A data transmission method for smart agricultural mobile scenarios

Abstract

The present invention discloses a data transmission method for smart agricultural mobile scenarios in the field of wireless communication technology, comprising a ground base station at a base station transmitting a signal to a drone cluster relay communication system, the drone cluster relay communication system receiving the signal and forming a virtual antenna array, the drone cluster relay communication system reconstructing and optimizing the beam, and using the APSSA-PSO algorithm to optimize the parameters of the communication system to ensure a stable connection between smart agricultural machinery in agricultural operation scenarios. The drone cluster relay communication system performs beam reconstruction based on the optimization results, determines the number of nodes and the optimal position of the drone cluster, and then generates distributed collaborative beamforming through the virtual antenna array to transmit data to ground agricultural equipment. The present invention solves the problems of insufficient long-distance signal coverage and unstable transmission when nodes move in traditional wireless communication technologies in smart agricultural scenarios by using drones as mobile relay nodes.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a data transmission method for smart agricultural mobile scenarios. Background Art

[0002] With the development of smart agriculture, a large number of intelligent devices and sensors are deployed in farmland to achieve real-time monitoring and precise control of the crop growth environment. These devices are often distributed over vast areas. With the advancement of wireless communication technology, drone relay communication systems are widely used to improve system capacity and signal gain. However, traditional communication systems face the following challenges when covering large areas of farmland:

[0003] 1. Signal blind spots: Due to the complex terrain of farmland, traditional fixed beamforming cannot effectively cover all areas. Numerous signal blind spots exist, resulting in poor communication quality and impacting device performance. Downlink communication links between ground base stations and mobile nodes are often affected by long distances, rugged terrain, and other interference. Implementing relay transmission can expand wireless coverage at a low cost. Traditional static relays are complex to deploy and expensive, and are significantly affected by environmental factors such as terrain and buildings, resulting in poor communication quality.

[0004] 2. Insufficient dynamic adjustment capabilities: Existing methods typically rely on fixed channel state information and cannot adapt to dynamic changes in devices and environments. As nodes move, the direction of the beam may change due to the uncertainty of their speed and direction. This can cause the main lobe of the beam to deviate from the original target direction, thereby reducing array gain.

[0005] 3. Energy efficiency optimization: Existing technologies struggle to optimize power consumption while ensuring communication quality, increasing the energy burden on devices and hindering long-term use. When node movement causes beam misalignment, the node's phase typically needs to be recalculated to realign it with the target. However, if a node moves too far from the base station, signal strength may weaken. Optimizing node deployment and weight allocation can enhance beam directivity and signal gain, leading to better communication performance. However, frequent optimization operations can lead to significant resource consumption.

[0006] To address the issue of insufficient transmission range for long-distance users, existing methods have implemented beamforming technology by equipping a single drone with an antenna array of a certain size, improving wireless network communication capabilities. One study proposed an artificial bee colony algorithm, which, considering only the LoS component and the Ricean K coefficient, determines the optimal beamforming vector for each user, maximizing the system and bit rate. However, the limited payload of the drone precludes the use of heavier, high-power single or multiple antennas. Secondly, to address beam failures caused by node mobility, controlling beamwidth can improve mobile link coverage and reliability, thereby mitigating the degradation in beam performance caused by node mobility. Another study proposed a hybrid control scheme that jointly optimizes the excitation current weights and position for mobile beamforming while ensuring quality of service. However, the computational overhead of the optimization algorithm employed is prohibitive for practical sensor networks.

[0007] In summary, drone relays utilizing beamforming technology can significantly improve system capacity, coverage, and link reliability. While existing research has explored DCB in mobile scenarios, these approaches typically rely on node movement to fixed locations, and their optimization methods are not fully applicable to actual agricultural operations. Therefore, in order to better address the challenges posed by continuous node mobility, more effective methods are needed to overcome the degradation in beamforming performance caused by node mobility. Summary of the Invention

[0008] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid blurring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0009] Therefore, the purpose of the present invention is to provide a data transmission method for smart agricultural mobile scenarios. By using drones as mobile relay nodes, it solves the problems of insufficient signal coverage and unstable transmission of traditional wireless communication technologies in smart agricultural scenarios and effectively realizes data collection and transmission tasks in agricultural operations.

[0010] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0011] A data transmission method for smart agricultural mobile scenarios, comprising:

[0012] S1, the ground base station at the base station transmits a signal to the drone cluster relay communication system, which receives the signal and forms a virtual antenna array;

[0013] S2. The UAV cluster relay communication system builds an adaptive beam reconstruction function based on UAV-assisted relay and uses the APSSA-PSO algorithm to solve it to optimize the beam performance under the node movement state;

[0014] S3. The drone cluster relay communication system reconstructs the beam based on the optimization results, determines the number of nodes and the optimal position of the drone cluster, and then generates distributed collaborative beamforming through the virtual antenna array to transmit data with ground agricultural equipment.

[0015] As a preferred solution of the data transmission method for smart agricultural mobile scenarios described in the present invention, in step S1, a ground base station with M×B antennas is equipped at the base station, and B rotor drones are set up to form a drone cluster relay communication system as mobile relays deployed between the base station and ground users. The drones are equipped with uniform array antennas to form a virtual antenna array.

[0016] As a preferred solution of the data transmission method for smart agricultural mobile scenarios described in the present invention, the beam reconstruction process can be divided into two parts: determining the number of nodes and optimizing the node positions. Considering the maximization of beam width and signal strength, the node number function is determined as:

[0017] g1(N)=k1*RSSI(N)+k2*θ(N)

[0018]

[0019] stC1:RSS>-90

[0020] C2:1 <N≤50

[0021] Where θ is the beam width, k1 and k2 are weighting factors. For node location optimization, the objective function for simultaneously reducing the maximum sidelobe, reducing energy consumption, and reducing delay while meeting real-time requirements is:

[0022]

[0023] maxg2

[0024] stC1:i≤i max

[0025]

[0026]

[0027] C4:θ0=arg max|AF(θ)|,θ∈[-π,π]

[0028] C5:θ SL ∈[-π,θFN1 )∪(θ FN2 ,π]

[0029]

[0030] Among them, w1, w2 and w3 are weighting factors, and are the side lobe and main lobe directions respectively, i is the number of algorithm iterations, P M is the motor power, T i is the time to move from the current position to the destination position, constraint C1 gives the upper limit constraint of variable i, and the maximum number of iterations cannot exceed i max , constraint C2 gives the position range of the node on the x-axis, between the position boundary x min and x max Constraint C3 gives the position range of the node on the y axis, between the position boundary y min and y max Constraint C4 gives the main lobe position, constraint C5 gives the side lobe position, constraint C6 gives the system time constraint, AF(θ) represents the array factor of the distributed beam emitted by the virtual antenna array composed of UAV nodes, θ FN1 is the lower bound angle of the main lobe, θ FN2 is the upper boundary angle of the main lobe, represents the movement time of the i-th sensor node m during its movement, t c represents the computational time for solving the optimization problem, T L Indicates the maximum communication time allowed by the system, T i is the time to move from the current position to the destination position, expressed as:

[0031]

[0032] Among them, X i ,X ie ,Y i ,Y ie are the current position coordinates and expected position coordinates of the i-th node respectively, and V is the node moving speed.

[0033] As a preferred solution of the data transmission method for smart agricultural mobile scenarios described in the present invention, the specific optimization steps of the APSSA-PSO algorithm are as follows:

[0034] Generate the initial population based on the good point set and calculate the discoverer position and follower position;

[0035] Select SD×A sparrows as sentinels and calculate the positions of the scouts;

[0036] Calculate the fitness of each individual and sort them to find the current best and worst individuals;

[0037] Calculate the current optimal individual disturbance position and choose whether to replace the optimal sparrow position;

[0038] Output the optimal solution.

[0039] As a preferred solution of the data transmission method for smart agricultural mobile scenarios described in the present invention, in step S3, the ground agricultural equipment includes smart devices and sensors deployed in the farmland, and agricultural machinery and data transmission equipment used by farmers.

[0040] Compared to existing technologies, this invention offers the following advantages: by using drones as mobile relay nodes, it addresses the issues of insufficient signal coverage and unstable transmission during node mobility in traditional wireless communication technologies in smart agriculture scenarios, effectively enabling data collection and transmission tasks in agricultural operations. The drone's high maneuverability and flexibility enable flexible deployment in areas with insufficient signal coverage, significantly enhancing the system's signal coverage. The application of beam reconstruction technology enables the drone to dynamically adjust signal transmission direction, ensuring stable and reliable signal transmission even in complex terrain and with node mobility. Working in conjunction with ground sensors, the drone can collect large amounts of agricultural data in real time, ensuring data accuracy and timeliness, thereby supporting scientific management of agricultural production. Furthermore, by optimizing the drone's flight path and antenna excitation parameters, this invention not only optimizes communication efficiency and energy consumption, reducing drone energy consumption, but also optimizes the number and location of nodes to maximize beam width and signal strength, enabling the overall communication system to operate stably and for extended periods despite node mobility. This innovative approach not only improves the efficiency and stability of agricultural data collection but also provides strong technical support for precision agriculture, laying a solid foundation for future agricultural management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort. Among them:

[0042] Figure 1 This is a flow chart of a data transmission method for smart agricultural mobile scenarios according to the present invention;

[0043] Figure 2 This is a flow chart of the APSSA-PSO algorithm provided by the present invention. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0045] The present invention considers a UAV relay-assisted wireless communication system, in which the ground base station is responsible for providing uplink communication services for remote users. Due to the undulating terrain and obstacles such as buildings, large-scale fading such as shadow effects may occur, affecting the communication effect. In the smart agriculture scenario considered by the invention, a ground base station with M×B antennas is equipped at the base station, and B rotor drones are set up to form a UAV cluster relay communication system as a mobile relay deployed between the base station and the ground user. The UAV is equipped with a uniform array antenna to form a virtual antenna array, and uses distributed collaborative beamforming to communicate with M groups of ground users to reduce path loss and improve system performance. For ease of analysis, a three-dimensional rectangular coordinate system is established with the base station as the origin. Relative to the height of the UAV cluster relay communication system, the height difference between the base station and the user can be ignored. Therefore, the base station and the user are at the same height in the system model, where (0,0,0), and Represent the locations of the base station, the Bth drone, and the Mth ground user, respectively. In a smart agriculture scenario, there are B mobile sensor nodes within a sensing area, and these nodes perform data collection tasks within that area. Since each node cannot communicate directly with the base station, nearby nodes should move in coordination with the source node and form a virtual antenna array to communicate with the base station.

[0046] Considering the downlink UAV colony relay to user (U2U) link, when the UAV colony communicates with the mth ground user, the gain in the direction of the mth user is:

[0047]

[0048] in, represents the direction of the mth ground user, is the amplitude of the far-field beam pattern of a UAV, η∈[0,1] is the antenna array efficiency, represents the array factor of the distributed beam emitted by the virtual antenna array composed of UAV nodes, Indicates the signal power in a certain direction.

[0049] The signal-to-noise ratio between the UAV cluster based on the virtual antenna array and the mth ground user can be expressed as:

[0050]

[0051] Among them, K0 is the constant path loss coefficient between the UAV and the ground user, σ is the standard deviation of the noise component in the received signal, which is used to characterize the strength of the noise. When calculating the signal-to-noise ratio, the power of the noise is usually proportional to the square of σ, that is, σ 2 represents the noise power, P t is the total transmission power of the UAV cluster, α represents the path loss index, d m is the distance between the center position of all UAVs and the mth ground user, expressed as:

[0052]

[0053] According to Shannon's theorem, the theoretical upper limit of the communication rate of the virtual antenna array can be deduced. The transmission rate between the UAV cluster based on the virtual antenna array and the mth ground user can be expressed as:

[0054] R m =B U2U log2(1+γ m )

[0055] By adding the transmission rate of the drone cluster and each user, the channel capacity of the U2U link can be obtained as:

[0056]

[0057] Among them, B U2U is the channel bandwidth between the base station and the UAV cluster.

[0058] In the U2U link of a drone-assisted relay communication system, because drones are far away from ground users, when drones need to transmit data to ground users, the drone cluster forms a virtual antenna array and transmits data to the ground users using DCB. The electromagnetic waves emitted by the virtual antenna array, composed of multiple randomly deployed drones, overlap and offset each other during transmission, thereby enhancing or weakening the signal strength in certain directions. By determining the optimal position of the drones, a high-gain main lobe is generated to align with the user while effectively reducing the gain in directions outside the main lobe. This increases the transmission rate from the drone antenna array to the ground user and maximizes the system capacity of the U2U link.

[0059] In mobile scenarios, node movement significantly impacts beam coverage and signal strength. Node movement perpendicular to the base station and high speed can make beam direction and beam gain difficult to control, resulting in the beam being unable to align with the base station or insufficient signal strength, leading to communication failure. Beam reconstruction is necessary in these situations. There are two conditions for beam reconstruction: the main lobe deviates from the target direction or the signal strength is insufficient for communication. Since main lobe deviation can lead to insufficient signal strength, the probability of beam reconstruction can be determined by signal strength. The reconstruction probability is calculated as follows:

[0060]

[0061] Where P is the reconstruction probability and RSS is the signal strength.

[0062] If the corrected signal strength meets the communication requirements, there is no need to rebuild the beam; simply adjust the node phase to align with the target direction. If the maximum signal strength generated by the current number and position of nodes is not higher than the threshold, the current beam cannot meet the communication requirements and needs to be rebuilt. Otherwise, only beam alignment is required. The judgment formula is as follows:

[0063]

[0064] When f is 1, beam reconstruction is performed, and when f is 0, beam alignment is performed. Where RSS is the signal strength, G max is the maximum beam gain that can be achieved with the current number of nodes and node position, and (x0, y0) is the position of the source node.

[0065] like Figure 1 As shown, the data transmission method for smart agriculture mobile scenarios has the following steps:

[0066] S1. The ground base station at the base station transmits a signal to the drone cluster relay communication system. The drone cluster relay communication system receives the signal and forms a virtual antenna array. Specifically, a ground base station with M×B antennas is equipped at the base station, and B rotorcraft drones are set up to form a drone cluster relay communication system as mobile relays deployed between the base station and the ground user. The drones are equipped with uniform array antennas to form a virtual antenna array.

[0067] S2. The UAV cluster relay communication system builds an adaptive beam reconstruction function based on UAV-assisted relay and uses the APSSA-PSO algorithm to solve it to optimize the beam performance under the node movement state;

[0068] S3. The drone cluster relay communication system reconstructs the beam based on the optimization results, determines the number of nodes and the optimal position of the drone cluster, and then generates distributed collaborative beamforming through a virtual antenna array to transmit data to ground agricultural equipment. The ground agricultural equipment includes smart devices and sensors deployed in farmland, as well as agricultural machinery and data transmission equipment used by farmers.

[0069] In step S2, node movement causes the main lobe to deviate, requiring frequent beam reconstruction, which significantly increases computational energy consumption and node movement energy consumption. Considering the real-time nature of communication, the number of nodes is determined and their positions are optimized to achieve reconstruction and optimize beam performance. When determining the number of nodes, maximizing both beam width and signal strength is considered, resulting in:

[0070] g1(N)=k1*RSSI(N)+k2*θ(N)

[0071]

[0072] stC1:RSS>-90

[0073] C2:1 <N≤50

[0074] Where θ is the beam width, and k1 and k2 are weighting factors.

[0075] When reconstructing the beam, the objective function for simultaneously achieving the reduction of maximum sidelobe, energy consumption, and delay while meeting the real-time requirements is:

[0076]

[0077] maxg2

[0078] stC1:i≤i max

[0079]

[0080]

[0081] C4:θ0=arg max|AF(θ)|,θ∈[-π,π]

[0082] C5:θ SL ∈[-π,θ FN1 )∪(θ FN2 ,π]

[0083]

[0084] Among them, w1, w2 and w3 are weighting factors, and are the side lobe and main lobe directions respectively, and i is the number of algorithm iterations. M is the motor power, T i is the time to move from the current position to the destination position, expressed as:

[0085]

[0086] Among them, X i ,X ie ,Y i ,Y ie are the current position coordinates and expected position coordinates of the i-th node, respectively, and V is the node moving speed. In the above optimization problem, constraint C1 gives the upper limit constraint of variable i, and the maximum number of iterations cannot exceed i max, the constraint C2 gives the position range of the node on the x-axis, which is between the position boundaries x min and x max . The constraint C3 gives the position range of the node on the y-axis, which is between the position boundaries y min and y max . The constraint C4 gives the main lobe position, the constraint C5 gives the sidelobe position, the constraint C6 gives the system time constraint. AF(θ) represents the array factor of the distributed beam emitted by the virtual antenna array composed of UAV nodes, and θ FN1 is the lower bound angle of the main lobe, and θ FN2 is the upper bound angle of the main lobe. represents the movement time during the movement of the i-th sensor node m, and t c represents the calculation time for solving the optimization problem, and T L represents the maximum communication time allowed by the system. They determine the first null beamwidth (FNBW) of the radiation pattern, where max(t m ) represents the longest movement time spent during the movement of all mobile sensor nodes, and t c is the calculation time for solving the optimization problem.

[0087] Due to the real-time requirement of the reconstruction beam optimization, the algorithm for solving the above problem should have the characteristics of high efficiency and speed. In the SSA algorithm, the initialization of the sparrow population is divided into three subgroups, namely discoverers, followers, and vigilant ones. The sparrow population is in the space of A×D, where A is the total number of sparrows and D is the space dimension. Then the position of the i-th sparrow in the space is X i =(x i,1 , x i,2 , …, x i,d ), i∈[1, N], d∈[1, D], and x i,d represents the position of the i-th sparrow in the D-dimensional space. Among them, the one that guides the movement of the population is called the discoverer, and its position update formula is as follows:

[0088]

[0089] where t represents the current iteration number; iter is the maximum number of iterations; α is a uniform random number between (0, 1]; Q is a random number of normal distribution; L is a matrix with all elements being 1, and its size is 1×d; R2∈[0, 1] represents the warning value; ST∈[0.5, 1] represents the safety value. When R2 < ST, it means that the population is not in danger and the discoverer continues to search; when R2≥ST, it means that the vigilant one has discovered a predator and immediately alarms other sparrows. The population immediately makes anti-predation behavior and flies to a safe area to forage. The followers mainly follow and monitor the movement of the discoverer and strive to get a better position. Its update formula is as follows;

[0090]

[0091] Among them, x worst Indicates the global worst position, A + =AT(AAT) -1 . When i>n / 2, it means that the i-th joiner has not obtained food and needs to fly to other places to find food. When i≤n / 2, it means that the i-th joiner is close to the global optimal position and is foraging randomly. When foraging, the population will select a small number of sparrows to be responsible for vigilance. When a natural enemy appears, both the discoverer and the follower will give up the current food and fly to a new location. Each generation randomly selects SD (usually 10% to 20%) sparrows from the population for early warning behavior. The position update formula is:

[0092]

[0093] Among them, β represents the step size control parameter, which is a random number that obeys the normal distribution with a mean of 0 and a variance of 1; K represents the moving direction of the sparrow, which is a random number in the interval [-1, 1]; ε is a small constant; f i represents the fitness of the i-th sparrow; f g represents the optimal fitness of the current sparrow population; f w Represents the worst fitness of the current sparrow population. i >f g When f i =f g When , it means that the i-th sparrow is at the center of the population. Because it is aware of the threat, it needs to move closer to other sparrows to reduce the risk of being captured.

[0094] The one-to-one transfer of global optimal information in SSA results in slow convergence. In traditional SSA algorithms, the method for updating the optimal individual depends on the population update at each iteration. However, when the optimal individual reaches an extreme value in the local space, this can cause the algorithm to become trapped in a local optimum and converge prematurely. To accelerate convergence and improve the ability to escape local optima, this paper proposes an adaptive hybrid perturbation strategy that perturbs the optimal individual after each iteration.

[0095] Particle Swarm Optimization (PSO) is a type of swarm intelligence algorithm. Its basic concept is to simulate the behavior of biological groups, such as flocks of birds or schools of fish, in a search space. Specifically, each solution is considered a particle in the search space, each with two properties: speed and position. Based on its historical best position (pbest) and global best position (gbest), the particle adjusts its speed and position in the hope of finding the optimal solution. The specific method is as follows:

[0096]

[0097]

[0098] in, and Represent the speed and current position of the i-th particle, ω represents the inertia factor, r i is a random number between (0,1), c1 and c2 are learning factors, It indicates the position where the evaluation function value is optimal among the positions passed by the particle during the iteration process, that is, the global optimal position.

[0099] APSSA-PSO introduces the social learning strategy of PSO into the position update equation of followers in SSA, and uses the determination coefficient r and adaptive transition probability P i Perform mixed perturbation on the optimal sparrow position. When r≤P i When the perturbation is large, reverse learning is used; otherwise, Lévy flight is used for small perturbations.

[0100]

[0101]

[0102] Among them, x' best represents the optimal individual position after updating through the adaptive hybrid perturbation strategy (i.e., after Lévy flight wandering), x lb Represents the lower bound of the search space, which is the minimum value of the search space where the optimal individual position is located, and represents the minimum allowable value of this dimension, x ub Represents the upper bound of the search space, is the maximum value of the search space where the optimal individual position is located, and represents the maximum allowable value of this dimension. lb and x ub Determine the boundary of particle search, x j Indicates the position of the j-th sparrow in this dimensional space, represents the position of the i-th sparrow at the t-th iteration, k and γ are both random numbers that conform to the normal distribution. is a random step size that follows the Lévy distribution, α represents the step size control coefficient, r is a random number between 0 and 1, and i is the number of iterations. i The number of iterations decreases as the number of iterations increases. In the early stages of the algorithm's iterations, reverse learning is performed on the optimal position, causing a large-scale perturbation to quickly find the global optimal solution. Conversely, in the later iterations of the algorithm, small-scale perturbations of the optimal position using Lévy flights are performed to escape the local optimum. The improved algorithm, while increasing the random distribution of its own position, leverages global information, enhances the tendency of individuals to move to food, and accelerates convergence.

[0103] Since the position of the sparrow after disturbance is not necessarily better than the original position, the replacement is only performed when the fitness value of the disturbance solution is better than the original solution. The formula is as follows:

[0104]

[0105] like Figure 2 As shown in Figure 2, the APSSA-PSO algorithm process is as follows:

[0106] Generate the initial population based on the good point set and calculate the discoverer position and follower position;

[0107] Select SD×A sparrows as sentinels and calculate the positions of the scouts;

[0108] Calculate the fitness of each individual and sort them to find the current best and worst individuals;

[0109] Calculate the current optimal individual disturbance position and choose whether to replace the optimal sparrow position;

[0110] Output the optimal solution.

[0111] By using drones as mobile relay nodes, this invention addresses the challenges of insufficient signal coverage and unstable transmission during mobile node deployment in traditional wireless communication technologies for smart agriculture, effectively enabling data collection and transmission in agricultural operations. The drone's high maneuverability and flexibility enable flexible deployment in areas with limited signal coverage, significantly enhancing the system's signal coverage. The application of beam reconstruction technology enables the drone to dynamically adjust signal transmission direction, ensuring stable and reliable signal transmission even in complex terrain. Through advanced adaptive beam reconstruction methods, this invention effectively improves system capacity and transmission efficiency. Working collaboratively with ground sensors, the drone can collect large amounts of agricultural data in real time, ensuring data accuracy and timeliness, thereby supporting scientific management of agricultural production. Furthermore, by optimizing the drone's flight path and antenna excitation parameters, this invention not only optimizes communication efficiency and energy consumption, reducing drone energy consumption, but also maximizes beam width and signal strength by optimizing the number and location of nodes, enabling the overall communication system to operate stably and for extended periods even with mobile nodes. This innovative approach not only improves the efficiency and stability of agricultural data collection but also provides strong technical support for precision agriculture, laying a solid foundation for future agricultural management and decision-making.

[0112] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A data transmission method for smart agricultural mobile scenarios, characterized in that: include: S1, the ground base station at the base station transmits a signal to the drone cluster relay communication system, and the drone cluster relay communication system forms a virtual antenna array to transmit the signal to the ground user; S2. The UAV cluster relay communication system constructs an adaptive beam reconstruction function based on UAV-assisted relay and uses the APSSA-PSO algorithm to solve it to optimize the beam performance under the node movement state. APSSA-PSO introduces the social learning strategy of PSO into the position update equation of the follower in SSA, and uses the determination coefficient r and the adaptive transition probability P i Perform mixed perturbation on the optimal sparrow position; when r≤P i When , reverse learning is used for perturbation; otherwise, Lévy flight is used for perturbation. Where r is a random number between 0 and 1, and i is the number of iterations; S3, the drone cluster relay communication system reconstructs the beam based on the optimization results, determines the optimal location and number of nodes in the drone cluster, and then uses the virtual antenna array to generate distributed collaborative beamforming to transmit data with ground agricultural equipment; In step S2, the beam is reconstructed and optimized based on the number of nodes and node positions to improve the robustness of the communication system. The node number function is determined as: g1(N)=k1*RSSI(N)+k2*θ(N) stC1:RSSI>-90 C2:1 <N≤50 Where θ is the beam width, k1 and k2 are weighting factors, set to 0.5 and 0.5 respectively. When reconstructing the beam, the objective function of reducing the maximum sidelobe, reducing energy consumption, and reducing delay while meeting the real-time requirements is: maxg2 s.t.C1:i≤i max C4:θ0=arg max|AF(θ)|,θ∈[-π,π] C5:θ SL ∈[-π,θ FN1 )∪(θ FN2 ,p] C6: Among them, w1, w2 and w3 are weighting factors, and are the side lobe and main lobe directions respectively, i is the number of algorithm iterations, P M is the motor power, T i is the time to move from the current position to the destination position, constraint C1 gives the upper limit constraint of variable i, and the maximum number of iterations cannot exceed i max , constraint C2 gives the position range of the node on the x-axis, between the position boundary x min and x max Constraint C3 gives the position range of the node on the y axis, between the position boundary y min and y max Constraint C4 gives the main lobe position, constraint C5 gives the side lobe position, constraint C6 gives the system time constraint, AF(θ) represents the array factor of the distributed beam emitted by the virtual antenna array composed of UAV nodes, θ FN1 is the lower bound angle of the main lobe, θ FN2 is the upper boundary angle of the main lobe, represents the movement time of the i-th sensor node m during its movement, t c represents the computational time for solving the optimization problem, T L Indicates the maximum communication time allowed by the system.

2. The data transmission method for smart agriculture mobile scenarios according to claim 1 is characterized in that: In step S1, a ground base station with M×B antennas is equipped at the base station, and B rotorcraft drones are set up to form a drone cluster relay communication system as mobile relays deployed between the base station and ground users. The drone cluster forms a virtual antenna array.

3. The data transmission method for smart agriculture mobile scenarios according to claim 1 is characterized in that: The specific optimization steps of the APSSA-PSO algorithm are as follows: Generate the initial population based on the good point set and calculate the discoverer position and follower position; Select SD×A sparrows as sentinels and calculate the positions of the scouts; Calculate the fitness of each individual and sort them to find the current best and worst individuals; Calculate the current optimal individual disturbance position and choose whether to replace the optimal sparrow position; Output the optimal solution.

4. The data transmission method for smart agriculture mobile scenarios according to claim 1 is characterized in that: In step S3, ground agricultural equipment includes smart devices and sensors deployed in farmland, as well as agricultural machinery and data transmission equipment used by farmers.

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