Data transmission method for smart agriculture mobile scene

Abstract

The invention discloses a data transmission method for a smart agriculture mobile scene in the technical field of wireless communication, and the method comprises the steps: a ground base station at a base station transmits a signal to an unmanned aerial vehicle cluster relay communication system, and the unmanned aerial vehicle cluster relay communication system receives the signal and forms a virtual antenna array; an unmanned aerial vehicle cluster relay communication system reconstructs an optimized beam, and parameter optimization is performed on the communication system by adopting an APSSA-PSO algorithm, so that stable connection of communication between smart agricultural machinery in an agricultural operation scene is ensured. And the unmanned aerial vehicle cluster relay communication system performs beam reconstruction based on an optimization result, determines the number of nodes and the optimal position of the unmanned aerial vehicle cluster, and then generates distributed cooperative beam forming through a virtual antenna array to transmit data with ground agricultural equipment. According to the invention, the unmanned aerial vehicle is used as a mobile relay node, so that the problems of insufficient long-distance signal coverage in an intelligent agriculture scene and unstable transmission during node movement in a traditional wireless communication technology are solved.

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 smart devices and sensors are deployed in farmland to achieve real-time monitoring and precise control of the crop growth environment. These devices are usually distributed over a wide area. With the development 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 problems in covering large areas of farmland: 1. Signal blind spots: Due to the complex terrain of farmland, it is difficult for traditional fixed beams to effectively cover all areas. There are a large number of signal blind spots, resulting in poor communication quality and affecting the normal communication of equipment. The downlink communication link between the ground base station and the mobile node is always affected by long distances, undulating terrain or other interference. Relay transmission can expand the wireless coverage at a lower cost. Traditional static relays have complex deployment and high costs, and are greatly affected by environmental factors such as terrain and buildings, resulting in poor communication quality.

[0003] 2. Insufficient dynamic adjustment capability: Existing methods usually rely on fixed channel state information and cannot adapt to dynamic changes in equipment and environment. During node movement, the direction of the beam may change due to the uncertainty of the node's movement speed and direction, which causes the main lobe of the beam to deviate from the original target direction, thereby reducing the array gain.

[0004] 3. Energy efficiency optimization problem: Existing technologies make it difficult to optimize power consumption while ensuring communication quality, which increases the energy consumption burden of the equipment and is not conducive to long-term use. When the movement of the node causes the beam to be misaligned, it is usually necessary to recalculate the phase of the node to realign it with the target direction. However, if the node moves too far away from the base station, the signal strength may weaken. Optimizing node deployment and weight allocation can enhance the directivity and signal gain of the beam, thereby achieving better communication effects. However, frequent optimization operations will result in huge resource consumption.

[0005] In order to solve the problem of insufficient transmission distance caused by long-distance users, existing methods have implemented beamforming technology by equipping a single drone with an antenna array of a certain size to improve the communication capability of wireless networks. Some studies have proposed an artificial bee colony algorithm, which only considers the LoS component and the Rice K coefficient to find the optimal beamforming vector for each user, so that the system and bit rate can reach the maximum. However, the limited load of the drone in this method will make it impossible to equip it with a heavier high-power single antenna or multiple antennas. Secondly, for beam failure caused by node movement, controlling the beam width can improve the coverage and reliability of the mobile link, thereby alleviating the degradation of beam performance caused by node movement. Some studies have proposed a hybrid control scheme that jointly optimizes the excitation current weights and positions of mobile beamforming while ensuring the quality of service. However, the optimization algorithm used has too much computational overhead and is not suitable for actual sensor networks.

[0006] In summary, the use of beamforming technology in drone relay can significantly improve the capacity, coverage and link reliability of the system. Although existing technologies have conducted some research on DCB in mobile scenarios, they are usually based on the movement of nodes to fixed points, and the optimization methods are not fully applicable to actual agricultural operations. Therefore, in order to better cope with the challenges brought by node movement when nodes are constantly moving, it is necessary to explore more effective ways to overcome the problem of beam performance degradation caused by node movement. Summary of the invention

[0007] 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 specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0008] 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, the problems of insufficient signal coverage and unstable transmission of traditional wireless communication technologies in smart agricultural scenarios and node mobility are solved, and data collection and transmission tasks in agricultural operations are effectively realized.

[0009] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions: A data transmission method for a smart agricultural mobile scenario, comprising: 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 receives the signal and forms a virtual antenna array; 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 mobile state; S3. The UAV cluster relay communication system reconstructs the beam based on the optimization results, determines the number of nodes and the optimal position of the UAV cluster, and then generates distributed collaborative beamforming through the virtual antenna array to transmit data with ground agricultural equipment.

[0010] 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×N antennas is equipped at the base station, and N rotor drones are set to form a drone cluster relay communication system as mobile relays deployed between the base station and ground users, and the drones are equipped with uniform array antennas to form a virtual antenna array.

[0011] 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 function for determining the number of nodes is:

[0012]

[0013]

[0014]

[0015] in, is the beam width, , is a weighting factor. For node location optimization, the objective function of reducing the maximum sidelobe, reducing energy consumption and reducing delay while meeting real-time requirements is:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] in, and is the weighting factor, and are the side lobe and main lobe directions respectively, i is the number of algorithm iterations, is the motor power, is the time to move from the current position to the destination, constraint Given variables The upper limit constraint is that the maximum number of iterations cannot exceed ,constraint Gives the node's x-axis position range, between the position boundaries and Space, Constraint Gives the node's y-axis position range, between the position boundaries and Space, Constraint Given the main lobe position, constraint Given the sidelobe position, constraint Given the system time constraints, represents the array factor of the distributed beam emitted by the virtual antenna array composed of UAV nodes, is the lower limit angle of the main lobe, is the upper boundary angle of the main lobe, Indicates Sensor Nodes Movement time during movement, represents the computational time for solving the optimization problem, Indicates the maximum communication time allowed by the system. is the time to move from the current position to the destination position, expressed as:

[0024] in, , , , are the current position coordinates and expected position coordinates of the i-th node respectively, and V is the node moving speed.

[0025] 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: Generate the initial population based on the good point set and calculate the discoverer position and follower position; Select SD×N 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.

[0026] 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.

[0027] Compared with the prior art, the present invention has the following beneficial effects: by using drones as mobile relay nodes, the problem of insufficient signal coverage and unstable transmission in the mobile state of nodes in traditional wireless communication technology in smart agricultural scenarios is solved, and the data collection and transmission tasks in agricultural operations are effectively realized. The high maneuverability and flexibility of drones enable them to be flexibly deployed in areas with insufficient signal coverage, significantly enhancing the signal coverage of the system. The application of beam reconstruction technology enables drones to dynamically adjust the direction of signal transmission to ensure the stability and reliability of signal transmission under conditions of complex terrain and mobile nodes. The drones work in conjunction with ground sensors to collect a large amount of agricultural data in real time, ensure the accuracy and timeliness of the data, and thus support the scientific management of agricultural production. In addition, by optimizing the flight path and antenna excitation parameters of the drone, the present invention not only achieves the optimization between communication efficiency and energy consumption, reduces the energy consumption of the drone, but also optimizes the number and position of nodes, maximizes the beam width and signal strength, and enables the overall communication system to operate stably for a long time in the mobile state of nodes. This innovative method 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

[0028] 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 in combination with the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them: Figure 1 This is a flow chart of a data transmission method for a smart agricultural mobile scenario of the present invention; Figure 2 The flowchart of the APSSA-PSO algorithm provided by the present invention. DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] 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 obstacles such as undulating terrain and 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×N antennas is equipped at the base station, and N rotor drones are set to form a drone cluster relay communication system as a mobile relay deployed between the base station and the ground users. The drones are equipped with uniform array antennas to form a virtual antenna array, and use 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 drone 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 Nth drone, and the Mth ground user respectively. In the next sensing area of ​​the smart agriculture scenario, there are N Mobile sensor nodes are created and these nodes perform data collection tasks in the area. Since each node cannot communicate directly with the base station, nearby nodes should move in coordination with the source node and collaborate to form a virtual antenna array to communicate with the base station.

[0031] 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:

[0032] in,[ ] represents the direction of the mth ground user, is the amplitude of the far-field beam pattern of a UAV, 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.

[0033] The signal-to-noise ratio between the UAV cluster and the mth ground user based on the virtual antenna array can be expressed as:

[0034] in, is the constant path loss coefficient between the UAV and the ground user, It 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 is proportional to the square of represents the noise power, is the total transmission power of the UAV cluster, represents the path loss exponent, The center position of all drones and m The distance between ground users is expressed as:

[0035] According to Shannon's theorem, the theoretical upper limit of the communication rate of the virtual antenna array can be deduced. Among them, the transmission rate between the UAV cluster based on the virtual antenna array and the mth ground user can be expressed as:

[0036] Adding the transmission rate of the drone cluster and each user, the channel capacity of the U2U link is:

[0037] in, is the channel bandwidth between the base station and the drone cluster.

[0038] In the U2U link of the drone relay-assisted communication system, because the distance between the drone and the ground user is relatively long, when the drone needs to transmit data to the ground user, the drone cluster forms a virtual antenna array and uses DCB to transmit data to the ground user. The electromagnetic waves emitted by the virtual antenna array composed of multiple randomly deployed drones will overlap and offset each other during the transmission process, thereby enhancing or weakening the signal strength in certain specific directions. By determining the optimal position of the drone, a main lobe with high gain can be generated to align with the user while effectively reducing the gain in other directions outside the main lobe, thereby increasing the transmission rate from the drone antenna array to the ground user and maximizing the system capacity of the U2U link.

[0039] In mobile scenarios, node movement will have a significant impact on beam coverage and signal strength. If the node moves in a direction perpendicular to the base station or moves at a fast speed, the beam direction and beam gain will be difficult to control, causing the beam to be unable to align with the base station or the signal strength to be insufficient, resulting in communication failure. At this time, beam reconstruction is required. There are two conditions for beam reconstruction: the main lobe deviates from the target direction or the signal strength is insufficient for communication. Since the deviation of the main lobe will lead to insufficient signal strength, the probability of beam reconstruction can be judged by the signal strength. The reconstruction probability calculation formula is as follows:

[0040] Among them, P is the reconstruction probability and RSS is the signal strength.

[0041] If the corrected signal strength meets the communication requirements, there is no need to rebuild the beam, and only the node phase needs to be adjusted 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:

[0042] When f is 1, beam reconstruction is performed, and when f is 0, beam alignment is performed. In the formula, RSS is the signal strength, is the maximum beam gain that can be achieved with the current number of nodes and node positions, ( ) is the location of the source node.

[0043] like Figure 1 As shown, the data transmission method for the smart agriculture mobile scenario has the following steps: 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 receives the signal and forms a virtual antenna array. Specifically, a ground base station with M×N antennas is equipped at the base station, and N rotor drones are set to form a drone cluster relay communication system as a mobile relay deployed between the base station and the ground user. The drones are equipped with uniform array antennas to form a virtual antenna array; 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 mobile state; S3. The UAV cluster relay communication system reconstructs the beam based on the optimization results, determines the number of nodes and the optimal position of the UAV 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.

[0044] In step S2, the node movement causes the main lobe to deviate and frequently rebuild the beam, which will lead to a significant increase in computing energy consumption and node movement energy consumption. Considering the real-time nature of communication, the number of nodes is determined and the node positions are optimized to complete the reconstruction and optimize the performance of the beam. When determining the number of nodes, considering maximizing both the beam width and signal strength, we can get:

[0045]

[0046]

[0047]

[0048] in, is the beam width, , is the weighting factor.

[0049] 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:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] in, and is the weighting factor, and are the side lobe and main lobe directions respectively, and i is the number of algorithm iterations. is the motor power, is the time to move from the current position to the destination position, expressed as:

[0058] in, , , , are the current position coordinates and expected position coordinates of the ith node, respectively, and V is the node moving speed. In the above optimization problem, the constraints Given variables The upper limit constraint is that the maximum number of iterations cannot exceed ,constraint Gives the node's x-axis position range, between the position boundaries and Space, Constraint Gives the node's y-axis position range, between the position boundaries and Space, Constraint Given the main lobe position, constraint Given the sidelobe position, constraint Given the system time constraints, represents the array factor of the distributed beam emitted by the virtual antenna array composed of UAV nodes, is the lower limit angle of the main lobe, is the upper boundary angle of the main lobe, Indicates Sensor Nodes Movement time during movement, represents the computational time for solving the optimization problem, Represents the maximum communication time allowed by the system, which determines the first null width (FNBW) of the directional pattern, where represents the longest movement time spent by all mobile sensor nodes during their movement. is the computation time to solve the optimization problem.

[0059] Since the reconstruction beam optimization has a real-time requirement, the algorithm for solving the above problem should be efficient and fast. In the SSA algorithm, the initialization sparrow population is divided into three subgroups, namely, discoverers, followers, and guards. N×D In the space of N is the total number of sparrows, D is the spatial dimension, then i The position of the sparrow in space is X i =( x i,1 ，x i,2 ，…，x i,d ), i ∈[1, N ], d ∈[1, D ], x i,d Indicates i The position of a bird in D-dimensional space. The one that guides the movement of the population is called a discoverer, and its position update formula is as follows:

[0060] in, t Indicates the current iteration number; iter is the maximum number of iterations; is a uniform random number between (0, 1]; Q is a normally distributed random number; L is a matrix whose elements are all 1 and whose size is 1× d ; R2 ∈[0, 1] indicates the warning value; ST ∈[0.5, 1] ​​represents a safe value. R2 <ST When , it means that the population is not in danger and the discoverer continues to search; when R2≥ST When , it means that the vigilant sparrow has discovered the predator and immediately alerted other sparrows. The population immediately takes anti-predator action and flies to a safe area to forage. The follower mainly follows and monitors the movement of the discoverer and strives for a better position. The update formula is as follows;

[0061] in, represents the global worst position, = AT(AAT) -1 .when i >n / 2, which means i A participant does not get food and needs to fly to other places to find food. i ≤n / 2, it means that i Each joiner approaches the global optimal position and randomly forages for food. When foraging, the population selects a small number of sparrows to be responsible for vigilance. When a natural enemy appears, both the discoverer and the follower will abandon the current food and fly to a new location. Each generation randomly selects a sparrow from the population. SD (Usually 10%~20%) sparrows perform warning behavior. The position update formula is:

[0062] in, 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 Indicates the moving direction of the sparrow. The value is an interval A random number in is a small constant; f i Indicates i The fitness of a sparrow; f g Indicates the optimal fitness of the current sparrow population; f w represents the worst fitness of the current sparrow population. f i > f g When, it means i The sparrows are at the edge of the population and are vulnerable to predators; f i = f g When, it means i The sparrow is at the center of the group and because it perceives the threat, it needs to stay close to other sparrows to reduce the risk of capture.

[0063] The one-to-one transmission mechanism of global optimal information in SSA makes the convergence speed slow. In the traditional SSA algorithm, the way to update the optimal individual depends on the update of the population in each iteration. However, when the optimal individual finds an extreme value in the local space, this will cause the algorithm to fall into the local optimum and converge prematurely. In order to speed up the convergence speed and improve the ability to jump out of the local optimum, the present invention proposes an adaptive hybrid perturbation strategy to perturb the optimal individual after each iteration.

[0064] Particle Swarm Optimization (PSO) is a type of swarm intelligence algorithm. Its basic idea is to simulate the behavior of biological groups such as bird flocks or fish schools in the search space. Specifically, each solution is regarded as a particle in the search space, and each particle has two properties: speed and position. Particles are assigned to the optimal position according to their own historical optimal position ( pbest ) and the global optimal position ( gbest ) to adjust its own speed and position in the hope of finding the optimal solution. The specific method is as follows:

[0065]

[0066] in, and Represent the velocity and current position of the i-th particle respectively, represents the inertia factor, 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.

[0067] 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 Perform mixed disturbance on the optimal sparrow position. r ≤ When the perturbation is large, reverse learning is used; otherwise, Lévy flight is used for small perturbations.

[0068]

[0069]

[0070] in, represents the optimal individual position after updating by the adaptive hybrid perturbation strategy (i.e., after Lévy flight wandering), Represents the lower bound of the search space, is the minimum value of the search space where the optimal individual position is located, and represents the minimum allowed value of this dimension. It 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 allowed value of this dimension. and The boundary of the particle search is determined. Indicates The position of a sparrow in this dimensional space, Indicates A sparrow in The position of the iteration, and are all random numbers that conform to the normal distribution. is a random step size following a Lévy distribution, represents the step size control coefficient, r is a random number between 0 and 1. i is the number of iterations. As the number of iterations increases, it continues to decrease. In the early stage of the algorithm iteration, the optimal position needs to be perturbed with reverse learning in a large range so that the algorithm can quickly find the global optimal solution in the early stage; on the contrary, in the later iterations of the algorithm, the optimal position is perturbed in a small range by Lévy flight to jump out of the local optimum. On the basis of increasing the random distribution of its own position, the improved algorithm makes full use of global information, enhances the tendency of individuals to move to food, and accelerates the convergence speed.

[0071] 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 that of the original solution. The formula is as follows:

[0072] like Figure 2 As shown in the figure, the APSSA-PSO algorithm process is as follows: Generate the initial population based on the good point set and calculate the discoverer position and follower position; Select SD×N 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.

[0073] The present invention solves the problem of insufficient signal coverage and unstable transmission in the mobile state of nodes in traditional wireless communication technology in smart agriculture scenarios by using drones as mobile relay nodes, and effectively realizes the data collection and transmission tasks in agricultural operations. The high maneuverability and flexibility of drones enable them to be flexibly deployed in areas with insufficient signal coverage, significantly enhancing the signal coverage of the system. The application of beam reconstruction technology enables drones to dynamically adjust the direction of signal transmission to ensure the stability and reliability of signal transmission under complex terrain conditions. Through advanced adaptive beam reconstruction methods, the present invention effectively improves the system capacity and transmission efficiency. The drone works in conjunction with ground sensors to collect a large amount of agricultural data in real time, ensuring the accuracy and timeliness of the data, thereby supporting the scientific management of agricultural production. In addition, by optimizing the flight path and antenna excitation parameters of the drone, the present invention not only achieves the optimization between communication efficiency and energy consumption, reduces the energy consumption of the drone, but also optimizes the number and position of nodes, maximizes the beam width and signal strength, and enables the overall communication system to operate stably for a long time in the mobile state of nodes. This innovative method 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.

[0074] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. 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 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 mobile state; S3. The UAV cluster relay communication system reconstructs the beam based on the optimization results, determines the optimal position and number of nodes of the UAV cluster, and then generates distributed collaborative beamforming through the virtual antenna array to transmit data with ground agricultural equipment.

2. According to claim 1, a data transmission method for smart agricultural mobile scenarios is characterized in that: In step S1, a ground base station with M×N antennas is equipped at the base station, and N 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 drone cluster forms a virtual antenna array.

3. According to claim 1, a data transmission method for smart agricultural mobile scenarios is characterized in that: In step S2, the optimized beam is reconstructed according to the number of nodes and the node positions to improve the robustness of the communication system, and the node number function is determined as: ； in, is the beam width, , are weighting factors, which are 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: ； ； in, , and is the weighting factor, and are the side lobe and main lobe directions respectively, i is the number of algorithm iterations, is the motor power, is the time to move from the current position to the destination, and the constraint Given variables The upper limit constraint is that the maximum number of iterations cannot exceed ,constraint Gives the node's x-axis position range, between the position boundaries and Space, Constraint Gives the node's y-axis position range, between the position boundaries and Space, Constraint Given the main lobe position, constraint Given the sidelobe position, constraint Given the system time constraints, represents the array factor of the distributed beam emitted by the virtual antenna array composed of UAV nodes, is the lower limit angle of the main lobe, is the upper boundary angle of the main lobe, Indicates Sensor Nodes Movement time during movement, represents the computational time for solving the optimization problem, Indicates the maximum communication time allowed by the system.

4. According to claim 1, a data transmission method for smart agricultural mobile scenarios 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×N 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.

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

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