System

Abstract

A system is provided.SOLUTION: A system comprising: a data collection means for collecting environmental data of an agricultural land in real time and storing the environmental data in a database on a cloud; a prediction means for analyzing the collected environmental data and planning a supply and demand prediction and a sales strategy; a control means for controlling an agricultural machine for automatically performing agricultural work based on the planned supply and demand prediction and sales strategy; and an order management means for receiving an order from a customer through online sales and automatically performing inventory management and delivery arrangement.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Social issues related to agriculture include a low food self-sufficiency rate, a decrease in the number of agricultural workers due to aging, and an increase in abandoned farmland. Against this backdrop, there is a shortage of independent farmers, making it difficult to ensure a stable supply of agricultural products. In addition, the heavy burden of farm work creates an extremely difficult environment for aging agricultural workers. Furthermore, progress in agricultural work efficiency has not progressed, and there is a need to improve productivity. To solve these problems, it is necessary to provide a fully automated agricultural platform that will automate and streamline agriculture. [Means for solving the problem]

[0005] The present invention provides a data collection means for collecting environmental data from agricultural land in real time and storing it in a cloud database. It also includes a forecasting means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies. It also includes a control means for controlling agricultural machinery to perform automated farm work based on the formulated supply and demand forecasts and sales strategies. Furthermore, it includes an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, thereby streamlining the entire process from agricultural production to sales and delivery. Furthermore, by adding a means for obtaining weather data from an external API and a monitoring means for monitoring the operating status of agricultural machinery in real time and detecting abnormalities, even greater efficiency and reliability are ensured.

[0006] "Agricultural land" means land used for growing agricultural crops.

[0007] "Environmental data" refers to data such as temperature, humidity, amount of sunlight, and nutrient content of the soil in agricultural land.

[0008] A "cloud database" is a database stored on a remote server accessible via the Internet.

[0009] A "data collection means" is a system that collects environmental data using sensors, gateways, etc. and transmits it to a database on the cloud.

[0010] "Predictive tools" are AI models and algorithms that analyze collected environmental data and develop supply and demand forecasts and sales strategies.

[0011] The "control means" is a system that controls agricultural machinery based on the planned supply and demand forecast and sales strategy.

[0012] "Agricultural machinery" refers to mechanical devices that automatically perform tasks such as sowing seeds, watering, weeding, and harvesting.

[0013] "Online sales" refers to the method of selling agricultural products directly to consumers via the Internet.

[0014] "Order management means" refers to a system that manages orders received through online sales and automatically manages inventory and arranges delivery.

[0015] An "External API" is an application programming interface for communicating with external services or databases.

[0016] "Monitoring means" refers to sensors and systems that monitor the operating status of agricultural machinery in real time and detect abnormalities. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention provides a data collection means for collecting environmental data of agricultural land in real time and storing the data in a database on a cloud. Hereinafter, an embodiment of the present invention will be specifically described.

[0039] Fully automated agricultural platform program

[0040] The program includes the following main processing steps:

[0041] 1. Data Collection and Management

[0042] 2. Supply and demand forecasting and sales strategy planning

[0043] 3. Automating agricultural work

[0044] 4. Online sales and delivery management

[0045] Program processing

[0046] 1. Data Collection and Management:

[0047] The server collects environmental data such as temperature, humidity, sunlight, and soil nutrients in real time from various sensors installed in agricultural fields. This data is sent from the sensors via a gateway to a database on the cloud. The server efficiently organizes the collected data and prepares it for analysis.

[0048] 2. Supply and demand forecasting and sales strategy planning:

[0049] The server uses AI to run a supply and demand forecasting model based on the collected environmental data. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict the next month's demand and supply. It also formulates a sales strategy based on these forecast results. Specifically, it determines the type, quantity, timing, and pricing of crops to be harvested.

[0050] 3. Automating agricultural work:

[0051] The server generates a schedule for each work process on the farm based on the planned supply and demand forecast and sales strategy. Based on this, the server automatically controls agricultural machinery. For example, an automatic seed sower sows seeds on the specified date, an automatic irrigation system supplies water at the appropriate time, a weeder removes weeds when necessary, and an automatic harvester harvests the crop.

[0052] 4. Online sales and delivery management:

[0053] Users purchase produce through a website or mobile app. The server receives order information from users and updates the inventory management system. Once stock is available, the server automatically contacts a delivery company to arrange delivery. The delivery company follows instructions and delivers the produce to the consumer. The server also notifies users of the order status and delivery status, and provides tracking information.

[0054] Specific examples

[0055] Data collection and management: At 6:00 a.m., the temperature and humidity sensor measures the latest data and sends it to a cloud database via the gateway. The server receives this data and corrects missing or abnormal values.

[0056] Supply and demand forecasting and sales strategy planning: The server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this, it plans the necessary sowing and harvesting schedules and sets the optimal price to adjust the harvest volume.

[0057] Automated farming: An automatic seed sower sows cabbage seeds at the specified date and time, then an automatic irrigation system waters them at the appropriate time. An automatic weeder removes weeds during the growing season, and an automatic harvester harvests the cabbages at the harvest time.

[0058] Online sales and delivery management: When a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server sends the user an order confirmation email and a delivery tracking number.

[0059] In this way, the fully automated agricultural platform of the present invention is a system that automates and streamlines the entire agricultural process, from production planning to harvesting and sales, thereby improving food self-sufficiency, reducing the burden on farmers, and making effective use of abandoned farmland.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The server collects real-time environmental data such as temperature, humidity, sunlight, and soil nutrients from various sensors installed in agricultural fields. These sensors measure data every 30 minutes and send it to a cloud-based database via a gateway.

[0063] Step 2:

[0064] The server stores the received environmental data in a database and verifies whether there are any missing or outliers. For example, it verifies the data collected that day at midnight, fills in any missing data, and filters out outliers.

[0065] Step 3:

[0066] The server periodically retrieves weather data from an external API and stores it in a cloud database. Every morning at 6:00, the server accesses the weather data provider's API to retrieve the current day's and weekly forecasts.

[0067] Step 4:

[0068] The server inputs preprocessed environmental and weather data into an AI model to make supply and demand forecasts. For example, it analyzes consumer purchasing data from the past three years to predict the supply and demand for cabbage for the next month. Based on these results, a sales strategy is developed.

[0069] Step 5:

[0070] The server generates a schedule for farm work based on supply and demand forecasts and sales strategies. For example, it creates a specific work plan for the next day, such as automatic sowing, watering, weeding, and harvesting, and sends it to agricultural machinery.

[0071] Step 6:

[0072] Based on the farming schedule, the server controls agricultural machinery such as automatic seeding machines, automatic irrigation systems, automatic weeders, and automatic harvesters. For example, an automatic seeding machine sows cabbage seeds on a specified date, and an automatic irrigation system waters them at the appropriate time.

[0073] Step 7:

[0074] The server monitors the operating status of agricultural machinery in real time and takes immediate action if an abnormality is detected. The sensor monitors the operating status of agricultural machinery and notifies the server if an abnormality is detected.

[0075] Step 8:

[0076] Users order produce through a website or mobile app. The server receives the order information and updates inventory data. For example, when a user orders cabbage through the app, the server reduces the inventory and accepts the order.

[0077] Step 9:

[0078] The server automatically issues delivery instructions to the delivery company based on the order information. It then contacts the delivery company using a logistics API to arrange for the delivery of the produce. The delivery company follows the instructions and delivers the produce to the consumer.

[0079] Step 10:

[0080] The server notifies the user of the order and delivery status. For example, the server sends the user an order confirmation email, a delivery tracking number, and provides real-time updates on the delivery status.

[0081] In this way, the entire agricultural process, from production planning to harvesting and sales, is automated and efficiently managed through each step. This platform will improve food self-sufficiency, reduce the burden on farmers, and make effective use of abandoned farmland.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] In order to reduce the burden on farmers and improve agricultural production efficiency, it is necessary to collect environmental data on agricultural land in real time and develop supply and demand forecasts and sales strategies. There is also a need for integrated management of work schedules, streamlining of online sales, automatic control of agricultural machinery, and anomaly detection. Furthermore, there is a need for a system that can centrally manage the entire process from agricultural production to sales, efficiently and effectively, by automating order information processing and delivery arrangements.

[0085] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0086] In this invention, the server includes: a collection unit for collecting environmental data from agricultural land in real time and storing the environmental data in a cloud database; a prediction unit for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies; a control unit for controlling machine tools for automating agricultural work based on the formulated supply and demand forecasts and sales strategies; an order management unit for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries; a unit for transmitting data from sensors installed in the agricultural land through a gateway and correcting the received data; a unit for using the collected environmental data to predict supply and demand based on a generative AI model; a unit for controlling an automatic seed sower, an automatic irrigation system, a weeder, and an automatic harvester based on the generated schedule; and a communication unit for providing order confirmation information and tracking information to customers. This makes it possible to automate and streamline the entire agricultural process, from production planning to harvesting and sales.

[0087] The "data collection means" is a means for collecting environmental data of agricultural land in real time and storing the environmental data in a database on the cloud.

[0088] The "prediction means" is a means for analyzing collected environmental data and formulating supply and demand forecasts and sales strategies.

[0089] The "control means" is a means for controlling machine tools for automatically carrying out agricultural work based on the planned supply and demand forecast and sales strategy.

[0090] "Order management means" refers to a means for accepting orders from customers through online sales and automatically managing inventory and arranging delivery.

[0091] A "sensor" is a measuring device used to measure environmental data such as temperature, humidity, amount of sunlight, and soil nutrients in agricultural land.

[0092] A "gateway" is a communications device that aggregates data sent from sensors and transfers it to a database on the cloud.

[0093] A "generative AI model" is an artificial intelligence model that uses collected environmental data to make supply and demand forecasts.

[0094] An "automatic seeder" is an automated agricultural machine that sows crop seeds according to a specified schedule.

[0095] An "automatic irrigation system" is an automated irrigation device that supplies water to crops at the appropriate time.

[0096] A "weeder" is a machine used to automatically remove weeds from agricultural land.

[0097] An "automatic harvester" is an agricultural machine for automatically harvesting mature crops.

[0098] "Communication Method" means the electronic communication method used to provide order confirmation and tracking information to Customer.

[0099] The present invention provides a data collection means for collecting environmental data of agricultural land in real time and storing the data in a database on a cloud. Hereinafter, an embodiment of the present invention will be specifically described.

[0100] Fully automated agricultural platform program

[0101] The program includes the following main processing steps:

[0102] 1. Data Collection and Management

[0103] 2. Supply and demand forecasting and sales strategy planning

[0104] 3. Automating agricultural work

[0105] 4. Online sales and delivery management

[0106] Program processing

[0107] Data Collection and Management

[0108] The server collects environmental data in real time from various sensors (temperature, humidity, amount of sunlight, soil nutrients, etc.) installed in agricultural fields. This data is sent from the sensors to a database on the cloud via a gateway. The server efficiently organizes the received data and prepares it for analysis. For example, at 6:00 a.m., the temperature and humidity sensor measures the latest data, which is sent to the database on the cloud via the gateway. The server receives this data and corrects any missing or abnormal values.

[0109] Supply and demand forecasting and sales strategy planning

[0110] The server uses a generative AI model based on the collected environmental data to perform supply and demand forecasts. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict demand and supply for the next month. It also formulates sales strategies based on the forecast results. This includes the type, quantity, timing, and pricing of crops to be harvested. Specifically, the server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this, it formulates the necessary sowing and harvest schedules and sets the optimal price to adjust the harvest volume.

[0111] Automating agricultural work

[0112] The server generates a schedule for each work process on the farm based on the planned supply and demand forecast and sales strategy. Based on this, the server automatically controls the automatic seeding machine, automatic irrigation system, weeder, and automatic harvester. For example, the automatic seeding machine sows cabbage seeds at a specified date and time, and then the automatic irrigation system waters them at the appropriate time. The weeder removes weeds during the growing season, and the automatic harvester harvests the cabbage when it's time to harvest.

[0113] Online sales and delivery management

[0114] A user purchases produce through a website or mobile app. The server receives order information from the user and updates the inventory management system. Once stock is available, the server automatically contacts a delivery company to arrange delivery. The delivery company follows instructions and delivers the produce to the consumer. The server also notifies the user of the order and delivery status and provides tracking information. For example, when a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server then sends the user an order confirmation email and a delivery tracking number.

[0115] Examples of prompt statements

[0116] "Please forecast the supply and demand for cabbage for the next month and create a harvest plan."

[0117] In this way, the fully automated agricultural platform of the present invention is a system that automates and streamlines the entire agricultural process, from production planning to harvesting and sales, thereby improving food self-sufficiency, reducing the burden on farmers, and making effective use of abandoned farmland.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] Data Collection and Management

[0121] Input: Environmental data such as temperature, humidity, solar radiation, and soil nutrients collected from sensors installed in agricultural fields.

[0122] Processing: The server receives this data in real time and stores it in a database on the cloud via a gateway from the sensor. The server organizes the data and corrects missing or outlier values.

[0123] Output: A cleaned and corrected dataset is produced.

[0124] Specific behavior:

[0125] At 6:00 a.m., the temperature and humidity sensor measures environmental data and transmits it wirelessly to the gateway. The gateway receives the data and transfers it to a cloud database. The server receives this data, fills in missing values ​​with historical average values, and sends an alert to the administrator if an abnormal value is detected.

[0126] Step 2:

[0127] Supply and demand forecasting and sales strategy planning

[0128] Inputs: Collected environmental data, historical harvest data, consumption trend data, and weather forecast data.

[0129] Processing: The server uses generative AI models to perform supply and demand forecasts, and develops sales strategies, including the type, quantity, timing, and pricing of crops to harvest.

[0130] Output: Demand and supply forecast results and sales strategy details are generated.

[0131] Specific behavior:

[0132] The server inputs collected environmental data, past harvest data, and consumption trend data into a generative AI model to predict the next month's demand and supply. For example, it predicts the supply and demand for cabbage for the next month, predicting demand of 50 tons. Based on this, it plans crop sowing and harvest schedules and sets optimal prices to adjust the harvest volume.

[0133] Step 3:

[0134] Automating agricultural work

[0135] Input: Supply and demand forecast results and planned sales strategy.

[0136] Processing: The server generates schedules for each work process in the agricultural field and controls machine tools such as automatic seeding machines, automatic irrigation systems, weeders, and automatic harvesters.

[0137] Output: Specific agricultural work schedules and automation instructions.

[0138] Specific behavior:

[0139] The server generates a schedule for the automatic seeding machine to sow cabbage seeds at the specified date and time, then the automatic irrigation system waters them at the appropriate time, the weeder removes weeds during the growing season, and the automatic harvester harvests the cabbages at the harvest time.

[0140] Step 4:

[0141] Online sales and delivery management

[0142] Input: Order information from customer.

[0143] Processing: The server processes the order information received through the website or mobile app, updates the inventory management system, and once the item is in stock, contacts the delivery company to arrange delivery.

[0144] Output: Inventory updates, shipping arrangements, order confirmations and tracking information for customers.

[0145] Specific behavior:

[0146] When a user orders 10 cabbages through the app, the server receives the order information, checks the inventory, and updates it. Once the inventory is available, the server automatically sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server then sends the user an order confirmation email and a delivery tracking number.

[0147] (Application example 1)

[0148] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0149] There is a need to collect environmental data in real time on agricultural land and in factories and use that data to develop optimal supply and demand forecasts and production management strategies. However, to achieve this, efficient data collection and analysis methods are essential, as well as a system for automatically controlling agricultural work and production processes. There is also a need for efficient online order management and inventory management. The lack of a system that can manage these multiple processes in an integrated manner is easily cited as a problem.

[0150] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0151] In this invention, the server includes a data collection means for collecting environmental data of agricultural land and production environment data within factories in real time and storing the environmental data in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and production management strategies, a control means for controlling machines that automate agricultural work and production processes based on the formulated supply and demand forecasts and production management strategies, and an inventory management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, thereby enabling efficient management and optimization of the entire agricultural and factory production process.

[0152] "Agricultural land" is land used for agricultural activities, such as growing crops, raising livestock, and horticulture.

[0153] "Environmental data" is numerical data that represents specific environmental conditions, such as temperature, humidity, amount of sunlight, soil nutrients, vibration, and safety conditions in agricultural land or within a factory.

[0154] "Production environment data" is numerical data that represents environmental conditions related to production activities, such as temperature, humidity, vibration, and safety conditions within a factory.

[0155] A "cloud database" is a data storage system accessible via the Internet for storing and managing collected data in real time.

[0156] "Data collection means" refers to devices or methods for acquiring environmental data in real time using sensors, gateways, etc., and transmitting the data to a cloud database.

[0157] "Prediction tools" are systems and algorithms that analyze collected environmental data and use AI models to develop supply and demand forecasts and production management strategies.

[0158] A "production management strategy" is a plan or policy established to optimize production activities, and includes production schedules and inventory management.

[0159] "Control means" refers to devices and systems that automatically operate and adjust agricultural machinery and machinery in factories based on planned supply and demand forecasts and production management strategies.

[0160] "Inventory management tools" are systems and algorithms used to accept orders from customers through online sales, manage inventory status in real time, and make appropriate delivery arrangements.

[0161] An "external API" is a standard program interface for obtaining weather and environmental data from external servers.

[0162] "Monitoring means" refers to sensors, systems, software, etc. that monitor the operating status of machines in real time and detect abnormalities.

[0163] This invention is a system that collects environmental data from agricultural fields and factories in real time, stores the data in a cloud database, and analyzes it to create supply and demand forecasts and production management strategies. Furthermore, this system automatically controls agricultural machinery and production machinery in factories, enabling efficient online sales and inventory management.

[0164] System configuration

[0165] 1. Server

[0166] The server mainly collects, analyzes, controls, and manages data. Specific hardware used can be a cloud server or an edge server, and software uses programming languages ​​such as Python or Java.

[0167] 2. Data Collection Methods

[0168] Various sensors (temperature, humidity, vibration, etc.) are installed in agricultural fields and factories to collect environmental data in real time, which is then sent to a cloud database via a gateway.

[0169] 3. Prediction methods

[0170] The server runs an AI model to analyze the collected environmental data. This AI model uses past and current environmental data to forecast supply and demand and develop production management strategies. The AI ​​model used here is implemented using deep learning frameworks such as TensorFlow and PyTorch.

[0171] 4. Control Measures

[0172] The server controls agricultural machinery (automatic seed sowing machines, automatic irrigation systems, etc.) and production machinery within the factory based on the planned supply and demand forecast and production management strategy, thereby realizing automation and efficiency of work processes.

[0173] 5. Inventory Management Methods

[0174] The server receives order information from customers through online sales, automatically manages inventory and arranges delivery. Order information is stored in a cloud database, and inventory status is updated in real time.

[0175] Program processing overview

[0176] The server collects environmental data in real time from various sensors installed in agricultural fields and factories. The collected data is sent to a cloud database via a gateway. The server checks the collected data for missing or outlier values ​​and makes corrections as necessary.

[0177] The server then runs an AI model based on the collected data to create supply and demand forecasts and production management strategies. This AI model combines past harvest data, production data, consumption trend data, weather forecasts, and other data to make predictions.

[0178] Based on the devised strategy, the server automatically controls agricultural machinery and production machinery in factories. For example, an automatic seed sowing machine sows seeds on a specified date, an automatic irrigation system supplies water at the appropriate time, and the operation schedule of a production line in a factory is optimized.

[0179] In online sales, when a user places an order via a mobile app or website, the information is sent to a server, which updates the inventory management system. The server then automatically contacts a delivery company and arranges delivery.

[0180] Specific examples

[0181] For example, at 6:00 a.m., temperature and humidity sensors in a factory measure the latest data and send it to a cloud-based database via a gateway. The server receives the data and corrects missing or outlier values. Next, an AI model is used to optimize production schedules and predict when necessary maintenance will be performed.

[0182] Prompt Sentence Examples

[0183] "Write a Python program to collect temperature and humidity data in a factory and send it to a cloud database in real time. The program uses a Raspberry Pi and a DHT22 sensor to collect and send data every minute. The data should include the current temperature, humidity, and a timestamp."

[0184] As described above, this invention is a system that efficiently manages and optimizes the entire production process in agriculture and factories.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] The server collects environmental data in real time from various sensors installed in agricultural fields and factories. At this time, data such as temperature, humidity, and vibration is sent from the sensors to the server via a gateway. The input data is raw environmental data obtained from the sensors, and the output is data stored in a database on the cloud. Specifically, the temperature sensor measures temperature data every 30 seconds, which is received by the gateway and sent to the cloud.

[0188] Step 2:

[0189] The server stores the collected environmental data in a database on the cloud. Here, the data is time-stamped and missing or outlier values ​​are corrected. The input data is the raw environmental data sent from the sensors, and the output is the corrected environmental data. Specifically, the server uses a Python script to interpolate missing values ​​with the average value.

[0190] Step 3:

[0191] The server runs an AI model based on data stored in a cloud database to create supply and demand forecasts and production management strategies. Weather data and past production data are also obtained through external APIs. The input data are the environmental data, weather data, and past production data stored in the cloud database, and the output is the supply and demand forecast results and production management strategies. Specifically, the AI ​​model uses TensorFlow to run a supply and demand forecasting algorithm and predict demand for the next month.

[0192] Step 4:

[0193] The server automatically controls agricultural machinery and production machinery in factories based on predicted supply and demand data and production management strategies. The server generates machine operation schedules and sends control instructions to each machine. The input data are the supply and demand forecast results and production management strategies, and the output is machine operation control instructions. Specifically, an automatic seed drill sows seeds on a specified date, and an automatic irrigation system supplies water at the appropriate time.

[0194] Step 5:

[0195] Users order produce or products through a mobile app or website. The order information is sent from the device to a server, which updates the inventory management system. The input data is the customer order information, and the output is the updated inventory information. For example, when a user orders 10 cabbages through the mobile app, the server updates the inventory database and decrements the stock status.

[0196] Step 6:

[0197] Once inventory is secured, the server automatically contacts the delivery company and arranges delivery. The delivery arrangement information and delivery status are notified to the user. The input data is the customer's order information and inventory status, and the output is delivery instructions and delivery status information. Specifically, the server sends an API request to the delivery company and arranges delivery at the specified date and time.

[0198] Through these processing steps, the entire process, from environmental data collection to supply and demand forecasting, agricultural and factory automation, online sales, inventory management, and delivery management, can be carried out efficiently.

[0199] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0200] The present invention provides a data collection means for collecting environmental data on agricultural land in real time and storing it in a cloud database. It also includes a forecasting means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, and a control means for controlling agricultural machinery to perform automated farm work based on the formulated supply and demand forecasts and sales strategies. The system also includes an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries. The system also incorporates an emotion engine that recognizes user emotions, improving the user experience.

[0201] Fully automated agricultural platform program

[0202] The program includes the following features:

[0203] 1. Data Collection and Management

[0204] 2. Supply and demand forecasting and sales strategy planning

[0205] 3. Automating agricultural work

[0206] 4. Online sales and delivery management

[0207] 5. Recognizing and responding to user emotions using an emotion engine

[0208] Program processing

[0209] 1. Data Collection and Management:

[0210] The server collects environmental data such as temperature, humidity, sunlight, and soil nutrients in real time from sensors installed in agricultural fields. The sensors measure data every 30 minutes and send it to a cloud-based database via a gateway. The server receives this data, organizes it, and stores it.

[0211] 2. Supply and demand forecasting and sales strategy planning:

[0212] The server analyzes the collected environmental data and uses AI to run a supply and demand forecasting model. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict demand and supply for the next month. Based on these forecast results, it then creates an optimal sales strategy and determines the selling price and harvest schedule.

[0213] 3. Automating agricultural work:

[0214] Based on the planned supply and demand forecast and sales strategy, the server generates a detailed schedule of farm work and sends instructions to agricultural machinery. For example, an automatic seed sower sows seeds on the specified date, an automatic irrigation system waters at the appropriate time, an automatic weeder removes weeds as needed, and an automatic harvester harvests the crop.

[0215] 4. Online sales and delivery management:

[0216] Users order produce through a website or mobile app. The server receives the order information and updates the inventory management system. Once the product is in stock, the server contacts a delivery company to arrange for delivery of the produce. The delivery company picks up the produce and delivers it to the consumer. The server then sends the user an order confirmation email and a tracking number.

[0217] 5. Emotion engine for recognizing and responding to user emotions:

[0218] The emotion engine uses sensors such as a webcam and microphone to recognize the user's emotions. While the user is using the system, the emotion engine analyzes the user's facial expressions and voice in real time to determine the user's emotional state.

[0219] The server receives data from the emotion engine and responds appropriately based on the recognition results. For example, if the user is feeling stressed, the system suggests promotional offers or contacting customer support.

[0220] The emotion engine can recognize the user's emotions and recommend products based on the emotion. For example, if the user is recognized as tired, it can recommend nutritious agricultural products.

[0221] Specific examples

[0222] Data collection and management: The server measures the latest data from the temperature and humidity sensor at 6:00 a.m. and sends it to the cloud via the gateway. The server receives this data and corrects missing or abnormal values.

[0223] Supply and demand forecasting and sales strategy planning: The server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this forecast, it sets the necessary sowing and harvesting schedules and pricing.

[0224] Automated farming: An automatic seeding machine sows cabbage seeds at a specified time, then an automatic irrigation system waters them at the appropriate time. At harvest time, an automatic harvester harvests the cabbages.

[0225] Online sales and delivery management: When a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company, which delivers the cabbages to the consumer.

[0226] Emotion engine recognizes and responds to user emotions: If a user is feeling stressed while ordering online, the emotion engine will recognize this and the server will notify them of promotional offers. If the server senses that the user is tired, it will recommend nutritious produce.

[0227] In this way, the fully automated agricultural platform of the present invention completely automates agricultural production planning, harvesting, and sales, and combines an emotion engine to improve the user experience, enabling efficient and effective agricultural operations. This platform will improve food self-sufficiency, reduce the burden on farmers, and make effective use of abandoned farmland.

[0228] The processing flow will be explained below.

[0229] Step 1:

[0230] The server collects real-time environmental data, such as temperature, humidity, sunlight, and soil nutrients, from various sensors installed in agricultural fields. The sensors measure the data every 30 minutes and send it to a cloud-based database via a gateway.

[0231] Step 2:

[0232] The server stores the received environmental data in a database on the cloud. At that time, it checks for missing or abnormal values, fills in any missing data, and corrects any abnormal values. For example, at midnight every night, it verifies all data collected that day and makes any necessary corrections.

[0233] Step 3:

[0234] The server uses the API of an external weather data acquisition service to obtain the latest weather forecast daily. It accesses the API every morning at 6:00 to obtain the data for the current day and the week, and stores this in the database.

[0235] Step 4:

[0236] The server uses AI to run a supply and demand forecasting model based on the pre-processed environmental data. For example, it combines past harvest data with consumption trend data to predict the supply and demand for cabbage for the next month. Based on these results, it then plans an optimal sales strategy.

[0237] Step 5:

[0238] The server generates a detailed schedule for farm work based on supply and demand forecasts and sales strategies. Specifically, it determines the next day's schedule for automatic sowing, watering, weeding, and harvesting, and sends this to agricultural machinery.

[0239] Step 6:

[0240] The server sends instructions to control agricultural machinery based on the farming schedule. For example, it might send a command to an automatic seed drill to sow cabbage seeds at 8:00 the next morning, and then send a command to an automatic irrigation system to tell it when to water the crops.

[0241] Step 7:

[0242] The server monitors the operating status of agricultural machinery in real time. Various sensors collect operational information about the machinery and notify the server if an abnormality is detected. For example, a sensor monitors whether an automatic seed drill is operating normally, and sends an alert to the server if an abnormality occurs.

[0243] Step 8:

[0244] Users order produce through a website or mobile app. The server receives the order and updates the inventory management system. If a user orders 10 cabbages, the server decrements the inventory and processes the order confirmation.

[0245] Step 9:

[0246] The server arranges delivery using the delivery company's API based on the order information. It sends the necessary information to the delivery company to ensure smooth delivery. For example, the server sends the user's delivery address and order information to the delivery company's system.

[0247] Step 10:

[0248] The server notifies the user of the order and delivery status in real time, sending a confirmation email when the order is accepted and providing a tracking number when delivery begins. For example, the server updates the delivery status of a cabbage and sends a notification email to the user.

[0249] Step 11:

[0250] The emotion engine uses sensors such as a webcam and microphone to recognize the user's emotions. For example, while a user is shopping online, the emotion engine can analyze the user's facial expressions and voice recorded by the webcam and recognize that the user is feeling stressed.

[0251] Step 12:

[0252] The server receives data from the emotion engine and responds appropriately based on the recognition results. For example, if the server recognizes that the user is feeling stressed, it will display a promotional offer and make suggestions to the user to relax.

[0253] Step 13:

[0254] The server then makes customized product recommendations based on the emotion engine data. For example, if the server detects that the user is tired, it will recommend nutritious produce and suggest products that meet the user's needs.

[0255] In this way, each processing step, from agricultural production planning to harvesting and sales, is fully automated, and by providing services that take user emotions into consideration, efficient and effective agricultural operations and improved customer satisfaction are achieved.

[0256] Example 2

[0257] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0258] Conventional agricultural systems require efficient and integrated processing of environmental data collection, supply and demand forecasting, farm work automation, online sales, inventory management, and delivery arrangements, but lack a means to consistently manage these elements. In particular, recognizing and responding to user emotions in real time would improve the user experience, but no system with such functionality existed. Improving the accuracy of crop supply and demand forecasts and providing a sales experience that satisfies users are key challenges in modern digital agriculture.

[0259] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0260] In this invention, the server includes a data collection means for collecting environmental data of agricultural land in real time and storing it in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, a control means for controlling machines that perform agricultural work automatically based on the formulated supply and demand forecasts and sales strategies, an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, and an emotion recognition means for recognizing and responding to user emotions. This makes it possible to consistently and efficiently manage agricultural production, sales, and delivery, and further improve the user experience.

[0261] "Data collection means" refers to devices and systems for collecting environmental data on agricultural land in real time and storing that data in a database on the cloud.

[0262] "Prediction methods" are methods and algorithms for analyzing collected environmental data and formulating supply and demand forecasts and sales strategies for agricultural products.

[0263] The "control means" is a means for controlling machines and devices for automatically carrying out agricultural work based on the planned supply and demand forecast and sales strategy.

[0264] "Order management means" refers to a system and method for accepting orders from customers through online sales and automatically managing inventory and arranging delivery.

[0265] "Emotion recognition means" refers to technology and devices for recognizing a user's emotions and taking appropriate action based on those emotions.

[0266] A "cloud database" is a data management system for storing data on a network that is accessible via the Internet.

[0267] "Environmental data" refers to the measurement results of agricultural land temperature, humidity, amount of sunlight, soil nutrients, etc., and is data that contains information necessary for agricultural work and predictions.

[0268] "Supply and demand forecasting" is the process of predicting the supply and demand of agricultural products based on historical data and current environmental data.

[0269] "Sales strategy" is a method of planning and formulating the selling price, sales method, marketing strategy, etc. of agricultural products based on supply and demand forecasts.

[0270] "Machinery for automated operations" refers to machines and robots used for the purpose of automating agricultural work, such as devices that perform tasks such as sowing, irrigation, weeding, and harvesting.

[0271] The system of this invention includes a data collection means for collecting environmental data of agricultural land in real time and storing it in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, a control means for controlling machines that perform agricultural work automatically based on the formulated supply and demand forecasts and sales strategies, an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, and an emotion recognition means for recognizing and responding to user emotions.

[0272] Data collection methods

[0273] The server collects environmental data from temperature and humidity sensors and soil sensors placed in agricultural fields. This includes measurements every 30 minutes, and information such as temperature, humidity, amount of sunlight, and soil nutrients is sent to the cloud. Specifically, for example, the temperature and humidity sensor measures the latest data at 6:00 a.m. and sends it to the cloud via the gateway.

[0274] Prediction methods

[0275] The server uses an AI model to predict supply and demand based on the stored environmental data. This prediction includes past harvest data, consumption trend data, weather forecasts, and more. For example, the server uses data from the past three years to predict that the demand for cabbage next month will be 50 tons. Based on this prediction, the server then plans sales prices, harvest schedules, and marketing strategies.

[0276] Control means

[0277] Based on the planned supply and demand forecast and sales strategy, the server generates a detailed farming schedule and sends it to agricultural machinery. Specifically, it sends instructions to the automatic seeding machine to sow cabbage seeds on March 15th, and to the automatic irrigation system to water at the appropriate time. When it's time for harvesting, the automatic harvester will harvest the cabbages.

[0278] Order Management Methods

[0279] Users order produce through a website or mobile app. The server receives the order information and updates the inventory management system. Based on the available stock, the server contacts a delivery company to arrange delivery. For example, if a user orders 10 cabbages through the app, the information is updated in the inventory system and the appropriate delivery arrangements are made.

[0280] emotion recognition means

[0281] The emotion engine analyzes facial expressions and voices while the user is using the system to recognize the user's emotional state. Emotions are analyzed in real time, and the server receives the results. For example, if a user feels stressed while placing an online order, the emotion engine will recognize this and the server will notify them of a promotional offer. If the server detects that the user is tired, it will recommend nutritious agricultural products.

[0282] This will enable consistent and efficient management of agricultural production, sales, and delivery, and further improve the user experience. For example, by inputting the following prompts into the generative AI model, supply and demand forecasts and emotion recognition can be further optimized.

[0283] Example prompt:

[0284] Predict the demand for cabbage for next month.

[0285] Analyze the user's current emotional state and notify them of their stress level.

[0286] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0287] Step 1: Data collection

[0288] Input: Environmental data from temperature and humidity sensors and soil sensors installed in agricultural fields

[0289] Processing: The sensors measure temperature, humidity, solar radiation, and soil nutrient data every 30 minutes.

[0290] Output: Measured environmental data

[0291] Specific operation: The server receives data from the temperature sensor at 6am, which indicates 25 degrees.

[0292] Step 2: Send data to the cloud

[0293] Input: Environmental data obtained from sensors

[0294] Processing: The gateway receives data from the sensors and sends it to a database on the cloud.

[0295] Output: Environmental data stored in the cloud

[0296] Specific operation: The gateway sends data on temperature of 25 degrees and humidity of 60% to the cloud.

[0297] Step 3: Organize and store your data

[0298] Input: Environmental data sent to the cloud

[0299] Processing: The server corrects missing or outliers in the data and records them in the database.

[0300] Output: Environmental data stored in an organized database

[0301] Specific behavior: The server checks the data for missing or outlier values ​​and corrects them.

[0302] Step 4: Run the supply and demand forecasting model

[0303] Input: Organized environmental data, past harvest data, and consumption trend data

[0304] Processing: The server uses the AI ​​model to predict supply and demand.

[0305] Output: Supply and demand forecast results (e.g., next month's demand for cabbage: 50 tons)

[0306] Specific operation: The server performs supply and demand forecasts based on data from the past three years.

[0307] Step 5: Develop a sales strategy

[0308] Input: Supply and demand forecast results

[0309] Processing: The server creates an optimal sales strategy based on the supply and demand forecast results, determining the selling price, harvest schedule, and marketing strategy.

[0310] Output: Sales strategy plan

[0311] Specific operation: The server sets the price of cabbage for the next month at 200 yen per cabbage and determines the harvest date.

[0312] Step 6: Generate and instruct farm work schedules

[0313] Input: Sales Strategy Plan

[0314] Processing: The server generates a detailed farming schedule and sends it to the farming machines.

[0315] Output: Work instructions for agricultural machinery

[0316] Specific operation: Send the instruction "Sow cabbage seeds on March 15th" to the automatic seed sowing machine.

[0317] Step 7: Controlling agricultural machinery

[0318] Input: Work instructions for agricultural machinery

[0319] Processing: Agricultural machinery performs tasks automatically based on instructions from the server.

[0320] Output: Completed farm work data

[0321] What happens: The automatic irrigation system waters on April 1st.

[0322] Step 8: Processing online orders

[0323] Input: User's online ordering information

[0324] Processing: The server receives the order information and updates the inventory management system.

[0325] Output: Updated inventory data

[0326] Specific action: A user orders 10 cabbages in the app.

[0327] Step 9: Inventory management and shipping arrangements

[0328] Input: Updated inventory data and order information

[0329] Processing: The server checks the inventory and arranges for delivery to the delivery company.

[0330] Output: Delivery arrangement information and instructions to the delivery company

[0331] Specific Action: Check cabbage inventory and send pickup and delivery instructions to delivery company.

[0332] Step 10: Recognizing user emotions

[0333] Input: User facial and voice data captured through a webcam and microphone

[0334] Processing: The emotion engine analyzes the user's emotions in real time.

[0335] Output: Analyzed user emotion data

[0336] Specific behavior: Recognize that the user is stressed.

[0337] Step 11: Respond based on user sentiment

[0338] Input: Parsed user emotion data

[0339] Processing: The server responds appropriately based on the user's sentiment, offering promotional offers and customer support.

[0340] Output: User action (e.g., notification of promotional offer)

[0341] Specific behavior: If the user is recognized as tired, the server sends a notification recommending nutritious produce.

[0342] (Application example 2)

[0343] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0344] Conventional agricultural and industrial land management systems have struggled to consistently automate environmental data collection, analysis, forecasting, production control, inventory management, and customer support. Furthermore, there was no way to recognize user emotions and utilize that information in production activities. This limited the improvements to production efficiency and user experience.

[0345] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a data collection means for collecting environmental data from agricultural or industrial land in real time and storing the environmental data in a database on the cloud; a prediction means for analyzing the collected environmental data and formulating a supply and demand forecast and a production strategy; a control means for controlling machines to perform tasks automatically based on the formulated supply and demand forecast and production strategy; an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries; and an emotion recognition means including an emotion engine that recognizes user emotions. This enables consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[0346] "Data collection means" is a function that uses various sensors to acquire data in real time from specified environments and conditions, and stores that data on the cloud.

[0347] A "cloud database" is a system for managing data stored on a server accessible via the Internet, and allows for the storage, analysis, and sharing of large amounts of data.

[0348] "Prediction methods" are functions that use AI and statistical models to create supply and demand forecasts and production strategies based on collected data.

[0349] "Control means" refers to a function that accurately operates automated machines and systems based on planned supply and demand forecasts and production strategies.

[0350] "Order management means" refers to the system's functions that automatically manage inventory and arrange delivery after accepting an online sales order.

[0351] "Emotion recognition means" is a function that recognizes emotions in real time by analyzing the user's facial expressions, voice, etc.

[0352] "Environmental data" is a general term for data related to specific conditions or environments, such as temperature, humidity, amount of sunlight, soil nutrients, vibration, and noise levels.

[0353] "Supply and demand forecasting" is an analytical process that predicts future supply and demand based on past data and current conditions.

[0354] A "production strategy" is a plan based on supply and demand forecasts to optimize production processes and aim for efficient operations.

[0355] "Online sales" is a sales method that provides products and services to customers via the Internet.

[0356] The embodiments of the present invention will be described in detail below.

[0357] First, we will explain the overview of the entire system. This system is equipped with a data collection means to collect environmental data from agricultural or industrial land in real time and store it in a database on the cloud. This ensures that the server always has the latest environmental data, which can be used for the next step of analysis.

[0358] The collected environmental data is then analyzed using AI-based predictive methods. These methods are used to develop supply and demand forecasts and production strategies. Predictive models are run using historical data, current environmental data, and external weather data. For example, AWS cloud services are used to process large amounts of data and run predictive models using TensorFlow and PyTorch libraries.

[0359] Furthermore, based on the planned supply and demand forecast and production strategy, the control means automatically controls the machines. Here, control units such as PLC and Arduino are used. This allows, for example, automated robots and machines to perform tasks automatically based on a schedule.

[0360] For order management, orders are accepted from customers through online sales, and then inventory management and delivery arrangements are automated. Order information is sent to a server via a website or mobile app, and inventory status is updated. Database systems such as AWS RDS and Google BigQuery are used.

[0361] Finally, an emotion recognition mechanism is built in to recognize the user's emotions in real time. For example, the user's facial expressions and voice are collected via a webcam or microphone, and analyzed by an emotion engine (AI model). Based on the results determined by the emotion engine, a notification is sent to a user who is feeling stressed, suggesting relaxation activities.

[0362] For example,

[0363] 1. Data collection: Temperature sensors in the factory collect the latest data at 6:00 a.m. every morning and send it to AWS via Raspberry Pi.

[0364] 2. Supply and demand forecasting: Run the AI ​​model monthly to forecast demand for product A for the next month and optimize the production schedule.

[0365] 3. Production automation: The automated robotic arm begins assembling product A based on the set schedule.

[0366] 4. Inventory management and logistics: If there is a shortage of materials, AWS RDS checks the inventory status and automatically places an order.

[0367] 5. Emotion Recognition: If an employee is tired, the emotion engine will recognize this and the server will send a notification suggesting a relaxation activity.

[0368] Examples of prompts include:

[0369] "Please run the monthly supply and demand forecasting model to forecast the demand for product A next month and optimize the production schedule."

[0370] "Detect abnormalities from factory environment data and make necessary corrections."

[0371] "Analyze the employee's emotional state and generate messages that suggest responses."

[0372] In this way, the system of the present invention achieves consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[0373] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0374] Step 1:

[0375] Temperature, humidity, and vibration sensors are used to collect environmental data. The sensors measure data every 30 minutes and send it to a database on the cloud via a gateway device. The server receives this data, corrects outliers and missing values, and organizes and stores it. The input is raw data from the various sensors, and the output is processed environmental data.

[0376] Step 2:

[0377] The server runs an AI prediction model based on the collected environmental data. It combines this with past data and weather data to create supply and demand forecasts and production strategies. TensorFlow and PyTorch are used as prediction methods. The inputs are organized environmental data, past harvest data, and weather forecast data, and the output is the supply and demand forecast results and the creation of a production strategy.

[0378] Step 3:

[0379] Based on the supply and demand forecast results and production strategy obtained from the predictive model, the server sends instructions to control units such as PLCs and Arduinos to control automated machinery. Specifically, automated robotic arms begin assembling parts based on a schedule. The inputs are the supply and demand forecast results and production strategy data, and the output is operation instructions for the automated machinery.

[0380] Step 4:

[0381] When a user places an order through online sales, the terminal sends the order information to the server. The server receives the order information and updates the inventory management system. If necessary, it also arranges delivery. The input is the order information from the user, and the output is updated inventory information and delivery instructions.

[0382] Step 5:

[0383] While the user is using the system, the emotion engine recognizes the user's emotions in real time via a webcam or microphone. The server receives data from the emotion engine and takes appropriate action based on the results. For example, if the user is feeling stressed, it will send a notification suggesting relaxation activities. The input is voice or facial expression data from the webcam or microphone, and the output is the user's emotional state and countermeasures.

[0384] These steps will enable consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[0385] Examples of generative AI models and prompts include:

[0386] "Please run the monthly supply and demand forecasting model to forecast the demand for product A next month and optimize the production schedule."

[0387] "Detect abnormalities from factory environment data and make necessary corrections."

[0388] "Analyze the employee's emotional state and generate messages that suggest responses."

[0389] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0390] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0391] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0392] [Second embodiment]

[0393] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0394] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0395] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0396] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0397] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0398] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0399] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0400] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0401] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0402] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0403] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0404] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0405] The present invention provides a data collection means for collecting environmental data of agricultural land in real time and storing the data in a database on a cloud. Hereinafter, an embodiment of the present invention will be specifically described.

[0406] Fully automated agricultural platform program

[0407] The program includes the following main processing steps:

[0408] 1. Data Collection and Management

[0409] 2. Supply and demand forecasting and sales strategy planning

[0410] 3. Automating agricultural work

[0411] 4. Online sales and delivery management

[0412] Program processing

[0413] 1. Data Collection and Management:

[0414] The server collects environmental data such as temperature, humidity, sunlight, and soil nutrients in real time from various sensors installed in agricultural fields. This data is sent from the sensors via a gateway to a database on the cloud. The server efficiently organizes the collected data and prepares it for analysis.

[0415] 2. Supply and demand forecasting and sales strategy planning:

[0416] The server uses AI to run a supply and demand forecasting model based on the collected environmental data. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict the next month's demand and supply. It also formulates a sales strategy based on these forecast results. Specifically, it determines the type, quantity, timing, and pricing of crops to be harvested.

[0417] 3. Automating agricultural work:

[0418] The server generates a schedule for each work process on the farm based on the planned supply and demand forecast and sales strategy. Based on this, the server automatically controls agricultural machinery. For example, an automatic seed sower sows seeds on the specified date, an automatic irrigation system supplies water at the appropriate time, a weeder removes weeds when necessary, and an automatic harvester harvests the crop.

[0419] 4. Online sales and delivery management:

[0420] Users purchase produce through a website or mobile app. The server receives order information from users and updates the inventory management system. Once stock is available, the server automatically contacts a delivery company to arrange delivery. The delivery company follows instructions and delivers the produce to the consumer. The server also notifies users of the order status and delivery status, and provides tracking information.

[0421] Specific examples

[0422] Data collection and management: At 6:00 a.m., the temperature and humidity sensor measures the latest data and sends it to a cloud database via the gateway. The server receives this data and corrects missing or abnormal values.

[0423] Supply and demand forecasting and sales strategy planning: The server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this, it plans the necessary sowing and harvesting schedules and sets the optimal price to adjust the harvest volume.

[0424] Automated farming: An automatic seed sower sows cabbage seeds at the specified date and time, then an automatic irrigation system waters them at the appropriate time. An automatic weeder removes weeds during the growing season, and an automatic harvester harvests the cabbages at the harvest time.

[0425] Online sales and delivery management: When a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server sends the user an order confirmation email and a delivery tracking number.

[0426] In this way, the fully automated agricultural platform of the present invention is a system that automates and streamlines the entire agricultural process, from production planning to harvesting and sales, thereby improving food self-sufficiency, reducing the burden on farmers, and making effective use of abandoned farmland.

[0427] The processing flow will be explained below.

[0428] Step 1:

[0429] The server collects real-time environmental data such as temperature, humidity, sunlight, and soil nutrients from various sensors installed in agricultural fields. These sensors measure data every 30 minutes and send it to a cloud-based database via a gateway.

[0430] Step 2:

[0431] The server stores the received environmental data in a database and verifies whether there are any missing or outliers. For example, it verifies the data collected that day at midnight, fills in any missing data, and filters out outliers.

[0432] Step 3:

[0433] The server periodically retrieves weather data from an external API and stores it in a cloud database. Every morning at 6:00, the server accesses the weather data provider's API to retrieve the current day's and weekly forecasts.

[0434] Step 4:

[0435] The server inputs preprocessed environmental and weather data into an AI model to make supply and demand forecasts. For example, it analyzes consumer purchasing data from the past three years to predict the supply and demand for cabbage for the next month. Based on these results, a sales strategy is developed.

[0436] Step 5:

[0437] The server generates a schedule for farm work based on supply and demand forecasts and sales strategies. For example, it creates a specific work plan for the next day, such as automatic sowing, watering, weeding, and harvesting, and sends it to agricultural machinery.

[0438] Step 6:

[0439] Based on the farming schedule, the server controls agricultural machinery such as automatic seeding machines, automatic irrigation systems, automatic weeders, and automatic harvesters. For example, an automatic seeding machine sows cabbage seeds on a specified date, and an automatic irrigation system waters them at the appropriate time.

[0440] Step 7:

[0441] The server monitors the operating status of agricultural machinery in real time and takes immediate action if an abnormality is detected. The sensor monitors the operating status of agricultural machinery and notifies the server if an abnormality is detected.

[0442] Step 8:

[0443] Users order produce through a website or mobile app. The server receives the order information and updates inventory data. For example, when a user orders cabbage through the app, the server reduces the inventory and accepts the order.

[0444] Step 9:

[0445] The server automatically issues delivery instructions to the delivery company based on the order information. It then contacts the delivery company using a logistics API to arrange for the delivery of the produce. The delivery company follows the instructions and delivers the produce to the consumer.

[0446] Step 10:

[0447] The server notifies the user of the order and delivery status. For example, the server sends the user an order confirmation email, a delivery tracking number, and provides real-time updates on the delivery status.

[0448] In this way, the entire agricultural process, from production planning to harvesting and sales, is automated and efficiently managed through each step. This platform will improve food self-sufficiency, reduce the burden on farmers, and make effective use of abandoned farmland.

[0449] Example 1

[0450] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0451] In order to reduce the burden on farmers and improve agricultural production efficiency, it is necessary to collect environmental data on agricultural land in real time and develop supply and demand forecasts and sales strategies. There is also a need for integrated management of work schedules, streamlining of online sales, automatic control of agricultural machinery, and anomaly detection. Furthermore, there is a need for a system that can centrally manage the entire process from agricultural production to sales, efficiently and effectively, by automating order information processing and delivery arrangements.

[0452] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0453] In this invention, the server includes: a collection unit for collecting environmental data from agricultural land in real time and storing the environmental data in a cloud database; a prediction unit for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies; a control unit for controlling machine tools for automating agricultural work based on the formulated supply and demand forecasts and sales strategies; an order management unit for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries; a unit for transmitting data from sensors installed in the agricultural land through a gateway and correcting the received data; a unit for using the collected environmental data to predict supply and demand based on a generative AI model; a unit for controlling an automatic seed sower, an automatic irrigation system, a weeder, and an automatic harvester based on the generated schedule; and a communication unit for providing order confirmation information and tracking information to customers. This makes it possible to automate and streamline the entire agricultural process, from production planning to harvesting and sales.

[0454] The "data collection means" is a means for collecting environmental data of agricultural land in real time and storing the environmental data in a database on the cloud.

[0455] The "prediction means" is a means for analyzing collected environmental data and formulating supply and demand forecasts and sales strategies.

[0456] The "control means" is a means for controlling machine tools for automatically carrying out agricultural work based on the planned supply and demand forecast and sales strategy.

[0457] "Order management means" refers to a means for accepting orders from customers through online sales and automatically managing inventory and arranging delivery.

[0458] A "sensor" is a measuring device used to measure environmental data such as temperature, humidity, amount of sunlight, and soil nutrients in agricultural land.

[0459] A "gateway" is a communications device that aggregates data sent from sensors and transfers it to a database on the cloud.

[0460] A "generative AI model" is an artificial intelligence model that uses collected environmental data to make supply and demand forecasts.

[0461] An "automatic seeder" is an automated agricultural machine that sows crop seeds according to a specified schedule.

[0462] An "automatic irrigation system" is an automated irrigation device that supplies water to crops at the appropriate time.

[0463] A "weeder" is a machine used to automatically remove weeds from agricultural land.

[0464] An "automatic harvester" is an agricultural machine for automatically harvesting mature crops.

[0465] "Communication Method" means the electronic communication method used to provide order confirmation and tracking information to Customer.

[0466] The present invention provides a data collection means for collecting environmental data of agricultural land in real time and storing the data in a database on a cloud. Hereinafter, an embodiment of the present invention will be specifically described.

[0467] Fully automated agricultural platform program

[0468] The program includes the following main processing steps:

[0469] 1. Data Collection and Management

[0470] 2. Supply and demand forecasting and sales strategy planning

[0471] 3. Automating agricultural work

[0472] 4. Online sales and delivery management

[0473] Program processing

[0474] Data Collection and Management

[0475] The server collects environmental data in real time from various sensors (temperature, humidity, amount of sunlight, soil nutrients, etc.) installed in agricultural fields. This data is sent from the sensors to a database on the cloud via a gateway. The server efficiently organizes the received data and prepares it for analysis. For example, at 6:00 a.m., the temperature and humidity sensor measures the latest data, which is sent to the database on the cloud via the gateway. The server receives this data and corrects any missing or abnormal values.

[0476] Supply and demand forecasting and sales strategy planning

[0477] The server uses a generative AI model based on the collected environmental data to perform supply and demand forecasts. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict demand and supply for the next month. It also formulates sales strategies based on the forecast results. This includes the type, quantity, timing, and pricing of crops to be harvested. Specifically, the server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this, it formulates the necessary sowing and harvest schedules and sets the optimal price to adjust the harvest volume.

[0478] Automating agricultural work

[0479] The server generates a schedule for each work process on the farm based on the planned supply and demand forecast and sales strategy. Based on this, the server automatically controls the automatic seeding machine, automatic irrigation system, weeder, and automatic harvester. For example, the automatic seeding machine sows cabbage seeds at a specified date and time, and then the automatic irrigation system waters them at the appropriate time. The weeder removes weeds during the growing season, and the automatic harvester harvests the cabbage when it's time to harvest.

[0480] Online sales and delivery management

[0481] A user purchases produce through a website or mobile app. The server receives order information from the user and updates the inventory management system. Once stock is available, the server automatically contacts a delivery company to arrange delivery. The delivery company follows instructions and delivers the produce to the consumer. The server also notifies the user of the order and delivery status and provides tracking information. For example, when a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server then sends the user an order confirmation email and a delivery tracking number.

[0482] Examples of prompt statements

[0483] "Please forecast the supply and demand for cabbage for the next month and create a harvest plan."

[0484] In this way, the fully automated agricultural platform of the present invention is a system that automates and streamlines the entire agricultural process, from production planning to harvesting and sales, thereby improving food self-sufficiency, reducing the burden on farmers, and making effective use of abandoned farmland.

[0485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0486] Step 1:

[0487] Data Collection and Management

[0488] Input: Environmental data such as temperature, humidity, solar radiation, and soil nutrients collected from sensors installed in agricultural fields.

[0489] Processing: The server receives this data in real time and stores it in a database on the cloud via a gateway from the sensor. The server organizes the data and corrects missing or outlier values.

[0490] Output: A cleaned and corrected dataset is produced.

[0491] Specific behavior:

[0492] At 6:00 a.m., the temperature and humidity sensor measures environmental data and transmits it wirelessly to the gateway. The gateway receives the data and transfers it to a cloud database. The server receives this data, fills in missing values ​​with historical average values, and sends an alert to the administrator if an abnormal value is detected.

[0493] Step 2:

[0494] Supply and demand forecasting and sales strategy planning

[0495] Inputs: Collected environmental data, historical harvest data, consumption trend data, and weather forecast data.

[0496] Processing: The server uses generative AI models to perform supply and demand forecasts, and develops sales strategies, including the type, quantity, timing, and pricing of crops to harvest.

[0497] Output: Demand and supply forecast results and sales strategy details are generated.

[0498] Specific behavior:

[0499] The server inputs collected environmental data, past harvest data, and consumption trend data into a generative AI model to predict the next month's demand and supply. For example, it predicts the supply and demand for cabbage for the next month, predicting demand of 50 tons. Based on this, it plans crop sowing and harvest schedules and sets optimal prices to adjust the harvest volume.

[0500] Step 3:

[0501] Automating agricultural work

[0502] Input: Supply and demand forecast results and planned sales strategy.

[0503] Processing: The server generates schedules for each work process in the agricultural field and controls machine tools such as automatic seeding machines, automatic irrigation systems, weeders, and automatic harvesters.

[0504] Output: Specific agricultural work schedules and automation instructions.

[0505] Specific behavior:

[0506] The server generates a schedule for the automatic seeding machine to sow cabbage seeds at the specified date and time, then the automatic irrigation system waters them at the appropriate time, the weeder removes weeds during the growing season, and the automatic harvester harvests the cabbages at the harvest time.

[0507] Step 4:

[0508] Online sales and delivery management

[0509] Input: Order information from customer.

[0510] Processing: The server processes the order information received through the website or mobile app, updates the inventory management system, and once the item is in stock, contacts the delivery company to arrange delivery.

[0511] Output: Inventory updates, shipping arrangements, order confirmations and tracking information for customers.

[0512] Specific behavior:

[0513] When a user orders 10 cabbages through the app, the server receives the order information, checks the inventory, and updates it. Once the inventory is available, the server automatically sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server then sends the user an order confirmation email and a delivery tracking number.

[0514] (Application example 1)

[0515] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0516] There is a need to collect environmental data in real time on agricultural land and in factories and use that data to develop optimal supply and demand forecasts and production management strategies. However, to achieve this, efficient data collection and analysis methods are essential, as well as a system for automatically controlling agricultural work and production processes. There is also a need for efficient online order management and inventory management. The lack of a system that can manage these multiple processes in an integrated manner is easily cited as a problem.

[0517] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0518] In this invention, the server includes a data collection means for collecting environmental data of agricultural land and production environment data within factories in real time and storing the environmental data in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and production management strategies, a control means for controlling machines that automate agricultural work and production processes based on the formulated supply and demand forecasts and production management strategies, and an inventory management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, thereby enabling efficient management and optimization of the entire agricultural and factory production process.

[0519] "Agricultural land" is land used for agricultural activities, such as growing crops, raising livestock, and horticulture.

[0520] "Environmental data" is numerical data that represents specific environmental conditions, such as temperature, humidity, amount of sunlight, soil nutrients, vibration, and safety conditions in agricultural land or within a factory.

[0521] "Production environment data" is numerical data that represents environmental conditions related to production activities, such as temperature, humidity, vibration, and safety conditions within a factory.

[0522] A "cloud database" is a data storage system accessible via the Internet for storing and managing collected data in real time.

[0523] "Data collection means" refers to devices or methods for acquiring environmental data in real time using sensors, gateways, etc., and transmitting the data to a cloud database.

[0524] "Prediction tools" are systems and algorithms that analyze collected environmental data and use AI models to develop supply and demand forecasts and production management strategies.

[0525] A "production management strategy" is a plan or policy established to optimize production activities, and includes production schedules and inventory management.

[0526] "Control means" refers to devices and systems that automatically operate and adjust agricultural machinery and machinery in factories based on planned supply and demand forecasts and production management strategies.

[0527] "Inventory management tools" are systems and algorithms used to accept orders from customers through online sales, manage inventory status in real time, and make appropriate delivery arrangements.

[0528] An "external API" is a standard program interface for obtaining weather and environmental data from external servers.

[0529] "Monitoring means" refers to sensors, systems, software, etc. that monitor the operating status of machines in real time and detect abnormalities.

[0530] This invention is a system that collects environmental data from agricultural fields and factories in real time, stores the data in a cloud database, and analyzes it to create supply and demand forecasts and production management strategies. Furthermore, this system automatically controls agricultural machinery and production machinery in factories, enabling efficient online sales and inventory management.

[0531] System configuration

[0532] 1. Server

[0533] The server mainly collects, analyzes, controls, and manages data. Specific hardware used can be a cloud server or an edge server, and software uses programming languages ​​such as Python or Java.

[0534] 2. Data Collection Methods

[0535] Various sensors (temperature, humidity, vibration, etc.) are installed in agricultural fields and factories to collect environmental data in real time, which is then sent to a cloud database via a gateway.

[0536] 3. Prediction methods

[0537] The server runs an AI model to analyze the collected environmental data. This AI model uses past and current environmental data to forecast supply and demand and develop production management strategies. The AI ​​model used here is implemented using deep learning frameworks such as TensorFlow and PyTorch.

[0538] 4. Control Measures

[0539] The server controls agricultural machinery (automatic seed sowing machines, automatic irrigation systems, etc.) and production machinery within the factory based on the planned supply and demand forecast and production management strategy, thereby realizing automation and efficiency of work processes.

[0540] 5. Inventory Management Methods

[0541] The server receives order information from customers through online sales, automatically manages inventory and arranges delivery. Order information is stored in a cloud database, and inventory status is updated in real time.

[0542] Program processing overview

[0543] The server collects environmental data in real time from various sensors installed in agricultural fields and factories. The collected data is sent to a cloud database via a gateway. The server checks the collected data for missing or outlier values ​​and makes corrections as necessary.

[0544] The server then runs an AI model based on the collected data to create supply and demand forecasts and production management strategies. This AI model combines past harvest data, production data, consumption trend data, weather forecasts, and other data to make predictions.

[0545] Based on the devised strategy, the server automatically controls agricultural machinery and production machinery in factories. For example, an automatic seed sowing machine sows seeds on a specified date, an automatic irrigation system supplies water at the appropriate time, and the operation schedule of a production line in a factory is optimized.

[0546] In online sales, when a user places an order via a mobile app or website, the information is sent to a server, which updates the inventory management system. The server then automatically contacts a delivery company and arranges delivery.

[0547] Specific examples

[0548] For example, at 6:00 a.m., temperature and humidity sensors in a factory measure the latest data and send it to a cloud-based database via a gateway. The server receives the data and corrects missing or outlier values. Next, an AI model is used to optimize production schedules and predict when necessary maintenance will be performed.

[0549] Prompt Sentence Examples

[0550] "Write a Python program to collect temperature and humidity data in a factory and send it to a cloud database in real time. The program uses a Raspberry Pi and a DHT22 sensor to collect and send data every minute. The data should include the current temperature, humidity, and a timestamp."

[0551] As described above, this invention is a system that efficiently manages and optimizes the entire production process in agriculture and factories.

[0552] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0553] Step 1:

[0554] The server collects environmental data in real time from various sensors installed in agricultural fields and factories. At this time, data such as temperature, humidity, and vibration is sent from the sensors to the server via a gateway. The input data is raw environmental data obtained from the sensors, and the output is data stored in a database on the cloud. Specifically, the temperature sensor measures temperature data every 30 seconds, which is received by the gateway and sent to the cloud.

[0555] Step 2:

[0556] The server stores the collected environmental data in a database on the cloud. Here, the data is time-stamped and missing or outlier values ​​are corrected. The input data is the raw environmental data sent from the sensors, and the output is the corrected environmental data. Specifically, the server uses a Python script to interpolate missing values ​​with the average value.

[0557] Step 3:

[0558] The server runs an AI model based on data stored in a cloud database to create supply and demand forecasts and production management strategies. Weather data and past production data are also obtained through external APIs. The input data are the environmental data, weather data, and past production data stored in the cloud database, and the output is the supply and demand forecast results and production management strategies. Specifically, the AI ​​model uses TensorFlow to run a supply and demand forecasting algorithm and predict demand for the next month.

[0559] Step 4:

[0560] The server automatically controls agricultural machinery and production machinery in factories based on predicted supply and demand data and production management strategies. The server generates machine operation schedules and sends control instructions to each machine. The input data are the supply and demand forecast results and production management strategies, and the output is machine operation control instructions. Specifically, an automatic seed drill sows seeds on a specified date, and an automatic irrigation system supplies water at the appropriate time.

[0561] Step 5:

[0562] Users order produce or products through a mobile app or website. The order information is sent from the device to a server, which updates the inventory management system. The input data is the customer order information, and the output is the updated inventory information. For example, when a user orders 10 cabbages through the mobile app, the server updates the inventory database and decrements the stock status.

[0563] Step 6:

[0564] Once inventory is secured, the server automatically contacts the delivery company and arranges delivery. The delivery arrangement information and delivery status are notified to the user. The input data is the customer's order information and inventory status, and the output is delivery instructions and delivery status information. Specifically, the server sends an API request to the delivery company and arranges delivery at the specified date and time.

[0565] Through these processing steps, the entire process, from environmental data collection to supply and demand forecasting, agricultural and factory automation, online sales, inventory management, and delivery management, can be carried out efficiently.

[0566] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0567] The present invention provides a data collection means for collecting environmental data on agricultural land in real time and storing it in a cloud database. It also includes a forecasting means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, and a control means for controlling agricultural machinery to perform automated farm work based on the formulated supply and demand forecasts and sales strategies. The system also includes an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries. The system also incorporates an emotion engine that recognizes user emotions, improving the user experience.

[0568] Fully automated agricultural platform program

[0569] The program includes the following features:

[0570] 1. Data Collection and Management

[0571] 2. Supply and demand forecasting and sales strategy planning

[0572] 3. Automating agricultural work

[0573] 4. Online sales and delivery management

[0574] 5. Recognizing and responding to user emotions using an emotion engine

[0575] Program processing

[0576] 1. Data Collection and Management:

[0577] The server collects environmental data such as temperature, humidity, sunlight, and soil nutrients in real time from sensors installed in agricultural fields. The sensors measure data every 30 minutes and send it to a cloud-based database via a gateway. The server receives this data, organizes it, and stores it.

[0578] 2. Supply and demand forecasting and sales strategy planning:

[0579] The server analyzes the collected environmental data and uses AI to run a supply and demand forecasting model. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict demand and supply for the next month. Based on these forecast results, it then creates an optimal sales strategy and determines the selling price and harvest schedule.

[0580] 3. Automating agricultural work:

[0581] Based on the planned supply and demand forecast and sales strategy, the server generates a detailed schedule of farm work and sends instructions to agricultural machinery. For example, an automatic seed sower sows seeds on the specified date, an automatic irrigation system waters at the appropriate time, an automatic weeder removes weeds as needed, and an automatic harvester harvests the crop.

[0582] 4. Online sales and delivery management:

[0583] Users order produce through a website or mobile app. The server receives the order information and updates the inventory management system. Once the product is in stock, the server contacts a delivery company to arrange for delivery of the produce. The delivery company picks up the produce and delivers it to the consumer. The server then sends the user an order confirmation email and a tracking number.

[0584] 5. Emotion engine for recognizing and responding to user emotions:

[0585] The emotion engine uses sensors such as a webcam and microphone to recognize the user's emotions. While the user is using the system, the emotion engine analyzes the user's facial expressions and voice in real time to determine the user's emotional state.

[0586] The server receives data from the emotion engine and responds appropriately based on the recognition results. For example, if the user is feeling stressed, the system suggests promotional offers or contacting customer support.

[0587] The emotion engine can recognize the user's emotions and recommend products based on the emotion. For example, if the user is recognized as tired, it can recommend nutritious agricultural products.

[0588] Specific examples

[0589] Data collection and management: The server measures the latest data from the temperature and humidity sensor at 6:00 a.m. and sends it to the cloud via the gateway. The server receives this data and corrects missing or abnormal values.

[0590] Supply and demand forecasting and sales strategy planning: The server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this forecast, it sets the necessary sowing and harvesting schedules and pricing.

[0591] Automated farming: An automatic seeding machine sows cabbage seeds at a specified time, then an automatic irrigation system waters them at the appropriate time. At harvest time, an automatic harvester harvests the cabbages.

[0592] Online sales and delivery management: When a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company, which delivers the cabbages to the consumer.

[0593] Emotion engine recognizes and responds to user emotions: If a user is feeling stressed while ordering online, the emotion engine will recognize this and the server will notify them of promotional offers. If the server senses that the user is tired, it will recommend nutritious produce.

[0594] In this way, the fully automated agricultural platform of the present invention completely automates agricultural production planning, harvesting, and sales, and combines an emotion engine to improve the user experience, enabling efficient and effective agricultural operations. This platform will improve food self-sufficiency, reduce the burden on farmers, and make effective use of abandoned farmland.

[0595] The processing flow will be explained below.

[0596] Step 1:

[0597] The server collects real-time environmental data, such as temperature, humidity, sunlight, and soil nutrients, from various sensors installed in agricultural fields. The sensors measure the data every 30 minutes and send it to a cloud-based database via a gateway.

[0598] Step 2:

[0599] The server stores the received environmental data in a database on the cloud. At that time, it checks for missing or abnormal values, fills in any missing data, and corrects any abnormal values. For example, at midnight every night, it verifies all data collected that day and makes any necessary corrections.

[0600] Step 3:

[0601] The server uses the API of an external weather data acquisition service to obtain the latest weather forecast daily. It accesses the API every morning at 6:00 to obtain the data for the current day and the week, and stores this in the database.

[0602] Step 4:

[0603] The server uses AI to run a supply and demand forecasting model based on the pre-processed environmental data. For example, it combines past harvest data with consumption trend data to predict the supply and demand for cabbage for the next month. Based on these results, it then plans an optimal sales strategy.

[0604] Step 5:

[0605] The server generates a detailed schedule for farm work based on supply and demand forecasts and sales strategies. Specifically, it determines the next day's schedule for automatic sowing, watering, weeding, and harvesting, and sends this to agricultural machinery.

[0606] Step 6:

[0607] The server sends instructions to control agricultural machinery based on the farming schedule. For example, it might send a command to an automatic seed drill to sow cabbage seeds at 8:00 the next morning, and then send a command to an automatic irrigation system to tell it when to water the crops.

[0608] Step 7:

[0609] The server monitors the operating status of agricultural machinery in real time. Various sensors collect operational information about the machinery and notify the server if an abnormality is detected. For example, a sensor monitors whether an automatic seed drill is operating normally, and sends an alert to the server if an abnormality occurs.

[0610] Step 8:

[0611] Users order produce through a website or mobile app. The server receives the order and updates the inventory management system. If a user orders 10 cabbages, the server decrements the inventory and processes the order confirmation.

[0612] Step 9:

[0613] The server arranges delivery using the delivery company's API based on the order information. It sends the necessary information to the delivery company to ensure smooth delivery. For example, the server sends the user's delivery address and order information to the delivery company's system.

[0614] Step 10:

[0615] The server notifies the user of the order and delivery status in real time, sending a confirmation email when the order is accepted and providing a tracking number when delivery begins. For example, the server updates the delivery status of a cabbage and sends a notification email to the user.

[0616] Step 11:

[0617] The emotion engine uses sensors such as a webcam and microphone to recognize the user's emotions. For example, while a user is shopping online, the emotion engine can analyze the user's facial expressions and voice recorded by the webcam and recognize that the user is feeling stressed.

[0618] Step 12:

[0619] The server receives data from the emotion engine and responds appropriately based on the recognition results. For example, if the server recognizes that the user is feeling stressed, it will display a promotional offer and make suggestions to the user to relax.

[0620] Step 13:

[0621] The server then makes customized product recommendations based on the emotion engine data. For example, if the server detects that the user is tired, it will recommend nutritious produce and suggest products that meet the user's needs.

[0622] In this way, each processing step, from agricultural production planning to harvesting and sales, is fully automated, and by providing services that take user emotions into consideration, efficient and effective agricultural operations and improved customer satisfaction are achieved.

[0623] Example 2

[0624] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0625] Conventional agricultural systems require efficient and integrated processing of environmental data collection, supply and demand forecasting, farm work automation, online sales, inventory management, and delivery arrangements, but lack a means to consistently manage these elements. In particular, recognizing and responding to user emotions in real time would improve the user experience, but no system with such functionality existed. Improving the accuracy of crop supply and demand forecasts and providing a sales experience that satisfies users are key challenges in modern digital agriculture.

[0626] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0627] In this invention, the server includes a data collection means for collecting environmental data of agricultural land in real time and storing it in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, a control means for controlling machines that perform agricultural work automatically based on the formulated supply and demand forecasts and sales strategies, an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, and an emotion recognition means for recognizing and responding to user emotions. This makes it possible to consistently and efficiently manage agricultural production, sales, and delivery, and further improve the user experience.

[0628] "Data collection means" refers to devices and systems for collecting environmental data on agricultural land in real time and storing that data in a database on the cloud.

[0629] "Prediction methods" are methods and algorithms for analyzing collected environmental data and formulating supply and demand forecasts and sales strategies for agricultural products.

[0630] The "control means" is a means for controlling machines and devices for automatically carrying out agricultural work based on the planned supply and demand forecast and sales strategy.

[0631] "Order management means" refers to a system and method for accepting orders from customers through online sales and automatically managing inventory and arranging delivery.

[0632] "Emotion recognition means" refers to technology and devices for recognizing a user's emotions and taking appropriate action based on those emotions.

[0633] A "cloud database" is a data management system for storing data on a network that is accessible via the Internet.

[0634] "Environmental data" refers to the measurement results of agricultural land temperature, humidity, amount of sunlight, soil nutrients, etc., and is data that contains information necessary for agricultural work and predictions.

[0635] "Supply and demand forecasting" is the process of predicting the supply and demand of agricultural products based on historical data and current environmental data.

[0636] "Sales strategy" is a method of planning and formulating the selling price, sales method, marketing strategy, etc. of agricultural products based on supply and demand forecasts.

[0637] "Machinery for automated operations" refers to machines and robots used for the purpose of automating agricultural work, such as devices that perform tasks such as sowing, irrigation, weeding, and harvesting.

[0638] The system of this invention includes a data collection means for collecting environmental data of agricultural land in real time and storing it in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, a control means for controlling machines that perform agricultural work automatically based on the formulated supply and demand forecasts and sales strategies, an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, and an emotion recognition means for recognizing and responding to user emotions.

[0639] Data collection methods

[0640] The server collects environmental data from temperature and humidity sensors and soil sensors placed in agricultural fields. This includes measurements every 30 minutes, and information such as temperature, humidity, amount of sunlight, and soil nutrients is sent to the cloud. Specifically, for example, the temperature and humidity sensor measures the latest data at 6:00 a.m. and sends it to the cloud via the gateway.

[0641] Prediction methods

[0642] The server uses an AI model to predict supply and demand based on the stored environmental data. This prediction includes past harvest data, consumption trend data, weather forecasts, and more. For example, the server uses data from the past three years to predict that the demand for cabbage next month will be 50 tons. Based on this prediction, the server then plans sales prices, harvest schedules, and marketing strategies.

[0643] Control means

[0644] Based on the planned supply and demand forecast and sales strategy, the server generates a detailed farming schedule and sends it to agricultural machinery. Specifically, it sends instructions to the automatic seeding machine to sow cabbage seeds on March 15th, and to the automatic irrigation system to water at the appropriate time. When it's time for harvesting, the automatic harvester will harvest the cabbages.

[0645] Order Management Methods

[0646] Users order produce through a website or mobile app. The server receives the order information and updates the inventory management system. Based on the available stock, the server contacts a delivery company to arrange delivery. For example, if a user orders 10 cabbages through the app, the information is updated in the inventory system and the appropriate delivery arrangements are made.

[0647] emotion recognition means

[0648] The emotion engine analyzes facial expressions and voices while the user is using the system to recognize the user's emotional state. Emotions are analyzed in real time, and the server receives the results. For example, if a user feels stressed while placing an online order, the emotion engine will recognize this and the server will notify them of a promotional offer. If the server detects that the user is tired, it will recommend nutritious agricultural products.

[0649] This will enable consistent and efficient management of agricultural production, sales, and delivery, and further improve the user experience. For example, by inputting the following prompts into the generative AI model, supply and demand forecasts and emotion recognition can be further optimized.

[0650] Example prompt:

[0651] Predict the demand for cabbage for next month.

[0652] Analyze the user's current emotional state and notify them of their stress level.

[0653] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0654] Step 1: Data collection

[0655] Input: Environmental data from temperature and humidity sensors and soil sensors installed in agricultural fields

[0656] Processing: The sensors measure temperature, humidity, solar radiation, and soil nutrient data every 30 minutes.

[0657] Output: Measured environmental data

[0658] Specific operation: The server receives data from the temperature sensor at 6am, which indicates 25 degrees.

[0659] Step 2: Send data to the cloud

[0660] Input: Environmental data obtained from sensors

[0661] Processing: The gateway receives data from the sensors and sends it to a database on the cloud.

[0662] Output: Environmental data stored in the cloud

[0663] Specific operation: The gateway sends data on temperature of 25 degrees and humidity of 60% to the cloud.

[0664] Step 3: Organize and store your data

[0665] Input: Environmental data sent to the cloud

[0666] Processing: The server corrects missing or outliers in the data and records them in the database.

[0667] Output: Environmental data stored in an organized database

[0668] Specific behavior: The server checks the data for missing or outlier values ​​and corrects them.

[0669] Step 4: Run the supply and demand forecasting model

[0670] Input: Organized environmental data, past harvest data, and consumption trend data

[0671] Processing: The server uses the AI ​​model to predict supply and demand.

[0672] Output: Supply and demand forecast results (e.g., next month's demand for cabbage: 50 tons)

[0673] Specific operation: The server performs supply and demand forecasts based on data from the past three years.

[0674] Step 5: Develop a sales strategy

[0675] Input: Supply and demand forecast results

[0676] Processing: The server creates an optimal sales strategy based on the supply and demand forecast results, determining the selling price, harvest schedule, and marketing strategy.

[0677] Output: Sales strategy plan

[0678] Specific operation: The server sets the price of cabbage for the next month at 200 yen per cabbage and determines the harvest date.

[0679] Step 6: Generate and instruct farm work schedules

[0680] Input: Sales Strategy Plan

[0681] Processing: The server generates a detailed farming schedule and sends it to the farming machines.

[0682] Output: Work instructions for agricultural machinery

[0683] Specific operation: Send the instruction "Sow cabbage seeds on March 15th" to the automatic seed sowing machine.

[0684] Step 7: Controlling agricultural machinery

[0685] Input: Work instructions for agricultural machinery

[0686] Processing: Agricultural machinery performs tasks automatically based on instructions from the server.

[0687] Output: Completed farm work data

[0688] What happens: The automatic irrigation system waters on April 1st.

[0689] Step 8: Processing online orders

[0690] Input: User's online ordering information

[0691] Processing: The server receives the order information and updates the inventory management system.

[0692] Output: Updated inventory data

[0693] Specific action: A user orders 10 cabbages in the app.

[0694] Step 9: Inventory management and shipping arrangements

[0695] Input: Updated inventory data and order information

[0696] Processing: The server checks the inventory and arranges for delivery to the delivery company.

[0697] Output: Delivery arrangement information and instructions to the delivery company

[0698] Specific Action: Check cabbage inventory and send pickup and delivery instructions to delivery company.

[0699] Step 10: Recognizing user emotions

[0700] Input: User facial and voice data captured through a webcam and microphone

[0701] Processing: The emotion engine analyzes the user's emotions in real time.

[0702] Output: Analyzed user emotion data

[0703] Specific behavior: Recognize that the user is stressed.

[0704] Step 11: Respond based on user sentiment

[0705] Input: Parsed user emotion data

[0706] Processing: The server responds appropriately based on the user's sentiment, offering promotional offers and customer support.

[0707] Output: User action (e.g., notification of promotional offer)

[0708] Specific behavior: If the user is recognized as tired, the server sends a notification recommending nutritious produce.

[0709] (Application example 2)

[0710] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0711] Conventional agricultural and industrial land management systems have struggled to consistently automate environmental data collection, analysis, forecasting, production control, inventory management, and customer support. Furthermore, there was no way to recognize user emotions and utilize that information in production activities. This limited the improvements to production efficiency and user experience.

[0712] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a data collection means for collecting environmental data from agricultural or industrial land in real time and storing the environmental data in a database on the cloud; a prediction means for analyzing the collected environmental data and formulating a supply and demand forecast and a production strategy; a control means for controlling machines to perform tasks automatically based on the formulated supply and demand forecast and production strategy; an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries; and an emotion recognition means including an emotion engine that recognizes user emotions. This enables consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[0713] "Data collection means" is a function that uses various sensors to acquire data in real time from specified environments and conditions, and stores that data on the cloud.

[0714] A "cloud database" is a system for managing data stored on a server accessible via the Internet, and allows for the storage, analysis, and sharing of large amounts of data.

[0715] "Prediction methods" are functions that use AI and statistical models to create supply and demand forecasts and production strategies based on collected data.

[0716] "Control means" refers to a function that accurately operates automated machines and systems based on planned supply and demand forecasts and production strategies.

[0717] "Order management means" refers to the system's functions that automatically manage inventory and arrange delivery after accepting an online sales order.

[0718] "Emotion recognition means" is a function that recognizes emotions in real time by analyzing the user's facial expressions, voice, etc.

[0719] "Environmental data" is a general term for data related to specific conditions or environments, such as temperature, humidity, amount of sunlight, soil nutrients, vibration, and noise levels.

[0720] "Supply and demand forecasting" is an analytical process that predicts future supply and demand based on past data and current conditions.

[0721] A "production strategy" is a plan based on supply and demand forecasts to optimize production processes and aim for efficient operations.

[0722] "Online sales" is a sales method that provides products and services to customers via the Internet.

[0723] The embodiments of the present invention will be described in detail below.

[0724] First, we will explain the overview of the entire system. This system is equipped with a data collection means to collect environmental data from agricultural or industrial land in real time and store it in a database on the cloud. This ensures that the server always has the latest environmental data, which can be used for the next step of analysis.

[0725] The collected environmental data is then analyzed using AI-based predictive methods. These methods are used to develop supply and demand forecasts and production strategies. Predictive models are run using historical data, current environmental data, and external weather data. For example, AWS cloud services are used to process large amounts of data and run predictive models using TensorFlow and PyTorch libraries.

[0726] Furthermore, based on the planned supply and demand forecast and production strategy, the control means automatically controls the machines. Here, control units such as PLC and Arduino are used. This allows, for example, automated robots and machines to perform tasks automatically based on a schedule.

[0727] For order management, orders are accepted from customers through online sales, and then inventory management and delivery arrangements are automated. Order information is sent to a server via a website or mobile app, and inventory status is updated. Database systems such as AWS RDS and Google BigQuery are used.

[0728] Finally, an emotion recognition mechanism is built in to recognize the user's emotions in real time. For example, the user's facial expressions and voice are collected via a webcam or microphone, and analyzed by an emotion engine (AI model). Based on the results determined by the emotion engine, a notification is sent to a user who is feeling stressed, suggesting relaxation activities.

[0729] For example,

[0730] 1. Data collection: Temperature sensors in the factory collect the latest data at 6:00 a.m. every morning and send it to AWS via Raspberry Pi.

[0731] 2. Supply and demand forecasting: Run the AI ​​model monthly to forecast demand for product A for the next month and optimize the production schedule.

[0732] 3. Production automation: The automated robotic arm begins assembling product A based on the set schedule.

[0733] 4. Inventory management and logistics: If there is a shortage of materials, AWS RDS checks the inventory status and automatically places an order.

[0734] 5. Emotion Recognition: If an employee is tired, the emotion engine will recognize this and the server will send a notification suggesting a relaxation activity.

[0735] Examples of prompts include:

[0736] "Please run the monthly supply and demand forecasting model to forecast the demand for product A next month and optimize the production schedule."

[0737] "Detect abnormalities from factory environment data and make necessary corrections."

[0738] "Analyze the employee's emotional state and generate messages that suggest responses."

[0739] In this way, the system of the present invention achieves consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[0740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0741] Step 1:

[0742] Temperature, humidity, and vibration sensors are used to collect environmental data. The sensors measure data every 30 minutes and send it to a database on the cloud via a gateway device. The server receives this data, corrects outliers and missing values, and organizes and stores it. The input is raw data from the various sensors, and the output is processed environmental data.

[0743] Step 2:

[0744] The server runs an AI prediction model based on the collected environmental data. It combines this with past data and weather data to create supply and demand forecasts and production strategies. TensorFlow and PyTorch are used as prediction methods. The inputs are organized environmental data, past harvest data, and weather forecast data, and the output is the supply and demand forecast results and the creation of a production strategy.

[0745] Step 3:

[0746] Based on the supply and demand forecast results and production strategy obtained from the predictive model, the server sends instructions to control units such as PLCs and Arduinos to control automated machinery. Specifically, automated robotic arms begin assembling parts based on a schedule. The inputs are the supply and demand forecast results and production strategy data, and the output is operation instructions for the automated machinery.

[0747] Step 4:

[0748] When a user places an order through online sales, the terminal sends the order information to the server. The server receives the order information and updates the inventory management system. If necessary, it also arranges delivery. The input is the order information from the user, and the output is updated inventory information and delivery instructions.

[0749] Step 5:

[0750] While the user is using the system, the emotion engine recognizes the user's emotions in real time via a webcam or microphone. The server receives data from the emotion engine and takes appropriate action based on the results. For example, if the user is feeling stressed, it will send a notification suggesting relaxation activities. The input is voice or facial expression data from the webcam or microphone, and the output is the user's emotional state and countermeasures.

[0751] These steps will enable consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[0752] Examples of generative AI models and prompts include:

[0753] "Please run the monthly supply and demand forecasting model to forecast the demand for product A next month and optimize the production schedule."

[0754] "Detect abnormalities from factory environment data and make necessary corrections."

[0755] "Analyze the employee's emotional state and generate messages that suggest responses."

[0756] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0757] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0758] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0759] [Third embodiment]

[0760] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0761] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0762] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0763] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0764] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0765] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0766] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0767] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0768] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0769] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0770] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0771] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0772] The present invention provides a data collection means for collecting environmental data of agricultural land in real time and storing the data in a database on a cloud. Hereinafter, an embodiment of the present invention will be specifically described.

[0773] Fully automated agricultural platform program

[0774] The program includes the following main processing steps:

[0775] 1. Data Collection and Management

[0776] 2. Supply and demand forecasting and sales strategy planning

[0777] 3. Automating agricultural work

[0778] 4. Online sales and delivery management

[0779] Program processing

[0780] 1. Data Collection and Management:

[0781] The server collects environmental data such as temperature, humidity, sunlight, and soil nutrients in real time from various sensors installed in agricultural fields. This data is sent from the sensors via a gateway to a database on the cloud. The server efficiently organizes the collected data and prepares it for analysis.

[0782] 2. Supply and demand forecasting and sales strategy planning:

[0783] The server uses AI to run a supply and demand forecasting model based on the collected environmental data. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict the next month's demand and supply. It also formulates a sales strategy based on these forecast results. Specifically, it determines the type, quantity, timing, and pricing of crops to be harvested.

[0784] 3. Automating agricultural work:

[0785] The server generates a schedule for each work process on the farm based on the planned supply and demand forecast and sales strategy. Based on this, the server automatically controls agricultural machinery. For example, an automatic seed sower sows seeds on the specified date, an automatic irrigation system supplies water at the appropriate time, a weeder removes weeds when necessary, and an automatic harvester harvests the crop.

[0786] 4. Online sales and delivery management:

[0787] Users purchase produce through a website or mobile app. The server receives order information from users and updates the inventory management system. Once stock is available, the server automatically contacts a delivery company to arrange delivery. The delivery company follows instructions and delivers the produce to the consumer. The server also notifies users of the order status and delivery status, and provides tracking information.

[0788] Specific examples

[0789] Data collection and management: At 6:00 a.m., the temperature and humidity sensor measures the latest data and sends it to a cloud database via the gateway. The server receives this data and corrects missing or abnormal values.

[0790] Supply and demand forecasting and sales strategy planning: The server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this, it plans the necessary sowing and harvesting schedules and sets the optimal price to adjust the harvest volume.

[0791] Automated farming: An automatic seed sower sows cabbage seeds at the specified date and time, then an automatic irrigation system waters them at the appropriate time. An automatic weeder removes weeds during the growing season, and an automatic harvester harvests the cabbages at the harvest time.

[0792] Online sales and delivery management: When a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server sends the user an order confirmation email and a delivery tracking number.

[0793] In this way, the fully automated agricultural platform of the present invention is a system that automates and streamlines the entire agricultural process, from production planning to harvesting and sales, thereby improving food self-sufficiency, reducing the burden on farmers, and making effective use of abandoned farmland.

[0794] The processing flow will be explained below.

[0795] Step 1:

[0796] The server collects real-time environmental data such as temperature, humidity, sunlight, and soil nutrients from various sensors installed in agricultural fields. These sensors measure data every 30 minutes and send it to a cloud-based database via a gateway.

[0797] Step 2:

[0798] The server stores the received environmental data in a database and verifies whether there are any missing or outliers. For example, it verifies the data collected that day at midnight, fills in any missing data, and filters out outliers.

[0799] Step 3:

[0800] The server periodically retrieves weather data from an external API and stores it in a cloud database. Every morning at 6:00, the server accesses the weather data provider's API to retrieve the current day's and weekly forecasts.

[0801] Step 4:

[0802] The server inputs preprocessed environmental and weather data into an AI model to make supply and demand forecasts. For example, it analyzes consumer purchasing data from the past three years to predict the supply and demand for cabbage for the next month. Based on these results, a sales strategy is developed.

[0803] Step 5:

[0804] The server generates a schedule for farm work based on supply and demand forecasts and sales strategies. For example, it creates a specific work plan for the next day, such as automatic sowing, watering, weeding, and harvesting, and sends it to agricultural machinery.

[0805] Step 6:

[0806] Based on the farming schedule, the server controls agricultural machinery such as automatic seeding machines, automatic irrigation systems, automatic weeders, and automatic harvesters. For example, an automatic seeding machine sows cabbage seeds on a specified date, and an automatic irrigation system waters them at the appropriate time.

[0807] Step 7:

[0808] The server monitors the operating status of agricultural machinery in real time and takes immediate action if an abnormality is detected. The sensor monitors the operating status of agricultural machinery and notifies the server if an abnormality is detected.

[0809] Step 8:

[0810] Users order produce through a website or mobile app. The server receives the order information and updates inventory data. For example, when a user orders cabbage through the app, the server reduces the inventory and accepts the order.

[0811] Step 9:

[0812] The server automatically issues delivery instructions to the delivery company based on the order information. It then contacts the delivery company using a logistics API to arrange for the delivery of the produce. The delivery company follows the instructions and delivers the produce to the consumer.

[0813] Step 10:

[0814] The server notifies the user of the order and delivery status. For example, the server sends the user an order confirmation email, a delivery tracking number, and provides real-time updates on the delivery status.

[0815] In this way, the entire agricultural process, from production planning to harvesting and sales, is automated and efficiently managed through each step. This platform will improve food self-sufficiency, reduce the burden on farmers, and make effective use of abandoned farmland.

[0816] Example 1

[0817] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0818] In order to reduce the burden on farmers and improve agricultural production efficiency, it is necessary to collect environmental data on agricultural land in real time and develop supply and demand forecasts and sales strategies. There is also a need for integrated management of work schedules, streamlining of online sales, automatic control of agricultural machinery, and anomaly detection. Furthermore, there is a need for a system that can centrally manage the entire process from agricultural production to sales, efficiently and effectively, by automating order information processing and delivery arrangements.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0820] In this invention, the server includes: a collection unit for collecting environmental data from agricultural land in real time and storing the environmental data in a cloud database; a prediction unit for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies; a control unit for controlling machine tools for automating agricultural work based on the formulated supply and demand forecasts and sales strategies; an order management unit for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries; a unit for transmitting data from sensors installed in the agricultural land through a gateway and correcting the received data; a unit for using the collected environmental data to predict supply and demand based on a generative AI model; a unit for controlling an automatic seed sower, an automatic irrigation system, a weeder, and an automatic harvester based on the generated schedule; and a communication unit for providing order confirmation information and tracking information to customers. This makes it possible to automate and streamline the entire agricultural process, from production planning to harvesting and sales.

[0821] The "data collection means" is a means for collecting environmental data of agricultural land in real time and storing the environmental data in a database on the cloud.

[0822] The "prediction means" is a means for analyzing collected environmental data and formulating supply and demand forecasts and sales strategies.

[0823] The "control means" is a means for controlling machine tools for automatically carrying out agricultural work based on the planned supply and demand forecast and sales strategy.

[0824] "Order management means" refers to a means for accepting orders from customers through online sales and automatically managing inventory and arranging delivery.

[0825] A "sensor" is a measuring device used to measure environmental data such as temperature, humidity, amount of sunlight, and soil nutrients in agricultural land.

[0826] A "gateway" is a communications device that aggregates data sent from sensors and transfers it to a database on the cloud.

[0827] A "generative AI model" is an artificial intelligence model that uses collected environmental data to make supply and demand forecasts.

[0828] An "automatic seeder" is an automated agricultural machine that sows crop seeds according to a specified schedule.

[0829] An "automatic irrigation system" is an automated irrigation device that supplies water to crops at the appropriate time.

[0830] A "weeder" is a machine used to automatically remove weeds from agricultural land.

[0831] An "automatic harvester" is an agricultural machine for automatically harvesting mature crops.

[0832] "Communication Method" means the electronic communication method used to provide order confirmation and tracking information to Customer.

[0833] The present invention provides a data collection means for collecting environmental data of agricultural land in real time and storing the data in a database on a cloud. Hereinafter, an embodiment of the present invention will be specifically described.

[0834] Fully automated agricultural platform program

[0835] The program includes the following main processing steps:

[0836] 1. Data Collection and Management

[0837] 2. Supply and demand forecasting and sales strategy planning

[0838] 3. Automating agricultural work

[0839] 4. Online sales and delivery management

[0840] Program processing

[0841] Data Collection and Management

[0842] The server collects environmental data in real time from various sensors (temperature, humidity, amount of sunlight, soil nutrients, etc.) installed in agricultural fields. This data is sent from the sensors to a database on the cloud via a gateway. The server efficiently organizes the received data and prepares it for analysis. For example, at 6:00 a.m., the temperature and humidity sensor measures the latest data, which is sent to the database on the cloud via the gateway. The server receives this data and corrects any missing or abnormal values.

[0843] Supply and demand forecasting and sales strategy planning

[0844] The server uses a generative AI model based on the collected environmental data to perform supply and demand forecasts. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict demand and supply for the next month. It also formulates sales strategies based on the forecast results. This includes the type, quantity, timing, and pricing of crops to be harvested. Specifically, the server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this, it formulates the necessary sowing and harvest schedules and sets the optimal price to adjust the harvest volume.

[0845] Automating agricultural work

[0846] The server generates a schedule for each work process on the farm based on the planned supply and demand forecast and sales strategy. Based on this, the server automatically controls the automatic seeding machine, automatic irrigation system, weeder, and automatic harvester. For example, the automatic seeding machine sows cabbage seeds at a specified date and time, and then the automatic irrigation system waters them at the appropriate time. The weeder removes weeds during the growing season, and the automatic harvester harvests the cabbage when it's time to harvest.

[0847] Online sales and delivery management

[0848] A user purchases produce through a website or mobile app. The server receives order information from the user and updates the inventory management system. Once stock is available, the server automatically contacts a delivery company to arrange delivery. The delivery company follows instructions and delivers the produce to the consumer. The server also notifies the user of the order and delivery status and provides tracking information. For example, when a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server then sends the user an order confirmation email and a delivery tracking number.

[0849] Examples of prompt statements

[0850] "Please forecast the supply and demand for cabbage for the next month and create a harvest plan."

[0851] In this way, the fully automated agricultural platform of the present invention is a system that automates and streamlines the entire agricultural process, from production planning to harvesting and sales, thereby improving food self-sufficiency, reducing the burden on farmers, and making effective use of abandoned farmland.

[0852] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] Data Collection and Management

[0855] Input: Environmental data such as temperature, humidity, solar radiation, and soil nutrients collected from sensors installed in agricultural fields.

[0856] Processing: The server receives this data in real time and stores it in a database on the cloud via a gateway from the sensor. The server organizes the data and corrects missing or outlier values.

[0857] Output: A cleaned and corrected dataset is produced.

[0858] Specific behavior:

[0859] At 6:00 a.m., the temperature and humidity sensor measures environmental data and transmits it wirelessly to the gateway. The gateway receives the data and transfers it to a cloud database. The server receives this data, fills in missing values ​​with historical average values, and sends an alert to the administrator if an abnormal value is detected.

[0860] Step 2:

[0861] Supply and demand forecasting and sales strategy planning

[0862] Inputs: Collected environmental data, historical harvest data, consumption trend data, and weather forecast data.

[0863] Processing: The server uses generative AI models to perform supply and demand forecasts, and develops sales strategies, including the type, quantity, timing, and pricing of crops to harvest.

[0864] Output: Demand and supply forecast results and sales strategy details are generated.

[0865] Specific behavior:

[0866] The server inputs collected environmental data, past harvest data, and consumption trend data into a generative AI model to predict the next month's demand and supply. For example, it predicts the supply and demand for cabbage for the next month, predicting demand of 50 tons. Based on this, it plans crop sowing and harvest schedules and sets optimal prices to adjust the harvest volume.

[0867] Step 3:

[0868] Automating agricultural work

[0869] Input: Supply and demand forecast results and planned sales strategy.

[0870] Processing: The server generates schedules for each work process in the agricultural field and controls machine tools such as automatic seeding machines, automatic irrigation systems, weeders, and automatic harvesters.

[0871] Output: Specific agricultural work schedules and automation instructions.

[0872] Specific behavior:

[0873] The server generates a schedule for the automatic seeding machine to sow cabbage seeds at the specified date and time, then the automatic irrigation system waters them at the appropriate time, the weeder removes weeds during the growing season, and the automatic harvester harvests the cabbages at the harvest time.

[0874] Step 4:

[0875] Online sales and delivery management

[0876] Input: Order information from customer.

[0877] Processing: The server processes the order information received through the website or mobile app, updates the inventory management system, and once the item is in stock, contacts the delivery company to arrange delivery.

[0878] Output: Inventory updates, shipping arrangements, order confirmations and tracking information for customers.

[0879] Specific behavior:

[0880] When a user orders 10 cabbages through the app, the server receives the order information, checks the inventory, and updates it. Once the inventory is available, the server automatically sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server then sends the user an order confirmation email and a delivery tracking number.

[0881] (Application example 1)

[0882] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0883] There is a need to collect environmental data in real time on agricultural land and in factories and use that data to develop optimal supply and demand forecasts and production management strategies. However, to achieve this, efficient data collection and analysis methods are essential, as well as a system for automatically controlling agricultural work and production processes. There is also a need for efficient online order management and inventory management. The lack of a system that can manage these multiple processes in an integrated manner is easily cited as a problem.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0885] In this invention, the server includes a data collection means for collecting environmental data of agricultural land and production environment data within factories in real time and storing the environmental data in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and production management strategies, a control means for controlling machines that automate agricultural work and production processes based on the formulated supply and demand forecasts and production management strategies, and an inventory management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, thereby enabling efficient management and optimization of the entire agricultural and factory production process.

[0886] "Agricultural land" is land used for agricultural activities, such as growing crops, raising livestock, and horticulture.

[0887] "Environmental data" is numerical data that represents specific environmental conditions, such as temperature, humidity, amount of sunlight, soil nutrients, vibration, and safety conditions in agricultural land or within a factory.

[0888] "Production environment data" is numerical data that represents environmental conditions related to production activities, such as temperature, humidity, vibration, and safety conditions within a factory.

[0889] A "cloud database" is a data storage system accessible via the Internet for storing and managing collected data in real time.

[0890] "Data collection means" refers to devices or methods for acquiring environmental data in real time using sensors, gateways, etc., and transmitting the data to a cloud database.

[0891] "Prediction tools" are systems and algorithms that analyze collected environmental data and use AI models to develop supply and demand forecasts and production management strategies.

[0892] A "production management strategy" is a plan or policy established to optimize production activities, and includes production schedules and inventory management.

[0893] "Control means" refers to devices and systems that automatically operate and adjust agricultural machinery and machinery in factories based on planned supply and demand forecasts and production management strategies.

[0894] "Inventory management tools" are systems and algorithms used to accept orders from customers through online sales, manage inventory status in real time, and make appropriate delivery arrangements.

[0895] An "external API" is a standard program interface for obtaining weather and environmental data from external servers.

[0896] "Monitoring means" refers to sensors, systems, software, etc. that monitor the operating status of machines in real time and detect abnormalities.

[0897] This invention is a system that collects environmental data from agricultural fields and factories in real time, stores the data in a cloud database, and analyzes it to create supply and demand forecasts and production management strategies. Furthermore, this system automatically controls agricultural machinery and production machinery in factories, enabling efficient online sales and inventory management.

[0898] System configuration

[0899] 1. Server

[0900] The server mainly collects, analyzes, controls, and manages data. Specific hardware used can be a cloud server or an edge server, and software uses programming languages ​​such as Python or Java.

[0901] 2. Data Collection Methods

[0902] Various sensors (temperature, humidity, vibration, etc.) are installed in agricultural fields and factories to collect environmental data in real time, which is then sent to a cloud database via a gateway.

[0903] 3. Prediction methods

[0904] The server runs an AI model to analyze the collected environmental data. This AI model uses past and current environmental data to forecast supply and demand and develop production management strategies. The AI ​​model used here is implemented using deep learning frameworks such as TensorFlow and PyTorch.

[0905] 4. Control Measures

[0906] The server controls agricultural machinery (automatic seed sowing machines, automatic irrigation systems, etc.) and production machinery within the factory based on the planned supply and demand forecast and production management strategy, thereby realizing automation and efficiency of work processes.

[0907] 5. Inventory Management Methods

[0908] The server receives order information from customers through online sales, automatically manages inventory and arranges delivery. Order information is stored in a cloud database, and inventory status is updated in real time.

[0909] Program processing overview

[0910] The server collects environmental data in real time from various sensors installed in agricultural fields and factories. The collected data is sent to a cloud database via a gateway. The server checks the collected data for missing or outlier values ​​and makes corrections as necessary.

[0911] The server then runs an AI model based on the collected data to create supply and demand forecasts and production management strategies. This AI model combines past harvest data, production data, consumption trend data, weather forecasts, and other data to make predictions.

[0912] Based on the devised strategy, the server automatically controls agricultural machinery and production machinery in factories. For example, an automatic seed sowing machine sows seeds on a specified date, an automatic irrigation system supplies water at the appropriate time, and the operation schedule of a production line in a factory is optimized.

[0913] In online sales, when a user places an order via a mobile app or website, the information is sent to a server, which updates the inventory management system. The server then automatically contacts a delivery company and arranges delivery.

[0914] Specific examples

[0915] For example, at 6:00 a.m., temperature and humidity sensors in a factory measure the latest data and send it to a cloud-based database via a gateway. The server receives the data and corrects missing or outlier values. Next, an AI model is used to optimize production schedules and predict when necessary maintenance will be performed.

[0916] Prompt Sentence Examples

[0917] "Write a Python program to collect temperature and humidity data in a factory and send it to a cloud database in real time. The program uses a Raspberry Pi and a DHT22 sensor to collect and send data every minute. The data should include the current temperature, humidity, and a timestamp."

[0918] As described above, this invention is a system that efficiently manages and optimizes the entire production process in agriculture and factories.

[0919] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0920] Step 1:

[0921] The server collects environmental data in real time from various sensors installed in agricultural fields and factories. At this time, data such as temperature, humidity, and vibration is sent from the sensors to the server via a gateway. The input data is raw environmental data obtained from the sensors, and the output is data stored in a database on the cloud. Specifically, the temperature sensor measures temperature data every 30 seconds, which is received by the gateway and sent to the cloud.

[0922] Step 2:

[0923] The server stores the collected environmental data in a database on the cloud. Here, the data is time-stamped and missing or outlier values ​​are corrected. The input data is the raw environmental data sent from the sensors, and the output is the corrected environmental data. Specifically, the server uses a Python script to interpolate missing values ​​with the average value.

[0924] Step 3:

[0925] The server runs an AI model based on data stored in a cloud database to create supply and demand forecasts and production management strategies. Weather data and past production data are also obtained through external APIs. The input data are the environmental data, weather data, and past production data stored in the cloud database, and the output is the supply and demand forecast results and production management strategies. Specifically, the AI ​​model uses TensorFlow to run a supply and demand forecasting algorithm and predict demand for the next month.

[0926] Step 4:

[0927] The server automatically controls agricultural machinery and production machinery in factories based on predicted supply and demand data and production management strategies. The server generates machine operation schedules and sends control instructions to each machine. The input data are the supply and demand forecast results and production management strategies, and the output is machine operation control instructions. Specifically, an automatic seed drill sows seeds on a specified date, and an automatic irrigation system supplies water at the appropriate time.

[0928] Step 5:

[0929] Users order produce or products through a mobile app or website. The order information is sent from the device to a server, which updates the inventory management system. The input data is the customer order information, and the output is the updated inventory information. For example, when a user orders 10 cabbages through the mobile app, the server updates the inventory database and decrements the stock status.

[0930] Step 6:

[0931] Once inventory is secured, the server automatically contacts the delivery company and arranges delivery. The delivery arrangement information and delivery status are notified to the user. The input data is the customer's order information and inventory status, and the output is delivery instructions and delivery status information. Specifically, the server sends an API request to the delivery company and arranges delivery at the specified date and time.

[0932] Through these processing steps, the entire process, from environmental data collection to supply and demand forecasting, agricultural and factory automation, online sales, inventory management, and delivery management, can be carried out efficiently.

[0933] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0934] The present invention provides a data collection means for collecting environmental data on agricultural land in real time and storing it in a cloud database. It also includes a forecasting means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, and a control means for controlling agricultural machinery to perform automated farm work based on the formulated supply and demand forecasts and sales strategies. The system also includes an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries. The system also incorporates an emotion engine that recognizes user emotions, improving the user experience.

[0935] Fully automated agricultural platform program

[0936] The program includes the following features:

[0937] 1. Data Collection and Management

[0938] 2. Supply and demand forecasting and sales strategy planning

[0939] 3. Automating agricultural work

[0940] 4. Online sales and delivery management

[0941] 5. Recognizing and responding to user emotions using an emotion engine

[0942] Program processing

[0943] 1. Data Collection and Management:

[0944] The server collects environmental data such as temperature, humidity, sunlight, and soil nutrients in real time from sensors installed in agricultural fields. The sensors measure data every 30 minutes and send it to a cloud-based database via a gateway. The server receives this data, organizes it, and stores it.

[0945] 2. Supply and demand forecasting and sales strategy planning:

[0946] The server analyzes the collected environmental data and uses AI to run a supply and demand forecasting model. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict demand and supply for the next month. Based on these forecast results, it then creates an optimal sales strategy and determines the selling price and harvest schedule.

[0947] 3. Automating agricultural work:

[0948] Based on the planned supply and demand forecast and sales strategy, the server generates a detailed schedule of farm work and sends instructions to agricultural machinery. For example, an automatic seed sower sows seeds on the specified date, an automatic irrigation system waters at the appropriate time, an automatic weeder removes weeds as needed, and an automatic harvester harvests the crop.

[0949] 4. Online sales and delivery management:

[0950] Users order produce through a website or mobile app. The server receives the order information and updates the inventory management system. Once the product is in stock, the server contacts a delivery company to arrange for delivery of the produce. The delivery company picks up the produce and delivers it to the consumer. The server then sends the user an order confirmation email and a tracking number.

[0951] 5. Emotion engine for recognizing and responding to user emotions:

[0952] The emotion engine uses sensors such as a webcam and microphone to recognize the user's emotions. While the user is using the system, the emotion engine analyzes the user's facial expressions and voice in real time to determine the user's emotional state.

[0953] The server receives data from the emotion engine and responds appropriately based on the recognition results. For example, if the user is feeling stressed, the system suggests promotional offers or contacting customer support.

[0954] The emotion engine can recognize the user's emotions and recommend products based on the emotion. For example, if the user is recognized as tired, it can recommend nutritious agricultural products.

[0955] Specific examples

[0956] Data collection and management: The server measures the latest data from the temperature and humidity sensor at 6:00 a.m. and sends it to the cloud via the gateway. The server receives this data and corrects missing or abnormal values.

[0957] Supply and demand forecasting and sales strategy planning: The server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this forecast, it sets the necessary sowing and harvesting schedules and pricing.

[0958] Automated farming: An automatic seeding machine sows cabbage seeds at a specified time, then an automatic irrigation system waters them at the appropriate time. At harvest time, an automatic harvester harvests the cabbages.

[0959] Online sales and delivery management: When a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company, which delivers the cabbages to the consumer.

[0960] Emotion engine recognizes and responds to user emotions: If a user is feeling stressed while ordering online, the emotion engine will recognize this and the server will notify them of promotional offers. If the server senses that the user is tired, it will recommend nutritious produce.

[0961] In this way, the fully automated agricultural platform of the present invention completely automates agricultural production planning, harvesting, and sales, and combines an emotion engine to improve the user experience, enabling efficient and effective agricultural operations. This platform will improve food self-sufficiency, reduce the burden on farmers, and make effective use of abandoned farmland.

[0962] The processing flow will be explained below.

[0963] Step 1:

[0964] The server collects real-time environmental data, such as temperature, humidity, sunlight, and soil nutrients, from various sensors installed in agricultural fields. The sensors measure the data every 30 minutes and send it to a cloud-based database via a gateway.

[0965] Step 2:

[0966] The server stores the received environmental data in a database on the cloud. At that time, it checks for missing or abnormal values, fills in any missing data, and corrects any abnormal values. For example, at midnight every night, it verifies all data collected that day and makes any necessary corrections.

[0967] Step 3:

[0968] The server uses the API of an external weather data acquisition service to obtain the latest weather forecast daily. It accesses the API every morning at 6:00 to obtain the data for the current day and the week, and stores this in the database.

[0969] Step 4:

[0970] The server uses AI to run a supply and demand forecasting model based on the pre-processed environmental data. For example, it combines past harvest data with consumption trend data to predict the supply and demand for cabbage for the next month. Based on these results, it then plans an optimal sales strategy.

[0971] Step 5:

[0972] The server generates a detailed schedule for farm work based on supply and demand forecasts and sales strategies. Specifically, it determines the next day's schedule for automatic sowing, watering, weeding, and harvesting, and sends this to agricultural machinery.

[0973] Step 6:

[0974] The server sends instructions to control agricultural machinery based on the farming schedule. For example, it might send a command to an automatic seed drill to sow cabbage seeds at 8:00 the next morning, and then send a command to an automatic irrigation system to tell it when to water the crops.

[0975] Step 7:

[0976] The server monitors the operating status of agricultural machinery in real time. Various sensors collect operational information about the machinery and notify the server if an abnormality is detected. For example, a sensor monitors whether an automatic seed drill is operating normally, and sends an alert to the server if an abnormality occurs.

[0977] Step 8:

[0978] Users order produce through a website or mobile app. The server receives the order and updates the inventory management system. If a user orders 10 cabbages, the server decrements the inventory and processes the order confirmation.

[0979] Step 9:

[0980] The server arranges delivery using the delivery company's API based on the order information. It sends the necessary information to the delivery company to ensure smooth delivery. For example, the server sends the user's delivery address and order information to the delivery company's system.

[0981] Step 10:

[0982] The server notifies the user of the order and delivery status in real time, sending a confirmation email when the order is accepted and providing a tracking number when delivery begins. For example, the server updates the delivery status of a cabbage and sends a notification email to the user.

[0983] Step 11:

[0984] The emotion engine uses sensors such as a webcam and microphone to recognize the user's emotions. For example, while a user is shopping online, the emotion engine can analyze the user's facial expressions and voice recorded by the webcam and recognize that the user is feeling stressed.

[0985] Step 12:

[0986] The server receives data from the emotion engine and responds appropriately based on the recognition results. For example, if the server recognizes that the user is feeling stressed, it will display a promotional offer and make suggestions to the user to relax.

[0987] Step 13:

[0988] The server then makes customized product recommendations based on the emotion engine data. For example, if the server detects that the user is tired, it will recommend nutritious produce and suggest products that meet the user's needs.

[0989] In this way, each processing step, from agricultural production planning to harvesting and sales, is fully automated, and by providing services that take user emotions into consideration, efficient and effective agricultural operations and improved customer satisfaction are achieved.

[0990] Example 2

[0991] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0992] Conventional agricultural systems require efficient and integrated processing of environmental data collection, supply and demand forecasting, farm work automation, online sales, inventory management, and delivery arrangements, but lack a means to consistently manage these elements. In particular, recognizing and responding to user emotions in real time would improve the user experience, but no system with such functionality existed. Improving the accuracy of crop supply and demand forecasts and providing a sales experience that satisfies users are key challenges in modern digital agriculture.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0994] In this invention, the server includes a data collection means for collecting environmental data of agricultural land in real time and storing it in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, a control means for controlling machines that perform agricultural work automatically based on the formulated supply and demand forecasts and sales strategies, an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, and an emotion recognition means for recognizing and responding to user emotions. This makes it possible to consistently and efficiently manage agricultural production, sales, and delivery, and further improve the user experience.

[0995] "Data collection means" refers to devices and systems for collecting environmental data on agricultural land in real time and storing that data in a database on the cloud.

[0996] "Prediction methods" are methods and algorithms for analyzing collected environmental data and formulating supply and demand forecasts and sales strategies for agricultural products.

[0997] The "control means" is a means for controlling machines and devices for automatically carrying out agricultural work based on the planned supply and demand forecast and sales strategy.

[0998] "Order management means" refers to a system and method for accepting orders from customers through online sales and automatically managing inventory and arranging delivery.

[0999] "Emotion recognition means" refers to technology and devices for recognizing a user's emotions and taking appropriate action based on those emotions.

[1000] A "cloud database" is a data management system for storing data on a network that is accessible via the Internet.

[1001] "Environmental data" refers to the measurement results of agricultural land temperature, humidity, amount of sunlight, soil nutrients, etc., and is data that contains information necessary for agricultural work and predictions.

[1002] "Supply and demand forecasting" is the process of predicting the supply and demand of agricultural products based on historical data and current environmental data.

[1003] "Sales strategy" is a method of planning and formulating the selling price, sales method, marketing strategy, etc. of agricultural products based on supply and demand forecasts.

[1004] "Machinery for automated operations" refers to machines and robots used for the purpose of automating agricultural work, such as devices that perform tasks such as sowing, irrigation, weeding, and harvesting.

[1005] The system of this invention includes a data collection means for collecting environmental data of agricultural land in real time and storing it in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, a control means for controlling machines that perform agricultural work automatically based on the formulated supply and demand forecasts and sales strategies, an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, and an emotion recognition means for recognizing and responding to user emotions.

[1006] Data collection methods

[1007] The server collects environmental data from temperature and humidity sensors and soil sensors placed in agricultural fields. This includes measurements every 30 minutes, and information such as temperature, humidity, amount of sunlight, and soil nutrients is sent to the cloud. Specifically, for example, the temperature and humidity sensor measures the latest data at 6:00 a.m. and sends it to the cloud via the gateway.

[1008] Prediction methods

[1009] The server uses an AI model to predict supply and demand based on the stored environmental data. This prediction includes past harvest data, consumption trend data, weather forecasts, and more. For example, the server uses data from the past three years to predict that the demand for cabbage next month will be 50 tons. Based on this prediction, the server then plans sales prices, harvest schedules, and marketing strategies.

[1010] Control means

[1011] Based on the planned supply and demand forecast and sales strategy, the server generates a detailed farming schedule and sends it to agricultural machinery. Specifically, it sends instructions to the automatic seeding machine to sow cabbage seeds on March 15th, and to the automatic irrigation system to water at the appropriate time. When it's time for harvesting, the automatic harvester will harvest the cabbages.

[1012] Order Management Methods

[1013] Users order produce through a website or mobile app. The server receives the order information and updates the inventory management system. Based on the available stock, the server contacts a delivery company to arrange delivery. For example, if a user orders 10 cabbages through the app, the information is updated in the inventory system and the appropriate delivery arrangements are made.

[1014] emotion recognition means

[1015] The emotion engine analyzes facial expressions and voices while the user is using the system to recognize the user's emotional state. Emotions are analyzed in real time, and the server receives the results. For example, if a user feels stressed while placing an online order, the emotion engine will recognize this and the server will notify them of a promotional offer. If the server detects that the user is tired, it will recommend nutritious agricultural products.

[1016] This will enable consistent and efficient management of agricultural production, sales, and delivery, and further improve the user experience. For example, by inputting the following prompts into the generative AI model, supply and demand forecasts and emotion recognition can be further optimized.

[1017] Example prompt:

[1018] Predict the demand for cabbage for next month.

[1019] Analyze the user's current emotional state and notify them of their stress level.

[1020] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1021] Step 1: Data collection

[1022] Input: Environmental data from temperature and humidity sensors and soil sensors installed in agricultural fields

[1023] Processing: The sensors measure temperature, humidity, solar radiation, and soil nutrient data every 30 minutes.

[1024] Output: Measured environmental data

[1025] Specific operation: The server receives data from the temperature sensor at 6am, which indicates 25 degrees.

[1026] Step 2: Send data to the cloud

[1027] Input: Environmental data obtained from sensors

[1028] Processing: The gateway receives data from the sensors and sends it to a database on the cloud.

[1029] Output: Environmental data stored in the cloud

[1030] Specific operation: The gateway sends data on temperature of 25 degrees and humidity of 60% to the cloud.

[1031] Step 3: Organize and store your data

[1032] Input: Environmental data sent to the cloud

[1033] Processing: The server corrects missing or outliers in the data and records them in the database.

[1034] Output: Environmental data stored in an organized database

[1035] Specific behavior: The server checks the data for missing or outlier values ​​and corrects them.

[1036] Step 4: Run the supply and demand forecasting model

[1037] Input: Organized environmental data, past harvest data, and consumption trend data

[1038] Processing: The server uses the AI ​​model to predict supply and demand.

[1039] Output: Supply and demand forecast results (e.g., next month's demand for cabbage: 50 tons)

[1040] Specific operation: The server performs supply and demand forecasts based on data from the past three years.

[1041] Step 5: Develop a sales strategy

[1042] Input: Supply and demand forecast results

[1043] Processing: The server creates an optimal sales strategy based on the supply and demand forecast results, determining the selling price, harvest schedule, and marketing strategy.

[1044] Output: Sales strategy plan

[1045] Specific operation: The server sets the price of cabbage for the next month at 200 yen per cabbage and determines the harvest date.

[1046] Step 6: Generate and instruct farm work schedules

[1047] Input: Sales Strategy Plan

[1048] Processing: The server generates a detailed farming schedule and sends it to the farming machines.

[1049] Output: Work instructions for agricultural machinery

[1050] Specific operation: Send the instruction "Sow cabbage seeds on March 15th" to the automatic seed sowing machine.

[1051] Step 7: Controlling agricultural machinery

[1052] Input: Work instructions for agricultural machinery

[1053] Processing: Agricultural machinery performs tasks automatically based on instructions from the server.

[1054] Output: Completed farm work data

[1055] What happens: The automatic irrigation system waters on April 1st.

[1056] Step 8: Processing online orders

[1057] Input: User's online ordering information

[1058] Processing: The server receives the order information and updates the inventory management system.

[1059] Output: Updated inventory data

[1060] Specific action: A user orders 10 cabbages in the app.

[1061] Step 9: Inventory management and shipping arrangements

[1062] Input: Updated inventory data and order information

[1063] Processing: The server checks the inventory and arranges for delivery to the delivery company.

[1064] Output: Delivery arrangement information and instructions to the delivery company

[1065] Specific Action: Check cabbage inventory and send pickup and delivery instructions to delivery company.

[1066] Step 10: Recognizing user emotions

[1067] Input: User facial and voice data captured through a webcam and microphone

[1068] Processing: The emotion engine analyzes the user's emotions in real time.

[1069] Output: Analyzed user emotion data

[1070] Specific behavior: Recognize that the user is stressed.

[1071] Step 11: Respond based on user sentiment

[1072] Input: Parsed user emotion data

[1073] Processing: The server responds appropriately based on the user's sentiment, offering promotional offers and customer support.

[1074] Output: User action (e.g., notification of promotional offer)

[1075] Specific behavior: If the user is recognized as tired, the server sends a notification recommending nutritious produce.

[1076] (Application example 2)

[1077] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1078] Conventional agricultural and industrial land management systems have struggled to consistently automate environmental data collection, analysis, forecasting, production control, inventory management, and customer support. Furthermore, there was no way to recognize user emotions and utilize that information in production activities. This limited the improvements to production efficiency and user experience.

[1079] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a data collection means for collecting environmental data from agricultural or industrial land in real time and storing the environmental data in a database on the cloud; a prediction means for analyzing the collected environmental data and formulating a supply and demand forecast and a production strategy; a control means for controlling machines to perform tasks automatically based on the formulated supply and demand forecast and production strategy; an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries; and an emotion recognition means including an emotion engine that recognizes user emotions. This enables consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[1080] "Data collection means" is a function that uses various sensors to acquire data in real time from specified environments and conditions, and stores that data on the cloud.

[1081] A "cloud database" is a system for managing data stored on a server accessible via the Internet, and allows for the storage, analysis, and sharing of large amounts of data.

[1082] "Prediction methods" are functions that use AI and statistical models to create supply and demand forecasts and production strategies based on collected data.

[1083] "Control means" refers to a function that accurately operates automated machines and systems based on planned supply and demand forecasts and production strategies.

[1084] "Order management means" refers to the system's functions that automatically manage inventory and arrange delivery after accepting an online sales order.

[1085] "Emotion recognition means" is a function that recognizes emotions in real time by analyzing the user's facial expressions, voice, etc.

[1086] "Environmental data" is a general term for data related to specific conditions or environments, such as temperature, humidity, amount of sunlight, soil nutrients, vibration, and noise levels.

[1087] "Supply and demand forecasting" is an analytical process that predicts future supply and demand based on past data and current conditions.

[1088] A "production strategy" is a plan based on supply and demand forecasts to optimize production processes and aim for efficient operations.

[1089] "Online sales" is a sales method that provides products and services to customers via the Internet.

[1090] The embodiments of the present invention will be described in detail below.

[1091] First, we will explain the overview of the entire system. This system is equipped with a data collection means to collect environmental data from agricultural or industrial land in real time and store it in a database on the cloud. This ensures that the server always has the latest environmental data, which can be used for the next step of analysis.

[1092] The collected environmental data is then analyzed using AI-based predictive methods. These methods are used to develop supply and demand forecasts and production strategies. Predictive models are run using historical data, current environmental data, and external weather data. For example, AWS cloud services are used to process large amounts of data and run predictive models using TensorFlow and PyTorch libraries.

[1093] Furthermore, based on the planned supply and demand forecast and production strategy, the control means automatically controls the machines. Here, control units such as PLC and Arduino are used. This allows, for example, automated robots and machines to perform tasks automatically based on a schedule.

[1094] For order management, orders are accepted from customers through online sales, and then inventory management and delivery arrangements are automated. Order information is sent to a server via a website or mobile app, and inventory status is updated. Database systems such as AWS RDS and Google BigQuery are used.

[1095] Finally, an emotion recognition mechanism is built in to recognize the user's emotions in real time. For example, the user's facial expressions and voice are collected via a webcam or microphone, and analyzed by an emotion engine (AI model). Based on the results determined by the emotion engine, a notification is sent to a user who is feeling stressed, suggesting relaxation activities.

[1096] For example,

[1097] 1. Data collection: Temperature sensors in the factory collect the latest data at 6:00 a.m. every morning and send it to AWS via Raspberry Pi.

[1098] 2. Supply and demand forecasting: Run the AI ​​model monthly to forecast demand for product A for the next month and optimize the production schedule.

[1099] 3. Production automation: The automated robotic arm begins assembling product A based on the set schedule.

[1100] 4. Inventory management and logistics: If there is a shortage of materials, AWS RDS checks the inventory status and automatically places an order.

[1101] 5. Emotion Recognition: If an employee is tired, the emotion engine will recognize this and the server will send a notification suggesting a relaxation activity.

[1102] Examples of prompts include:

[1103] "Please run the monthly supply and demand forecasting model to forecast the demand for product A next month and optimize the production schedule."

[1104] "Detect abnormalities from factory environment data and make necessary corrections."

[1105] "Analyze the employee's emotional state and generate messages that suggest responses."

[1106] In this way, the system of the present invention achieves consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[1107] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1108] Step 1:

[1109] Temperature, humidity, and vibration sensors are used to collect environmental data. The sensors measure data every 30 minutes and send it to a database on the cloud via a gateway device. The server receives this data, corrects outliers and missing values, and organizes and stores it. The input is raw data from the various sensors, and the output is processed environmental data.

[1110] Step 2:

[1111] The server runs an AI prediction model based on the collected environmental data. It combines this with past data and weather data to create supply and demand forecasts and production strategies. TensorFlow and PyTorch are used as prediction methods. The inputs are organized environmental data, past harvest data, and weather forecast data, and the output is the supply and demand forecast results and the creation of a production strategy.

[1112] Step 3:

[1113] Based on the supply and demand forecast results and production strategy obtained from the predictive model, the server sends instructions to control units such as PLCs and Arduinos to control automated machinery. Specifically, automated robotic arms begin assembling parts based on a schedule. The inputs are the supply and demand forecast results and production strategy data, and the output is operation instructions for the automated machinery.

[1114] Step 4:

[1115] When a user places an order through online sales, the terminal sends the order information to the server. The server receives the order information and updates the inventory management system. If necessary, it also arranges delivery. The input is the order information from the user, and the output is updated inventory information and delivery instructions.

[1116] Step 5:

[1117] While the user is using the system, the emotion engine recognizes the user's emotions in real time via a webcam or microphone. The server receives data from the emotion engine and takes appropriate action based on the results. For example, if the user is feeling stressed, it will send a notification suggesting relaxation activities. The input is voice or facial expression data from the webcam or microphone, and the output is the user's emotional state and countermeasures.

[1118] These steps will enable consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[1119] Examples of generative AI models and prompts include:

[1120] "Please run the monthly supply and demand forecasting model to forecast the demand for product A next month and optimize the production schedule."

[1121] "Detect abnormalities from factory environment data and make necessary corrections."

[1122] "Analyze the employee's emotional state and generate messages that suggest responses."

[1123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1125] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1126] [Fourth embodiment]

[1127] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1131] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1134] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1136] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1138] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1139] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1140] The present invention provides a data collection means for collecting environmental data of agricultural land in real time and storing the data in a database on a cloud. Hereinafter, an embodiment of the present invention will be specifically described.

[1141] Fully automated agricultural platform program

[1142] The program includes the following main processing steps:

[1143] 1. Data Collection and Management

[1144] 2. Supply and demand forecasting and sales strategy planning

[1145] 3. Automating agricultural work

[1146] 4. Online sales and delivery management

[1147] Program processing

[1148] 1. Data Collection and Management:

[1149] The server collects environmental data such as temperature, humidity, sunlight, and soil nutrients in real time from various sensors installed in agricultural fields. This data is sent from the sensors via a gateway to a database on the cloud. The server efficiently organizes the collected data and prepares it for analysis.

[1150] 2. Supply and demand forecasting and sales strategy planning:

[1151] The server uses AI to run a supply and demand forecasting model based on the collected environmental data. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict the next month's demand and supply. It also formulates a sales strategy based on these forecast results. Specifically, it determines the type, quantity, timing, and pricing of crops to be harvested.

[1152] 3. Automating agricultural work:

[1153] The server generates a schedule for each work process on the farm based on the planned supply and demand forecast and sales strategy. Based on this, the server automatically controls agricultural machinery. For example, an automatic seed sower sows seeds on the specified date, an automatic irrigation system supplies water at the appropriate time, a weeder removes weeds when necessary, and an automatic harvester harvests the crop.

[1154] 4. Online sales and delivery management:

[1155] Users purchase produce through a website or mobile app. The server receives order information from users and updates the inventory management system. Once stock is available, the server automatically contacts a delivery company to arrange delivery. The delivery company follows instructions and delivers the produce to the consumer. The server also notifies users of the order status and delivery status, and provides tracking information.

[1156] Specific examples

[1157] Data collection and management: At 6:00 a.m., the temperature and humidity sensor measures the latest data and sends it to a cloud database via the gateway. The server receives this data and corrects missing or abnormal values.

[1158] Supply and demand forecasting and sales strategy planning: The server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this, it plans the necessary sowing and harvesting schedules and sets the optimal price to adjust the harvest volume.

[1159] Automated farming: An automatic seed sower sows cabbage seeds at the specified date and time, then an automatic irrigation system waters them at the appropriate time. An automatic weeder removes weeds during the growing season, and an automatic harvester harvests the cabbages at the harvest time.

[1160] Online sales and delivery management: When a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server sends the user an order confirmation email and a delivery tracking number.

[1161] In this way, the fully automated agricultural platform of the present invention is a system that automates and streamlines the entire agricultural process, from production planning to harvesting and sales, thereby improving food self-sufficiency, reducing the burden on farmers, and making effective use of abandoned farmland.

[1162] The processing flow will be explained below.

[1163] Step 1:

[1164] The server collects real-time environmental data such as temperature, humidity, sunlight, and soil nutrients from various sensors installed in agricultural fields. These sensors measure data every 30 minutes and send it to a cloud-based database via a gateway.

[1165] Step 2:

[1166] The server stores the received environmental data in a database and verifies whether there are any missing or outliers. For example, it verifies the data collected that day at midnight, fills in any missing data, and filters out outliers.

[1167] Step 3:

[1168] The server periodically retrieves weather data from an external API and stores it in a cloud database. Every morning at 6:00, the server accesses the weather data provider's API to retrieve the current day's and weekly forecasts.

[1169] Step 4:

[1170] The server inputs preprocessed environmental and weather data into an AI model to make supply and demand forecasts. For example, it analyzes consumer purchasing data from the past three years to predict the supply and demand for cabbage for the next month. Based on these results, a sales strategy is developed.

[1171] Step 5:

[1172] The server generates a schedule for farm work based on supply and demand forecasts and sales strategies. For example, it creates a specific work plan for the next day, such as automatic sowing, watering, weeding, and harvesting, and sends it to agricultural machinery.

[1173] Step 6:

[1174] Based on the farming schedule, the server controls agricultural machinery such as automatic seeding machines, automatic irrigation systems, automatic weeders, and automatic harvesters. For example, an automatic seeding machine sows cabbage seeds on a specified date, and an automatic irrigation system waters them at the appropriate time.

[1175] Step 7:

[1176] The server monitors the operating status of agricultural machinery in real time and takes immediate action if an abnormality is detected. The sensor monitors the operating status of agricultural machinery and notifies the server if an abnormality is detected.

[1177] Step 8:

[1178] Users order produce through a website or mobile app. The server receives the order information and updates inventory data. For example, when a user orders cabbage through the app, the server reduces the inventory and accepts the order.

[1179] Step 9:

[1180] The server automatically issues delivery instructions to the delivery company based on the order information. It then contacts the delivery company using a logistics API to arrange for the delivery of the produce. The delivery company follows the instructions and delivers the produce to the consumer.

[1181] Step 10:

[1182] The server notifies the user of the order and delivery status. For example, the server sends the user an order confirmation email, a delivery tracking number, and provides real-time updates on the delivery status.

[1183] In this way, the entire agricultural process, from production planning to harvesting and sales, is automated and efficiently managed through each step. This platform will improve food self-sufficiency, reduce the burden on farmers, and make effective use of abandoned farmland.

[1184] Example 1

[1185] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1186] In order to reduce the burden on farmers and improve agricultural production efficiency, it is necessary to collect environmental data on agricultural land in real time and develop supply and demand forecasts and sales strategies. There is also a need for integrated management of work schedules, streamlining of online sales, automatic control of agricultural machinery, and anomaly detection. Furthermore, there is a need for a system that can centrally manage the entire process from agricultural production to sales, efficiently and effectively, by automating order information processing and delivery arrangements.

[1187] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1188] In this invention, the server includes: a collection unit for collecting environmental data from agricultural land in real time and storing the environmental data in a cloud database; a prediction unit for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies; a control unit for controlling machine tools for automating agricultural work based on the formulated supply and demand forecasts and sales strategies; an order management unit for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries; a unit for transmitting data from sensors installed in the agricultural land through a gateway and correcting the received data; a unit for using the collected environmental data to predict supply and demand based on a generative AI model; a unit for controlling an automatic seed sower, an automatic irrigation system, a weeder, and an automatic harvester based on the generated schedule; and a communication unit for providing order confirmation information and tracking information to customers. This makes it possible to automate and streamline the entire agricultural process, from production planning to harvesting and sales.

[1189] The "data collection means" is a means for collecting environmental data of agricultural land in real time and storing the environmental data in a database on the cloud.

[1190] The "prediction means" is a means for analyzing collected environmental data and formulating supply and demand forecasts and sales strategies.

[1191] The "control means" is a means for controlling machine tools for automatically carrying out agricultural work based on the planned supply and demand forecast and sales strategy.

[1192] "Order management means" refers to a means for accepting orders from customers through online sales and automatically managing inventory and arranging delivery.

[1193] A "sensor" is a measuring device used to measure environmental data such as temperature, humidity, amount of sunlight, and soil nutrients in agricultural land.

[1194] A "gateway" is a communications device that aggregates data sent from sensors and transfers it to a database on the cloud.

[1195] A "generative AI model" is an artificial intelligence model that uses collected environmental data to make supply and demand forecasts.

[1196] An "automatic seeder" is an automated agricultural machine that sows crop seeds according to a specified schedule.

[1197] An "automatic irrigation system" is an automated irrigation device that supplies water to crops at the appropriate time.

[1198] A "weeder" is a machine used to automatically remove weeds from agricultural land.

[1199] An "automatic harvester" is an agricultural machine for automatically harvesting mature crops.

[1200] "Communication Method" means the electronic communication method used to provide order confirmation and tracking information to Customer.

[1201] The present invention provides a data collection means for collecting environmental data of agricultural land in real time and storing the data in a database on a cloud. Hereinafter, an embodiment of the present invention will be specifically described.

[1202] Fully automated agricultural platform program

[1203] The program includes the following main processing steps:

[1204] 1. Data Collection and Management

[1205] 2. Supply and demand forecasting and sales strategy planning

[1206] 3. Automating agricultural work

[1207] 4. Online sales and delivery management

[1208] Program processing

[1209] Data Collection and Management

[1210] The server collects environmental data in real time from various sensors (temperature, humidity, amount of sunlight, soil nutrients, etc.) installed in agricultural fields. This data is sent from the sensors to a database on the cloud via a gateway. The server efficiently organizes the received data and prepares it for analysis. For example, at 6:00 a.m., the temperature and humidity sensor measures the latest data, which is sent to the database on the cloud via the gateway. The server receives this data and corrects any missing or abnormal values.

[1211] Supply and demand forecasting and sales strategy planning

[1212] The server uses a generative AI model based on the collected environmental data to perform supply and demand forecasts. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict demand and supply for the next month. It also formulates sales strategies based on the forecast results. This includes the type, quantity, timing, and pricing of crops to be harvested. Specifically, the server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this, it formulates the necessary sowing and harvest schedules and sets the optimal price to adjust the harvest volume.

[1213] Automating agricultural work

[1214] The server generates a schedule for each work process on the farm based on the planned supply and demand forecast and sales strategy. Based on this, the server automatically controls the automatic seeding machine, automatic irrigation system, weeder, and automatic harvester. For example, the automatic seeding machine sows cabbage seeds at a specified date and time, and then the automatic irrigation system waters them at the appropriate time. The weeder removes weeds during the growing season, and the automatic harvester harvests the cabbage when it's time to harvest.

[1215] Online sales and delivery management

[1216] A user purchases produce through a website or mobile app. The server receives order information from the user and updates the inventory management system. Once stock is available, the server automatically contacts a delivery company to arrange delivery. The delivery company follows instructions and delivers the produce to the consumer. The server also notifies the user of the order and delivery status and provides tracking information. For example, when a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server then sends the user an order confirmation email and a delivery tracking number.

[1217] Examples of prompt statements

[1218] "Please forecast the supply and demand for cabbage for the next month and create a harvest plan."

[1219] In this way, the fully automated agricultural platform of the present invention is a system that automates and streamlines the entire agricultural process, from production planning to harvesting and sales, thereby improving food self-sufficiency, reducing the burden on farmers, and making effective use of abandoned farmland.

[1220] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1221] Step 1:

[1222] Data Collection and Management

[1223] Input: Environmental data such as temperature, humidity, solar radiation, and soil nutrients collected from sensors installed in agricultural fields.

[1224] Processing: The server receives this data in real time and stores it in a database on the cloud via a gateway from the sensor. The server organizes the data and corrects missing or outlier values.

[1225] Output: A cleaned and corrected dataset is produced.

[1226] Specific behavior:

[1227] At 6:00 a.m., the temperature and humidity sensor measures environmental data and transmits it wirelessly to the gateway. The gateway receives the data and transfers it to a cloud database. The server receives this data, fills in missing values ​​with historical average values, and sends an alert to the administrator if an abnormal value is detected.

[1228] Step 2:

[1229] Supply and demand forecasting and sales strategy planning

[1230] Inputs: Collected environmental data, historical harvest data, consumption trend data, and weather forecast data.

[1231] Processing: The server uses generative AI models to perform supply and demand forecasts, and develops sales strategies, including the type, quantity, timing, and pricing of crops to harvest.

[1232] Output: Demand and supply forecast results and sales strategy details are generated.

[1233] Specific behavior:

[1234] The server inputs collected environmental data, past harvest data, and consumption trend data into a generative AI model to predict the next month's demand and supply. For example, it predicts the supply and demand for cabbage for the next month, predicting demand of 50 tons. Based on this, it plans crop sowing and harvest schedules and sets optimal prices to adjust the harvest volume.

[1235] Step 3:

[1236] Automating agricultural work

[1237] Input: Supply and demand forecast results and planned sales strategy.

[1238] Processing: The server generates schedules for each work process in the agricultural field and controls machine tools such as automatic seeding machines, automatic irrigation systems, weeders, and automatic harvesters.

[1239] Output: Specific agricultural work schedules and automation instructions.

[1240] Specific behavior:

[1241] The server generates a schedule for the automatic seeding machine to sow cabbage seeds at the specified date and time, then the automatic irrigation system waters them at the appropriate time, the weeder removes weeds during the growing season, and the automatic harvester harvests the cabbages at the harvest time.

[1242] Step 4:

[1243] Online sales and delivery management

[1244] Input: Order information from customer.

[1245] Processing: The server processes the order information received through the website or mobile app, updates the inventory management system, and once the item is in stock, contacts the delivery company to arrange delivery.

[1246] Output: Inventory updates, shipping arrangements, order confirmations and tracking information for customers.

[1247] Specific behavior:

[1248] When a user orders 10 cabbages through the app, the server receives the order information, checks the inventory, and updates it. Once the inventory is available, the server automatically sends a request to the delivery company. The delivery company picks up the cabbages and delivers them to the user. The server then sends the user an order confirmation email and a delivery tracking number.

[1249] (Application example 1)

[1250] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1251] There is a need to collect environmental data in real time on agricultural land and in factories and use that data to develop optimal supply and demand forecasts and production management strategies. However, to achieve this, efficient data collection and analysis methods are essential, as well as a system for automatically controlling agricultural work and production processes. There is also a need for efficient online order management and inventory management. The lack of a system that can manage these multiple processes in an integrated manner is easily cited as a problem.

[1252] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1253] In this invention, the server includes a data collection means for collecting environmental data of agricultural land and production environment data within factories in real time and storing the environmental data in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and production management strategies, a control means for controlling machines that automate agricultural work and production processes based on the formulated supply and demand forecasts and production management strategies, and an inventory management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, thereby enabling efficient management and optimization of the entire agricultural and factory production process.

[1254] "Agricultural land" is land used for agricultural activities, such as growing crops, raising livestock, and horticulture.

[1255] "Environmental data" is numerical data that represents specific environmental conditions, such as temperature, humidity, amount of sunlight, soil nutrients, vibration, and safety conditions in agricultural land or within a factory.

[1256] "Production environment data" is numerical data that represents environmental conditions related to production activities, such as temperature, humidity, vibration, and safety conditions within a factory.

[1257] A "cloud database" is a data storage system accessible via the Internet for storing and managing collected data in real time.

[1258] "Data collection means" refers to devices or methods for acquiring environmental data in real time using sensors, gateways, etc., and transmitting the data to a cloud database.

[1259] "Prediction tools" are systems and algorithms that analyze collected environmental data and use AI models to develop supply and demand forecasts and production management strategies.

[1260] A "production management strategy" is a plan or policy established to optimize production activities, and includes production schedules and inventory management.

[1261] "Control means" refers to devices and systems that automatically operate and adjust agricultural machinery and machinery in factories based on planned supply and demand forecasts and production management strategies.

[1262] "Inventory management tools" are systems and algorithms used to accept orders from customers through online sales, manage inventory status in real time, and make appropriate delivery arrangements.

[1263] An "external API" is a standard program interface for obtaining weather and environmental data from external servers.

[1264] "Monitoring means" refers to sensors, systems, software, etc. that monitor the operating status of machines in real time and detect abnormalities.

[1265] This invention is a system that collects environmental data from agricultural fields and factories in real time, stores the data in a cloud database, and analyzes it to create supply and demand forecasts and production management strategies. Furthermore, this system automatically controls agricultural machinery and production machinery in factories, enabling efficient online sales and inventory management.

[1266] System configuration

[1267] 1. Server

[1268] The server mainly collects, analyzes, controls, and manages data. Specific hardware used can be a cloud server or an edge server, and software uses programming languages ​​such as Python or Java.

[1269] 2. Data Collection Methods

[1270] Various sensors (temperature, humidity, vibration, etc.) are installed in agricultural fields and factories to collect environmental data in real time, which is then sent to a cloud database via a gateway.

[1271] 3. Prediction methods

[1272] The server runs an AI model to analyze the collected environmental data. This AI model uses past and current environmental data to forecast supply and demand and develop production management strategies. The AI ​​model used here is implemented using deep learning frameworks such as TensorFlow and PyTorch.

[1273] 4. Control Measures

[1274] The server controls agricultural machinery (automatic seed sowing machines, automatic irrigation systems, etc.) and production machinery within the factory based on the planned supply and demand forecast and production management strategy, thereby realizing automation and efficiency of work processes.

[1275] 5. Inventory Management Methods

[1276] The server receives order information from customers through online sales, automatically manages inventory and arranges delivery. Order information is stored in a cloud database, and inventory status is updated in real time.

[1277] Program processing overview

[1278] The server collects environmental data in real time from various sensors installed in agricultural fields and factories. The collected data is sent to a cloud database via a gateway. The server checks the collected data for missing or outlier values ​​and makes corrections as necessary.

[1279] The server then runs an AI model based on the collected data to create supply and demand forecasts and production management strategies. This AI model combines past harvest data, production data, consumption trend data, weather forecasts, and other data to make predictions.

[1280] Based on the devised strategy, the server automatically controls agricultural machinery and production machinery in factories. For example, an automatic seed sowing machine sows seeds on a specified date, an automatic irrigation system supplies water at the appropriate time, and the operation schedule of a production line in a factory is optimized.

[1281] In online sales, when a user places an order via a mobile app or website, the information is sent to a server, which updates the inventory management system. The server then automatically contacts a delivery company and arranges delivery.

[1282] Specific examples

[1283] For example, at 6:00 a.m., temperature and humidity sensors in a factory measure the latest data and send it to a cloud-based database via a gateway. The server receives the data and corrects missing or outlier values. Next, an AI model is used to optimize production schedules and predict when necessary maintenance will be performed.

[1284] Prompt Sentence Examples

[1285] "Write a Python program to collect temperature and humidity data in a factory and send it to a cloud database in real time. The program uses a Raspberry Pi and a DHT22 sensor to collect and send data every minute. The data should include the current temperature, humidity, and a timestamp."

[1286] As described above, this invention is a system that efficiently manages and optimizes the entire production process in agriculture and factories.

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] The server collects environmental data in real time from various sensors installed in agricultural fields and factories. At this time, data such as temperature, humidity, and vibration is sent from the sensors to the server via a gateway. The input data is raw environmental data obtained from the sensors, and the output is data stored in a database on the cloud. Specifically, the temperature sensor measures temperature data every 30 seconds, which is received by the gateway and sent to the cloud.

[1290] Step 2:

[1291] The server stores the collected environmental data in a database on the cloud. Here, the data is time-stamped and missing or outlier values ​​are corrected. The input data is the raw environmental data sent from the sensors, and the output is the corrected environmental data. Specifically, the server uses a Python script to interpolate missing values ​​with the average value.

[1292] Step 3:

[1293] The server runs an AI model based on data stored in a cloud database to create supply and demand forecasts and production management strategies. Weather data and past production data are also obtained through external APIs. The input data are the environmental data, weather data, and past production data stored in the cloud database, and the output is the supply and demand forecast results and production management strategies. Specifically, the AI ​​model uses TensorFlow to run a supply and demand forecasting algorithm and predict demand for the next month.

[1294] Step 4:

[1295] The server automatically controls agricultural machinery and production machinery in factories based on predicted supply and demand data and production management strategies. The server generates machine operation schedules and sends control instructions to each machine. The input data are the supply and demand forecast results and production management strategies, and the output is machine operation control instructions. Specifically, an automatic seed drill sows seeds on a specified date, and an automatic irrigation system supplies water at the appropriate time.

[1296] Step 5:

[1297] Users order produce or products through a mobile app or website. The order information is sent from the device to a server, which updates the inventory management system. The input data is the customer order information, and the output is the updated inventory information. For example, when a user orders 10 cabbages through the mobile app, the server updates the inventory database and decrements the stock status.

[1298] Step 6:

[1299] Once inventory is secured, the server automatically contacts the delivery company and arranges delivery. The delivery arrangement information and delivery status are notified to the user. The input data is the customer's order information and inventory status, and the output is delivery instructions and delivery status information. Specifically, the server sends an API request to the delivery company and arranges delivery at the specified date and time.

[1300] Through these processing steps, the entire process, from environmental data collection to supply and demand forecasting, agricultural and factory automation, online sales, inventory management, and delivery management, can be carried out efficiently.

[1301] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1302] The present invention provides a data collection means for collecting environmental data on agricultural land in real time and storing it in a cloud database. It also includes a forecasting means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, and a control means for controlling agricultural machinery to perform automated farm work based on the formulated supply and demand forecasts and sales strategies. The system also includes an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries. The system also incorporates an emotion engine that recognizes user emotions, improving the user experience.

[1303] Fully automated agricultural platform program

[1304] The program includes the following features:

[1305] 1. Data Collection and Management

[1306] 2. Supply and demand forecasting and sales strategy planning

[1307] 3. Automating agricultural work

[1308] 4. Online sales and delivery management

[1309] 5. Recognizing and responding to user emotions using an emotion engine

[1310] Program processing

[1311] 1. Data Collection and Management:

[1312] The server collects environmental data such as temperature, humidity, sunlight, and soil nutrients in real time from sensors installed in agricultural fields. The sensors measure data every 30 minutes and send it to a cloud-based database via a gateway. The server receives this data, organizes it, and stores it.

[1313] 2. Supply and demand forecasting and sales strategy planning:

[1314] The server analyzes the collected environmental data and uses AI to run a supply and demand forecasting model. For example, it combines past harvest data, consumer trend data, weather forecasts, and other data to predict demand and supply for the next month. Based on these forecast results, it then creates an optimal sales strategy and determines the selling price and harvest schedule.

[1315] 3. Automating agricultural work:

[1316] Based on the planned supply and demand forecast and sales strategy, the server generates a detailed schedule of farm work and sends instructions to agricultural machinery. For example, an automatic seed sower sows seeds on the specified date, an automatic irrigation system waters at the appropriate time, an automatic weeder removes weeds as needed, and an automatic harvester harvests the crop.

[1317] 4. Online sales and delivery management:

[1318] Users order produce through a website or mobile app. The server receives the order information and updates the inventory management system. Once the product is in stock, the server contacts a delivery company to arrange for delivery of the produce. The delivery company picks up the produce and delivers it to the consumer. The server then sends the user an order confirmation email and a tracking number.

[1319] 5. Emotion engine for recognizing and responding to user emotions:

[1320] The emotion engine uses sensors such as a webcam and microphone to recognize the user's emotions. While the user is using the system, the emotion engine analyzes the user's facial expressions and voice in real time to determine the user's emotional state.

[1321] The server receives data from the emotion engine and responds appropriately based on the recognition results. For example, if the user is feeling stressed, the system suggests promotional offers or contacting customer support.

[1322] The emotion engine can recognize the user's emotions and recommend products based on the emotion. For example, if the user is recognized as tired, it can recommend nutritious agricultural products.

[1323] Specific examples

[1324] Data collection and management: The server measures the latest data from the temperature and humidity sensor at 6:00 a.m. and sends it to the cloud via the gateway. The server receives this data and corrects missing or abnormal values.

[1325] Supply and demand forecasting and sales strategy planning: The server runs a supply and demand forecasting model monthly and predicts that the demand for cabbage for the next month will be 50 tons. Based on this forecast, it sets the necessary sowing and harvesting schedules and pricing.

[1326] Automated farming: An automatic seeding machine sows cabbage seeds at a specified time, then an automatic irrigation system waters them at the appropriate time. At harvest time, an automatic harvester harvests the cabbages.

[1327] Online sales and delivery management: When a user orders 10 cabbages through the app, the server updates the inventory and sends a request to the delivery company, which delivers the cabbages to the consumer.

[1328] Emotion engine recognizes and responds to user emotions: If a user is feeling stressed while ordering online, the emotion engine will recognize this and the server will notify them of promotional offers. If the server senses that the user is tired, it will recommend nutritious produce.

[1329] In this way, the fully automated agricultural platform of the present invention completely automates agricultural production planning, harvesting, and sales, and combines an emotion engine to improve the user experience, enabling efficient and effective agricultural operations. This platform will improve food self-sufficiency, reduce the burden on farmers, and make effective use of abandoned farmland.

[1330] The processing flow will be explained below.

[1331] Step 1:

[1332] The server collects real-time environmental data, such as temperature, humidity, sunlight, and soil nutrients, from various sensors installed in agricultural fields. The sensors measure the data every 30 minutes and send it to a cloud-based database via a gateway.

[1333] Step 2:

[1334] The server stores the received environmental data in a database on the cloud. At that time, it checks for missing or abnormal values, fills in any missing data, and corrects any abnormal values. For example, at midnight every night, it verifies all data collected that day and makes any necessary corrections.

[1335] Step 3:

[1336] The server uses the API of an external weather data acquisition service to obtain the latest weather forecast daily. It accesses the API every morning at 6:00 to obtain the data for the current day and the week, and stores this in the database.

[1337] Step 4:

[1338] The server uses AI to run a supply and demand forecasting model based on the pre-processed environmental data. For example, it combines past harvest data with consumption trend data to predict the supply and demand for cabbage for the next month. Based on these results, it then plans an optimal sales strategy.

[1339] Step 5:

[1340] The server generates a detailed schedule for farm work based on supply and demand forecasts and sales strategies. Specifically, it determines the next day's schedule for automatic sowing, watering, weeding, and harvesting, and sends this to agricultural machinery.

[1341] Step 6:

[1342] The server sends instructions to control agricultural machinery based on the farming schedule. For example, it might send a command to an automatic seed drill to sow cabbage seeds at 8:00 the next morning, and then send a command to an automatic irrigation system to tell it when to water the crops.

[1343] Step 7:

[1344] The server monitors the operating status of agricultural machinery in real time. Various sensors collect operational information about the machinery and notify the server if an abnormality is detected. For example, a sensor monitors whether an automatic seed drill is operating normally, and sends an alert to the server if an abnormality occurs.

[1345] Step 8:

[1346] Users order produce through a website or mobile app. The server receives the order and updates the inventory management system. If a user orders 10 cabbages, the server decrements the inventory and processes the order confirmation.

[1347] Step 9:

[1348] The server arranges delivery using the delivery company's API based on the order information. It sends the necessary information to the delivery company to ensure smooth delivery. For example, the server sends the user's delivery address and order information to the delivery company's system.

[1349] Step 10:

[1350] The server notifies the user of the order and delivery status in real time, sending a confirmation email when the order is accepted and providing a tracking number when delivery begins. For example, the server updates the delivery status of a cabbage and sends a notification email to the user.

[1351] Step 11:

[1352] The emotion engine uses sensors such as a webcam and microphone to recognize the user's emotions. For example, while a user is shopping online, the emotion engine can analyze the user's facial expressions and voice recorded by the webcam and recognize that the user is feeling stressed.

[1353] Step 12:

[1354] The server receives data from the emotion engine and responds appropriately based on the recognition results. For example, if the server recognizes that the user is feeling stressed, it will display a promotional offer and make suggestions to the user to relax.

[1355] Step 13:

[1356] The server then makes customized product recommendations based on the emotion engine data. For example, if the server detects that the user is tired, it will recommend nutritious produce and suggest products that meet the user's needs.

[1357] In this way, each processing step, from agricultural production planning to harvesting and sales, is fully automated, and by providing services that take user emotions into consideration, efficient and effective agricultural operations and improved customer satisfaction are achieved.

[1358] Example 2

[1359] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1360] Conventional agricultural systems require efficient and integrated processing of environmental data collection, supply and demand forecasting, farm work automation, online sales, inventory management, and delivery arrangements, but lack a means to consistently manage these elements. In particular, recognizing and responding to user emotions in real time would improve the user experience, but no system with such functionality existed. Improving the accuracy of crop supply and demand forecasts and providing a sales experience that satisfies users are key challenges in modern digital agriculture.

[1361] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1362] In this invention, the server includes a data collection means for collecting environmental data of agricultural land in real time and storing it in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, a control means for controlling machines that perform agricultural work automatically based on the formulated supply and demand forecasts and sales strategies, an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, and an emotion recognition means for recognizing and responding to user emotions. This makes it possible to consistently and efficiently manage agricultural production, sales, and delivery, and further improve the user experience.

[1363] "Data collection means" refers to devices and systems for collecting environmental data on agricultural land in real time and storing that data in a database on the cloud.

[1364] "Prediction methods" are methods and algorithms for analyzing collected environmental data and formulating supply and demand forecasts and sales strategies for agricultural products.

[1365] The "control means" is a means for controlling machines and devices for automatically carrying out agricultural work based on the planned supply and demand forecast and sales strategy.

[1366] "Order management means" refers to a system and method for accepting orders from customers through online sales and automatically managing inventory and arranging delivery.

[1367] "Emotion recognition means" refers to technology and devices for recognizing a user's emotions and taking appropriate action based on those emotions.

[1368] A "cloud database" is a data management system for storing data on a network that is accessible via the Internet.

[1369] "Environmental data" refers to the measurement results of agricultural land temperature, humidity, amount of sunlight, soil nutrients, etc., and is data that contains information necessary for agricultural work and predictions.

[1370] "Supply and demand forecasting" is the process of predicting the supply and demand of agricultural products based on historical data and current environmental data.

[1371] "Sales strategy" is a method of planning and formulating the selling price, sales method, marketing strategy, etc. of agricultural products based on supply and demand forecasts.

[1372] "Machinery for automated operations" refers to machines and robots used for the purpose of automating agricultural work, such as devices that perform tasks such as sowing, irrigation, weeding, and harvesting.

[1373] The system of this invention includes a data collection means for collecting environmental data of agricultural land in real time and storing it in a database on the cloud, a prediction means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies, a control means for controlling machines that perform agricultural work automatically based on the formulated supply and demand forecasts and sales strategies, an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries, and an emotion recognition means for recognizing and responding to user emotions.

[1374] Data collection methods

[1375] The server collects environmental data from temperature and humidity sensors and soil sensors placed in agricultural fields. This includes measurements every 30 minutes, and information such as temperature, humidity, amount of sunlight, and soil nutrients is sent to the cloud. Specifically, for example, the temperature and humidity sensor measures the latest data at 6:00 a.m. and sends it to the cloud via the gateway.

[1376] Prediction methods

[1377] The server uses an AI model to predict supply and demand based on the stored environmental data. This prediction includes past harvest data, consumption trend data, weather forecasts, and more. For example, the server uses data from the past three years to predict that the demand for cabbage next month will be 50 tons. Based on this prediction, the server then plans sales prices, harvest schedules, and marketing strategies.

[1378] Control means

[1379] Based on the planned supply and demand forecast and sales strategy, the server generates a detailed farming schedule and sends it to agricultural machinery. Specifically, it sends instructions to the automatic seeding machine to sow cabbage seeds on March 15th, and to the automatic irrigation system to water at the appropriate time. When it's time for harvesting, the automatic harvester will harvest the cabbages.

[1380] Order Management Methods

[1381] Users order produce through a website or mobile app. The server receives the order information and updates the inventory management system. Based on the available stock, the server contacts a delivery company to arrange delivery. For example, if a user orders 10 cabbages through the app, the information is updated in the inventory system and the appropriate delivery arrangements are made.

[1382] emotion recognition means

[1383] The emotion engine analyzes facial expressions and voices while the user is using the system to recognize the user's emotional state. Emotions are analyzed in real time, and the server receives the results. For example, if a user feels stressed while placing an online order, the emotion engine will recognize this and the server will notify them of a promotional offer. If the server detects that the user is tired, it will recommend nutritious agricultural products.

[1384] This will enable consistent and efficient management of agricultural production, sales, and delivery, and further improve the user experience. For example, by inputting the following prompts into the generative AI model, supply and demand forecasts and emotion recognition can be further optimized.

[1385] Example prompt:

[1386] Predict the demand for cabbage for next month.

[1387] Analyze the user's current emotional state and notify them of their stress level.

[1388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1389] Step 1: Data collection

[1390] Input: Environmental data from temperature and humidity sensors and soil sensors installed in agricultural fields

[1391] Processing: The sensors measure temperature, humidity, solar radiation, and soil nutrient data every 30 minutes.

[1392] Output: Measured environmental data

[1393] Specific operation: The server receives data from the temperature sensor at 6am, which indicates 25 degrees.

[1394] Step 2: Send data to the cloud

[1395] Input: Environmental data obtained from sensors

[1396] Processing: The gateway receives data from the sensors and sends it to a database on the cloud.

[1397] Output: Environmental data stored in the cloud

[1398] Specific operation: The gateway sends data on temperature of 25 degrees and humidity of 60% to the cloud.

[1399] Step 3: Organize and store your data

[1400] Input: Environmental data sent to the cloud

[1401] Processing: The server corrects missing or outliers in the data and records them in the database.

[1402] Output: Environmental data stored in an organized database

[1403] Specific behavior: The server checks the data for missing or outlier values ​​and corrects them.

[1404] Step 4: Run the supply and demand forecasting model

[1405] Input: Organized environmental data, past harvest data, and consumption trend data

[1406] Processing: The server uses the AI ​​model to predict supply and demand.

[1407] Output: Supply and demand forecast results (e.g., next month's demand for cabbage: 50 tons)

[1408] Specific operation: The server performs supply and demand forecasts based on data from the past three years.

[1409] Step 5: Develop a sales strategy

[1410] Input: Supply and demand forecast results

[1411] Processing: The server creates an optimal sales strategy based on the supply and demand forecast results, determining the selling price, harvest schedule, and marketing strategy.

[1412] Output: Sales strategy plan

[1413] Specific operation: The server sets the price of cabbage for the next month at 200 yen per cabbage and determines the harvest date.

[1414] Step 6: Generate and instruct farm work schedules

[1415] Input: Sales Strategy Plan

[1416] Processing: The server generates a detailed farming schedule and sends it to the farming machines.

[1417] Output: Work instructions for agricultural machinery

[1418] Specific operation: Send the instruction "Sow cabbage seeds on March 15th" to the automatic seed sowing machine.

[1419] Step 7: Controlling agricultural machinery

[1420] Input: Work instructions for agricultural machinery

[1421] Processing: Agricultural machinery performs tasks automatically based on instructions from the server.

[1422] Output: Completed farm work data

[1423] What happens: The automatic irrigation system waters on April 1st.

[1424] Step 8: Processing online orders

[1425] Input: User's online ordering information

[1426] Processing: The server receives the order information and updates the inventory management system.

[1427] Output: Updated inventory data

[1428] Specific action: A user orders 10 cabbages in the app.

[1429] Step 9: Inventory management and shipping arrangements

[1430] Input: Updated inventory data and order information

[1431] Processing: The server checks the inventory and arranges for delivery to the delivery company.

[1432] Output: Delivery arrangement information and instructions to the delivery company

[1433] Specific Action: Check cabbage inventory and send pickup and delivery instructions to delivery company.

[1434] Step 10: Recognizing user emotions

[1435] Input: User facial and voice data captured through a webcam and microphone

[1436] Processing: The emotion engine analyzes the user's emotions in real time.

[1437] Output: Analyzed user emotion data

[1438] Specific behavior: Recognize that the user is stressed.

[1439] Step 11: Respond based on user sentiment

[1440] Input: Parsed user emotion data

[1441] Processing: The server responds appropriately based on the user's sentiment, offering promotional offers and customer support.

[1442] Output: User action (e.g., notification of promotional offer)

[1443] Specific behavior: If the user is recognized as tired, the server sends a notification recommending nutritious produce.

[1444] (Application example 2)

[1445] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1446] Conventional agricultural and industrial land management systems have struggled to consistently automate environmental data collection, analysis, forecasting, production control, inventory management, and customer support. Furthermore, there was no way to recognize user emotions and utilize that information in production activities. This limited the improvements to production efficiency and user experience.

[1447] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a data collection means for collecting environmental data from agricultural or industrial land in real time and storing the environmental data in a database on the cloud; a prediction means for analyzing the collected environmental data and formulating a supply and demand forecast and a production strategy; a control means for controlling machines to perform tasks automatically based on the formulated supply and demand forecast and production strategy; an order management means for accepting orders from customers through online sales and automatically managing inventory and arranging deliveries; and an emotion recognition means including an emotion engine that recognizes user emotions. This enables consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[1448] "Data collection means" is a function that uses various sensors to acquire data in real time from specified environments and conditions, and stores that data on the cloud.

[1449] A "cloud database" is a system for managing data stored on a server accessible via the Internet, and allows for the storage, analysis, and sharing of large amounts of data.

[1450] "Prediction methods" are functions that use AI and statistical models to create supply and demand forecasts and production strategies based on collected data.

[1451] "Control means" refers to a function that accurately operates automated machines and systems based on planned supply and demand forecasts and production strategies.

[1452] "Order management means" refers to the system's functions that automatically manage inventory and arrange delivery after accepting an online sales order.

[1453] "Emotion recognition means" is a function that recognizes emotions in real time by analyzing the user's facial expressions, voice, etc.

[1454] "Environmental data" is a general term for data related to specific conditions or environments, such as temperature, humidity, amount of sunlight, soil nutrients, vibration, and noise levels.

[1455] "Supply and demand forecasting" is an analytical process that predicts future supply and demand based on past data and current conditions.

[1456] A "production strategy" is a plan based on supply and demand forecasts to optimize production processes and aim for efficient operations.

[1457] "Online sales" is a sales method that provides products and services to customers via the Internet.

[1458] The embodiments of the present invention will be described in detail below.

[1459] First, we will explain the overview of the entire system. This system is equipped with a data collection means to collect environmental data from agricultural or industrial land in real time and store it in a database on the cloud. This ensures that the server always has the latest environmental data, which can be used for the next step of analysis.

[1460] The collected environmental data is then analyzed using AI-based predictive methods. These methods are used to develop supply and demand forecasts and production strategies. Predictive models are run using historical data, current environmental data, and external weather data. For example, AWS cloud services are used to process large amounts of data and run predictive models using TensorFlow and PyTorch libraries.

[1461] Furthermore, based on the planned supply and demand forecast and production strategy, the control means automatically controls the machines. Here, control units such as PLC and Arduino are used. This allows, for example, automated robots and machines to perform tasks automatically based on a schedule.

[1462] For order management, orders are accepted from customers through online sales, and then inventory management and delivery arrangements are automated. Order information is sent to a server via a website or mobile app, and inventory status is updated. Database systems such as AWS RDS and Google BigQuery are used.

[1463] Finally, an emotion recognition mechanism is built in to recognize the user's emotions in real time. For example, the user's facial expressions and voice are collected via a webcam or microphone, and analyzed by an emotion engine (AI model). Based on the results determined by the emotion engine, a notification is sent to a user who is feeling stressed, suggesting relaxation activities.

[1464] For example,

[1465] 1. Data collection: Temperature sensors in the factory collect the latest data at 6:00 a.m. every morning and send it to AWS via Raspberry Pi.

[1466] 2. Supply and demand forecasting: Run the AI ​​model monthly to forecast demand for product A for the next month and optimize the production schedule.

[1467] 3. Production automation: The automated robotic arm begins assembling product A based on the set schedule.

[1468] 4. Inventory management and logistics: If there is a shortage of materials, AWS RDS checks the inventory status and automatically places an order.

[1469] 5. Emotion Recognition: If an employee is tired, the emotion engine will recognize this and the server will send a notification suggesting a relaxation activity.

[1470] Examples of prompts include:

[1471] "Please run the monthly supply and demand forecasting model to forecast the demand for product A next month and optimize the production schedule."

[1472] "Detect abnormalities from factory environment data and make necessary corrections."

[1473] "Analyze the employee's emotional state and generate messages that suggest responses."

[1474] In this way, the system of the present invention achieves consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[1475] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1476] Step 1:

[1477] Temperature, humidity, and vibration sensors are used to collect environmental data. The sensors measure data every 30 minutes and send it to a database on the cloud via a gateway device. The server receives this data, corrects outliers and missing values, and organizes and stores it. The input is raw data from the various sensors, and the output is processed environmental data.

[1478] Step 2:

[1479] The server runs an AI prediction model based on the collected environmental data. It combines this with past data and weather data to create supply and demand forecasts and production strategies. TensorFlow and PyTorch are used as prediction methods. The inputs are organized environmental data, past harvest data, and weather forecast data, and the output is the supply and demand forecast results and the creation of a production strategy.

[1480] Step 3:

[1481] Based on the supply and demand forecast results and production strategy obtained from the predictive model, the server sends instructions to control units such as PLCs and Arduinos to control automated machinery. Specifically, automated robotic arms begin assembling parts based on a schedule. The inputs are the supply and demand forecast results and production strategy data, and the output is operation instructions for the automated machinery.

[1482] Step 4:

[1483] When a user places an order through online sales, the terminal sends the order information to the server. The server receives the order information and updates the inventory management system. If necessary, it also arranges delivery. The input is the order information from the user, and the output is updated inventory information and delivery instructions.

[1484] Step 5:

[1485] While the user is using the system, the emotion engine recognizes the user's emotions in real time via a webcam or microphone. The server receives data from the emotion engine and takes appropriate action based on the results. For example, if the user is feeling stressed, it will send a notification suggesting relaxation activities. The input is voice or facial expression data from the webcam or microphone, and the output is the user's emotional state and countermeasures.

[1486] These steps will enable consistent management and analysis of data, automation of all production activities, efficient inventory management and delivery arrangements, and an improved user experience.

[1487] Examples of generative AI models and prompts include:

[1488] "Please run the monthly supply and demand forecasting model to forecast the demand for product A next month and optimize the production schedule."

[1489] "Detect abnormalities from factory environment data and make necessary corrections."

[1490] "Analyze the employee's emotional state and generate messages that suggest responses."

[1491] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1492] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1493] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1494] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1495] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1496] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1497] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1498] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1499] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1500] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1501] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1502] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1503] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1504] 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.

[1505] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1506] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1507] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1508] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1509] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1510] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1511] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1512] The following is further disclosed regarding the above embodiment.

[1513] (Claim 1)

[1514] a data collection means for collecting environmental data of agricultural land in real time and storing the environmental data in a database on the cloud;

[1515] A forecasting means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies;

[1516] a control means for controlling agricultural machinery for automatically performing agricultural work based on the planned supply and demand forecast and sales strategy;

[1517] An order management tool for accepting orders from customers through online sales and automatically managing inventory and arranging delivery;

[1518] A system including:

[1519] (Claim 2)

[1520] 10. The system of claim 1, further comprising: means for obtaining weather data from an external API.

[1521] (Claim 3)

[1522] 2. The system according to claim 1, further comprising a monitoring means for monitoring the operating status of the agricultural machine in real time and detecting abnormalities.

[1523] "Example 1"

[1524] (Claim 1)

[1525] a collection means for collecting environmental data of agricultural land in real time and storing the environmental data in a database on a cloud;

[1526] A forecasting means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies;

[1527] a control means for controlling a machine tool for automatically performing agricultural work based on the planned supply and demand forecast and sales strategy;

[1528] An order management tool for accepting orders from customers through online sales and automatically managing inventory and arranging delivery;

[1529] A means for transmitting data from sensors installed in agricultural land through a gateway and for the server to correct the received data;

[1530] A means for performing supply and demand forecasting based on a generative AI model using collected environmental data;

[1531] a means for controlling an automatic seeding machine, an automatic irrigation system, a weeder, and an automatic harvester based on the generated schedule;

[1532] A means of communication to provide order confirmation and tracking information to customers;

[1533] A system including:

[1534] (Claim 2)

[1535] 10. The system of claim 1, further comprising: means for obtaining weather data from an external API.

[1536] (Claim 3)

[1537] 2. The system according to claim 1, further comprising a monitoring means for monitoring the operating status of the agricultural machine in real time and detecting abnormalities.

[1538] "Application Example 1"

[1539] (Claim 1)

[1540] a data collection means for collecting environmental data of agricultural land and production environment data in factories in real time and storing the environmental data in a database on the cloud;

[1541] a forecasting means for analyzing the collected environ...

Claims

1. a data collection means for collecting environmental data of agricultural land in real time and storing the environmental data in a database on the cloud; A forecasting means for analyzing the collected environmental data and formulating supply and demand forecasts and sales strategies; a control means for controlling agricultural machinery for automatically performing agricultural work based on the planned supply and demand forecast and sales strategy; An order management tool for accepting orders from customers through online sales and automatically managing inventory and arranging delivery; A system including:

2. The system of claim 1 , further comprising: means for obtaining weather data from an external API.

3. 2. The system according to claim 1, further comprising a monitoring means for monitoring the operating conditions of the agricultural machine in real time and detecting abnormalities.

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