Unmanned aerial vehicle communication method for agricultural data perception

Abstract

The invention discloses an agricultural data perception-oriented unmanned aerial vehicle communication method in the technical field of agricultural machinery equipment, and the method comprises the following steps: grouping sensors according to the maximum width of a constructable wave beam of an antenna array equipped on an agricultural unmanned aerial vehicle and the distance between field sensors, and bringing the wave beam width into signal intensity calculation; the method comprises the following steps: by taking maximization of a user group transmission rate as a target, alternately optimizing hovering positions and beam widths of unmanned aerial vehicles by means of a block coordinate optimization algorithm and in combination with a chaos dynamic crow search algorithm, respectively obtaining optimal hovering points and optimal beam widths of the agricultural unmanned aerial vehicles corresponding to different groups, and obtaining optimal hovering points and optimal beam widths of the agricultural unmanned aerial vehicles based on the optimized hovering positions of the unmanned aerial vehicles. According to the invention, the energy efficiency of the communication system is improved, the service life of the agricultural unmanned aerial vehicle is prolonged, and the efficient communication between the field sensor and the agricultural unmanned aerial vehicle is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural machinery equipment, and particularly to an unmanned aerial vehicle communication method for agricultural data perception. Background Art

[0002] When communicating with in-field sensors for data transmission and reception, the communication distance between the ground base station and the in-field sensors is far and is easily blocked by tall trees. Therefore, agricultural unmanned aerial vehicles are often used as aerial agricultural machinery to communicate with in-field sensors to achieve services such as collecting crop data in the field. However, its communication link is easily interfered by environmental factors, resulting in communication link interruption, and the energy of the unmanned aerial vehicle is limited. How to reduce energy consumption while ensuring high communication rate is an important problem to be solved.

[0003] In the existing technology, the transmission technology between agricultural machinery and farmland sensors mostly relies on the 4G and LoRa communication technologies of traditional communication, and adopts a one-to-one mode to achieve communication. In the scenario of communication between conventional unmanned aerial vehicles and sensors, based on the enhanced transmission beamforming communication technology combined with NOMA, multiple sensors share the same communication resource, and the communication energy efficiency is maximized by jointly optimizing the unmanned aerial vehicle trajectory and signal transmission power.

[0004] In the current agricultural scenario, the beamforming technology is rarely combined to improve the communication energy efficiency. In addition, in the existing scenario of communication between unmanned aerial vehicles and target sensors, for the coverage of multiple users using the NOMA technology, the parameter of beam width is not included in the calculation of beam gain, that is, the existing scheme cannot reasonably change the width of the constructed beam according to the distribution of different users and the hovering position of the unmanned aerial vehicle during the communication process, so as to ensure the smoothness of the communication link between the unmanned aerial vehicle and the sensor. In the face of user groups that are far apart, communication interruption will occur during transmission. Summary of the Invention

[0005] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. In this part, as well as in the abstract and title of the specification of this application, some simplifications or omissions may be made to avoid obscuring the purpose of this part, the abstract of the specification, and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] Therefore, the purpose of the present invention is to provide an unmanned aerial vehicle communication method for agricultural data perception. By incorporating the beam width into the calculation of beam gain, and using the proposed CDCSA algorithm and DCSA algorithm to dynamically adjust the beam width and the trajectory of the agricultural unmanned aerial vehicle, the energy efficiency of the communication system is improved, the service life of the agricultural unmanned aerial vehicle is extended, and the high-efficiency communication between the in-field sensors and the agricultural unmanned aerial vehicle is ensured.

[0007] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions: A UAV communication method for agricultural data perception, the steps are as follows: S1. Based on the NOMA technology, group the sensors according to the maximum width of the beam that can be constructed by the antenna array equipped on the agricultural UAV and the distance between the field sensors; S2. Incorporate the beam width into the signal strength calculation. With the goal of maximizing the transmission rate of the user group, by means of the block coordinate optimization algorithm, combined with the chaotic dynamic crow search algorithm, alternately optimize the UAV hovering position and the beam width to obtain the optimal hovering point and the optimal beam width of the agricultural UAV corresponding to different groups respectively; S3. Based on the optimized UAV hovering position, discretize the trajectory as a TSP problem, use the discrete dynamic crow search algorithm to optimize and obtain the optimal trajectory, and use the discrete dynamic crow search algorithm to optimize and obtain the optimal flight trajectory of the UAV.

[0008] As a preferred scheme of the UAV communication method for agricultural data perception described in the present invention, in step S1, based on the NOMA technology, the steps of grouping the sensors according to the maximum width of the beam that can be constructed by the antenna array equipped on the agricultural UAV and the distance between the field sensors are as follows: First, combined with the characteristics of the NOMA technology, according to the distance between the field sensors, group the two sensors with closer distances; If the distance between users is greater than the maximum width θmax of the beam that can be constructed by the antenna array equipped on the agricultural UAV, then these two field sensors will each form a group, and create a circular area with the distance between the sensors as the radius. Determine whether there are other users in this area. If there are, disassemble them to prevent the signal transmission from being interfered by sensors outside the group.

[0009] As a preferred scheme of the UAV communication method for agricultural data perception described in the present invention, the incorporation of the beam width into the signal strength calculation is to calculate the relevant parameters through formulas (1)-(9), specifically as follows: (1) Among them, are the beam widths of the beams constructed by linear arrays with the number of antennas Nx and Ny respectively, and their calculation formulas are as follows: (2) (3) The signal strength y received by sensor l from UAV i l,i is calculated as follows: (4) where d l represents the distance between the UAV i and the sensor l: (5) where the position coordinates of the sensor l are (x l , y l , 0), the altitude of the UAV i is set to h, and its coordinates are represented as (x i , y i , h), and w l represents the beam weight vector of the UAV i with respect to the sensor l, and is specifically represented as follows: (6) a l represents the signal direction vector of the UAV i based on the sensor l, and is specifically represented as follows (7) u l and v l represent the direction angles of the sensor l relative to the UAV i, and are specifically represented as follows (8) (9).

[0010] As a preferred solution of a UAV communication method for agricultural data perception according to the present invention, in step S2, the objective expression with the goal of maximizing the transmission rate Rate m,i of user i within user group m is: (10) where Rate l represents the maximum transmission rate that the field UAV can provide, and the transmission rate of the signal received by the sensor l from the UAV i is calculated as follows: (11).

[0011] As a preferred solution of a UAV communication method for agricultural data perception according to the present invention, in step S2, when alternately optimizing by means of the block coordinate optimization algorithm, first set the beam width to the minimum value, that is, initialize the number of antennas to the maximum number of antennas available in the scenario, fix the beam width and use the chaotic dynamic crow search algorithm to optimize to obtain the optimal hovering position of the agricultural UAV, and then fix the hovering position of the UAV and optimize the selection of the optimal number of antennas in a traversing manner.

[0012] As a preferred solution of a drone communication method for agricultural data perception according to the present invention, the chaotic dynamic crow search algorithm includes chaotic initialization and dynamic search. The chaotic initialization uses the Logistic - tent chaotic method to obtain a more diverse crow population, and the specific formula is: (12) where r is a fixed constant and x n is a random number belonging to (0, 1); The dynamic search improves the value of AP in the original crow search algorithm, and the formula is: (13) where iter represents the current iteration number of the algorithm, and iter_max represents the maximum iteration number set for the algorithm. In the early stage of iteration, the value of AP iter is small, and in the later stage of iteration, the value of AP iter becomes larger; According to the value of AP, global search is performed in the initial stage of iteration, and local search is performed in the later stage. The position update rule formula is as follows: (14) where, represents the position of crow i at the t-th iteration. Subsequently, the fitness of and is compared, and the position with smaller fitness is set as the value of .

[0013] As a preferred solution of a drone communication method for agricultural data perception according to the present invention, the discrete dynamic crow search algorithm includes discrete initialization and dynamic crossover mutation update. The discrete initialization generates the initial population of the algorithm by randomly generating a [1×K] - dimensional array with non - repeating elements. The dynamic crossover mutation update performs crossover without mutation update between each crow individual and other crows in the early stage of iteration, and self - crossover update in the later stage of iteration. The update rule formula is as follows: (15) where represents the rule of crossover mutation between individual and individual . The calculation formula for the rule of crossover mutation between individual and individual is: (16).

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention combines NOMA and beamforming technologies, and uses agricultural drones and field sensors to achieve communication. In the process of solving the problem, the present invention first groups the sensors according to the maximum width of the beam that the drone can construct and the positions of the sensors, and considers the beam width in the calculation of the beam gain and the sensor outgoing signal transmission rate, so as to transform the optimization of the beam width into the optimization of the number of antennas. Subsequently, the proposed chaotic dynamic crow search algorithm and block coordinate optimization algorithm can be combined to achieve the hovering position of the drone for different user groups of fields and the optimal beam width, that is, the number of antennas used, improving the energy efficiency of the communication system and ensuring the smoothness of the communication link between the sensor and the field drone. In addition, according to the hovering position of the drone obtained by optimization, the present invention transforms the trajectory problem of the drone into a TSP problem, and uses the proposed discrete dynamic crow search algorithm to optimize and obtain the optimal flight trajectory of the drone, reducing the energy consumption of the drone and extending the service life of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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 drawings and specific embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts. Among them: Figure 1 is the overall flowchart of a drone communication method for agricultural data perception according to the present invention; Figure 2 is the flowchart of the alternating optimization of the beam width and the hovering position of the drone provided by the present invention; Figure 3 is the flowchart of the chaotic dynamic crow search algorithm provided by the present invention; Figure 4 is the flowchart of the discrete dynamic crow search algorithm provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings.

[0017] Secondly, the present invention will be described in detail in combination with the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention here. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will further describe in detail the embodiments of the present invention with reference to the accompanying drawings.

[0019] To improve the communication between field sensors and agricultural drones, the present invention combines NOMA technology and beam tracking technology and applies them to the scenario. To better adapt to the proposed scenario and extend the service life of agricultural drones, as Figure 1 shown, the present invention proposes a drone communication method for agricultural data perception, including the following steps: S1. Based on NOMA technology, group the sensors according to the maximum width of the beam that can be constructed by the antenna array equipped on the agricultural drone and the distance from the field sensors. S2. Incorporate the beam width into the signal strength calculation, aiming to maximize the transmission rate of the user group. With the help of the block coordinate optimization algorithm, alternately optimize the hovering position and beam width of the drone by combining the chaotic dynamic crow search algorithm to obtain the optimal hovering point and the optimal beam width of the agricultural drone corresponding to different groups respectively. S3. Based on the optimized hovering position of the drone, discretize the trajectory as a TSP problem, and use the discrete dynamic crow search algorithm to optimize and obtain the optimal trajectory, and use the discrete dynamic crow search algorithm to optimize and obtain the optimal flight trajectory of the drone.

[0020] The following will introduce the above steps S1 - S3 in detail: Step S1. Grouping of field sensors In the scenario of the present invention, 2×N field sensors are randomly scattered in the field within a fixed range and their positions are known. The present invention designs the grouping steps as follows: First, combining the characteristics of NOMA technology, grouping two sensors into a group can effectively decode the information transmitted and received by different sensors and reduce the interference between sensors within the same group. Therefore, the present invention proposes to group the two sensors with a closer distance according to the distance between the field sensors. It should be noted that considering the finiteness of the beam width, if the distance between users is greater than the maximum width θmax of the beam that can be constructed by the antenna array equipped on the agricultural drone, then these two field sensors will each form a group. Subsequently, create a circular area with the distance between the sensors as the radius, and determine whether there are other users within this area. If there are, break them up to prevent the signal transmission from being interfered by sensors outside the group.

[0021] Step S2: Optimization of the hovering position and beam width of the drone To achieve this goal, the present invention first incorporates the beam width into the calculation of the signal strength y received by sensor l from drone i l,i as follows: (1) where They are the beam widths of the beams constructed by linear arrays with the number of antennas being Nx and Ny respectively, and their calculation formulas are as follows: (2) (3) And the signal strength y received by sensor l from UAV i l,i is calculated as follows (4) where d l represents the distance between UAV i and sensor l: (5) where the position coordinates of field sensor l are (x l , y l , 0), the altitude of UAV i is set to h, and its coordinates can be expressed as (x i , y i , h), and w l represents the beam weight vector of UAV i with respect to sensor l, which is specifically expressed as follows (6) a l represents the signal direction vector of UAV i based on sensor l, which is specifically expressed as follows: (7) u l and v l represent the direction angles of sensor l relative to UAV i, which are specifically expressed as follows (8) (9) That is, the present invention combines the calculation of the beam width with the calculation of the signal strength, and sets the signal strength received by users not within the coverage range of the half-power beam width to 0.

[0022] So far, the optimization of the beam width can be quantified as the optimization of the number of antennas. In addition, the present invention aims to maximize the transmission rate Rate m,i of user i within user group m, which is specifically expressed as: (10) where Rate l represents the maximum transmission rate that the field UAV can provide, and the transmission rate of the signal received by sensor l from UAV i is calculated as follows (11) To obtain the optimal result more quickly and accurately, the present invention proposes to alternately optimize the beam width and the optimal hovering position of the UAV based on the block coordinate optimization algorithm. The specific steps are as follows Figure 2 shown.

[0023] First, set the beam width to the minimum value, that is, initialize the number of antennas to the maximum number of antennas that can be provided in the scene. Then, fix the beam width and use the proposed CDCSA algorithm to optimize the optimal hovering position of the agricultural UAV. Next, fix the hovering position of the UAV and optimize the selection of the optimal number of antennas in a traversal manner. The process of the proposed chaotic dynamic crow search algorithm (CDCSA) is as follows Figure 3 shown.

[0024] The present invention proposes that the chaotic dynamic crow search algorithm includes chaotic initialization and dynamic search.

[0025] 1. Chaotic initialization Chaotic initialization uses the Logistic - tent chaotic method to obtain a more diverse crow population. The specific formula is as follows: (12) where r is a fixed constant and x n is a random number belonging to (0, 1).

[0026] 2. Dynamic search To enhance the global search ability of the algorithm at the initial stage and the local search ability at the later stage for rapid convergence, the present invention proposes to improve the value of AP in the original search algorithm as follows: (13) where iter represents the current iteration number of the algorithm and iter_max represents the maximum iteration number set for the algorithm. At the early stage of iteration, the value of AP iter is small, and at the later stage of iteration, the value of AP iter becomes large. Therefore, the present invention proposes that the crow individuals randomly generate solutions again in the solution space at the initial stage of iteration for global search to improve the diversity of the crow population, while at the later stage, position updates are performed around the crow individuals to achieve local search of the algorithm. The specific update rule is as follows (14) where represents the position of crow i at the t - th iteration. Subsequently, compare with in terms of fitness, and set the position with smaller fitness as value.

[0027] Step S3, UAV trajectory optimization To solve this problem, based on the position of the agricultural drone obtained in the previous stage of optimization, the present invention discretizes the trajectory and regards it as a TSP problem to reduce the problem complexity, and proposes a discrete dynamic crow search algorithm (DCSA). The specific steps are as follows Figure 4 shown. Among them, the discrete dynamic crow search algorithm includes discrete initialization and dynamic crossover mutation update.

[0028] The discrete initialization generates the initial population of the algorithm by randomly generating a [1×K] - dimensional array with non - repeating elements. In the early stage of iteration, each crow individual crosses with other crows without mutation update, and in the later stage of iteration, self - crossover update is performed. The update rule formula is as follows: (15) Where represents the rule of crossover mutation between individual and individual . The calculation formula for the rule of crossover mutation between individual and individual is: (16) So far, with the progress of iteration, according to the dynamically set value of AP, the probability that AP is greater than 0.8 gradually increases. The proposed DCSA is more inclined to search around the crow individuals for local optimization, and finally converges to optimize to obtain the optimal trajectory of the agricultural drone.

[0029] Although the present invention has been described above with reference to the embodiments, various improvements can be made to it and its components can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments of the present invention can be combined with each other in any way. The exhaustive description of these combinations is not given in this specification only for the sake of saving space and resources. Therefore, the present invention is not limited to the specific embodiments disclosed in the text, but includes all technical solutions falling within the scope of the claims.

Claims

1. A drone communication method for agricultural data perception, characterized in that, The steps are as follows: S1. Based on the NOMA technology, group the sensors according to the maximum width of the beam that can be constructed by the antenna array equipped on the agricultural UAV and the distance between the sensors in the field; S2. Incorporate the beam width into the signal strength calculation. With the goal of maximizing the transmission rate of the user group, by means of the block coordinate optimization algorithm, combined with the chaotic dynamic crow search algorithm, alternately optimize the hovering position and beam width of the UAV to obtain the optimal hovering point and optimal beam width of the agricultural UAV corresponding to different groups respectively; S3. Based on the optimized hovering position of the UAV, discretize the trajectory as a TSP problem, use the discrete dynamic crow search algorithm to optimize and obtain the optimal trajectory, and use the discrete dynamic crow search algorithm to optimize and obtain the optimal flight trajectory of the UAV.

2. The drone communication method for agricultural data perception according to claim 1, characterized in that In step S1, based on the NOMA technology, the steps of grouping the sensors according to the maximum width of the beam that can be constructed by the antenna array equipped on the agricultural UAV and the distance between the sensors in the field are as follows: First, combined with the characteristics of the NOMA technology, according to the distance between the sensors in the field, group the two sensors with closer distances into one group; If the distance between users is greater than the maximum width θmax of the beam that can be constructed by the antenna array equipped on the agricultural UAV, then these two sensors in the field will each form a group, and create a circular area with the distance between the sensors as the radius. Determine whether there are other users in this area. If there are, disassemble them to prevent the signal transmission from being interfered by sensors outside the group.

3. The UAV communication method for agricultural data perception according to claim 1, wherein The incorporation of the beam width into the signal strength calculation is to perform relevant parameter calculations through formulas (1)-(9), specifically as follows: （1）； Among them, are respectively the beam widths of the beams constructed by linear arrays with the number of antennas Nx and Ny, and their calculation formulas are as follows: （2）； （3）； The signal strength y received by sensor l from drone i l,i is calculated as follows: (4)； where d l represents the distance between the drone i and the sensor l: (5)； Among them, the position coordinates of sensor l are (x l , y l , 0), the altitude of UAV i is set to h, and its coordinates are expressed as (x i , y i , h), and w l represents the beam weight vector of UAV i with respect to sensor l, which is specifically expressed as follows: (6)； a l The signal direction vector of the drone i based on the sensor l is specifically represented as follows (7)； u l and v l represent the orientation angle of the sensor l relative to the drone i, which is specifically represented as follows (8)； （9）。 4. The UAV communication method for agricultural data perception according to claim 1, wherein In step S2, the objective expression aiming at maximizing the transmission rate Rate of user i within user group m is as follows: m,i is: （10）； Among them, Rate l represents the maximum transmission rate that the field drone can provide, and the transmission rate of the signal received by sensor l from drone i is calculated as follows: （11）。 5. The method for drone communication for agricultural data perception according to claim 1, characterized in that In step S2, when alternately optimizing by means of the block coordinate optimization algorithm, first set the beam width to the minimum value, that is, initialize the number of antennas to the maximum number of antennas that can be provided in the scenario. Fix the beam width and use the chaotic dynamic crow search algorithm to optimize and obtain the optimal hovering position of the agricultural UAV, and then fix the hovering position of the UAV and optimize and select the optimal number of antennas in a traversing manner.

6. The drone communication method for agricultural data perception according to claim 5, characterized in that, The chaotic dynamic crow search algorithm includes chaotic initialization and dynamic search. The chaotic initialization uses the Logistic - tent chaotic method to obtain a more diverse population of crows. The specific formula is: （12）； where r is a fixed constant and x n is a random number belonging to (0, 1); The dynamic search improves the value of AP in the original crow search algorithm. The formula is: （13）； where iter represents the current iteration number of the algorithm, and iter_max represents the maximum iteration number set for the algorithm. In the early stage of iteration, the value of AP iter is small, and in the later stage of iteration, the value of AP iter becomes larger; Conduct global search in the initial stage of iteration according to the AP value and local search in the later stage. The position update rule formula is as follows: （14）； Among them, represents the position of crow i at the t-th iteration, and then compare with in terms of fitness, and set the position with smaller fitness as value.

7. A drone communication method for agricultural data perception according to claim 1, characterized in that The discrete dynamic crow search algorithm includes discrete initialization and dynamic crossover and mutation update. The discrete initialization generates the initial population of the algorithm by randomly generating a [1×K] - dimensional array with non - repeating elements. The dynamic crossover and mutation update perform crossover without mutation update between each crow individual and other crows in the initial stage of iteration, and self - crossover update in the later stage of iteration. The update rule formula is as follows: （15）； Among them represents an individual and the individual cross-mutation rule, the individual and the individual The calculation formula of the cross-mutation rule is: （16）。

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