting. For example, the program automatically determines the appropriate irrigation days by considering a week's weather forecast.

[0755] Step 5:

[0756] The server notifies the user of the generated schedule and sends execution instructions to the work equipment. The generated schedule is used as input. The output consists of the notification sent to the user and control commands for the work equipment. For example, the user checks the next day's work plan using a smartphone app, and the agricultural robot is provided with confirmed route information.

[0757] Step 6:

[0758] The server collaborates with national and local government agencies and labor placement agencies to match workers. It receives information on urban farmers as input and outputs a list of suitable candidates for each agricultural organization. In particular, it can match experienced workers with farms that require short-term labor.

[0759] (Application Example 1)

[0760] 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".

[0761] As agricultural efficiency and sustainable production systems are increasingly demanded, accurate data collection, real-time information provision, and proper schedule management are crucial. However, conventional technologies remain insufficient to address challenges such as efficient management of individual agricultural projects and appropriate matching of urban labor.

[0762] 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.

[0763] In this invention, the server includes means for acquiring data on crop status, soil conditions, and weather conditions from an information collection device installed on an agricultural site; means for preprocessing the data collected by the data collection means and converting it into an analyzable format; and means for analyzing the data obtained by the data preprocessing means to predict crop health and growth. This enables real-time agricultural management based on accurate data, efficient labor matching, and intuitive information provision through a visualization device.

[0764] An "information gathering device" is a device installed on agricultural land that acquires data on crop conditions, soil conditions, and weather conditions.

[0765] "Data preprocessing means" refers to means of converting raw data collected by an information collection device into an analyzable format.

[0766] "Analysis means" refers to methods for predicting crop health and growth based on pre-processed data.

[0767] A "schedule generation method" is a means of generating an optimal farm work schedule based on the analysis results.

[0768] A "notification means" is a means of notifying the user of the generated schedule and instructing the work device to execute it.

[0769] A "talent matching method" is a means of matching individuals who wish to work in agriculture with agricultural organizations, in cooperation with the national and local governments and labor organizations.

[0770] A "visualization device" is a device that visually displays analysis results and generated schedules.

[0771] A "mobile information terminal" refers to a portable information processing device such as a smartphone, which is used to run applications that support agricultural management.

[0772] This invention's implementation system achieves agricultural efficiency through the cooperation of three entities: a server, a terminal, and a user.

[0773] The server collects data in real time from information gathering devices installed on agricultural land. These devices provide data related to crop conditions, soil conditions, and weather conditions. The server preprocesses this data, removing noise and filtering outliers to convert it into an analyzable format. Furthermore, it uses a generative AI model based on the preprocessed data to predict crop health and growth. The server then leverages machine learning models to generate an optimal farming schedule and notifies users and work equipment.

[0774] The terminal functions as a visualization device, displaying schedules and analysis results provided by the server to the user in real time. The terminal can include mobile information devices such as smartphones and head-mounted displays. This allows users to intuitively obtain information and give instructions for agricultural work.

[0775] As a concrete example, users can use their mobile devices to monitor crops in their fields in real time and receive immediate notifications when abnormalities are detected. Furthermore, they can rationally plan the harvest date for the following week based on weather forecasts. By utilizing this system, the efficiency of agricultural production can be maximized, and the effective use of environmental resources can be achieved.

[0776] Examples of prompts include, "Based on crop health data, please show the best way to generate a farming schedule for the next week," and "Manage multiple agricultural projects within the city and aggregate and display data in real time." Through these prompts, the system provides guidance for optimal farming management.

[0777] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0778] Step 1:

[0779] The server receives data on crop conditions, soil conditions, and weather conditions as input from information gathering devices. Specifically, it collects data in real time from sensors and cameras and transmits that information to the server. The output is a collection of raw data.

[0780] Step 2:

[0781] The server preprocesses the acquired raw data. Specifically, it performs data noise reduction, outlier filtering, and scaling. The output of this processing is clean data suitable for analysis.

[0782] Step 3:

[0783] The server inputs pre-processed data into a generating AI model to predict crop health and growth. At this stage, data calculations are used to detect crop anomalies and predict growth patterns. The output is a set of analysis results.

[0784] Step 4:

[0785] The server generates an optimal farming schedule based on the analysis results. Here, weather forecast data and analysis results are combined, and machine learning algorithms are used to create an efficient farming plan. The output is an optimized schedule.

[0786] Step 5:

[0787] The terminal displays the schedule and analysis results generated using a visualization device to the user. Specifically, the schedule and detailed data analysis results are visually displayed on the screen of a smartphone or head-mounted display. The output is the displayed information.

[0788] Step 6:

[0789] The user issues instructions to agricultural robots and work equipment based on the displayed schedule. Agricultural work begins by inputting instructions into the device via voice input or tap controls. The output is a set of instructions for the agricultural equipment.

[0790] Step 7:

[0791] When a user needs labor, the server collaborates with national and local governments and labor agencies to match them with suitable personnel. This process processes personnel information data and arranges the most suitable candidates. The output is data on the matched workers.

[0792] 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.

[0793] This invention combines a system designed to improve agricultural efficiency with an emotion engine that recognizes user emotions. This system includes functions for data collection, data preprocessing, analysis, schedule generation, notification, and personnel matching, and improves the user experience through feedback from the emotion engine.

[0794] Data Acquisition and Preprocessing

[0795] The server acquires data on crop conditions, soil conditions, and weather conditions through information gathering devices placed on agricultural land. The collected data is denoised and outlier-processed, and then converted into an analyzable format.

[0796] Data analysis and schedule generation

[0797] The server supplies pre-processed data to a machine learning model to predict crop growth and assess their health. Based on the analysis results, it generates an optimal farming schedule to maximize work efficiency.

[0798] Notifications and emotional feedback

[0799] When the server notifies the user of the schedule it has created, it uses an emotion engine to recognize the user's emotional state. The emotion engine evaluates emotions from voice and nonverbal behavior and provides feedback based on that. For example, if the user is feeling stressed, the system will send an encouraging message in a gentle tone.

[0800] Robot control

[0801] The terminal controls the farm robots according to schedules and instructions generated by the server. The robots effectively perform farm work in various areas of the field based on the specified schedule.

[0802] Talent matching and mental support

[0803] The server has the function of matching local agricultural organizations with urban labor, enabling efficient personnel allocation. In addition, the emotion engine provides regular mental health advice to support the motivation of agricultural workers.

[0804] This system aims to simultaneously improve productivity and reduce the psychological burden on farmers by combining data-driven agricultural management with emotion-based feedback.

[0805] The following describes the processing flow.

[0806] Step 1:

[0807] The server periodically acquires data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land. This data is provided as physical measurements from sensors and camera images.

[0808] Step 2:

[0809] The server performs data preprocessing on the acquired data. Specifically, it removes noise from the data and filters out unwanted outliers. It also extracts necessary features from the image data.

[0810] Step 3:

[0811] The server performs analysis using pre-processed data. Image data of crops is input into a machine learning model to detect their growth status and any abnormalities. For example, the health of the leaves can be used to assess the presence or absence of disease.

[0812] Step 4:

[0813] The server generates an optimal farming schedule based on the analysis results. This includes adjusting the work schedule to take weather forecast data into account. A schedule is created that includes specific work plans such as harvesting and fertilizing.

[0814] Step 5:

[0815] Following schedule generation, the server activates the emotion engine and initiates a feedback process to recognize the user's emotional state. It analyzes voice and nonverbal behavioral data to evaluate the user's emotional state.

[0816] Step 6:

[0817] The server takes the assessed emotional state into consideration and adjusts the content of notifications sent to the user. For example, if the user is feeling stressed, it may send a message encouraging them to relax.

[0818] Step 7:

[0819] The terminal controls the farm robots based on instructions from the server. The robots move and perform tasks according to the generated schedule to carry out specific work.

[0820] Step 8:

[0821] The server works in cooperation with national and local governments and staffing agencies to match urban labor with agricultural organizations. In this process, it uses an emotional engine to support placement, taking into account the motivation and suitability of prospective farmers.

[0822] In this way, this system improves agricultural productivity and worker satisfaction through advanced data processing and emotional feedback functions combined with an emotion engine.

[0823] (Example 2)

[0824] 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".

[0825] There are challenges in agriculture, including improving labor efficiency and reducing the psychological burden on agricultural workers. In today's agricultural environment, it is necessary to efficiently collect and analyze large amounts of information, such as plant conditions and weather conditions, and to take necessary measures. Furthermore, ensuring the mental well-being of agricultural workers and appropriately matching labor resources is also crucial.

[0826] 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.

[0827] In this invention, the server includes information gathering means, initial processing means, and analysis means. This enables efficient information gathering and data analysis in agriculture, and through the generation of optimal farm work schedules and psychological support for workers, it becomes possible to improve the efficiency of agriculture and reduce the psychological burden on workers.

[0828] "Information gathering means" refers to devices and processes for acquiring information on plant conditions, soil conditions, and weather conditions using sensors and measuring devices installed in agricultural areas.

[0829] "Initial processing means" refers to devices and algorithms that perform preprocessing, such as noise reduction and outlier processing, to convert information obtained by information gathering means into an analyzable format.

[0830] "Analysis means" refers to devices and processes that use machine learning models and analytical algorithms to predict the health and growth of plants using initially processed information.

[0831] The "schedule generation means" refers to a device and algorithm for generating the optimal schedule for agricultural work based on the analysis results obtained from the analysis means.

[0832] "Notification means" refers to devices and processes for notifying agricultural workers of the generated schedule and instructing work equipment to carry it out.

[0833] "Emotion recognition means" refers to a device and algorithm for recognizing the emotional state of a worker by analyzing their voice and nonverbal behavior, and for providing feedback that corresponds to that emotion.

[0834] A "labor force matching system" refers to a mechanism and process that works in cooperation with national and local governments and labor supply organizations to effectively match aspiring agricultural workers with agricultural organizations.

[0835] This invention is a system designed to improve agricultural efficiency, integrating functions such as information gathering, initial data processing, analysis, schedule generation, notification, emotional feedback, and labor force matching.

[0836] In information gathering, the server utilizes information gathering devices such as sensors and drones placed in agricultural areas. This allows it to acquire weather information such as plant growth status, soil moisture, temperature, and precipitation. The data transmitted from the sensors is stored in the server's central database in real time.

[0837] During initial data processing, the server uses a Python-based data cleansing script to remove noise from the collected data, process outliers, and format the data into a clean format suitable for analysis. Libraries such as NumPy and Pandas can be used for this process.

[0838] During the data analysis phase, the server uses the initially processed data to input into a machine learning model to predict plant growth and assess their health. This analysis utilizes analytical tools such as Scikit-learn and TensorFlow, which can improve the accuracy of crop predictions. For example, it can predict how certain weather patterns will affect crop growth.

[0839] Regarding schedule generation, the server generates the optimal schedule for farm work based on the analysis results and plans a schedule that maximizes work efficiency. This uses Python scripts and optimization algorithms.

[0840] As a notification and emotional feedback function, the server evaluates the user's emotional state from voice and nonverbal data through an emotion engine and provides appropriate feedback. For example, if the user is feeling stressed, the server can generate a friendly message as a prompt from the generated AI model, such as, "How is your farm work going today? Let's take a short break to relieve stress."

[0841] In labor matching, the server efficiently connects local agricultural organizations with urban labor. This utilizes database management and Python data processing techniques. Based on skills and experience, the server automatically suggests appropriate labor assignments, promoting efficient utilization of workers.

[0842] Thus, the present invention is a system that utilizes agricultural data and combines it with feedback based on the emotions of the workers to improve productivity and reduce the mental burden on workers.

[0843] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0844] Step 1:

[0845] Information gathering

[0846] The server acquires data on plant conditions, soil conditions, and weather conditions from sensors and drones installed in agricultural areas. Numerical data transmitted in real time from the sensors (e.g., temperature, humidity, soil pH) is recorded in a central database. Input is raw data from various sensors, and output is a set of initial state information stored in the database.

[0847] Step 2:

[0848] Data Initialization

[0849] The server processes the acquired raw data using a Python script. Specifically, it uses libraries such as NumPy and Pandas to remove noise and correct or delete outliers. The input is raw data, and the output is a clean dataset. This improves data quality and increases the accuracy of the analysis.

[0850] Step 3:

[0851] Data Analysis

[0852] The server performs analysis using machine learning models based on clean data. It runs growth prediction models and health assessment models using Scikit-learn and TensorFlow. The input is a pre-processed dataset, and the output is the growth prediction and health assessment results for individual plants. This provides crucial information for future farming plans.

[0853] Step 4:

[0854] Schedule generation

[0855] The server generates an optimized farming schedule based on the analysis results. It uses a Python optimization algorithm to create a farming schedule that takes weather forecast information into account. The inputs are the analysis results and weather forecast data, and the output is a specific schedule plan for the next farming operation.

[0856] Step 5:

[0857] Notifications and emotional feedback

[0858] The server notifies the user of the generated schedule and analyzes the user's emotional tendencies using an emotion engine. It evaluates voice and nonverbal data and provides emotionally appropriate feedback. The input is the response data collected from the user, and the output is the corresponding emotional feedback message. For example, if the user is feeling tired, the system will send a message such as, "Take a break and don't push yourself too hard today."

[0859] Step 6:

[0860] Robot control

[0861] The terminal controls agricultural robots based on schedule information. It instructs the robots to sow seeds, water, and harvest via a Raspberry Pi. The input is work instruction data sent from the server, and the output is the actual work action performed by the robot.

[0862] Step 7:

[0863] Labor matching

[0864] The server uses a database to allocate labor based on skills and experience. A Python script collaborates with national and local governments and labor organizations to select suitable workers and notify agricultural organizations. The input is worker skill data, and the output is a list of optimal workers.

[0865] (Application Example 2)

[0866] 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".

[0867] In the agricultural sector, efficient farming practices are key to increasing productivity. However, particularly in agriculture near urban areas, the shortage of farm workers and the need to consider their mental health are significant challenges. Furthermore, the stress levels and job satisfaction of farm workers directly impact productivity. Therefore, it is necessary to streamline work schedules, optimize personnel allocation, and consider the emotional state of farm workers to reduce their psychological burden.

[0868] 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.

[0869] In this invention, the server includes data collection means for acquiring data from information gathering devices installed on agricultural land, schedule generation means for analyzing the data and generating an optimal work schedule, and emotion recognition means for recognizing the user's emotions from voice and nonverbal actions. This enables efficient agricultural work while providing appropriate feedback according to the emotional state of agricultural workers.

[0870] An "information gathering device" is a device installed on agricultural land to acquire data on crop conditions, soil conditions, and weather conditions.

[0871] A "data collection means" is a means that has the function of collecting data acquired from an information collection device.

[0872] "Data preprocessing means" refers to means for processing collected data to convert it into an analyzable format.

[0873] "Analysis means" refers to methods for analyzing pre-processed data to predict crop health and growth.

[0874] The "schedule generation means" is a means for generating an optimal schedule for agricultural work based on the analysis results obtained by the analysis means.

[0875] A "notification means" is a means that has the function of notifying agricultural workers of the generated schedule and instructing them to carry out the work.

[0876] "Personnel matching methods" refer to means of matching individuals who wish to work in agriculture with agricultural organizations, in cooperation with local organizations and labor dispatch agencies.

[0877] An "emotion recognition means" is a means of recognizing the user's emotional state from their voice and nonverbal actions, and providing feedback appropriate to that state.

[0878] A "control means" is a means of giving instructions to an agricultural work management support system to control its actions.

[0879] The system of the present invention is realized by having information gathering devices placed in agricultural land and interacting via a network, with the server, terminals, and users each playing specific roles. The server acquires data such as crop conditions, soil conditions, and weather conditions from the information gathering devices. In this way, various environmental data of the agricultural land is collected.

[0880] Next, the data is preprocessed on the server to remove noise and convert it into a format suitable for analysis. The preprocessed data is then analyzed using machine learning models (e.g., TensorFlow or scikit-learn) to help predict crop growth and assess their health.

[0881] Based on the analysis results, the server generates an optimal farming schedule and notifies the terminal. In this process, the notification utilizes emotion recognition technology (e.g., Google Cloud Speech-to-Text API and Emotion Analysis API) to consider the user's emotional state and provide appropriate feedback. For example, if the emotion engine determines that the user is stressed, the system will provide advice in a calm tone.

[0882] The server also collaborates with local organizations to provide a personnel matching function, enabling the efficient allocation of agricultural labor. The terminals control agricultural robots based on the generated schedule and perform the actual farm work.

[0883] For example, if it rains continuously in a certain area, the server uses that data to evaluate the impact on crop growth, predict the next sunny period, and notifies farmers of their work schedule via their devices. At this time, the emotion engine might determine that the user is tired and send a message such as, "Take a break and resume work at the next opportune moment."

[0884] An example of a prompt might be: "Design an application that generates a work schedule that takes into account the current emotional state of agricultural workers. Include a feature that reads their emotions and provides appropriate feedback."

[0885] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0886] Step 1:

[0887] The server acquires data from information collection devices installed on agricultural land. The input consists of raw data on crop conditions, soil conditions, and weather conditions. After receiving this data, the server performs noise reduction processing and converts it into a format suitable for subsequent analysis. The output is a clean dataset.

[0888] Step 2:

[0889] The server feeds preprocessed data into a machine learning model and performs data analysis. The input is a clean dataset. Specifically, it uses scikit-learn and TensorFlow to predict crop growth and assess their health. The output generated as a result of the analysis is predicted data regarding the state of the crops.

[0890] Step 3:

[0891] The server generates an optimal schedule for agricultural work based on predictive data obtained through analysis. The input is predictive data. A generating AI model is used to create the schedule, setting work times and priorities. The output is a specific work schedule.

[0892] Step 4:

[0893] The server notifies the terminal of the generated schedule. During this process, the server evaluates the user's emotional state using emotion recognition technology. The input consists of the work schedule and the user's voice and non-verbal data. Using an emotion recognition API, the server adjusts the feedback message according to the user's emotions and notifies them as output.

[0894] Step 5:

[0895] The terminal controls the agricultural robot according to the received schedule. The input is the work schedule. Specifically, it instructs the robot to start work in each area, automating the task. In this process, the robot operates according to the set schedule and generates the results of the work execution as output.

[0896] Step 6:

[0897] The server collaborates with local organizations and labor placement agencies to provide a talent matching function. Inputs include registration information of agricultural workers and demand data from agricultural organizations. A matching algorithm is used to select the most suitable personnel, and the matching results are presented as output.

[0898] Step 7:

[0899] Users engage in farm work based on system feedback. Input to the user consists of feedback messages and schedule information. Users implement methods to improve work efficiency while taking stress-reducing advice into consideration. Output is work progress and results.

[0900] 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.

[0901] 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.

[0902] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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."

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] The following is further disclosed regarding the embodiments described above.

[0922] (Claim 1)

[0923] A data collection means that acquires data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land,

[0924] Data preprocessing means for preprocessing data collected by the data collection means and converting it into an analyzable format,

[0925] An analysis means analyzes the data obtained by the data preprocessing means and performs an analysis to predict the health status and growth of crops,

[0926] Based on the analysis results obtained by the aforementioned analysis means, a schedule generation means generates an optimal schedule for agricultural work,

[0927] A notification means that notifies agricultural workers of the schedule generated by the schedule generation means and instructs the work device to execute it,

[0928] In collaboration with the national government, local governments, and staffing agencies, we have developed a human resource matching system that matches aspiring agricultural workers with agricultural organizations.

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, characterized in that the analysis means uses a machine learning model to analyze image data of crops and detect abnormal conditions of crops.

[0932] (Claim 3)

[0933] The system according to claim 1, characterized in that the schedule generation means optimizes the work schedule by taking weather forecast data into consideration.

[0934] "Example 1"

[0935] (Claim 1)

[0936] An information gathering means that acquires information related to plant conditions, soil conditions, and weather conditions from data collection devices installed in agricultural areas,

[0937] Information preprocessing means for preprocessing information acquired by the information gathering means and converting it into a format that can be analyzed,

[0938] An analysis means for analyzing the information obtained by the information preprocessing means and predicting the health and growth of the plant,

[0939] A timetable generation means generates an optimal timetable for agricultural work based on the analysis results obtained by the aforementioned analysis means,

[0940] A notification means for informing agricultural workers of the timetable generated by the timetable generation means and instructing the work equipment to execute it,

[0941] In cooperation with the national and local government agencies and labor dispatch agencies, we will implement labor adjustment measures to match those who wish to work in agriculture with agricultural organizations.

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, characterized in that the analysis means uses an artificial intelligence model to analyze image information of plants and detect abnormal conditions in the plants.

[0945] (Claim 3)

[0946] The system according to claim 1, characterized in that the timetable generation means optimizes the work schedule taking weather forecast information into consideration.

[0947] "Application Example 1"

[0948] (Claim 1)

[0949] A means for acquiring data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land,

[0950] A means for preprocessing the data collected by the aforementioned data collection means and converting it into an analyzable format,

[0951] A means for analyzing the data obtained by the aforementioned data preprocessing means to predict the health and growth of crops,

[0952] A means for generating an optimal schedule for agricultural work based on the analysis results obtained by the aforementioned analysis means,

[0953] A means for notifying the user of the schedule generated by the schedule generation means and instructing the work device to execute it,

[0954] In cooperation with the national government, local governments, and labor organizations, a means of matching aspiring agricultural workers with agricultural organizations,

[0955] A means for visually displaying the analysis results and the generated schedule using a visualization device or a portable information terminal,

[0956] A system that includes this.

[0957] (Claim 2)

[0958] The system according to claim 1, characterized in that the analysis means uses a learning model to analyze image data of crops and detect abnormalities in crops.

[0959] (Claim 3)

[0960] The system according to claim 1, characterized in that the schedule generation means optimizes the work schedule by taking weather forecast data into consideration and notifies the user of the schedule via a mobile information terminal.

[0961] "Example 2 of combining an emotion engine"

[0962] (Claim 1)

[0963] Information gathering means that acquire information on plant conditions, soil conditions, and weather conditions from information gathering devices installed in agricultural areas,

[0964] An initial processing means for initial processing the information collected by the information collection means and converting it into an analyzable format,

[0965] An analysis means that analyzes the information obtained by the initial processing means and performs an analysis to predict the health status and growth of the plant,

[0966] Based on the analysis results obtained by the aforementioned analysis means, a schedule generation means generates an optimal schedule for agricultural work,

[0967] A notification means that notifies agricultural workers of the schedule generated by the aforementioned schedule generation means and instructs the work equipment to carry it out,

[0968] An emotion recognition means that analyzes voice and nonverbal behavior to recognize the emotional state of workers and provides feedback corresponding to those emotions,

[0969] In cooperation with the national government, local governments, and labor supply organizations, a labor matching system is in place to match aspiring agricultural workers with agricultural organizations.

[0970] A system that includes this.

[0971] (Claim 2)

[0972] The system according to claim 1, characterized in that the analysis means analyzes image information of a plant using a knowledge acquisition algorithm and detects an abnormal condition of the plant.

[0973] (Claim 3)

[0974] The system according to claim 1, characterized in that the schedule generation means optimizes the work schedule by taking weather forecast information into consideration.

[0975] "Application example 2 when combining with an emotional engine"

[0976] (Claim 1)

[0977] A data collection means that acquires data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land,

[0978] Data preprocessing means for preprocessing data collected by the data collection means and converting it into an analyzable format,

[0979] An analysis means analyzes the data obtained by the data preprocessing means and performs an analysis to predict the health status and growth of crops,

[0980] Based on the analysis results obtained by the aforementioned analysis means, a schedule generation means generates an optimal schedule for agricultural work,

[0981] A notification means that notifies agricultural workers of the schedule generated by the schedule generation means and instructs the work device to execute it,

[0982] In collaboration with local organizations and labor dispatch agencies, we have developed a human resource matching system to connect aspiring agricultural workers with agricultural organizations.

[0983] An emotion recognition means that recognizes the user's emotional state from voice and nonverbal actions and provides feedback according to that state,

[0984] A control means that gives instructions to an agricultural work management support system,

[0985] A system that includes this.

[0986] (Claim 2)

[0987] The system according to claim 1, characterized in that the analysis means uses a machine learning model to analyze image data of crops and detect abnormal conditions of crops.

[0988] (Claim 3)

[0989] The system according to claim 1, characterized in that the schedule generation means optimizes the work schedule by taking weather forecast data into consideration and makes adjustments according to the user's emotional state. [Explanation of symbols]

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Claims

1. A data collection means that acquires data on crop conditions, soil conditions, and weather conditions from information gathering devices installed on agricultural land, Data preprocessing means for preprocessing data collected by the data collection means and converting it into an analyzable format, An analysis means analyzes the data obtained by the data preprocessing means and performs an analysis to predict the health status and growth of crops, Based on the analysis results obtained by the aforementioned analysis means, a schedule generation means generates an optimal schedule for agricultural work, A notification means that notifies agricultural workers of the schedule generated by the schedule generation means and instructs the work device to execute it, In collaboration with the national government, local governments, and staffing agencies, we have developed a human resource matching system that matches aspiring agricultural workers with agricultural organizations. A system that includes this.

2. The system according to claim 1, characterized in that the analysis means uses a machine learning model to analyze image data of crops and detect abnormal conditions of crops.

3. The system according to claim 1, characterized in that the schedule generation means optimizes the work schedule by taking weather forecast data into consideration.

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