An agricultural monitoring method and system for a drone to communicate with ground internet of things nodes

Abstract

The application relates to an unmanned aerial vehicle and ground Internet of Things node communication agricultural monitoring system, belonging to the unmanned aerial vehicle auxiliary communication field, and solves the problem that existing methods cannot efficiently and timely acquire monitoring data acquired by an Internet of Things node. The system comprises an unmanned aerial vehicle module, which is arranged on the unmanned aerial vehicle body and is used for executing a multi-antenna unmanned aerial vehicle and ground Internet of Things node communication method, communicating with the Internet of Things node in a farmland, acquiring monitoring data sent by the Internet of Things node module, and sending the monitoring data of the multiple Internet of Things nodes to a command station module; the command station module is used for issuing an inspection instruction to the unmanned aerial vehicle and formulating a management strategy of crop irrigation, fertilization, disease and pest identification and prevention and treatment based on the monitoring data; the Internet of Things node module is used for acquiring monitoring data in the farmland and sending the monitoring data to the unmanned aerial vehicle module in a time slot when the unmanned aerial vehicle flies; and a user terminal module is used for being communicatively connected with the command station module, acquiring the management strategy and manually adjusting the strategy. The monitoring data can be efficiently acquired.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things and smart agriculture monitoring technology, and in particular to an agricultural monitoring system that allows communication between a drone and a ground-based Internet of Things node. Background Technology

[0002] Smart agriculture is the intelligent economy within agriculture. With the development of the times and the progress of science and technology, smart agriculture represents the deep integration of modern technology and agricultural planting to achieve unmanned, automated, and intelligent management of agricultural production.

[0003] In the new era, farmers not only need to understand the growth of crops but also need to monitor and analyze this growth in a timely manner, and determine management strategies for irrigation, fertilization, and pest and disease identification and control, achieving automated irrigation, fertilization, and pest control. Smart agriculture encompasses areas such as precision agriculture, intelligent equipment, IoT technology, big data analytics, cloud computing, and artificial intelligence, aiming to promote the modernization and technological advancement of traditional agriculture and achieve efficient and sustainable development of the agricultural industry. The development of smart agriculture is an inevitable trend in agricultural modernization. Through the widespread application of advanced technologies such as IoT, big data, and artificial intelligence, smart agriculture can achieve precise, intelligent, and efficient agricultural production management. How to efficiently and in real-time acquire monitoring data from field IoT nodes and promptly irrigate, fertilize, and control pests is a current challenge facing smart agriculture. Summary of the Invention

[0004] Based on the above analysis, the present invention aims to provide an agricultural monitoring system that enables communication between a drone and a ground-based Internet of Things (IoT) node, thereby solving the technical problem that existing methods cannot efficiently acquire monitoring data obtained by field IoT nodes in real time.

[0005] The objective of this invention is mainly achieved through the following technical solutions:

[0006] This invention provides an agricultural monitoring system for communication between a drone and a ground-based Internet of Things (IoT) node, comprising the following modules:

[0007] The drone module, deployed on the drone itself, is used to execute the multi-antenna drone-to-ground IoT node communication method, communicate with IoT nodes in the farmland, obtain monitoring data sent by IoT node modules, and send monitoring data from multiple IoT nodes to the command center module.

[0008] The command console module is used to issue inspection instructions to the drones and to formulate management strategies for crop irrigation, fertilization, pest and disease identification and control based on the monitoring data.

[0009] The Internet of Things (IoT) node module is used to acquire monitoring data in the farmland and send it to the drone module during the drone's flight time.

[0010] The user terminal module is used to communicate with the command console module, obtain the management strategy, and manually adjust the strategy.

[0011] Furthermore, the IoT node module includes:

[0012] Temperature and humidity sensors are used to monitor the temperature and humidity data of the air in farmland.

[0013] Light sensors are used to monitor light intensity data in farmland;

[0014] Soil moisture sensor, used to monitor soil moisture data;

[0015] Spectral sensors are used to monitor the spectral reflectance characteristics of plants;

[0016] pH sensors are used to monitor data on soil nutrient deficiencies or the effects of harmful chemicals on crop health;

[0017] Image sensors are used to capture images of crops in the field;

[0018] The communication module is used to transmit the monitoring data obtained by each sensor to the UAV module;

[0019] The monitoring data includes farmland air temperature and humidity data, light intensity data, soil moisture data, soil nutrient deficiency or harmful chemical substance data, and crop image data.

[0020] Furthermore, the UAV module includes a uniform planar antenna array, which performs circular flight over the farmland according to a predetermined flight path based on the inspection commands from the command console module;

[0021] The communication module of the IoT node module includes a single antenna device. When the drone flies by, the IoT node establishes a wireless communication connection with the drone. Each IoT node module receives a directional beam signal sent by the drone module in a certain time slot, and then responds and sends the monitoring data.

[0022] Furthermore, the communication method between the multi-antenna UAV and the ground IoT node includes:

[0023] Step S1: Calculate the turning vector from the UAV to each IoT node; calculate the channel gain from the UAV to each IoT node module based on the turning vector; calculate the communication rate between the UAV and each IoT node module based on the channel gain.

[0024] Step S2: Construct a drone-assisted IoT node communication optimization model with the objective function of maximizing the average communication rate between the drone and multiple IoT node modules, while satisfying IoT node scheduling constraints, drone communication performance constraints, drone transmit power constraints, and drone flight trajectory constraints.

[0025] Step S3: Solve the UAV-assisted IoT node communication optimization model to obtain the optimal communication solution between the UAV and multiple IoT nodes; use the optimal communication solution to perform precise communication with the IoT node module.

[0026] Furthermore, the UAV is equipped with a uniform planar antenna array deployed parallel to the ground. The total number of antennas in the uniform planar antenna array and the distance between antenna elements are divided along the x-axis and y-axis of a three-dimensional Cartesian coordinate system, respectively, to obtain the total number of antennas M = M x ×M y The distance d between the antenna and the coordinate system axis x =d y =λ / 2;

[0027] Among them, M x M y d represents the number of antennas arranged along the x-axis and y-axis, respectively; x d y λ represents the spacing of the antenna along the x-axis and y-axis, respectively; λ is the carrier wavelength.

[0028] Based on M x M y and d x d y Calculate the turning vector α(l(n), l) of the UAV to the k-th IoT node module. k The calculation is as follows:

[0029]

[0030] Where k = 1, 2, ..., K, K is the number of IoT nodes; l(n) is the position of the drone in the nth time slot. k Let θ(l(n), l) be the location of the k-th IoT node. k ), Φ(l(n), l k ) are the AoD elevation angle and AoD azimuth angle when the drone transmits signals to the k-th IoT node, respectively.

[0031] Furthermore, based on α(l(n), l k Calculate the channel gain h from the UAV to the k-th IoT node module. k，com (l(n), l) k The calculation is as follows:

[0032]

[0033] Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t For the antenna gain of the UAV transmitter, G b The antenna gain received by the IoT node module, d(l(n), l k )=||l(n)-l k || represents the Euclidean distance between the drone and the k-th IoT node;

[0034] Under unit bandwidth, based on h k，com (l(n), l) k Calculate the communication rate between the UAV and the k-th IoT node module. The calculation is as follows:

[0035]

[0036] Among them, c k (n) represents the IoT node scheduling of the drone in the nth time slot. Assign a communication beamforming vector to the UAV in the nth time slot. The noise follows a Gaussian random distribution, and H is the channel gain h. k，com (l(n), l) k The conjugate transpose of ); c k (n) is a binary discrete variable;

[0037] When the drone transmits a communication beam in time slot n to perform a communication task with the kth IoT node, c k (n) = 1, otherwise c k (n) = 0.

[0038] Furthermore, the drone-assisted IoT node communication optimization model is as follows:

[0039]

[0040] in, The communication rate between the UAV and the kth IoT node is denoted by ; K is the number of IoT nodes; n = 1, 2, ..., N, where N is the number of time slots in the UAV's flight cycle; C, W, and L are the IoT node scheduling to be optimized, the beamforming vector emitted by the UAV, and the UAV trajectory variables, respectively.

[0041]

[0042] C1 and C2 represent IoT node scheduling constraints, and C3 represents drone communication performance constraints. C4 represents the preset communication rate threshold between the UAV and the base station, C5 and C6 represent the UAV's transmit power constraint, and P represents the UAV's flight trajectory constraint. max D represents the maximum transmission power of the drone. max l represents the maximum flight distance of the UAV in each time slot. I This is the starting position of the drone.

[0043] Further, step S3 includes:

[0044] The UAV-assisted IoT node communication optimization model includes three nonlinear coupled variables: IoT node scheduling, beamforming vector emitted by the UAV, and UAV trajectory. The solution to this model is a non-convex optimization problem.

[0045] The non-convex optimization problem is decomposed into three convex optimization sub-problems: IoT node scheduling, beamforming vector of UAV launch, and UAV trajectory.

[0046] The three convex optimization subproblems are alternately and iteratively optimized to gradually approach the global optimal solution.

[0047] When the objective function converges, the obtained global optimal solution is the communication optimal solution between the UAV and multiple IoT nodes; wherein, the global optimal solution includes IoT node scheduling, beamforming vectors emitted by the UAV, and UAV trajectory;

[0048] The non-convex optimization problem includes IoT node scheduling. Beamforming vectors launched by UAVs drone trajectory Three non-linear coupled variables.

[0049] Furthermore, by alternately optimizing three convex optimization subproblems—IoT node scheduling, UAV launch beamforming vector, and UAV trajectory—the global optimum is gradually approximated, including:

[0050] In the m-th iteration, when solving the IoT node scheduling problem, given the preset beamforming vector emitted by the UAV and the initial UAV trajectory, the IoT node scheduling C is obtained by solving the UAV-assisted IoT node communication optimization model. (m) ;

[0051] When solving for the beamforming vector emitted by the UAV, based on C (m) The initial UAV trajectory is used to solve the UAV-assisted IoT node communication optimization model to obtain the beamforming vector W. (m) ;

[0052] When solving the UAV trajectory, based on W (m) C (m)Solving the UAV-assisted IoT node communication optimization model yields L. (m) ;

[0053] The solution is obtained by alternating and iterating until the objective function converges. At this point, the IoT node scheduling, the beamforming vector launched by the UAV, and the UAV trajectory in the current round are the global optimal solutions.

[0054] Furthermore, the command console module determines the crop type, crop growth height, and pest and disease status based on the field crop images;

[0055] Based on the temperature and humidity data of the farmland air, soil moisture data, and crop types, a crop irrigation management strategy is generated to determine whether to turn the farmland irrigation system on or off.

[0056] Based on the data on soil nutrient deficiencies or harmful chemicals, and the types of crops, a crop fertilization management strategy is generated.

[0057] Based on the plant spectral reflectance data, the spectral reflectance characteristics of crops are analyzed to determine the health status of crop growth.

[0058] Based on the crop images, the status of crop diseases and pests is determined, and crop disease and pest identification and control management strategies are generated.

[0059] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0060] 1. This invention enables real-time and efficient rapid collection of farmland monitoring data through direct communication between drones and ground-based IoT nodes, which greatly improves the efficiency and real-time performance of data collection compared to traditional manual collection or wired transmission methods.

[0061] 2. This invention maximizes the average communication rate between the UAV and multiple IoT nodes by constructing an optimization model, effectively allocates communication resources, and improves the operating efficiency of the entire agricultural monitoring system;

[0062] 3. The command console module of this invention can formulate more precise crop management strategies based on real-time collected multi-dimensional data (such as temperature, humidity, light, soil moisture, etc.), including irrigation, fertilization and pest and disease control, thereby improving crop yield and quality.

[0063] 4. The UAV of this invention is equipped with a uniform planar antenna array, which can adjust the flight path and communication strategy according to real-time monitoring data, making the system more flexible and adaptable to different farmland environments and crop growth conditions.

[0064] 5. The user terminal module of this invention allows agricultural experts to adjust automatically generated management strategies, making the system not only automated but also intelligent, providing a powerful decision support platform for smart agriculture.

[0065] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0066] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0067] Figure 1 A schematic diagram of an agricultural monitoring system module for communication between a drone and a ground-based Internet of Things node in an embodiment of the present invention;

[0068] Figure 2 This is a flowchart of an agricultural monitoring method for communication between a drone and a ground IoT node, as described in an embodiment of the present invention.

[0069] Figure 3 This is a schematic diagram of a multi-antenna UAV-assisted IoT node communication system in an embodiment of the present invention;

[0070] Figure 4 This is a schematic diagram of multi-antenna UAV trajectory optimization in an embodiment of the present invention;

[0071] Figure 5 This is a schematic diagram illustrating the scheduling of IoT nodes by a multi-antenna UAV in an embodiment of the present invention;

[0072] Figure 6 This is a schematic diagram of the planar beamforming at the IoT node receiver in a randomly selected time slot of the UAV in an embodiment of the present invention.

[0073] Figure 7 This is a three-dimensional beam diagram showing the signal-to-noise ratio at the IoT node receiver in a randomly selected time slot during an embodiment of the present invention.

[0074] Figure 8 This is a schematic diagram comparing the average communication rate of the system with the scheme of the present invention and the scheme of the preset trajectory under different maximum transmission power of the UAV in an embodiment of the present invention. Detailed Implementation

[0075] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0076] This technical solution addresses the shortcomings of existing technologies by proposing an agricultural monitoring system that facilitates communication between unmanned aerial vehicles (UAVs) and ground-based Internet of Things (IoT) nodes. The aim is to improve communication efficiency and data acquisition accuracy, reduce system costs, and enhance system stability and coverage by utilizing multi-antenna UAVs to assist multi-IoT node communication. Simultaneously, by integrating advanced sensor technologies and artificial intelligence decision-making capabilities, the system enhances the monitoring of farmland environment and pests and diseases, enabling more precise crop management strategies to meet future food demands and achieve green agricultural development.

[0077] A specific embodiment of the present invention, such as Figure 1 As shown, an agricultural monitoring system for communication between a drone and a ground-based Internet of Things (IoT) node is disclosed, comprising the following modules:

[0078] The drone module, deployed on the drone itself, is used to execute the multi-antenna drone-to-ground IoT node communication method, communicate with IoT nodes in the farmland, obtain monitoring data sent by IoT node modules, and send monitoring data from multiple IoT nodes to the command center module.

[0079] The command console module is used to issue inspection instructions to the drones and to formulate management strategies for crop irrigation, fertilization, pest and disease identification and control based on the monitoring data.

[0080] The Internet of Things (IoT) node module is used to acquire monitoring data in the farmland and send it to the drone module during the drone's flight time.

[0081] The user terminal module is used to communicate with the command console module, obtain the management strategy, and manually adjust the strategy.

[0082] This invention enables efficient collection, intelligent analysis, and precise management of farmland monitoring data through the collaborative work of drones and ground-based IoT nodes, thereby optimizing irrigation, fertilization, and pest and disease control strategies for crops.

[0083] The IoT node module includes:

[0084] Temperature and humidity sensors are used to monitor the temperature and humidity data of the air in farmland.

[0085] Light sensors are used to monitor light intensity data in farmland;

[0086] Soil moisture sensor, used to monitor soil moisture data;

[0087] Spectral sensors are used to monitor the spectral reflectance characteristics of plants;

[0088] pH sensors are used to monitor data on soil nutrient deficiencies or the effects of harmful chemicals on crop health;

[0089] Image sensors are used to capture images of crops in the field;

[0090] The communication module is used to transmit the monitoring data obtained by each sensor to the UAV module;

[0091] The monitoring data includes farmland air temperature and humidity data, light intensity data, soil moisture data, soil nutrient deficiency or harmful chemical substance data, and crop image data.

[0092] For example,

[0093] The temperature and humidity sensors are DHT11 or DHT22, which can simultaneously measure air temperature and relative humidity, providing basic climate data for agricultural environmental monitoring.

[0094] The light sensor is a BH1750 light intensity sensor, used to measure the light intensity received by farmland, which is crucial for plant photosynthesis and growth.

[0095] The soil moisture sensor is a capacitive soil moisture sensor that can detect the moisture content of the soil and help determine the best time for irrigation.

[0096] Spectral sensors are either multispectral or hyperspectral sensors, which can measure the spectral information reflected by plants to assess plant health and nutritional needs.

[0097] The pH sensor is an electrochemical pH sensor used to measure the acidity and alkalinity of soil, which is very important for determining soil nutrient status and adjusting fertilization strategies.

[0098] The image sensor is a high-definition camera or a multispectral camera, which can capture images of crops in the field to monitor crop growth and identify pests and diseases.

[0099] These sensors and communication modules together form the Internet of Things (IoT) node module. They work together to provide real-time and accurate field data for smart agriculture, supporting the implementation of smart agriculture.

[0100] The command platform module determines the crop type, crop growth height, and pest and disease status based on the field crop images.

[0101] Based on the temperature and humidity data of the farmland air, soil moisture data, and crop types, a crop irrigation management strategy is generated to determine whether to turn the farmland irrigation system on or off.

[0102] Based on the data on soil nutrient deficiencies or harmful chemicals, and the types of crops, a crop fertilization management strategy is generated.

[0103] Based on the plant spectral reflectance data, the spectral reflectance characteristics of crops are analyzed to determine the health status of crop growth.

[0104] Based on the crop images, the status of crop diseases and pests is determined, and crop disease and pest identification and control management strategies are generated.

[0105] Specifically, regarding the command console module...

[0106] (1) Determining the type, height, and pest and disease status of crops:

[0107] The command center module uses image sensors to capture images of crops in the field, combined with deep learning algorithms, to automatically identify and classify crop types. The deep learning algorithm improves the accuracy of identification by extracting key features from the images, especially against complex backgrounds.

[0108] By analyzing the size and shape of crops in images, the control panel module can estimate the growth height of crops, which is crucial for monitoring crop growth stages and adjusting planting strategies.

[0109] Using image recognition technology, the command console module can also identify crop diseases and pests, which is of great significance for taking timely prevention and control measures and reducing losses.

[0110] (2) Generation of crop irrigation management strategies:

[0111] The command console module analyzes and generates irrigation management strategies based on air temperature and humidity data provided by temperature and humidity sensors, soil moisture data from soil moisture sensors, and crop types.

[0112] This strategy includes determining the frequency and amount of irrigation, as well as whether to turn farmland irrigation systems on or off, to ensure that crops receive adequate water while avoiding water waste.

[0113] (3) The generation of crop fertilization management strategies:

[0114] The command console module generates personalized fertilization management strategies based on soil nutrient deficiency or harmful chemical data monitored by pH sensors, as well as the type of crop.

[0115] This strategy references the principles of the NE (Nutrient Expert) system, taking into account soil fertility, nutrient residue from the previous crop, nutrients brought in by straw return, crop rotation systems, and organic fertilizer application, and proposes a fertilization plan that conforms to the 4R nutrient management strategy (optimal fertilizer type, optimal amount, optimal application time, and optimal application location).

[0116] (4) Analysis of crop growth and health status:

[0117] The control module assesses crop nutrition and growth health by analyzing plant spectral reflectance data provided by spectral sensors. This analysis helps to identify nutrient deficiencies or excesses in a timely manner, allowing for adjustments to fertilization and irrigation plans accordingly.

[0118] (5) Development of strategies for identifying and controlling crop diseases and pests:

[0119] By combining field crop image data with deep learning algorithms, the control module can identify pests and diseases and generate control strategies. These strategies include selecting appropriate biological control methods or chemical pesticides, and determining the optimal time and method of application to minimize environmental impact and improve control effectiveness.

[0120] The above description details how the command console module utilizes various sensor data and advanced deep learning technology to provide precise management strategies for smart agriculture.

[0121] The emergence of beamforming technology provides an effective way to solve the above-mentioned technical problems. Beamforming technology is based on antenna arrays. By precisely adjusting the phase and amplitude of the signals transmitted by each antenna element, it can make the transmitted electromagnetic waves form a specific radiation pattern in space, concentrating the signal energy towards the target IoT node.

[0122] The unmanned aerial vehicle (UAV) module includes a uniform planar antenna array and performs circular flight over the farmland according to a predetermined flight path based on the inspection commands from the command console module.

[0123] The communication module of the IoT node module includes a single antenna device. When the drone flies by, the IoT node establishes a wireless communication connection with the drone. Each IoT node module receives a directional beam signal sent by the drone module in a certain time slot, and then responds and sends the monitoring data.

[0124] In drone-assisted IoT node communication scenarios, beamforming technology can significantly enhance the communication link performance between the drone and the IoT node. By precisely aligning a narrower communication beam with the target IoT node, it effectively reduces signal diffusion and waste in space, increases the signal strength reaching the IoT node, thereby improving the signal-to-noise ratio and reliability. This solves the problems of low communication quality and efficiency associated with single-antenna drones, providing a better solution for drone-assisted IoT node communication.

[0125] This invention constructs a drone-assisted communication network based on the ever-increasing demands of IoT nodes for communication quality and speed. It equips drones with multi-antenna arrays and utilizes beamforming technology to enhance signal strength and anti-interference capabilities. By jointly optimizing IoT node scheduling, the beamforming vectors emitted by the drones, and the drone's flight trajectory, precise network coverage is achieved, thereby effectively improving the average communication speed. This meets the communication quality expectations of IoT nodes in various complex scenarios and provides strong support for the in-depth application of drone communication technology.

[0126] like Figure 2 As shown, the communication method between the multi-antenna UAV and the ground IoT node includes:

[0127] Step S1: Calculate the turning vector from the UAV to each IoT node; calculate the channel gain from the UAV to each IoT node module based on the turning vector; calculate the communication rate between the UAV and each IoT node module based on the channel gain.

[0128] Step S2: Construct a drone-assisted IoT node communication optimization model with the objective function of maximizing the average communication rate between the drone and multiple IoT node modules, while satisfying IoT node scheduling constraints, drone communication performance constraints, drone transmit power constraints, and drone flight trajectory constraints.

[0129] Step S3: Solve the UAV-assisted IoT node communication optimization model to obtain the optimal communication solution between the UAV and multiple IoT nodes; use the optimal communication solution to perform precise communication with the IoT node module.

[0130] Step S1, specifically.

[0131] (1) Construct a multi-antenna UAV-assisted multi-IoT node communication system.

[0132] Based on the needs of practical application scenarios, a system for multi-antenna UAV-assisted IoT node communication is constructed, covering the channel model of the communication link and the UAV flight motion model. By adjusting the radiation direction of the antenna array, the signal energy can be focused and directionally transmitted to the target IoT node. At the same time, combined with the optimized design of the UAV flight trajectory, it is ensured that IoT nodes in different geographical locations can receive stable communication signals.

[0133] A multi-antenna UAV-assisted multi-IoT node communication system consists of a single UAV and multiple IoT nodes distributed across different locations in the disaster area. The UAV is equipped with a Uniform Planar Array (UPA) and serves as an aerial information acquisition and communication platform.

[0134] During a circular flight mission, the UAV transmits a communication beam at a constant altitude to a single-antenna IoT node on the ground, thereby collecting and transmitting information from the IoT node. To ensure that the UAV's position can be considered relatively stationary within each time slot, its flight cycle is divided into N equal-length time slots.

[0135] The communication channel is specifically as follows: the UAV is equipped with a uniform planar antenna array, deployed parallel to the ground. The total number of antennas in the uniform planar antenna array and the distance between antenna elements are divided along the x-axis and y-axis of a three-dimensional Cartesian coordinate system, respectively, to obtain the total number of antenna arrays M = M x ×M y The distance d between the antenna and the coordinate system axis x =d y =λ / 2;

[0136] Among them, M x M y d represents the number of antennas arranged along the x-axis and y-axis, respectively; x d y λ represents the spacing of the antenna along the x-axis and y-axis, respectively; λ is the carrier wavelength.

[0137] (2) Calculate the turning vector α(l(n),l) of the UAV to each of the IoT nodes. k ).

[0138] Based on M x M y and d x d y Calculate the turning vector α(l(n),l) of the UAV to the k-th IoT node module. k The calculation is as follows:

[0139]

[0140] Where k = 1, 2, ..., K, K is the number of IoT nodes; l(n) is the position of the drone in the nth time slot. k Let θ(l(n),l) be the location of the k-th IoT node. k ), Φ(l(n),l k ) are the AoD elevation angle and AoD azimuth angle when the drone transmits signals to the k-th IoT node, respectively.

[0141] j is the imaginary unit; T is the transpose of the matrix; θ(l(n),l k ) represents the Angle of Departure (AoD) elevation angle when the drone's transmitted signal reaches the k-th IoT node, reflecting the change in the signal's vertical angle relative to the drone's transmission angle; Φ(l(n),l k ) represents the AoD azimuth angle of the drone's transmitted signal to the k-th IoT node, describing the horizontal angle of the signal relative to the drone's transmission pointing direction.

[0142] (3) Calculate the channel gain h from the UAV to each IoT node based on the steering vector. k,com (l(n),l k ).

[0143] Considering that the downlink communication link between the drone and the IoT node is a line-of-sight channel, a free-space fading model is used to simulate the line-of-sight channel gain.

[0144] Based on α(l(n),l k Calculate the channel gain h from the UAV to the k-th IoT node module. k,com (l(n),l k The calculation is as follows:

[0145]

[0146] Where n = 1, 2, ..., N, N is the number of time slots in the UAV flight cycle; G t For the antenna gain of the UAV transmitter, G b The antenna gain received by the IoT node module, d(l(n),l k )=||l(n)-l k || represents the Euclidean distance between the drone and the k-th IoT node;

[0147] (4) Calculate the communication rate between the UAV and each IoT node based on the channel gain.

[0148] Under unit bandwidth, based on h k,com (l(n),l kCalculate the communication rate between the UAV and the k-th IoT node module. The calculation is as follows:

[0149]

[0150] Among them, c k (n) represents the IoT node scheduling of the drone in the nth time slot. Assign a communication beamforming vector to the UAV in the nth time slot. The noise follows a Gaussian random distribution, and H is the channel gain h. k,com (l(n),l k The conjugate transpose of ); c k (n) is a binary discrete variable;

[0151] When the drone transmits a communication beam in time slot n to perform a communication task with the kth IoT node, c k (n) = 1, otherwise c k (n) = 0.

[0152] c k (n) is a binary discrete variable used to describe the scheduling of IoT nodes by drones.

[0153] The noise used in the communication rate is a Gaussian random distribution, specifically the noise at the IoT node receiver. This noise has a mean of 0 and a variance of... Gaussian white noise, This describes the power distribution of the noise signal.

[0154] The motion model of the UAV includes kinematic constraints and flight position constraints; considering the UAV's own physical characteristics and flight safety requirements, the flight speed is always controlled at a preset maximum speed v during the UAV's flight. max the following.

[0155] For example, the preset maximum speed v max It is 40 m / s.

[0156] The drone has the following distance constraints in each time slot:

[0157]

[0158] Where l(n) is the coordinate of the UAV in the nth time slot, l(n-1) is the coordinate of the UAV in the (n-1)th time slot, and D max =v max δ T δ represents the maximum flight distance of the UAV in each time slot. T The length of each time slot, in seconds;

[0159] During the process of drones performing IoT node communication services, in order to ensure that their flight trajectory within a specific area has a clear start and end point, the following flight constraints must be followed:

[0160] l(1)=l(N)=l I Formula (5)

[0161] Among them, l I This indicates the starting position of the drone, and N is the total number of time slots in the drone's flight cycle.

[0162] The physical meaning of formula (5) is that the UAV returns to its starting position when the flight mission ends.

[0163] The power constraint of the UAV is specifically defined as follows: Let P(n) be the average power transmitted by the UAV in the nth time slot, then:

[0164]

[0165] in, It is a set containing all timeslots.

[0166] Since the average transmit power of a drone is limited by its maximum power, the following constraints must be met:

[0167] P(n)≤P max Formula (7)

[0168] Among them, P max This indicates the maximum transmission power of the drone, which is a parameter of the drone itself.

[0169] Step S1 aims to establish a multi-antenna UAV-assisted multi-IoT node communication system between a multi-antenna UAV and multiple IoT nodes, and to calculate key parameters of the communication link, including the turning vector α(l(n),l) between the UAV and each of the IoT nodes. k ), channel gain h k,com (l(n),l k ) and communication rate This provides a foundation for subsequent communication optimization.

[0170] Step S2, specifically.

[0171] The design optimization problem aims to maximize the average communication rate of the UAV while satisfying IoT node scheduling constraints, transmit power constraints, minimum communication rate constraints of IoT nodes, and kinematic constraints of UAV flight.

[0172] To maximize the average communication rate of the drone, the drone-assisted IoT node communication optimization model is as follows:

[0173]

[0174] in, The communication rate between the UAV and the k-th IoT node is denoted by K; the number of IoT nodes is denoted by n = 1, 2, ..., N, where N is the number of time slots in the UAV's flight cycle; C, W, and L are the IoT node scheduling to be optimized, the beamforming vector emitted by the UAV, and the UAV trajectory variables, respectively.

[0175]

[0176] C1 and C2 represent IoT node scheduling constraints, and C3 represents drone communication performance constraints. C4 represents the preset communication rate threshold between the UAV and the base station, C5 and C6 represent the UAV's transmit power constraint, and P represents the UAV's flight trajectory constraint. max D represents the maximum transmission power of the drone. max l represents the maximum flight distance of the UAV in each time slot. I This is the starting position of the drone.

[0177] C1 and C2 IoT node scheduling constraints mean that the drone focuses on providing services to a single IoT node in each time slot; C3 drone communication performance constraints require that the communication rate between the drone and the base station must exceed a preset threshold. Ensure the quality of signal data transmission; limit the transmission power of the C4 UAV to ensure that the transmission power of the UAV does not exceed the upper limit to ensure equipment safety; and constrain the flight trajectory of the C5 and C6 UAVs, ensuring that the start and end points coincide to ensure the flight safety and stability of the UAVs.

[0178] For example, a preset communication rate threshold Set to 16bps / Hz.

[0179] The purpose of step S2 is to design an optimization problem that aims to maximize the average communication rate between the UAV and the IoT nodes by adjusting the IoT node scheduling, beamforming vector, and UAV trajectory variables, while ensuring that the constraints of IoT node scheduling, communication performance, transmission power, and flight trajectory are met, so as to achieve efficient, stable and safe UAV-assisted communication.

[0180] Step S3 includes:

[0181] Step S3 includes:

[0182] The UAV-assisted IoT node communication optimization model includes three nonlinear coupled variables: IoT node scheduling, beamforming vector emitted by the UAV, and UAV trajectory. The solution to this model is a non-convex optimization problem.

[0183] The non-convex optimization problem is decomposed into three convex optimization sub-problems: IoT node scheduling, beamforming vector of UAV launch, and UAV trajectory.

[0184] The three convex optimization subproblems are alternately and iteratively optimized to gradually approach the global optimal solution.

[0185] When the objective function converges, the obtained global optimal solution is the communication optimal solution between the UAV and multiple IoT nodes; wherein, the global optimal solution includes IoT node scheduling, beamforming vectors emitted by the UAV, and UAV trajectory;

[0186] This optimization problem is a mixed integer non-convex optimization problem, which is difficult to solve directly.

[0187] The non-convex optimization problem includes IoT node scheduling. Beamforming vectors launched by UAVs drone trajectory Three non-linear coupled variables.

[0188] The non-convex optimization problem is decomposed into three sub-problems: IoT node scheduling optimization, UAV beamforming vector optimization, and UAV trajectory optimization. In solving each sub-problem, the Successive Convex Approximation (SCA) method and the Semidefinite Relaxation (SDR) technique are used to transform the non-convex problem into a convex problem.

[0189] Specifically as follows:

[0190] (1) For the IoT node scheduling optimization subproblem, by relaxing the binary scheduling variable into a continuous variable with a value range between 0 and 1, the problem is transformed into a convex optimization problem, and then the transformed convex problem can be solved directly with the help of the CVX toolbox in Matlab.

[0191] (2) For the beamforming vector optimization subproblem of UAV launch, SDR technology is used to transform the beamforming vector into a positive semi-definite matrix with rank 1. At this point, the problem is initially transformed into a convex problem. Then, the relaxed rank 1 constraint with strong non-convexity is added as a penalty factor to the objective function, so that it can be solved directly by the CVX toolbox;

[0192] (3) For the UAV trajectory optimization subproblem, the SCA method is used to perform Taylor first-order expansion on the non-convex parts of the objective function and constraints, transforming the problem into a convex problem that can be directly solved by CVX.

[0193] After solving each subproblem separately, an alternating optimization strategy is adopted, which switches between different subproblems through iterative optimization. This allows the solutions to the three subproblems to influence each other and evolve collaboratively, gradually approaching the global optimum, thereby achieving a joint optimization solution for the entire complex optimization problem.

[0194] The algorithm alternately optimizes three convex optimization subproblems: IoT node scheduling, beamforming vector for UAV launch, and UAV trajectory, gradually approaching the global optimum. These subproblems include:

[0195] In the m-th iteration, when solving the IoT node scheduling problem, given the preset beamforming vector emitted by the UAV and the initial UAV trajectory, the IoT node scheduling C is obtained by solving the UAV-assisted IoT node communication optimization model. (m) ;

[0196] When solving for the beamforming vector emitted by the UAV, based on C (m) The initial UAV trajectory is used to solve the UAV-assisted IoT node communication optimization model to obtain the beamforming vector W. (m) ;

[0197] When solving the UAV trajectory, based on W (m) C (m) Solving the UAV-assisted IoT node communication optimization model yields L. (m) ;

[0198] The solution is obtained by alternating and iterating until the objective function converges. At this point, the IoT node scheduling, the beamforming vector launched by the UAV, and the UAV trajectory in the current round are the global optimal solutions.

[0199] Here, the m-th round is the iteration round, m≥1 round, which is the iteration round before the objective function converges.

[0200] The optimization steps are explained in detail below:

[0201] The original optimization problem contains three nonlinearly coupled variables (IoT node scheduling). Beamforming vectors launched by UAVs drone trajectory Therefore, the optimization problem is a non-convex problem. To solve this problem, the original problem is decomposed into three subproblems, and each subproblem is solved iteratively until the objective function value converges.

[0202] Method for determining convergence of objective function: If the objective function in the current iteration minus the objective function in the previous iteration is less than a given threshold or reaches the set maximum number of iterations, the objective value can be considered to remain basically unchanged and has reached the optimum. The solution (C, W, L) at this time is the approximate global optimum.

[0203] For example, the maximum number of iterations is set to 20.

[0204] It is important to note that in each round of the solution process, each sub-problem is solved in sequence: the IoT node scheduling problem, the beamforming vector problem for UAV launch, and the UAV trajectory optimization problem. The approximate optimal solution for the variables in each sub-problem is then obtained and used in the solution of the next sub-problem, ultimately achieving the goal of joint solution.

[0205] For example, in the m-th iteration, when solving for the IoT node scheduling C, W and L are first given. Here, "given" refers to the pre-setting of the W value for all time slots during simulation. L is the position on the initial circular trajectory with radius r set to r = 250m, where the circumference is divided into N time slots, which is the initial trajectory of the UAV (given trajectory L). The C obtained after solving is... (m) ;

[0206] Similarly, when solving for the beamforming vector W emitted by the UAV, given C (m) L, where C is replaced with the solution C obtained above. (m) L remains the initial trajectory (since the trajectory subproblem has not yet been solved, it will not be replaced for now), and the solved W... (m) ;

[0207] When solving for the UAV trajectory L, given W (m) C (m) The solution is obtained, and C is finally obtained. (m) W (m) L (m) These three values ​​are the solutions for the current round. Then the next round begins, still following the solution order of C, W, L, until the maximum number of iterations is reached, eventually approaching the global optimum.

[0208] In step S3, the original problem is first divided into an IoT node scheduling optimization problem, a beamforming vector optimization problem for UAV launch, and a non-convex problem for UAV trajectory optimization. The non-convex problem is then transformed into a convex problem that can be directly solved using the CVX toolbox. Finally, the global optimal solution of the original optimization problem is approximated by an alternating iterative optimization algorithm.

[0209] The optimal solution consists of the following three decision variables:

[0210] (1) IoT node scheduling: Which IoT node should be selected for communication in each time slot;

[0211] (2) Beamforming vector of UAV: ​​Design beamforming vector of UAV for each time slot to ensure that the corresponding IoT node can receive the strongest signal;

[0212] (3) Drone trajectory: The flight path of the drone in each time slot to ensure the optimal communication rate.

[0213] These three decision variables are iteratively optimized under constraints to maximize the average communication rate of the drone. That is, considering all constraints, through multiple iterations and adjustments, the average communication rate between the drone and the IoT node is ultimately maximized.

[0214] The purpose of step S3 is to decompose the complex non-convex optimization problem into three solvable convex optimization subproblems and use an alternating iterative optimization strategy to gradually approach the global optimal solution, and finally determine the IoT node scheduling of the UAV, the beamforming vector of the UAV, and the flight trajectory of the UAV, so as to maximize the average communication rate between the UAV and the IoT node under all constraints.

[0215] Figure 3 In the network model shown, G is set... t =15dBi, G b =10dBi, σ k 2 = -110dBm, M x =M y =4, K=10, H=90m, T=75s, δ t =0.5s, P max =10w, v max = 40m / s.

[0216] σ k 2 = -110dBm converted to watts is 10 -14 W is the noise power value.

[0217] The center of the area where the UAV performs its mission (0, 0) is used as the center of the UAV's initial circular trajectory, with a radius of r = 250m. This ensures that when the UAV initiates its communication mission, its initial trajectory allows it to connect with surrounding target IoT nodes in a relatively balanced manner. During actual flight, the UAV further dynamically adjusts its flight trajectory based on real-time channel status information and the communication needs of the IoT nodes, effectively assisting in the communication of the IoT nodes.

[0218] Figure 4The optimized drone trajectory presented clearly demonstrates this adaptive adjustment process. Compared to the initial trajectory, the optimized trajectory shows that the drone actively moves closer to the IoT node. This is because, with the goal of maximizing communication speed, the reduced distance between the drone and the target IoT node means a significant decrease in path loss during signal propagation, allowing the target IoT node to obtain a higher signal strength at its receiving end.

[0219] The optimization objective is to maximize the average communication rate between the UAV and the IoT node, which is to maximize the sum of the communication rates across all time slots. According to formulas (2) and (3), the closer the UAV is to the IoT node, the higher the channel gain h. k,com (l(n),l k The larger the value, the higher the communication rate. The larger the value, the better. Therefore, after algorithm optimization, the drone will serve as close as possible to the IoT nodes. When the set drone flight time and speed are sufficient, the drone will serve directly above each IoT node, and then continue flying forward while serving. Figure 3 It is a two-dimensional planar diagram of the drone's flight trajectory. From the two-dimensional plane, the drone's flight trajectory is connected to multiple Internet of Things (IoT) nodes. In three-dimensional space, the drone itself has a certain altitude, and it is actually flying above the IoT nodes along this trajectory.

[0220] Figure 5 The demonstration shows the scheduling of IoT nodes by a UAV in different time slots. Starting from its initial position, the UAV prioritizes serving the nearest IoT node while ensuring that every IoT node is served. From a channel quality perspective, the UAV prioritizes scheduling IoT nodes with better channel conditions. This is because in a good channel environment, limited communication resources can be utilized more efficiently, increasing the data transmission rate per unit bandwidth. From the perspective of IoT node service quality assurance, the scheduling algorithm avoids over-serving some IoT nodes, leading to a shortage of communication resources for other IoT nodes. Through a tightly coupled scheduling and trajectory optimization mechanism, the UAV can make the most reasonable IoT node scheduling decisions in each time slot under complex and changing communication environments, thereby effectively improving the communication efficiency of the entire UAV-assisted communication network and providing continuous, stable, and efficient communication services to IoT nodes. Figure 4 The horizontal axis represents the number of time slots N in the drone's flight cycle, and the vertical axis represents the sequence number of the target IoT node, from 1 to 10. Figure 4 This explains how the drone schedules the corresponding IoT nodes during its flight time slots.

[0221] Figure 6 and Figure 7The diagrams show both planar beamforming and three-dimensional beamforming schematics of the signal-to-noise ratio (SNR) at the IoT node receiver in randomly selected time slots. The narrower communication beam is concentrated at the IoT node scheduled in the current time slot, indicating that by optimizing the UAV's communication beamforming vector, its uniform planar antenna array can precisely control the phase and amplitude of the transmitted signal. During communication with a specific IoT node in the current time slot, based on the IoT node's location information and channel characteristics, the transmitted signal energy is highly concentrated in the direction of that IoT node, resulting in a significant improvement in the IoT node's SNR. When beamforming improves the SNR, according to Shannon's theorem, the communication rate will significantly increase per unit bandwidth.

[0222] Figure 6 In the simulation, a 1000m*1000m area was used to locate 10 target IoT nodes. The innovation of this invention is that the UAV is combined with beamforming technology to assist communication, so the advantages of beamforming technology need to be demonstrated through simulation results. This invention optimizes the beamforming vector emitted by the UAV, which is a complex matrix containing amplitude and phase. By optimizing (adjusting the amplitude and phase of the matrix), the signals emitted by the antenna array on the UAV are all directed to an IoT node, thereby enhancing the communication rate in combination with formula (3), which meets the optimization goal. In each time slot, the UAV will only provide services to one IoT node. Therefore, in each time slot, the beam will be concentrated at a corresponding IoT node, meaning that only that location has a communication rate. Therefore, a time slot is randomly selected for observation ( Figure 6 and Figure 7 This corresponds to time slot 55, combined with Figure 4 As can be seen, the drone is currently serving the fifth target IoT node. Therefore, this is represented in the diagram by a narrow beam concentrated at the location of the fifth IoT node. A narrow beam indicates high energy. The beam height value corresponds to the signal-to-noise ratio.

[0223] Figure 7 Two-dimensional graph Figure 6 The three-dimensional perspective, where the x and y axes represent the simulation area (meters). The communication rate in formula (3) To display the beamform linearly (if communication rate were used as the ordinate, the logarithm would result in multiple beams, making it difficult to visually identify which IoT node had the highest communication rate in a given time slot), signal-to-noise ratio (SNR) is used as the ordinate. A higher SNR corresponds to a higher communication rate. In summary, using SNR as the ordinate provides a more intuitive graph while still satisfying the optimization requirements.

[0224] Figure 8This paper compares the communication rates of a preset trajectory scheme and an optimized trajectory scheme. As the maximum transmit power of the UAV increases, the communication rates of both schemes improve, with the optimized trajectory scheme consistently outperforming the preset trajectory scheme. This is because the optimized trajectory scheme does not solely rely on increased transmit power to improve communication. The UAV dynamically adjusts its flight path based on factors such as the distribution of IoT nodes and channel conditions, allowing it to approach IoT nodes with better signal transmission quality, thereby reducing path loss and interference during signal propagation. At the same transmit power, the optimized trajectory scheme can effectively transmit more signal energy to the receiver, improving both communication quality and speed. The preset trajectory scheme, lacking this flexibility, is relatively limited in terms of communication rate improvement. Furthermore, the figure also shows that the number of antenna elements affects the system's communication rate; a higher number of antenna elements significantly increases beamforming gain, resulting in a higher achievable communication rate.

[0225] Preset trajectory scheme refers to a fixed drone following a certain path. Figure 4 The initial trajectory flight scheme does not optimize the UAV's flight trajectory. Obviously, compared with the trajectory optimization scheme in this invention (adjusting the flight path to be closer to the IoT node), the unoptimized UAV flight trajectory is farther from the IoT node, and therefore the communication rate is lower at the same transmission power. Increasing the maximum transmission power of the UAV improves the communication rate of both schemes. Combining formulas (6) and (7), this is because the UAV's beamforming vector w c (n) With greater transmission power, a larger amplitude can be achieved, thus improving the communication rate between the drone and the IoT node, as shown in formula (3). Similarly, the more antennas there are, the stronger the enhanced signal can be generated at a specific IoT node, and the greater the communication rate between the drone and the IoT node.

[0226] The IoT node receives the directional beam signal emitted by the drone, converts the received analog signal into a digital signal, and extracts the transmitted data. It decodes the received data to recover the original information. Depending on the actual needs, the IoT node sends acknowledgment signals or feedback information to the drone, as well as requests, to achieve two-way communication. Through this two-way communication mechanism, the drone and the IoT node can achieve effective data exchange, supporting various applications such as remote monitoring, emergency rescue, data collection, and distribution.

[0227] In summary, the agricultural monitoring system for communication between a drone and a ground-based IoT node according to an embodiment of the present invention has the following beneficial effects:

[0228] 1. This invention enables real-time and efficient rapid collection of farmland monitoring data through direct communication between drones and ground-based IoT nodes, which greatly improves the efficiency and real-time performance of data collection compared to traditional manual collection or wired transmission methods.

[0229] 2. This invention maximizes the average communication rate between the UAV and multiple IoT nodes by constructing an optimization model, effectively allocates communication resources, and improves the operating efficiency of the entire agricultural monitoring system;

[0230] 3. The command console module of this invention can formulate more precise crop management strategies based on real-time collected multi-dimensional data (such as temperature, humidity, light, soil moisture, etc.), including irrigation, fertilization and pest and disease control, thereby improving crop yield and quality.

[0231] 4. The UAV of this invention is equipped with a uniform planar antenna array, which can adjust the flight path and communication strategy according to real-time monitoring data, making the system more flexible and adaptable to different farmland environments and crop growth conditions.

[0232] 5. The user terminal module of this invention allows agricultural experts to adjust automatically generated management strategies, making the system not only automated but also intelligent. Combined with artificial intelligence and cloud computing technologies, it provides a powerful decision support platform for smart agriculture.

[0233] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. Agricultural monitoring method for unmanned aerial vehicle and ground Internet of Things node communication, applied to an unmanned aerial vehicle and ground Internet of Things node communication agricultural monitoring system, characterized in that, The agricultural monitoring system comprises: An unmanned aerial vehicle module deployed on an unmanned aerial vehicle body, configured to perform a multi-antenna unmanned aerial vehicle and ground Internet of Things node communication method, communicate with Internet of Things nodes in farmland, obtain monitoring data sent by the Internet of Things node module, and send monitoring data of multiple Internet of Things nodes to a command center module; the unmanned aerial vehicle is equipped with a uniform planar antenna array, and the antenna array adjusts the phase and amplitude of the transmission signal of each antenna unit by using a beamforming vector, thereby concentrating the transmission signal energy to point to a target Internet of Things node; The command center module is configured to issue a patrol instruction to the unmanned aerial vehicle and develop a management strategy for crop irrigation, fertilization, pest and disease identification and control based on the monitoring data; The Internet of Things node module is configured to obtain monitoring data in farmland and send the monitoring data to the unmanned aerial vehicle module during a time slot when the unmanned aerial vehicle flies over; The user terminal module is configured to be communicatively connected to the command center module, obtain the management strategy, and manually adjust the strategy; The multi-antenna unmanned aerial vehicle and ground Internet of Things node communication method comprises: Step S1, calculating a turning vector of the unmanned aerial vehicle to each Internet of Things node, calculating a channel gain of the unmanned aerial vehicle to each Internet of Things node module based on the turning vector, and calculating a communication rate between the unmanned aerial vehicle and each Internet of Things node module based on the channel gain; Step S2, constructing an unmanned aerial vehicle-assisted Internet of Things node communication optimization model with an optimization objective function of maximizing the average communication rate of the unmanned aerial vehicle and multiple Internet of Things node modules, while satisfying Internet of Things node scheduling constraints, unmanned aerial vehicle communication performance constraints, unmanned aerial vehicle transmission power constraints, and unmanned aerial vehicle flight trajectory constraints; Step S3, solving the unmanned aerial vehicle-assisted Internet of Things node communication optimization model to obtain an optimal solution for communication between the unmanned aerial vehicle and multiple Internet of Things nodes; using the optimal solution for communication to perform precise communication with the Internet of Things node module; based on the optimal solution for communication, the average communication rate of the unmanned aerial vehicle and each Internet of Things node is maximized; the sum of the communication rates of all time slots of the unmanned aerial vehicle is maximized; the closer the distance between the unmanned aerial vehicle and the Internet of Things node, the greater the channel gain and the communication rate.

2. The method of claim 1, wherein, The Internet of Things node module comprises: A temperature and humidity sensor configured to monitor temperature and humidity data of air in farmland; An illumination sensor configured to monitor illumination intensity data of farmland; A soil moisture sensor configured to monitor soil moisture data; A spectrum sensor configured to monitor plant spectral reflectance characteristic data; A pH sensor configured to monitor data of soil nutrient deficiency or harmful chemical substances affecting the health of crops; An image sensor configured to capture images of crops in the field; A communication module configured to transmit monitoring data obtained by each sensor to the unmanned aerial vehicle module; The monitoring data comprises temperature and humidity data of air in farmland, illumination intensity data, soil moisture data, soil nutrient deficiency or harmful chemical substance data, and crop image data.

3. The method of claim 1, wherein, The unmanned aerial vehicle module comprises a uniform planar antenna array, and performs circumferential flight in the air above farmland according to a predetermined flight path based on the patrol instruction of the command center module. The communication module of the Internet of Things node module includes a single antenna device, and the Internet of Things node establishes a wireless communication connection with the unmanned aerial vehicle when the unmanned aerial vehicle flies over. Each Internet of Things node module receives a directional beam signal transmitted by the unmanned aerial vehicle module in a time slot, and then responds and transmits the monitoring data.

4. The method of claim 1, wherein, The UAV is equipped with a uniform planar antenna array deployed parallel to the ground. The total number of antennas in the uniform planar antenna array and the distance between antenna elements are divided along the x-axis and y-axis of a three-dimensional Cartesian coordinate system, respectively, to obtain the total number of antennas M = M x ×M y The distance d between the antenna and the coordinate system axis x =d y =λ / 2; where M x , M y are the number of antennas arranged along the x-axis and y-axis respectively; d x , d y are the spacing of the antennas along the x-axis and y-axis respectively; and λ is the carrier wavelength. Based on M x , M y and d x , d y , the steering vector a(l(n), l k ) of the unmanned aerial vehicle to the kth Internet of Things node module is calculated as follows: wherein k = 1, 2, …, K, K is the number of Internet of Things nodes; l(n) is the position of the unmanned aerial vehicle in the nth time slot, l k is the position of the kth Internet of Things node, θ(l(n), l k ), Φ(l(n), l k ) are the AoD elevation angle and the AoD azimuth angle, respectively, of the unmanned aerial vehicle when transmitting a signal to the kth Internet of Things node.

5. The method of claim 4, wherein, based on a (l(n), l k ) to calculate the channel gain h k,com (l(n), l k ) of the unmanned aerial vehicle to the kth Internet of Things node module, calculated as follows: Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t For the antenna gain of the UAV transmitter, G b The antenna gain received by the IoT node module, d(l(n),l k )=||l(n)-l k || represents the Euclidean distance between the drone and the k-th IoT node; At unit bandwidth, based on h k,com (l(n),l k ) the communication rate between the unmanned aerial vehicle and the kth Internet of Things node module is calculated is calculated as follows: wherein c k (n) is the IoT node scheduling of the UAV at the nth time slot, Hn is the communication beamforming vector of the UAV at the nth time slot, is a noise subject to a Gaussian random distribution, and H is a channel gain h k,com (l(n), l k ) is the conjugate transpose of Hn; c k (n) is a binary discrete variable; When the UAV transmits a communication beam to the kth Internet of Things node to perform a communication task at time slot n, c k (n) = 1, otherwise c k (n) = 0.

6. The method of claim 1, wherein, The unmanned aerial vehicle-assisted Internet of Things node communication optimization model comprises the following steps: wherein, is the communication rate between the UAV and the kth IoT node; K is the number of IoT nodes; n = 1, 2, … N, N is the number of time slots in the UAV flight period; C, W, L are the variables to be optimized, which are the IoT node scheduling, the beamforming vector of the UAV transmission and the UAV trajectory respectively; C1and C2represent the IoT node scheduling constraints, C3represents the UAV communication performance constraint, is the preset communication rate threshold between the UAV and the base station, C4represents the UAV transmission power constraint, C5and C6represent the UAV flight trajectory constraints, P max is the maximum transmission power of the UAV, D max is the maximum flight distance of the UAV in each time slot, l I is the starting position of the UAV.

7. The method of claim 6, wherein, The step S3 comprises the following steps: The unmanned aerial vehicle-assisted Internet of Things node communication optimization model comprises three non-linear coupled variables, namely, Internet of Things node scheduling, beamforming vector transmitted by the unmanned aerial vehicle, and unmanned aerial vehicle trajectory. The model is a non-convex optimization problem. The non-convex optimization problem is decomposed into three convex optimization sub-problems, namely, Internet of Things node scheduling, beamforming vector transmitted by the unmanned aerial vehicle, and unmanned aerial vehicle trajectory. The three convex optimization sub-problems are alternately iteratively optimized to gradually approach a global optimal solution. When the objective function converges, the global optimal solution obtained is the optimal communication solution of the unmanned aerial vehicle and the multiple Internet of Things nodes. The global optimal solution comprises Internet of Things node scheduling, beamforming vector transmitted by the unmanned aerial vehicle, and unmanned aerial vehicle trajectory. wherein the non-convex optimization problem includes internet of things node scheduling A drone launched beamforming vector A drone trajectory Three nonlinearly coupled variables.

8. The method of claim 7, wherein, The three convex optimization sub-problems of Internet of Things node scheduling, beamforming vector transmitted by the unmanned aerial vehicle, and unmanned aerial vehicle trajectory are alternately optimized to gradually approach a global optimal solution, comprising the following steps: In the mth round of iteration, in solving the Internet of Things node scheduling, given the preset unmanned aerial vehicle transmitted beamforming vector and initial unmanned aerial vehicle trajectory, the unmanned aerial vehicle assisted Internet of Things node communication optimization model is solved to obtain the Internet of Things node scheduling C (m) ; In solving the beamforming vector launched by the unmanned aerial vehicle, based on C (m) , the initial unmanned aerial vehicle trajectory, the beamforming vector W (m) obtained by solving the unmanned aerial vehicle assisted Internet of Things node communication optimization model In solving the UAV trajectory, based on W (m) , C (m) , solving the UAV auxiliary Internet of Things node communication optimization model L (m) ; The three convex optimization sub-problems of Internet of Things node scheduling, beamforming vector transmitted by the unmanned aerial vehicle, and unmanned aerial vehicle trajectory are alternately optimized to gradually approach a global optimal solution, comprising the following steps:

9. The method according to any one of claims 2 to 8, characterized in that, The command post module determines the crop type, crop growth height, and pest and disease conditions based on the field crop image. Based on the temperature and humidity data of the farmland air, the soil humidity data, and the crop type, a crop irrigation management strategy is generated to determine whether to open or close the farmland irrigation system. Based on the soil nutrient deficiency or harmful chemical substance data and the crop type, a crop fertilization management strategy is generated. Based on the plant spectral reflectance characteristic data, the crop growth and health status is determined by analyzing the crop spectral reflectance characteristics. Based on the crop image, the pest and disease conditions of the crop are determined to generate a crop pest and disease identification and control management strategy.

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