Intelligent agriculture fusion system based on Internet of Things and digital twinning

Abstract

The invention provides a smart agriculture fusion system based on Internet of Things and digital twinning, and relates to the technical field of data acquisition, and the system comprises an Internet of Things sensing network which is used for collecting data of soil humidity, soil oxygen, environmental parameters, meteorological information and crop growth states; the big data processing unit is used for receiving the data acquired by the sensor and feeding back the processed data to the digital twinborn model; establishing a digital twinborn model, and constructing the digital twinborn model of the farmland by using the real-time data and historical data of the farmland through a computer learning method and a deep learning algorithm; the control and decision-making unit is used for making farmland management strategies and decisions based on the analysis and prediction results of the digital twinborn model; a user remotely monitors and manages the operation state of the farmland through a terminal, and through the Internet of Things and the digital twinning technology, informatization and intelligentization of the whole chain of agricultural production are achieved, and the efficiency and quality of agricultural production are improved.

Description

Technical Field

[0001] The present invention relates to the field of data acquisition technology, and in particular to a smart agriculture integration system based on the Internet of Things and digital twins. Background Art

[0002] In recent years, with the growth of population and the increasing demand for agricultural production, traditional agriculture faces many challenges, including inefficient resource utilization, uncontrollable production process, environmental pollution and other problems. In order to improve the efficiency and quality of agricultural production, smart agriculture came into being. Smart agriculture uses modern information technology, such as the Internet of Things, to realize the informatization, intelligence and sustainable development of the entire chain of agricultural production.

[0003] However, the current smart agriculture system has some shortcomings. The traditional Internet of Things system cannot fully meet the needs in complex agricultural environments and has great defects in improving the quality and efficiency of agricultural production. Summary of the invention

[0004] The purpose of the present invention is to provide a smart agriculture integration system based on the Internet of Things and digital twins to solve the technical problems in the prior art.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A smart agriculture integration system based on the Internet of Things and digital twins, comprising: an Internet of Things sensing network as the basis of the present invention, multiple sensors, cameras, RFID tags and drones are arranged in the farmland to collect data on soil moisture, soil oxygen, environmental parameters, meteorological information, and crop growth status, and the collected data is transmitted to a big data processing unit through a wireless sensor network, a cellular network and a satellite network; the big data processing unit receives the data collected by the sensor, and evaluates and predicts the status of the farmland through data analysis and mining, data cleaning and preprocessing technology, and at the same time, feeds the processed data back to the digital twin model; establishes a digital twin model, and constructs a digital twin of the farmland through computer learning methods and deep learning algorithms, and uses real-time data and historical data of the farmland The model simulates and predicts the growth environment, crop growth process and yield indicators of farmland. The model can be continuously optimized and improved through interaction with actual farmland. The control and decision-making unit formulates farmland management strategies and decisions based on the analysis and prediction results of the digital twin model, and realizes precise management of farmland through control equipment and actuators, including intelligent agricultural equipment that can perform tasks autonomously, and irrigate, fertilize, and control pests and diseases on farmland. The remote monitoring and management platform uses the sensors, cameras, RFID tags and drones to monitor the environmental parameters and equipment operating conditions in the agricultural production process in real time, and realizes the control and management of the intelligent agricultural equipment through the Internet of Things system, providing a user interface and control panel to allow users to remotely monitor and manage the operating status of farmland through the terminal.

[0007] Furthermore, the big data processing unit includes: a network layer, a data management layer, and an application service layer; the network layer is used to transmit the data collected by the Internet of Things perception network to the data management layer, and the network layer includes a wireless sensor network, a cellular network, and a satellite network; the data management layer can provide scientific decision-making support for agricultural production by mining and analyzing a large amount of agricultural data. When the data management layer receives agricultural data from the Internet of Things perception network, it performs data cleaning, data filtering, and data normalization operations on the data to remove errors and inaccurate information; the application service layer is used to provide agricultural production support based on the analysis results of the data management layer.

[0008] Furthermore, the application service layer includes: an agricultural production management system, an agricultural expert system and an agricultural Internet of Things platform.

[0009] Furthermore, the establishment of the digital twin model includes: physical entity: the actual agricultural production environment, including farmland, crops, and agricultural machinery and equipment; digital model: a virtual model created by digital twin technology; data and information connection: the data flow and information exchange mechanism that connects the physical entity with the digital model; analysis algorithm: processing and analyzing the data collected from the physical entity, as well as algorithms and analysis tools that guide how the digital model responds to this data; visualization interface: generating a visualization interface through 3D maps and dashboards to display the status of the digital model and analysis results.

[0010] Furthermore, the intelligent agricultural equipment includes: agricultural mechanization equipment, which replaces manual labor to complete repetitive labor; agricultural information technology equipment, which collects a large amount of data on agricultural production, conducts scientific analysis, and provides agricultural assistance; agricultural biotechnology equipment, which uses biotechnology to carry out biological breeding and pest control of agricultural crops; agricultural environmental technology equipment, which is used to monitor and regulate the farmland environment, and carry out intelligent irrigation, intelligent fertilization, and intelligent spraying of farmland; agricultural robots, which use planting robots and picking robots to automate planting, management, and harvesting; and agricultural intelligent equipment management systems, which realize the interconnection of various intelligent devices by building an agricultural Internet of Things platform.

[0011] Compared with the prior art, the present invention has the following beneficial effects:

[0012] The present invention realizes real-time monitoring and data collection of key parameters such as farmland environment and crop growth status through the Internet of Things sensing network;

[0013] By using digital twin technology, virtual modeling and simulation of farmland can be achieved, and the status of farmland can be evaluated, optimized and predicted, thus improving the accuracy of farmland management and the scientific nature of decision-making;

[0014] By combining control and decision-making units, precise management and intelligent control of farmland can be achieved, including the use of intelligent agricultural equipment to irrigate, fertilize, and control pests and diseases on farmland;

[0015] By providing a remote monitoring and management platform, users can obtain farmland data, model results and decision-making suggestions anytime and anywhere, thus realizing remote agricultural management;

[0016] The present invention realizes the informatization and intelligence of the whole chain of farmland production, and has the beneficial effect of improving the efficiency and quality of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of the present invention; DETAILED DESCRIPTION

[0018] In order to make the content of the present invention more clearly understood, the technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. The same parts are represented by the same figure marks. It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to the directions in the drawings, and the words "inside" and "outside" refer to the directions toward or away from the geometric center of a specific component, respectively.

[0019] like Figure 1 As shown in the figure, a smart agriculture integration system based on the Internet of Things and digital twins includes:

[0020] The IoT sensing network deploys multiple sensors, cameras, RFID tags and drones in the farmland to collect data on soil moisture, soil oxygen, environmental parameters, meteorological information, and crop growth status. The RFID tags can help farmers accurately record data on each piece of farmland, such as land quality, planting types, planting cycles, and fertilization conditions, so as to better formulate agricultural plans and production management; and transmit the collected data to the big data processing unit through wireless sensor networks, cellular networks, and satellite networks. The wireless sensor networks, cellular networks, and satellite networks are all existing technologies and will not be elaborated on here.

[0021] The big data processing unit receives the data collected by the sensor, and evaluates and predicts the status of the farmland through data analysis and mining, data cleaning and preprocessing technology. The big data processing unit includes: a network layer, a data management layer, and an application service layer; the network layer is used to transmit the data collected by the Internet of Things perception network to the data management layer. The network layer includes a wireless sensor network, a cellular network and a satellite network, and also includes a wireless data transmission terminal DTU / data acquisition instrument / 5g4g industrial Internet of Things gateway as a network communication core to collect data from agricultural farming equipment, detectors, sensor instruments, etc.; the data management layer can provide scientific decision-making support for agricultural production by mining and analyzing a large amount of agricultural data. When the data management layer receives agricultural data from the Internet of Things perception network, it cleans the data. , data filtering, and data normalization operations to remove errors and inaccurate information to ensure data quality and consistency. The data management layer also includes data storage and privacy protection. Commonly used data storage technologies include Hadoop Distributed File System (HDFS), NoSQL databases, distributed databases, and cloud storage technologies; the application service layer is used to provide agricultural production support based on the analysis results of the data management layer. The application service layer includes: agricultural production management system, agricultural expert system, and agricultural Internet of Things platform. The agricultural production management system helps agricultural producers to achieve refined management, improve crop yield and quality, and reduce production costs through environmental monitoring, crop production simulation, yield forecasting, production resource allocation, production safety management and other steps, thereby ensuring the quality and safety of agricultural products; at the same time, the processed data is fed back to the digital twin model.

[0022] Establish a digital twin model, and use computer learning methods and deep learning algorithms, and use real-time and historical data of farmland to build a digital twin model of farmland. The computer learning method and deep learning algorithm are basic tools for processing large-scale data sets. These algorithms include random forests, support vector machines, gradient boosting trees (GBDT), XGBoost, deep feedforward neural networks, convolutional neural networks (CNN) and recurrent neural networks (RNN). These algorithms can learn and extract patterns from large amounts of data, and simulate and predict the growth environment of farmland through association rule learning algorithms and time series analysis techniques, identify the direct relationship between crop growth cycle and climatic conditions, crop growth process and yield indicators. The model can be continuously optimized and improved through interaction with actual farmland.

[0023] The establishment of the digital twin model includes:

[0024] Physical entity: the actual agricultural production environment, including farmland, crops, and agricultural machinery and equipment;

[0025] Digital model: A virtual model created through digital twin technology that can be a direct mapping of a physical entity or a simulation model built based on the characteristics of a physical entity;

[0026] Data and information connectivity: data flows and information exchange mechanisms that connect physical entities to digital models, ensuring interaction and synchronization between the two;

[0027] Analytical algorithms: algorithms and analytical tools that process and analyze the data collected from the physical entities, and guide how the digital models respond to this data;

[0028] Visualization interface: Generate a visualization interface through 3D maps and dashboards to display the status of the digital model and analysis results.

[0029] The control and decision-making unit formulates farmland management strategies and decisions based on the analysis and prediction results of the digital twin model. The control and management unit performs real-time monitoring, intelligent analysis, data storage and command execution on the farmland; realizes precise management of the farmland through control equipment and actuators, including intelligent agricultural equipment that can autonomously perform tasks, and irrigates, fertilizes, and controls pests and diseases on the farmland. The intelligent agricultural equipment includes: agricultural mechanization equipment, which replaces manual labor to complete repetitive labor; agricultural information technology equipment, which collects a large amount of data on agricultural production, conducts scientific analysis, and provides agricultural assistance; agricultural biotechnology settings, through the application of biotechnology, biological breeding and pest control of agricultural crops; agricultural environmental technology equipment, which is used to monitor and regulate the farmland environment, and perform intelligent irrigation, intelligent fertilization, and intelligent spraying on the farmland; agricultural robots, which perform automated planting, management, and harvesting through planting robots and picking robots; agricultural intelligent equipment management system, which realizes the interconnection of various intelligent devices by building an agricultural Internet of Things platform.

[0030] The remote monitoring and management platform monitors the environmental parameters and equipment operating conditions in the agricultural production process in real time through the sensors, cameras, RFID tags and drones, realizes the control and management of the smart agricultural equipment through the Internet of Things system, uses big data and artificial intelligence technology to provide intelligent decision-making support for agricultural production, and provides a user interface and control panel to allow users to remotely monitor and manage the operating status of farmland through the terminal.

[0031] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart agriculture integration system based on the Internet of Things and digital twins, characterized by: include: IoT sensing network, which deploys multiple sensors, cameras, RFID tags and drones in farmland to collect data on soil moisture, soil oxygen, environmental parameters, meteorological information and crop growth status, and transmits the collected data to the big data processing unit through wireless sensor networks, cellular networks and satellite networks; The big data processing unit receives the data collected by the sensors and evaluates and predicts the status of the farmland through data analysis and mining, data cleaning and preprocessing technologies. At the same time, the processed data is fed back to the digital twin model. Establish a digital twin model. Through computer learning methods and deep learning algorithms, and using real-time and historical data of farmland, build a digital twin model of farmland to simulate and predict the growth environment, crop growth process and yield indicators of farmland. The model can be continuously optimized and improved through interaction with actual farmland. The control and decision-making unit formulates farmland management strategies and decisions based on the analysis and prediction results of the digital twin model, and realizes precise management of farmland through control equipment and actuators, including intelligent agricultural equipment that can perform tasks autonomously, and irrigates, fertilizes, and controls pests and diseases on farmland; The remote monitoring and management platform monitors the environmental parameters and equipment operating conditions in the agricultural production process in real time through the sensors, cameras, RFID tags and drones, realizes the control and management of the smart agricultural equipment through the Internet of Things system, and provides a user interface and control panel, allowing users to remotely monitor and manage the operating status of the farmland through the terminal.

2. The smart agriculture integration system based on the Internet of Things and digital twins according to claim 1 is characterized by: The big data processing unit includes: a network layer, a data management layer, and an application service layer; the network layer is used to transmit the data collected by the Internet of Things perception network to the data management layer, and the network layer includes a wireless sensor network, a cellular network, and a satellite network; the data management layer, through the mining and analysis of a large amount of agricultural data, can provide scientific decision-making support for agricultural production, and when the data management layer receives agricultural data from the Internet of Things perception network, it performs data cleaning, data filtering, and data normalization operations on the data; the application service layer is used to provide agricultural production support according to the analysis results of the numerical control management layer.

3. The smart agriculture integration system based on the Internet of Things and digital twins according to claim 2 is characterized by: The application service layer includes: agricultural production management system, agricultural expert system and agricultural Internet of Things platform.

4. The smart agriculture integration system based on the Internet of Things and digital twins according to claim 1 is characterized by: The establishment of the digital twin model includes: Physical entity: the actual agricultural production environment, including farmland, crops, and agricultural machinery and equipment; Digital model: A virtual model created through digital twin technology; Data and information connectivity: data flows and information exchange mechanisms that connect physical entities to digital models; Analytical algorithms: algorithms and analytical tools that process and analyze the data collected from the physical entities, and guide the digital models to respond to this data; Visualization interface: Generate a visualization interface through 3D maps and dashboards to display the status of the digital model and analysis results.

5. The smart agriculture integration system based on the Internet of Things and digital twins according to claim 1 is characterized by: The smart agricultural equipment includes: agricultural mechanized equipment, which replaces manual labor to complete repetitive labor; agricultural information technology equipment, which collects a large amount of data on agricultural production, conducts scientific analysis, and provides agricultural assistance; agricultural biotechnology equipment, which uses biotechnology to carry out biological breeding and pest control of agricultural crops; agricultural environmental technology equipment, which is used to monitor and regulate the farmland environment, and carry out smart irrigation, smart fertilization, and smart spraying of farmland; agricultural robots, which use planting robots and picking robots to automate planting, management, and harvesting; and agricultural smart equipment management systems, which realize the interconnection of various smart devices by building an agricultural Internet of Things platform.

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