Agricultural monitoring system for communication between unmanned aerial vehicle and ground Internet of Things node

Abstract

The invention relates to an agricultural monitoring system for communication between an unmanned aerial vehicle and a ground Internet of Things node, belongs to the field of unmanned aerial vehicle auxiliary communication, and solves the problem that monitoring data acquired by the Internet of Things node cannot be efficiently acquired in real time in an existing method. Comprising an unmanned aerial vehicle module which is deployed in an unmanned aerial vehicle body and is used for executing a communication method between the multi-antenna unmanned aerial vehicle and a ground Internet of Things node, communicating with Internet of Things nodes in a farmland, acquiring monitoring data sent by an Internet of Things node module and sending the monitoring data of the Internet of Things nodes to a command set module; the command set module is used for issuing an inspection instruction to the unmanned aerial vehicle and formulating a management strategy of crop irrigation, fertilization, and pest and disease damage identification and prevention and control based on the monitoring data; the Internet of Things node module is used for acquiring monitoring data in a farmland and sending the monitoring data to the unmanned aerial vehicle module in a time slot where an unmanned aerial vehicle flies; and the user terminal module is used for being in communication connection with the command set module, acquiring a management strategy and manually adjusting the strategy. The monitoring data can be efficiently acquired.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things and smart agricultural monitoring technology, and in particular to an agricultural monitoring system for communicating between a drone and a ground Internet of Things node. Background Art

[0002] Smart agriculture is the smart economy in agriculture. With the development of the times and the advancement of science and technology, smart agriculture represents the in-depth 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 when planting crops, but also need to detect and analyze the growth of crops in a timely manner, as well as determine the management strategies for irrigation, fertilization, identification and prevention of pests and diseases, and realize automatic irrigation, fertilization and pest control; the fields involved in smart agriculture include precision agriculture, intelligent equipment, Internet of Things technology, big data analysis, cloud computing, artificial intelligence, etc., aiming to promote the transformation of traditional agriculture to modernization and technology, and realize the efficient and sustainable development of the agricultural industry. The development of smart agriculture is an inevitable trend of agricultural modernization. Through the widespread application of advanced technologies such as the Internet of Things, big data, and artificial intelligence, smart agriculture can realize the precision, intelligence, and efficiency of agricultural production management. How to efficiently and real-time obtain the monitoring data obtained by the field Internet of Things nodes, and timely irrigate, fertilize and pest control crops is the challenge currently faced by smart agriculture. Summary of the invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide an agricultural monitoring system for communicating between a drone and a ground IoT node, so as to solve the technical problem that the existing method cannot efficiently and real-time obtain the monitoring data obtained by the field IoT node.

[0005] The purpose of the present invention is mainly achieved through the following technical solutions:

[0006] The present invention provides an agricultural monitoring system for communicating between a drone and a ground IoT node, comprising the following modules:

[0007] The drone module is deployed on the drone body and is used to execute the communication method between the multi-antenna drone and the ground IoT node, communicate with the IoT nodes in the farmland, obtain the monitoring data sent by the IoT node module, and send the monitoring data of multiple IoT nodes to the command console module;

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

[0009] The IoT node module is used to obtain monitoring data in the farmland and send it to the drone module during the time slot when the drone flies over;

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

[0011] Furthermore, the Internet of Things node module includes:

[0012] Temperature and humidity sensors are used to monitor the temperature and humidity data of the air in the 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 sensor, used to monitor plant spectral reflectance characteristic data;

[0016] pH sensors to monitor soil nutrient deficiencies or harmful chemicals affecting crop health;

[0017] Image sensors for capturing images of crops in the field;

[0018] A communication module, used to transmit the monitoring data obtained by each sensor to the drone module;

[0019] The monitoring data include 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] Further, the drone module includes a uniform planar antenna array, and performs circular flight over the farmland according to a predetermined flight path based on the inspection instructions of the control station module;

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

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

[0023] Step S1, calculating the steering vector from the drone to each IoT node; calculating the channel gain from the drone to each IoT node module based on the steering vector; calculating the communication rate between the drone and each IoT node module based on the channel gain;

[0024] Step S2, constructing a UAV-assisted IoT node communication optimization model that takes maximizing the average communication rate between the UAV and multiple IoT node modules as the optimization objective function, while satisfying the IoT node scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints;

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

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

[0027] Among them, M x 、M y are the number of antennas arranged on 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; λ is the carrier wavelength;

[0028] Based on M x 、M y and d x ,d y Calculate the steering vector α(l(n), l from the drone to the kth IoT node module k ), calculated 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, l k is the position of the kth IoT node, θ(l(n), l k )、Φ(l(n),l k ) are the AoD elevation angle and AoD azimuth angle when the UAV transmits the signal to the kth IoT node.

[0031] Furthermore, based on α(l(n), l k ) Calculate the channel gain h from the UAV to the kth IoT node module k，com (l(n),l k ), calculated as follows:

[0032]

[0033] Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t is the antenna gain of the UAV transmitter, G b is 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 kth IoT node;

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

[0035]

[0036] Among them, c k (n) is the IoT node scheduling of the drone in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise that obeys Gaussian random distribution, H is the channel gain h k，com (l(n),l k )'s conjugate transpose; c k (n) is a binary discrete variable;

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

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

[0039]

[0040] in, 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 cycle; C, W, L are the IoT node scheduling to be optimized, the beamforming vector emitted by the UAV, and the trajectory variable of the UAV, respectively;

[0041]

[0042] C1 and C2 represent IoT node scheduling constraints, C3 represents UAV communication performance constraints, is the preset communication rate threshold between the UAV and the base station, C4 represents the UAV transmission power constraint, C5 and C6 represent the UAV flight trajectory constraints, and 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 drone.

[0043] Furthermore, the step S3 comprises:

[0044] The UAV-assisted IoT node communication optimization model includes three nonlinear coupling variables: IoT node scheduling, beamforming vectors emitted by the UAV, and UAV trajectory. The model is solved as a non-convex optimization problem.

[0045] Decomposing the non-convex optimization problem into three convex optimization sub-problems of IoT node scheduling, beamforming vectors emitted by drones, and drone trajectories;

[0046] Alternately iterate and optimize three convex optimization sub-problems to gradually approach the global optimal solution;

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

[0048] The non-convex optimization problem includes IoT node scheduling Beamforming vectors transmitted by drones Drone tracks Three nonlinear coupled variables.

[0049] Furthermore, the three convex optimization sub-problems of IoT node scheduling, UAV emission beamforming vectors, and UAV trajectory are alternately optimized to gradually approach the global optimal solution, including:

[0050] In the mth iteration, when solving the IoT node scheduling, given the preset UAV-transmitted beamforming vector and the initial UAV trajectory, the UAV-assisted IoT node communication optimization model is solved to obtain the IoT node scheduling C (m) ;

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

[0052] When solving the trajectory of the UAV, based on W (m) , C (m), solve the UAV-assisted IoT node communication optimization model to obtain L (m) ;

[0053] The solution is solved in an alternating cycle until the optimization objective function converges, and the current round of IoT node scheduling, the beamforming vector emitted by the drone, and the drone trajectory are the global optimal solution.

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

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

[0056] generating a crop fertilization management strategy based on the soil nutrient deficiency or harmful chemical substance data and the crop types;

[0057] Based on the plant spectral reflectance characteristic data, analyzing the spectral reflectance characteristics of crops to determine the growth health of crops;

[0058] Based on the crop images, the crop disease and insect pest conditions are determined, and crop disease and insect pest identification and prevention 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. The present invention realizes the real-time and efficient rapid collection of farmland monitoring data through direct communication between drones and ground IoT nodes. Compared with traditional manual collection or wired transmission methods, the efficiency and real-time performance of data collection are greatly improved;

[0061] 2. The present invention maximizes the average communication rate between the drone 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 the present invention can formulate more accurate crop management strategies based on multi-dimensional data collected in real time (such as temperature and humidity, light, soil moisture, etc.), including irrigation, fertilization and pest control, thereby improving the yield and quality of crops;

[0063] 4. The UAV of the present 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 able to adapt to different farmland environments and crop growth conditions;

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

[0065] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

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

[0068] Figure 2 This is a flow chart of an agricultural monitoring method for communication between a drone and a ground IoT node in an embodiment of the present invention;

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

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

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

[0072] Figure 6 A schematic diagram of a plane beam of the signal-to-noise ratio at the receiving point of the Internet of Things node when the drone is in a randomly selected time slot in an embodiment of the present invention;

[0073] Figure 7 A three-dimensional beam schematic diagram of the signal-to-noise ratio at the receiving point of the Internet of Things node of the drone in a randomly selected time slot in an embodiment of the present invention;

[0074] Figure 8 It is a schematic diagram comparing the average communication rate of the system of the present invention and the preset trajectory solution under different maximum transmission powers of the UAV in the embodiment of the present invention. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a 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 used to limit the scope of the present invention.

[0076] In view of the defects of the above-mentioned existing technologies, this technical solution proposes an agricultural monitoring system for drones to communicate with ground IoT nodes, aiming to improve communication efficiency and data collection accuracy, reduce system costs, and enhance system stability and coverage through multi-antenna drone-assisted multi-IoT node communication methods. At the same time, by integrating advanced sensor technology and artificial intelligence decision-making capabilities, the monitoring capabilities of farmland environment and pests and diseases are improved, and more accurate crop management strategies are achieved to meet future food needs and achieve green agricultural development.

[0077] A specific embodiment of the present invention, as Figure 1 As shown, an agricultural monitoring system for communicating between a drone and a ground IoT node is disclosed, including the following modules:

[0078] The drone module is deployed on the drone body and is used to execute the communication method between the multi-antenna drone and the ground IoT node, communicate with the IoT nodes in the farmland, obtain the monitoring data sent by the IoT node module, and send the monitoring data of multiple IoT nodes to the command console module;

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

[0080] The IoT node module is used to obtain monitoring data in the farmland and send it to the drone module during the time slot when the drone flies over;

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

[0082] The present invention realizes the efficient collection, intelligent analysis and precise management of farmland monitoring data through the collaborative work of drones and ground IoT nodes, so as to optimize the irrigation, fertilization and pest control strategies of crops.

[0083] The Internet of Things node module comprises:

[0084] Temperature and humidity sensors are used to monitor the temperature and humidity data of the air in the 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 sensor, used to monitor plant spectral reflectance characteristic data;

[0088] pH sensors to monitor soil nutrient deficiencies or harmful chemicals affecting crop health;

[0089] Image sensors for capturing images of crops in the field;

[0090] A communication module, used to transmit the monitoring data obtained by each sensor to the drone module;

[0091] The monitoring data include 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 measure the temperature and relative humidity of the air at the same time, providing basic climate data for agricultural environmental monitoring;

[0094] The light sensor is a BH1750 light intensity sensor, which is used to measure the light intensity received by the 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 to irrigate;

[0096] Spectral sensors are multispectral sensors or hyperspectral sensors that 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 pH of the soil, which is very important for determining the soil nutrient status and adjusting fertilization strategies;

[0098] Image sensors are high-definition cameras or multispectral cameras that can capture images of crops in the field for monitoring crop growth and identifying pests and diseases;

[0099] These sensors and communication modules together constitute the IoT node modules, which work together to provide real-time and accurate field data for smart agriculture and support the implementation of smart agriculture.

[0100] The command console module determines the type of crops, the growth height of crops and the condition of pests and diseases based on the field crop images;

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

[0102] generating a crop fertilization management strategy based on the soil nutrient deficiency or harmful chemical substance data and the crop types;

[0103] Based on the plant spectral reflectance characteristic data, analyzing the spectral reflectance characteristics of crops to determine the growth health of crops;

[0104] Based on the crop images, the crop disease and insect pest conditions are determined, and crop disease and insect pest identification and prevention and control management strategies are generated.

[0105] For the podium module, specifically.

[0106] (1) Determination of crop types, growth heights, and pest and disease conditions:

[0107] The command center module uses images of crops in the field taken by image sensors combined with deep learning algorithms to automatically identify and classify crop types. Deep learning algorithms can improve recognition accuracy by extracting key features in images, especially in complex backgrounds.

[0108] By analyzing the size and morphology of crops in the image, the command console 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 center module comprehensively analyzes and generates irrigation management strategies based on the air temperature and humidity data provided by the temperature and humidity sensor, the soil moisture data from the soil moisture sensor, and the type of crops.

[0112] The strategy includes determining the frequency and amount of irrigation, as well as whether to turn on or off the farm irrigation system to ensure that crops get the right amount of water while avoiding wasting water resources.

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

[0114] The command center module generates personalized fertilization management strategies based on soil nutrient deficiency or harmful chemical data monitored by the pH sensor and the type of crop.

[0115] This strategy refers to the principles of the NE system (Nutrient Expert), taking into account the basic soil fertility, residual nutrients of previous season's crops, nutrients brought in by returning straw to the fields, as well as the crop rotation system and the application of organic fertilizers, and proposes a fertilization plan that complies with the 4R nutrient management strategy (best fertilizer variety, best dosage, best application time and best application location).

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

[0117] The command center module evaluates the nutritional status and growth health of crops by analyzing the plant spectral reflectance characteristic data provided by the spectral sensor. This analysis helps to detect nutrient deficiency or excess problems in a timely manner and adjust fertilization and irrigation plans accordingly.

[0118] (5) Generation of crop pest and disease identification and control management strategies:

[0119] Combining image field crop image data and deep learning algorithms, the command center module is able to identify pests and diseases and generate a control strategy. The strategy includes selecting appropriate biological control methods or chemical pesticides, and determining the best time and method of application to minimize environmental impact and improve control effectiveness.

[0120] The above description elaborates on how the command console module uses 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 technical problems. Beamforming technology is based on antenna arrays. By accurately adjusting the phase and amplitude of the signal transmitted by each antenna unit, the transmitted electromagnetic waves can form a specific radiation pattern in space, concentrating the signal energy to the target IoT node.

[0122] The drone module includes a uniform planar antenna array, and performs circular flight over the farmland according to a predetermined flight path based on the inspection instructions of the command console module;

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

[0124] In the UAV-assisted IoT node communication scenario, beamforming technology can significantly enhance the performance of the communication link between the UAV and the IoT node. By accurately aiming the narrower communication beam at the target IoT node, the diffusion and waste of the signal in space can be effectively reduced, and the strength of the signal reaching the IoT node can be increased, thereby improving the signal-to-noise ratio and reliability, solving the problems of low communication quality and efficiency of the above-mentioned single antenna of the UAV, and providing a better solution for UAV-assisted IoT node communication.

[0125] The present invention builds a drone-assisted communication network based on the growing demand of IoT nodes for communication quality and rate, equips drones with multi-antenna arrays, and uses beamforming technology to enhance signal strength and anti-interference. By jointly optimizing the scheduling of IoT nodes, the beamforming vectors emitted by drones, and the flight trajectory of drones, accurate coverage of the communication network is achieved, thereby effectively improving the average communication rate to meet the expectations of IoT nodes for communication quality in various complex scenarios, providing strong support for the in-depth application of drone communication technology.

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

[0127] Step S1, calculating the steering vector from the drone to each IoT node; calculating the channel gain from the drone to each IoT node module based on the steering vector; calculating the communication rate between the drone and each IoT node module based on the channel gain;

[0128] Step S2, constructing a UAV-assisted IoT node communication optimization model that takes maximizing the average communication rate between the UAV and multiple IoT node modules as the optimization objective function, while satisfying the IoT node scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints;

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

[0130] Step S1, specifically.

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

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

[0133] The multi-antenna UAV-assisted multi-IoT node communication system includes a UAV and multiple IoT nodes distributed in different locations in the disaster area. The UAV is equipped with a uniform planar antenna array (UPA) and serves as an aerial information collection and communication platform.

[0134] When performing a circular flight mission, the drone transmits a communication beam to the single-antenna IoT node on the ground at a constant altitude, thereby collecting and transmitting information from the IoT node. To ensure that the drone's position in each time slot can be considered relatively stationary, 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, which is deployed parallel to the ground. The total number of antennas in the uniform planar antenna array and the distance between antenna units are divided along the x-axis and y-axis of the three-dimensional Cartesian coordinate system, and the total number of antenna arrays M = M x ×M y and the spacing d between the antennas along the coordinate system axis x =d y =λ / 2;

[0136] Among them, M x 、M y are the number of antennas arranged on 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; λ is the carrier wavelength;

[0137] (2) Calculate the steering vector α(l(n),l k ).

[0138] Based on M x 、M y and d x ,d y Calculate the steering vector α(l(n),l from the drone to the kth IoT node module k ), calculated 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, l k is the position of the kth IoT node, θ(l(n),l k )、Φ(l(n),l k ) are the AoD elevation angle and AoD azimuth angle when the UAV transmits the signal to the kth IoT node.

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

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

[0143] Considering that the downlink communication link between the UAV 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 kth IoT node module k,com (l(n),l k ), calculated as follows:

[0145]

[0146] Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t is the antenna gain of the UAV transmitter, G b is 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 kth IoT node;

[0147] (4) Calculating 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 k) Calculate the communication rate between the drone and the kth IoT node module The calculation is as follows:

[0149]

[0150] Among them, c k (n) is the IoT node scheduling of the drone in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise that obeys Gaussian random distribution, H is the channel gain h k,com (l(n),l k )'s conjugate transpose; c k (n) is a binary discrete variable;

[0151] When the UAV transmits a communication beam in time slot n to perform a communication task to 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 drones to IoT nodes.

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

[0154] The motion model of the drone includes kinematic constraints and flight position constraints. Considering the physical characteristics of the drone and the flight safety requirements, the flight speed of the drone will always be controlled at the preset maximum speed v during its flight. max the following.

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

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

[0157]

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

[0159] When the drone performs IoT node communication services, in order to ensure that its flight trajectory in a specific area has clear start and end settings, the following flight constraints must be followed:

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

[0161] Among them, l I represents the starting position of the UAV, and N is the total number of time slots in the UAV flight cycle.

[0162] The physical meaning of formula (5) is that the UAV flies back to the starting position at the end of the flight mission.

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

[0164]

[0165] in, is a set containing all time slot numbers.

[0166] Since the average transmission power of the drone is limited by the maximum power, the following constraints are met:

[0167] P(n)≤P max Formula (7)

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

[0169] The function of step S1 is to establish a multi-antenna UAV-assisted multi-IoT node communication system between a multi-antenna UAV and multiple IoT nodes, and calculate the key parameters of the communication link, including the steering 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 Provide basic support for subsequent communication optimization.

[0170] Step S2, specifically.

[0171] Design an optimization problem to maximize the average communication rate of the UAV while satisfying the IoT node scheduling constraints, transmission power constraints, minimum communication rate constraints of the IoT nodes, and kinematic constraints of the UAV flight;

[0172] In order to maximize the average communication rate of the UAV, the UAV-assisted IoT node communication optimization model is as follows:

[0173]

[0174] in, 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 cycle; C, W, L are the IoT node scheduling to be optimized, the beamforming vector emitted by the UAV, and the trajectory variable of the UAV, respectively;

[0175]

[0176] C1 and C2 represent IoT node scheduling constraints, C3 represents UAV communication performance constraints, is the preset communication rate threshold between the UAV and the base station, C4 represents the UAV transmission power constraint, C5 and C6 represent the UAV flight trajectory constraints, and 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 drone.

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

[0178] Exemplarily, the preset communication rate threshold Set to 16bps / Hz.

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

[0180] The step S3 comprises:

[0181] The step S3 comprises:

[0182] The UAV-assisted IoT node communication optimization model includes three nonlinear coupling variables: IoT node scheduling, beamforming vectors emitted by the UAV, and UAV trajectory. The model is solved as a non-convex optimization problem.

[0183] Decomposing the non-convex optimization problem into three convex optimization sub-problems of IoT node scheduling, beamforming vectors emitted by drones, and drone trajectories;

[0184] Alternately iterate and optimize three convex optimization sub-problems to gradually approach the global optimal solution;

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

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

[0187] The non-convex optimization problem includes IoT node scheduling Beamforming vectors transmitted by drones Drone tracks Three nonlinear coupled variables.

[0188] The non-convex optimization problem is decomposed into three sub-problems, namely, the IoT node scheduling optimization problem, the beamforming vector optimization problem of UAV launch, and the UAV trajectory optimization problem. In the process of 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] The details are as follows:

[0190] (1) For the IoT node scheduling optimization subproblem, the binary scheduling variables are relaxed into continuous variables with a value range between 0 and 1, so that the problem is transformed into a convex optimization problem. Then, the CVX toolbox in Matlab can be used to directly solve the transformed convex problem.

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

[0193] After solving each sub-problem separately, an alternating optimization strategy is adopted to switch optimization between different sub-problems through cyclic iteration, so that the solutions of the three sub-problems can influence and evolve co-evolved with each other, gradually approaching the global optimal solution, thereby achieving a joint optimization solution to the entire complex optimization problem.

[0194] The three convex optimization sub-problems of IoT node scheduling, UAV emission beamforming vectors, and UAV trajectory are alternately optimized to gradually approach the global optimal solution, including:

[0195] In the mth iteration, when solving the IoT node scheduling, given the preset UAV-transmitted beamforming vector and the initial UAV trajectory, the UAV-assisted IoT node communication optimization model is solved to obtain the IoT node scheduling C (m) ;

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

[0197] When solving the trajectory of the UAV, based on W (m) , C (m) , solve the UAV-assisted IoT node communication optimization model to obtain L (m) ;

[0198] The solution is solved in an alternating cycle until the optimization objective function converges, and the current round of IoT node scheduling, the beamforming vector emitted by the drone, and the drone trajectory are the global optimal solution.

[0199] Among them, the mth round is the iteration round, m≥1 round, and it is the iteration round before the objective function converges.

[0200] The optimization steps are explained in detail as follows:

[0201] There are three nonlinear coupling variables in the original optimization problem (IoT node scheduling Beamforming vectors transmitted by drones Drone tracks Therefore, the optimization problem is a non-convex problem. To solve this problem, the original problem is decomposed into three sub-problems, and each sub-problem is solved iteratively until the objective function value converges;

[0202] Method for determining convergence of objective function: When the objective function in the current number of iterations minus the objective function in the previous number of iterations is less than a given threshold or reaches the set maximum number of iterations, it can be considered that the objective value remains basically unchanged and has reached the optimal value. At this time, the solution (C, W, L) is an approximate global optimal solution.

[0203] Exemplarily, the maximum number of iterations is set to 20 times.

[0204] It should be noted that in each round of solving, each sub-problem is solved in sequence, including the IoT node scheduling problem, the beamforming vector problem of the drone launch, and the drone trajectory optimization problem, and the current approximate optimal solution of the variables in each sub-problem is found. This solution should be used in solving the next sub-problem, ultimately achieving the goal of joint solution.

[0205] For example, in the mth iteration, when solving the IoT node scheduling C, W and L are first given. The given here means that the W value of all time slots is pre-set during simulation. L is the position where the circle is divided into N time slots on the initial circular trajectory with a radius r set to r = 250m, which is the initial trajectory of the drone (given trajectory L). The C obtained after solving (m) ;

[0206] Similarly, when solving the beamforming vector W emitted by the drone, given C (m) , L, where C is replaced by the solution C obtained above (m) , L is still the initial trajectory (because the trajectory subproblem has not been solved yet, it will not be replaced yet), and the solved W (m) ;

[0207] When solving the UAV trajectory L, given W (m) , C (m) Solve and finally get C (m) , W (m) , L (m) , these three values ​​are the solutions of the current round, and then the next round begins, still following the solution order of C, W, and L until the maximum iteration round is reached and finally the global optimal solution is approached.

[0208] In step S3, the original problem is first divided into the non-convex problem of IoT node scheduling optimization problem, the beamforming vector optimization problem of UAV launch, and the UAV trajectory optimization problem. The non-convex problem is converted into a convex problem that can be directly solved by the CVX toolbox, and finally the global optimal solution of the original optimization problem is approached through 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 the UAV transmission: The beamforming vector of the UAV transmission is designed 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 the constraints to maximize the average communication rate of the drone. That is, after multiple iterations and adjustments, the average communication rate between the drone and the IoT node is maximized under all the constraints.

[0214] The purpose of step S3 is to decompose the complex non-convex optimization problem into three solvable convex optimization sub-problems, and adopt an alternating iterative optimization strategy to gradually approach the global optimal solution, and finally determine the IoT node scheduling of the drone, the beamforming vector emitted by the drone, and the flight trajectory of the drone, so as to maximize the average communication rate between the drone and the IoT node while satisfying 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 drone performs the mission (0, 0) is used as the center of the drone's circular initial trajectory, and the radius is set to r = 250m. In this way, when the drone starts the communication mission, its initial trajectory can contact the surrounding target IoT nodes in a relatively balanced manner. In the actual flight process, the drone further dynamically adjusts its flight trajectory according to the real-time channel state information and the communication needs of the IoT nodes to complete the task of effectively assisting the communication of IoT nodes.

[0218] Figure 4The optimized trajectory of the drone presented in Figure 1 clearly demonstrates this adaptive adjustment process. Compared with the initial trajectory, the optimized trajectory shows that the drone actively moves closer to the IoT node. Because under the goal of maximizing the communication rate, the shortened distance between the drone and the target IoT node means that the path loss during signal propagation will be significantly reduced, so that the target IoT node can obtain higher signal strength at its receiving end.

[0219] The optimization goal is to maximize the average communication rate between the drone and the IoT node, that is, the sum of the communication rates of all time slots. According to formula (2) and formula (3), the closer the distance between the drone and the IoT node, the greater the channel gain h. k,com (l(n),l k ) is larger, so the communication rate Therefore, after the algorithm is optimized, the drone will serve as close to the IoT node as possible. When the set flight time and speed of the drone are sufficient, the drone will serve directly above each IoT node, and then continue to fly forward while serving. Figure 3 It is a two-dimensional plane diagram of the drone's flight trajectory. From a two-dimensional perspective, the drone's flight trajectory is connected to multiple IoT nodes. In three-dimensional space, the drone itself will have a certain height, and it is actually flying above the IoT nodes with such a trajectory.

[0220] Figure 5 The figure shows the scheduling of IoT nodes by drones in different time slots. Starting from the starting position, the drone gives priority to serving the IoT node closest to it, while ensuring that every IoT node is served. From the perspective of channel quality, the drone gives priority to IoT nodes with better channel conditions for communication scheduling. This is because in a good channel environment, limited communication resources can be used more efficiently and the data transmission rate per unit bandwidth can be increased. From the perspective of IoT node service quality assurance, the scheduling algorithm avoids the situation where over-serving some IoT nodes leads to a lack of communication resources for other IoT nodes. Through the tightly coupled scheduling and trajectory optimization mechanism, the drone can make the most reasonable IoT node scheduling decision in each time slot under a complex and changeable communication environment, thereby effectively improving the communication efficiency of the entire drone-assisted communication network and providing continuous, stable and efficient communication services for IoT nodes. Figure 4 The horizontal axis refers to the number of time slots N in the UAV flight cycle, and the vertical axis refers to the serial number of the target IoT node from 1 to 10. Figure 4 It shows that the UAV dispatches the corresponding IoT nodes in the corresponding time slots of flight.

[0221] Figure 6 and Figure 7The plane beam schematic diagram and three-dimensional beam schematic diagram of the signal-to-noise ratio at the receiving end of the IoT node in a randomly selected time slot are shown respectively. The narrower communication beam is concentrated at the IoT node scheduled in the current time slot, indicating that by optimizing the communication beamforming vector of the drone, the uniform planar antenna array equipped with it can accurately control the phase and amplitude of the transmitted signal. In the communication process for a specific IoT node in the current time slot, according to the location information and channel characteristics of the IoT node, the transmitted signal energy is highly concentrated in the direction of the IoT node, which is reflected in the significant improvement of the signal-to-noise ratio of the IoT node. When beamforming improves the signal-to-noise ratio, according to Shannon's theorem, the communication rate will be significantly increased under unit bandwidth.

[0222] Figure 6 In the simulation, an area of ​​1000 meters * 1000 meters is simulated, and 10 target IoT nodes are arranged in this area. The innovation of the present invention is that the UAV is combined with beamforming technology to assist communication, so the advantages of beamforming technology need to be reflected through simulation results. The present 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, so that combined with formula (3), the communication rate is enhanced to meet the optimization goal. In each time slot, the UAV will only provide services for one IoT node. Therefore, in each time slot, there will be a beam concentrated at a corresponding IoT node, which means that only this position has a communication rate. Therefore, a time slot is randomly selected for observation ( Figure 6 and Figure 7 The corresponding time slot is 55, combined with Figure 4 It can be seen that the drone is serving the fifth target IoT node at this time), so the narrow beam is concentrated on the fifth IoT node in the figure. A narrow beam means high energy. The height value of the beam is the size of the signal-to-noise ratio.

[0223] Figure 7 For a two-dimensional graph Figure 6 The three-dimensional perspective of the simulation area is shown in Figure 3, where the x and y axes represent the simulation area (meters). The communication rate in formula (3) is In order to display the beam diagram linearly (if the communication rate is used as the ordinate, due to the existence of log, the image will produce multiple beams, and it is not possible to intuitively see which IoT node has the highest communication rate in this time slot), the signal-to-noise ratio is used as the ordinate, and the larger the signal-to-noise ratio, the greater the communication rate. In general, using the signal-to-noise ratio as the ordinate makes the image more intuitive and also satisfies the optimization problem.

[0224] Figure 8The comparison of the communication rate between the preset trajectory scheme and the optimized trajectory scheme is shown. As the maximum transmission power of the drone increases, the communication rates of both schemes are improved, and the communication rate of the optimized trajectory scheme is always higher than that of the preset trajectory scheme. This is because the optimized trajectory scheme does not rely solely on the increase in transmission power to improve the communication conditions. The drone will dynamically adjust the flight path based on factors such as the distribution of IoT nodes and channel status, so that it can be closer to IoT nodes with better signal transmission quality, thereby reducing the path loss and interference effects during signal propagation. Under the same transmission power, the optimized trajectory scheme can effectively transmit more signal energy to the receiving end, improving the communication quality and rate. However, due to the lack of such flexibility, the preset trajectory scheme is relatively limited in terms of communication rate improvement. In addition, it can be observed from the figure that the number of antenna units also has an impact on the communication rate of the system. The more antenna units there are, the more significantly the gain of beamforming will be improved, and the higher the communication rate that the system can achieve.

[0225] The preset trajectory plan refers to the fixed drone along Figure 4 The scheme of initial trajectory flight is that the flight trajectory of the drone is not optimized. Obviously, compared with the scheme of optimizing the trajectory in the present invention (adjusting the flight path to be close to the IoT node), the flight trajectory of the drone that is not optimized is farther away from the IoT node, so the communication rate is low under the same transmission power. With the increase of the maximum transmission power of the drone, the communication rate of both schemes is improved. Combined with formulas (6) and (7), this is because the beamforming vector w of the drone c (n) can have a larger amplitude at a higher transmission power, thus increasing the communication rate between the drone and the IoT node from formula (3). Similarly, the more antennas there are, the stronger the enhanced signal can be generated at a specific IoT node, and the higher the communication rate between the drone and the IoT node.

[0226] The IoT node receives the directional beam signal transmitted by the drone, converts the received analog signal into a digital signal, and extracts the transmitted data from it. The received data is decoded to restore the original information. According to the actual needs, the IoT node sends a confirmation signal or feedback information to the drone, as well as a request to achieve two-way communication. Through this two-way communication mechanism, the drone can achieve effective data exchange with the IoT node, supporting various applications such as remote monitoring, emergency rescue, data collection and distribution, etc.

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

[0228] 1. The present invention realizes the real-time and efficient rapid collection of farmland monitoring data through direct communication between drones and ground IoT nodes. Compared with traditional manual collection or wired transmission methods, the efficiency and real-time performance of data collection are greatly improved;

[0229] 2. The present invention maximizes the average communication rate between the drone 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 the present invention can formulate more accurate crop management strategies based on multi-dimensional data collected in real time (such as temperature and humidity, light, soil moisture, etc.), including irrigation, fertilization and pest control, thereby improving the yield and quality of crops;

[0231] 4. The UAV of the present 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 able to adapt to different farmland environments and crop growth conditions;

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

[0233] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. An agricultural monitoring system for communication between a drone and a ground IoT node, characterized in that: include: The drone module is deployed on the drone body and is used to execute the communication method between the multi-antenna drone and the ground IoT node, communicate with the IoT nodes in the farmland, obtain the monitoring data sent by the IoT node module, and send the monitoring data of multiple IoT nodes to the command console module; A command console module is used to issue inspection instructions to drones and formulate management strategies for crop irrigation, fertilization, and pest and disease identification and prevention based on the monitoring data; The IoT node module is used to obtain monitoring data in the farmland and send it to the drone module during the time slot when the drone flies over; The user terminal module is used to communicate with the command center module, obtain the management strategy and manually adjust the strategy.

2. The system according to claim 1, characterized in that: The Internet of Things node module comprises: Temperature and humidity sensors are used to monitor the temperature and humidity data of the air in the farmland; Light sensors are used to monitor light intensity data in farmland; Soil moisture sensor, used to monitor soil moisture data; Spectral sensor, used to monitor plant spectral reflectance characteristic data; pH sensors to monitor soil nutrient deficiencies or harmful chemicals affecting crop health; Image sensors for capturing images of crops in the field; A communication module, used to transmit the monitoring data obtained by each sensor to the drone module; The monitoring data include farmland air temperature and humidity data, light intensity data, soil moisture data, soil nutrient deficiency or harmful chemical substance data and crop image data.

3. The system according to claim 1, characterized in that: The drone module includes a uniform planar antenna array, and performs circular flight over the farmland according to a predetermined flight path based on the inspection instructions of the command console module; The communication module of the Internet of Things node module includes a single antenna device. When the drone flies over, the Internet of Things node establishes a wireless communication connection with the drone. Each Internet of Things node module receives a directional beam signal sent from the drone module within a certain time slot, and then responds and sends the monitoring data.

4. The system according to claim 1, characterized in that: The multi-antenna UAV and ground IoT node communication method comprises: Step S1, calculating the steering vector from the drone to each IoT node; calculating the channel gain from the drone to each IoT node module based on the steering vector; calculating the communication rate between the drone and each IoT node module based on the channel gain; Step S2, constructing a UAV-assisted IoT node communication optimization model that takes maximizing the average communication rate between the UAV and multiple IoT node modules as the optimization objective function, while satisfying the IoT node scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints; Step S3: Solve the drone-assisted IoT node communication optimization model to obtain the optimal communication solution between the drone and multiple IoT nodes; and use the optimal communication solution to communicate accurately with the IoT node module.

5. The method according to claim 4, characterized in that: The UAV is equipped with a uniform planar antenna array, which is deployed parallel to the ground. The total number of antennas in the uniform planar antenna array and the distance between antenna units are divided along the x-axis and y-axis of the three-dimensional Cartesian coordinate system to obtain the total number of antenna arrays M=M x ×M y and the spacing d between the antennas along the coordinate system axis x =d y =λ / 2; Among them, M x 、M y are the number of antennas arranged on 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; λ is the carrier wavelength; Based on M x 、M y and d x ,d y Calculate the steering vector α(l(n),l from the drone to the kth IoT node module k ), calculated as follows: 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, l k is the position of the kth IoT node, θ(l(n),l k )、Φ(l(n),l k ) are the AoD elevation angle and AoD azimuth angle when the UAV transmits the signal to the kth IoT node.

6. The method according to claim 5, characterized in that: Based on α(l(n),l k ) Calculate the channel gain h from the UAV to the kth IoT node module k,com (l(n),l k ), calculated as follows: Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t is the antenna gain of the UAV transmitter, G b is 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 kth IoT node; Under unit bandwidth, based on h k,com (l(n),l k ) Calculate the communication rate between the drone and the kth IoT node module The calculation is as follows: Among them, c k (n) is the IoT node scheduling of the drone in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise that obeys Gaussian random distribution, H is the channel gain h k,com (l(n),l k )'s conjugate transpose; c k (n) is a binary discrete variable; When the UAV transmits a communication beam in time slot n to perform a communication task to the kth IoT node, c k (n)=1, otherwise c k (n)=0.

7. The method according to claim 4, characterized in that: The UAV-assisted IoT node communication optimization model is as follows: C6:l(1)=l(N)=l I in, 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 cycle; C, W, L are the IoT node scheduling to be optimized, the beamforming vector emitted by the UAV, and the trajectory variable of the UAV, respectively; C1 and C2 represent IoT node scheduling constraints, C3 represents UAV communication performance constraints, is the preset communication rate threshold between the UAV and the base station, C4 represents the UAV transmission power constraint, C5 and C6 represent the UAV flight trajectory constraints, and 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 drone.

8. The method according to claim 7, characterized in that: The step S3 comprises: The UAV-assisted IoT node communication optimization model includes three nonlinear coupling variables: IoT node scheduling, beamforming vectors emitted by the UAV, and UAV trajectory. The model is solved as a non-convex optimization problem. Decomposing the non-convex optimization problem into three convex optimization sub-problems of IoT node scheduling, beamforming vectors emitted by drones, and drone trajectories; Alternately iterate and optimize three convex optimization sub-problems to gradually approach the global optimal solution; When the objective function converges, the global optimal solution obtained is the optimal solution for communication between the UAV and multiple IoT nodes; wherein the global optimal solution includes IoT node scheduling, beamforming vectors emitted by the UAV, and the trajectory of the UAV; The non-convex optimization problem includes IoT node scheduling Beamforming vectors transmitted by drones Drone tracks Three nonlinear coupled variables.

9. The method according to claim 8, characterized in that: The three convex optimization sub-problems of IoT node scheduling, UAV emission beamforming vectors, and UAV trajectory are alternately optimized to gradually approach the global optimal solution, including: In the mth iteration, when solving the IoT node scheduling, given the preset UAV-transmitted beamforming vector and the initial UAV trajectory, the UAV-assisted IoT node communication optimization model is solved to obtain the IoT node scheduling C (m) ; When solving the beamforming vector emitted by the UAV, based on C (m) , the initial UAV trajectory, solving the UAV-assisted IoT node communication optimization model to obtain the beamforming vector W (m) ; When solving the trajectory of the UAV, based on W (m) , C (m) , solve the UAV-assisted IoT node communication optimization model to obtain L (m) ; The solution is solved in an alternating cycle until the optimization objective function converges, and the current round of IoT node scheduling, the beamforming vector emitted by the drone, and the drone trajectory are the global optimal solution.

10. The system according to any one of claims 2 to 9, characterized in that: The command console module determines the type of crops, the growth height of crops and the condition of pests and diseases based on the field crop images; Based on the farmland air temperature and humidity data, soil moisture data and crop types, a crop irrigation management strategy is generated to determine whether to turn on or off the farmland irrigation system; generating a crop fertilization management strategy based on the soil nutrient deficiency or harmful chemical substance data and the crop types; Based on the plant spectral reflectance characteristic data, analyzing the spectral reflectance characteristics of crops to determine the growth health of crops; Based on the crop images, the crop disease and insect pest conditions are determined, and crop disease and insect pest identification and prevention and control management strategies are generated.

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