Dynamic adjustment method and device for UAV antenna supported by single Beidou

Abstract

The present invention discloses a method and device for dynamically adjusting the antenna of a UAV supported by a single Beidou, and relates to the field of antenna control technology. The method includes: positioning the target UAV to obtain a spatial positioning data signal; performing signal transmission and reception to obtain a signal transmission and reception record log; analyzing the flight area of ​​the target UAV to obtain terrain environment parameters, performing flight analysis, and generating flight attitude change information; performing operation calculations on the antenna module of the target UAV, generating antenna dynamic operation coefficients, and formulating antenna adjustment strategies; performing optimization, generating antenna adjustment optimization strategies, and performing dynamic adjustment. The present invention solves the technical problem in the prior art that the antenna adjustment accuracy of UAVs in complex environments is insufficient, resulting in poor communication and positioning accuracy and stability, and achieves the technical effect of realizing accurate tracking of the target signal source by the UAV antenna in complex environments, and improving the accuracy and stability of UAV communication and positioning.

Description

Technical Field

[0001] The present invention relates to the field of antenna control technology, and in particular to a method and device for dynamically adjusting an antenna of an unmanned aerial vehicle supported by a single Beidou. Background Art

[0002] In complex terrains such as urban canyons and mountainous areas, signals are easily blocked, reflected, and interfered with, resulting in a significant decrease in positioning accuracy, making it impossible for the drone to accurately determine its own position, which in turn affects the antenna adjustment accuracy, making it difficult to accurately point to the target signal source, causing communication interruption or signal quality deterioration. On the other hand, there is signal interference between multi-system GNSS, which increases positioning errors and reduces system reliability. Moreover, traditional drone antenna adjustment strategies are often unable to adapt to complex and changing flight environments in real time, lack comprehensive consideration of factors such as flight attitude changes, signal interference, and terrain environment, and are unable to perform accurate dynamic adjustments based on real-time conditions. These problems seriously restrict the efficient and stable operation of drones in complex environments.

[0003] The existing technology has the technical problem that the antenna adjustment accuracy of drones in complex environments is insufficient, resulting in poor communication and positioning accuracy and stability. Summary of the invention

[0004] The present application provides a method and device for dynamic adjustment of UAV antennas under single Beidou support, which is used to solve the technical problem in the prior art that the antenna adjustment accuracy of UAVs in complex environments is insufficient, resulting in poor communication and positioning accuracy and stability.

[0005] In view of the above problems, the present application provides a method and device for dynamic adjustment of UAV antennas under single Beidou support.

[0006] The first aspect of the present application provides a method for dynamically adjusting the antenna of a drone supported by a single Beidou, the method comprising:

[0007] The target UAV is positioned by the Beidou satellite navigation system to obtain a spatial positioning data signal of the target UAV; based on the spatial positioning data signal, the antenna module of the target UAV is activated to transmit and receive signals, and a signal transmission and reception record log is obtained; the flight area of ​​the target UAV is analyzed to obtain terrain environment parameters, and a flight analysis of the target UAV is performed based on the terrain environment parameters to generate flight attitude change information; according to the flight attitude change information and the signal transmission and reception record log, an operation calculation is performed on the antenna module of the target UAV to generate an antenna dynamic operation coefficient, and an operation analysis of the antenna module is performed according to the antenna dynamic operation coefficient to formulate an antenna adjustment strategy; the antenna adjustment strategy is simulated to perform gain control and a gain instruction is generated, the antenna adjustment strategy is optimized through the gain instruction, an antenna adjustment optimization strategy is generated, and the antenna module of the target UAV is dynamically adjusted through the antenna adjustment optimization strategy.

[0008] The second aspect of the present application provides an electronic device, including: a memory for storing executable instructions; a processor for executing the executable instructions stored in the memory to implement a method for dynamic adjustment of a drone antenna under single Beidou support.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] Position the target UAV and obtain the spatial positioning data signal of the target UAV; activate the antenna module of the target UAV to send and receive signals based on the spatial positioning data signal, and obtain the signal sending and receiving record log; analyze the flight area of ​​the target UAV to obtain the terrain environment parameters, perform flight analysis on the target UAV, and generate flight attitude change information; perform operation calculations on the antenna module of the target UAV in combination with the signal sending and receiving record log, generate the antenna dynamic operation coefficient, perform operation analysis on the antenna module, and formulate an antenna adjustment strategy; simulate the execution of the antenna adjustment strategy for gain control, optimize through gain instructions, generate an antenna adjustment optimization strategy, and dynamically adjust the antenna module of the target UAV. It achieves the precise tracking of the target signal source by the UAV antenna in a complex environment, and improves the accuracy and stability of the UAV communication and positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the 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 work.

[0012] Figure 1A schematic diagram of a flow chart of a method for dynamically adjusting a drone antenna under single Beidou support provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0014] Description of the reference numerals: input device 201 , processor 202 , memory 203 , output device 204 . DETAILED DESCRIPTION

[0015] This application provides a method and device for dynamic adjustment of UAV antennas under single Beidou support, which is used to solve the technical problem in the prior art that the antenna adjustment accuracy of UAVs in complex environments is insufficient, resulting in poor communication and positioning accuracy and stability.

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0017] Embodiment 1, as Figure 1 As shown, the present application provides a method for dynamically adjusting the antenna of a UAV supported by a single Beidou, the method comprising:

[0018] Step S100: Positioning the target UAV through the Beidou satellite navigation system to obtain the spatial positioning data signal of the target UAV.

[0019] Specifically, the Beidou satellite navigation system is crucial to the positioning process of the target UAV. First, the signal receiving module of the Beidou satellite navigation system is started. It will perform initialization analysis on the target UAV to determine the prior knowledge of the target UAV, such as the initial position, speed and other basic information of the UAV. Based on this prior knowledge, positioning analysis is performed to obtain multiple particles, which are the basis for subsequent accurate calculations. As time goes by, the time step is obtained, and the propagation of multiple particles is predicted according to the time step to obtain the particle propagation prediction information, and then resampled based on this information to generate a particle sampling data set. Through the analysis of the particle sampling data set, multiple particles are differentially corrected, and finally multiple particle positioning data are determined, and added to the spatial positioning data signal, so as to obtain the accurate spatial positioning data signal of the target UAV. Single Beidou plays an irreplaceable and key role in this process. It has an autonomously controlled satellite network and has significant advantages in positioning accuracy and reliability. In complex environments such as urban canyons and mountainous areas, other global navigation satellite systems (GNSS) are susceptible to interference and have large positioning errors, while the positioning signal of single Beidou can effectively reduce the error and provide high-precision positioning services. Its regional optimization characteristics can reduce the interference and errors of multi-system GNSS, provide stable and accurate positioning for drones, and ensure that when the drone dynamically adjusts the antenna, it can make fine adjustments based on the most accurate location information, so that the antenna can better align with the signal source and achieve accurate signal transmission and reception. In addition, the Beidou satellite system's advantages such as two-way communication capabilities, high-precision positioning, and differential services not only ensure stable communication during drone flight, but also meet the drone's high-precision navigation and positioning needs. Through independent and compatible signal transmission, the anti-interference ability of signal reception is enhanced, providing a solid positioning foundation for the dynamic adjustment of drone antennas, and strongly supporting the stable operation and efficient communication of drones in complex environments.

[0020] Step S200: activating the antenna module of the target UAV to perform signal transmission and reception based on the spatial positioning data signal, and obtaining a signal transmission and reception record log.

[0021] Specifically, the drone antenna module is a key component responsible for the emission and reception of electromagnetic waves in the drone system. It is activated by the spatial positioning data signal and once activated, it quickly starts working. In terms of signal transmission, the antenna module modulates the information processed inside the drone into electromagnetic waves of specific frequency and intensity, and transmits it to the outside world to realize the information output between the drone and the ground control station or other equipment; in the signal reception link, it captures the electromagnetic wave signal from the outside world and converts it into an electrical signal, which is transmitted to the internal system of the drone, so that the drone can obtain external instructions, data and other information. During the entire signal transmission and reception process, the key parameters of each signal transmission and reception, such as transmission time, signal strength, reception quality, and interference conditions, are fully recorded. These rich data are sorted and summarized in a specific format to generate a signal transmission and reception record log. The log records the working status of the antenna module in detail, which helps to deeply analyze its performance, and then provides indispensable data support for the subsequent formulation of precise adjustment strategies and optimization of antenna working conditions to ensure stable and efficient communication of drones.

[0022] Step S300: Analyze the flight area of ​​the target UAV to obtain terrain environment parameters, perform flight analysis on the target UAV based on the terrain environment parameters, and generate flight attitude change information.

[0023] Specifically, the target UAV uses its own sensors, such as lidar, altimeter, camera, etc., to collect all-round data of the flight area. These sensors continuously collect information such as terrain height, landform features, obstacle distribution, etc., and construct three-dimensional terrain data of the flight area through complex data processing algorithms. Based on these three-dimensional terrain data, the terrain parameters are further analyzed in depth, and various terrain features such as mountains, canyons, and buildings are identified and quantified into terrain environment parameters, which include a terrain environment model used to describe the overall characteristics of the terrain. Then, multiple preset or existing flight paths of the target UAV in the flight area are retrieved, and these paths are mapped to the terrain environment model for flight simulation. During the simulation process, multiple simulated flight trajectories are generated taking into account the impact of terrain on flight, such as airflow changes, signal blocking, etc. Finally, the inertial navigation module of the UAV is started based on these flight trajectories. The module obtains acceleration data sets and angular velocity data sets by measuring acceleration and angular velocity and combining flight trajectory points for calculation. The two data sets are fused in real time according to the flight trajectory points to obtain a flight state parameter set containing multiple flight attitude change values, which is then processed by a complementary filtering algorithm to finally generate information that can accurately reflect the flight attitude changes of the UAV in a complex terrain environment. This information is crucial for the subsequent precise adjustment of the drone antenna module to ensure stable signal transmission.

[0024] Step S400: performing operation calculations on the antenna module of the target UAV according to the flight attitude change information and the signal receiving and sending record log, generating an antenna dynamic operation coefficient, performing operation analysis on the antenna module according to the antenna dynamic operation coefficient, and formulating an antenna adjustment strategy.

[0025] Specifically, the multipath suppression algorithm is first used to process the signal transmission and reception record log according to the flight attitude change information. Since the change of the UAV's flight attitude will affect the signal propagation path and generate multipath pseudo signals, the multipath suppression algorithm can identify and mark these pseudo signals and use them as indexes to traverse and match the signal transmission and reception record log. Once the records corresponding to the multipath pseudo signals are identified, these interference information is removed from the log to obtain a more accurate signal transmission and reception record update log. Then, the regression analysis algorithm is used to calculate the operation of the antenna module in combination with the signal transmission strength value and signal reception quality value in the signal transmission and reception record update log. The regression analysis algorithm establishes a mathematical model to find the potential relationship between these parameters, thereby generating the antenna dynamic operation coefficient. This coefficient comprehensively reflects the operating status of the antenna under the current flight attitude and signal transmission and reception conditions. After obtaining the antenna dynamic operation coefficient, the weight calculation algorithm is used to combine the real-time flight status of the target UAV, such as flight speed, altitude, acceleration and other factors, to assign weights to different operating parameters and calculate multiple weight coefficients. For example, when flying at high speed, the flight speed has a greater impact on the antenna adjustment, and the corresponding weight will increase; in areas with weak signals, the weight of the signal reception quality value will increase. Finally, based on these weight coefficients, the control adjustment algorithm is used to control and adjust the antenna module and formulate an antenna adjustment strategy. The control adjustment algorithm determines the adjustment amount of the antenna in terms of direction, angle, gain, etc. according to the weight coefficient. For example, if the signal reception quality is poor and the drone is approaching the signal source, the algorithm will calculate an adjustment strategy to increase the antenna gain and fine-tune the antenna direction to ensure that the antenna can receive signals more effectively and achieve stable communication between the drone and external devices.

[0026] Step S500: simulate the execution of the antenna adjustment strategy to perform gain control, generate a gain instruction, optimize the antenna adjustment strategy through the gain instruction, generate an antenna adjustment optimization strategy, and dynamically adjust the antenna module of the target UAV through the antenna adjustment optimization strategy.

[0027] Specifically, the antenna adjustment strategy formulated is simulated and executed, and a simulation environment highly similar to the actual flight scene is constructed with the help of professional simulation software or algorithms, covering the flight attitude of the drone, the surrounding signal environment, terrain interference and other factors. In this simulation scenario, the operation of the antenna module is simulated and analyzed according to the established antenna adjustment strategy, and the signal receiving and transmitting effects of the antenna under different adjustment parameters are recorded in detail, including indicators such as signal strength and signal-to-noise ratio, so as to generate the antenna simulation adjustment results. Based on these simulation adjustment results, the signal quality is quantitatively evaluated using the signal evaluation algorithm to generate multiple signal scores. According to these scores, the gain analysis algorithm is used to conduct in-depth research to determine multiple parameters to be gained, such as the antenna gain angle parameter and the antenna gain direction parameter. These parameters reflect the key indicators that need to be adjusted for the antenna under the current simulation conditions to achieve better signal reception effects. Combined with the multiple weight coefficients obtained by calculation, the gain calculation algorithm is used to calculate the gain of the antenna adjustment strategy. Through this calculation, multiple gain fitnesses are obtained, which are used to measure the optimization degree of different adjustment strategies in the current simulation environment. According to the level of gain fitness, the antenna adjustment strategy is marked to be optimized, and then a gain instruction is generated. Finally, the original antenna adjustment strategy is optimized according to the generated gain instruction. During the optimization process, the key parameters in the antenna adjustment strategy are adjusted, such as the rotation angle range of the antenna, the adjustment amplitude of the signal gain, etc., to generate an antenna adjustment optimization strategy. This optimized strategy is applied to the antenna module of the target UAV, so that the antenna can dynamically adjust its working state according to the real-time flight status of the UAV and changes in the signal environment, such as automatically changing the direction of the antenna, adjusting the signal gain strength, etc., to ensure that the signal transmission between the UAV and the outside world is always maintained in the best state, and improve the communication quality and stability of the UAV.

[0028] In a possible implementation, step S100 further includes:

[0029] Step S110: Start the signal receiving module of the Beidou satellite navigation system to perform initialization analysis on the target UAV, determine the prior knowledge of the target UAV, perform positioning analysis based on the prior knowledge, and obtain multiple particles.

[0030] Step S120: Obtain a time step, perform propagation prediction on the multiple particles according to the time step, obtain particle propagation prediction information, perform resampling according to the particle propagation prediction information, and generate a particle sampling data set.

[0031] Step S130: performing differential correction on the plurality of particles based on the particle sampling data set to determine a plurality of particle positioning data.

[0032] Step S140: Add the plurality of particle positioning data to the spatial positioning data signal.

[0033] Specifically, at the beginning of using the Beidou satellite navigation system to locate the target drone, the signal receiving module is first started. The module quickly begins to perform initialization analysis operations on the target drone. It retrieves and integrates basic information related to the drone. This information comes from a wide range of sources, including the initial parameters preset when the drone leaves the factory, the position and speed recorded at the end of the last flight mission, and other data, in order to determine the prior knowledge of the target drone. This prior knowledge provides a key basis for subsequent positioning analysis. With the help of positioning algorithms, prior knowledge is fully utilized to conduct comprehensive searches and calculations in complex positioning spaces, thereby generating multiple particles. Each particle carries specific information and represents a possible positioning state of the drone in the current environment. These particles together constitute multiple groups of initial speculations on the positioning of the drone, laying an important foundation for subsequent more accurate positioning calculations.

[0034] The long short-term memory network (LSTM) is used to analyze and process the historical flight data of the drone, which includes information with time series characteristics such as flight speed, acceleration, and positioning error. With its gating mechanism, the LSTM network can effectively capture the long-term dependencies in the data and predict the most appropriate time step at the moment. After determining the time step, the particle filter algorithm based on the Monte Carlo method is used to propagate multiple particles for prediction. Each particle represents a possible positioning state of the drone, including information such as position and speed. The particle state is updated and predicted by using dynamic models such as the uniform motion model combined with the time step. In the prediction process, the convolutional neural network (CNN) is used to evaluate the signal quality. CNN performs convolution and pooling operations on the received satellite signal data, such as signal strength and frequency, and outputs the signal quality evaluation score through multi-layer neural network learning. Finally, the K-Means clustering algorithm is used for resampling based on the particle propagation prediction information and signal quality evaluation results. First, all particles are divided into K clusters according to signal quality and prediction state, and the average signal quality and center position of particles in each cluster are calculated. Next, the clusters are weighted according to their average signal quality. The higher the signal quality, the greater the weight of the cluster. Particles are extracted from each cluster according to the weight ratio to form a new particle sampling data set. The new data set can contain more particles representing reliable positioning states, which improves the accuracy and reliability of positioning.

[0035] Differential correction operations are performed on multiple particles to determine the positioning data of multiple particles. Since the positioning state represented by the particles in the particle sampling data set may have errors, differential correction is intended to reduce these errors and improve the accuracy of positioning. Reference particles with high credibility are screened out in the data set. These reference particles are usually particles with high signal quality and strong correlation with other particles. Then, the difference information of each particle relative to the reference particle is calculated, and the difference information includes position deviation, velocity deviation, etc. By analyzing these difference information, the correction calculation is performed using the pre-established differential correction model. The model takes into account a variety of factors that may affect positioning, such as propagation errors of satellite signals, environmental interference, etc. During the correction process, the correction parameters are dynamically adjusted according to the actual situation of the particles to ensure the effectiveness of the correction. After differential correction, the positioning state of the particles that originally had errors is optimized, thereby determining more accurate positioning data of multiple particles.

[0036] Add multiple particle positioning data to the spatial positioning data signal. Through calculation and correction of the obtained particle positioning data, the possible position state of the drone in space is accurately reflected. When these data are added to the spatial positioning data signal, the accuracy of the spatial positioning data signal is greatly improved. With this high-precision spatial positioning data signal, the drone antenna module can be activated more accurately in the future. Accurate positioning data allows the antenna module to know the relative position relationship between itself and the target signal source. When sending and receiving signals, it can determine the best sending and receiving direction according to the positioning data, reduce signal transmission loss, and improve signal quality. In addition, the optimization of the spatial positioning data signal also lays a solid foundation for the subsequent analysis of the terrain environment parameters of the flight area and the generation of flight attitude change information. This information is combined with the signal sending and receiving record log to calculate the antenna dynamic operation coefficient, and then formulate an antenna adjustment strategy that is more in line with the actual flight state of the drone. Whether it is adjusting the antenna angle, optimizing the gain, or coping with complex terrain and flight attitude changes, accurate spatial positioning data signals are the key to achieving precise antenna adjustment, ultimately ensuring that the antenna of the drone can always efficiently track the target signal source during flight and maintain stable communication and positioning functions.

[0037] In a possible implementation, step S200 further includes:

[0038] Step S210: performing real-time analysis on the plurality of particle positioning data, extracting signal noise parameters, performing signal interference analysis based on the signal noise parameters, and determining a plurality of interference factors.

[0039] Step S220: performing Kalman filtering on the spatial positioning data signal according to the multiple interference factors to generate a spatial positioning standard data signal.

[0040] Step S230: activating the antenna module of the target UAV to determine the antenna receiving and transmitting direction according to the spatial positioning standard data signal.

[0041] Step S240: Recording two-way communication with the target UAV through the antenna transceiver direction to obtain a plurality of two-way communication data, wherein the plurality of two-way communication data includes signal sending communication data and signal receiving communication data.

[0042] Step S250: performing signal analysis on the signal sending communication data to obtain a signal sending strength value, and performing signal analysis on the signal receiving communication data to obtain a signal receiving quality value.

[0043] Step S260: Associating and integrating the signal transmission strength value with the signal reception quality value to construct the signal transmission and reception record log.

[0044] Specifically, the multiple particle positioning data obtained in the previous positioning link are analyzed in real time. In this process, the signal noise parameters are extracted from the particle positioning data using the signal processing algorithm. These parameters reflect the degree of noise interference to the signal during transmission. Based on these signal noise parameters, signal interference analysis is further carried out. By comparing the normal signal characteristics with the actual received signal characteristics, multiple interference factors are determined. These interference factors may include electromagnetic interference from other electronic devices, signal attenuation caused by terrain obstruction, and other factors.

[0045] Based on the determined multiple interference factors, the spatial positioning data signal is processed by Kalman filtering. Kalman filtering is a highly efficient recursive filter that can use the system's dynamic model and measurement data to optimally estimate the signal. By incorporating the interference factors into the calculation process of Kalman filtering, the interfered spatial positioning data signal is optimized to remove the influence of noise and interference, generate a spatial positioning standard data signal, and make the positioning data more accurate and reliable.

[0046] After completing the Kalman filter processing of the spatial positioning data signal and obtaining the spatial positioning standard data signal, the antenna module of the target drone will be activated. As a key component for drones to achieve signal transmission, the antenna module can not only send out various data, instructions and other information inside the drone in the form of electromagnetic waves, realize information interaction with the ground control station, other drones or equipment, but also receive signals from the outside world, so that the drone can obtain various key instructions and data in a timely manner to ensure that it can fly and perform operations normally according to the scheduled tasks. The activation of the antenna module this time is based on the precise spatial positioning standard data signal. The signal accurately reflects the actual position of the drone in space. By analyzing this data and combining the approximate direction of the signal source, the antenna module can quickly and accurately determine its own sending and receiving direction. It can align the signal source at the best angle and direction. When sending signals, it can concentrate energy to the greatest extent, reduce signal scattering and attenuation, ensure that the signal is sent with the strongest intensity and the most accurate direction, and improve signal transmission efficiency and coverage. When receiving signals, it can effectively enhance the ability to capture target signals and reduce the impact of external interference signals, thereby improving the quality of signal reception, ensuring stable and efficient communication between the UAV and the outside world, and providing reliable communication guarantee for the UAV's subsequent mission execution, thereby ensuring the stable operation and precise operation of the UAV in complex environments.

[0047] After the antenna module determines the direction of transmission and reception, the drone records two-way communication through the antenna transmission and reception direction. During the communication process, the drone exchanges data with the ground control station or other equipment, and records multiple two-way communication data, which include signal transmission communication data and signal reception communication data. These communication data record in detail the process and results of the drone's information interaction with the outside world.

[0048] For the signal transmission communication data, the signal analysis algorithm is used to conduct in-depth analysis from multiple dimensions. First, the power characteristics of the signal are analyzed. According to the current, voltage and other parameters in the transmission process, combined with the physical model of signal propagation, the energy intensity of the signal at the moment of transmission is accurately calculated. At the same time, the spectrum distribution of the signal is analyzed to see whether there are frequency offsets, spurious and other conditions, because these factors will affect the actual effect of signal transmission. By comprehensively analyzing this information, the signal transmission strength value that can accurately reflect the signal transmission capability is finally obtained. For the signal reception communication data, an evaluation mechanism is used to analyze the signal reception quality. On the one hand, the signal strength stability is detected to determine whether there is a large fluctuation in the strength during the reception process, because unstable signal strength may increase the error rate of data transmission. On the other hand, the clarity of the signal is evaluated to see whether there are problems such as noise interference and signal distortion. The purity and accuracy of the signal are quantified by calculating indicators such as signal-to-noise ratio and bit error rate. Combining these analysis results, the signal reception quality value used to measure the quality of signal reception is obtained. These two key values ​​provide important data support for the subsequent comprehensive evaluation of the performance of the drone antenna module and the formulation of accurate antenna adjustment strategies.

[0049] After obtaining the signal transmission strength value and signal reception quality value, these two key data are associated and integrated to construct a signal transmission and reception record log. The signal transmission strength value reflects the ability of the drone to send signals, while the signal reception quality value reflects the quality of the drone's signal reception. Both are important indicators for measuring the working performance of the antenna module. First, a structured data framework is created as the basis for the signal transmission and reception record log. In this framework, different data fields are set to store the signal transmission strength value, signal reception quality value, and other related auxiliary information, such as recording time, corresponding flight status parameters, etc. Then, the signal transmission strength value and signal reception quality value are accurately filled into the corresponding data fields. The two are associated through timestamps, so that each set of signal transmission strength values ​​and signal reception quality values ​​can correspond to the working status of the drone at the same time. At the same time, in order to facilitate subsequent data analysis and processing, these data will be standardized to have a unified format and unit. After such association and integration, a complete signal transmission and reception record log is constructed. The log records the signal reception and transmission status of the drone antenna at different time points in detail and in an orderly manner, providing an intuitive and comprehensive data basis for the subsequent operation analysis and performance evaluation of the antenna module, as well as the formulation of scientific and reasonable antenna adjustment strategies, which helps to improve the communication stability and reliability of the drone in complex environments.

[0050] In a possible implementation, step S300 further includes:

[0051] Step S310: The target UAV traverses the flight area to collect data and construct three-dimensional terrain data of the flight area.

[0052] Step S320: Perform terrain parameter analysis based on the three-dimensional terrain data to obtain a plurality of terrain features, and construct terrain environment parameters according to the plurality of terrain features, wherein the terrain environment parameters include a terrain environment model.

[0053] Step S330: Retrieve multiple flight paths of the target UAV in the flight area, map the multiple flight paths to the terrain environment model for flight simulation, and generate multiple flight trajectories.

[0054] Step S340: starting the inertial navigation module of the target UAV according to the multiple flight trajectories to perform flight estimation on the target UAV and generate the flight attitude change information.

[0055] Specifically, the target drone conducts a comprehensive traversal flight in the flight area according to the preset flight plan. During the flight, the various data collection devices carried by the drone, such as laser radar, high-definition camera, altimeter, etc., work together to continuously collect various information about the flight area. The laser radar accurately obtains the height information of the terrain by emitting a laser beam and measuring the time of reflected light; the high-definition camera captures a large amount of image data to identify the texture and features of the terrain; the altimeter monitors the flight altitude of the drone in real time. The data collected by these devices are initially processed and integrated by the system to construct the three-dimensional terrain data of the flight area, which can intuitively and comprehensively present the topography of the flight area.

[0056] The edge detection algorithm is used to process the three-dimensional terrain data. The algorithm can keenly identify the boundaries of different terrain features in the terrain data, such as the boundary between mountains and plains, rivers and land. Through edge detection, different areas in the terrain data are divided, providing a basis for subsequent feature extraction. Then, the cluster analysis algorithm is used to cluster the divided terrain data. The algorithm clusters similar terrain areas into one category based on the height, slope, texture and other features of the terrain, thereby extracting multiple significant terrain features such as peaks, valleys, hills, lakes, and rivers. After obtaining multiple terrain features, the terrain environment parameters are constructed, and a series of quantitative indicators are calculated for each terrain feature. For example, for the terrain feature of the peak, its altitude, the slope range of the peak, the surface area of ​​the peak and other parameters will be calculated; for the river, its length, width, flow rate and other parameters will be calculated. At the same time, a terrain feature database is established to store each terrain feature and its corresponding quantitative indicators. In order to construct the terrain environment model, the three-dimensional terrain data is processed by a triangular meshing algorithm, which divides the terrain surface into a series of interconnected triangular meshes, each of which represents a small area on the terrain surface. In this way, the shape and undulation of the terrain can be described more accurately. Then, the extracted terrain features and the calculated quantitative indicators are mapped to the triangular mesh to construct a terrain environment model containing rich information. In this model, not only can the geomorphic features of the terrain be intuitively seen, but the corresponding terrain features and quantitative parameters can also be obtained by querying each triangular mesh. In addition, the spatial relationship between terrain features is also considered. For example, the distance between the peak and the river, the occlusion relationship of the mountain range to the valley, etc. are calculated. These spatial relationship information will also be incorporated into the terrain environment parameters to further improve the terrain environment model. Through the above series of implementation methods, terrain environment parameters and terrain environment models containing rich information are successfully constructed based on three-dimensional terrain data, providing accurate and comprehensive terrain information support for subsequent flight simulation and antenna adjustment strategy formulation.

[0057] Retrieve multiple pre-set flight paths of the target UAV in the flight area. These flight paths are planned according to the mission requirements and the characteristics of the flight area. Then, these flight paths are mapped to the constructed terrain environment model for flight simulation. During the simulation process, analyze the various effects of terrain on flight, such as changes in airflow, signal obstruction, etc. Through simulation, multiple flight trajectories are generated, which reflect the actual flight conditions of the UAV under different flight paths and under the influence of the terrain environment.

[0058] When the drone flies along multiple predetermined flight paths, its inertial navigation module begins to play a key role. The drone's inertial measurement unit (IMU) continuously collects data. The accelerometer accurately monitors the acceleration of the drone in the three axes of X, Y, and Z, and the gyroscope is responsible for real-time measurement of the angular velocity of the drone's rotation around these three axes. These sensors collect data at a very high frequency to ensure that the latest data can be obtained in a timely and uninterrupted manner. The real-time attitude information of the drone is inferred with the help of an integration method based on IMU data. For example, for acceleration data, in the X-axis direction, the velocity value at the initial moment is first determined, and then the velocity value measured by the accelerometer that changes with time can be obtained through integration operations to obtain the velocity in the X-axis direction that changes with time. Then, this velocity is integrated and combined with the initial position information to calculate the displacement of the drone in the X-axis direction. Similarly, similar integration operations are performed on the acceleration data in the Y-axis and Z-axis directions to obtain the velocity and displacement information in these two axes. For angular velocity data, taking the angular velocity around the X-axis as an example, starting from the initial pitch angle, the pitch angle of the drone changing over time in the X-axis direction can be calculated by integrating the angular velocity over time. Using the same method to process the angular velocity data in the Y-axis and Z-axis directions, real-time attitude information such as roll angle and yaw angle can be obtained. Because the sensor itself has certain errors and the external environment will also cause interference, the attitude information obtained by integration alone may not be very accurate. Therefore, it is necessary to use the Kalman filter algorithm based on IMU to fuse the acceleration and angular velocity data to improve the accuracy of attitude estimation. The Kalman filter algorithm treats the attitude change of the drone as a dynamic system, which mainly includes two links: prediction and update. In the prediction link, based on the attitude estimation value of the previous moment, combined with the acceleration and angular velocity data measured by the IMU, the kinematic model is used to predict the attitude value of the current moment. In the update link, the predicted attitude value is compared with the acceleration and angular velocity data actually measured, and the predicted value is adjusted by calculating the Kalman gain, thereby obtaining a more accurate attitude estimation value. By repeating the two steps of prediction and updating, the Kalman filter algorithm can integrate acceleration and angular velocity data in real time and accurately calculate the three-dimensional attitude changes of the drone. Finally, the three-dimensional attitude change information obtained after Kalman filter processing is sorted and recorded to generate flight attitude change information. This information records in detail the changes of the pitch angle, roll angle, yaw angle and other attitude parameters of the drone over time during flight, providing important data basis for the subsequent precise dynamic adjustment of the drone antenna module, ensuring that the drone can maintain stable communication and flight status in complex flight environments.

[0059] In a possible implementation, step S340 further includes:

[0060] Step S341: Segment the multiple flight trajectories according to the flight plan of the target UAV to determine multiple flight trajectory points.

[0061] Step S342: Utilize the inertial navigation module to calculate the multiple flight trajectory points to obtain an acceleration data set and an angular velocity data set of the target UAV.

[0062] Step S343: fusing the acceleration data set and the angular velocity data set in real time according to the multiple flight trajectory points to obtain a flight state parameter set, wherein the flight state parameter set includes multiple flight attitude change values.

[0063] Step S344: performing complementary filtering based on the multiple flight attitude change values ​​and the multiple flight trajectories to obtain the flight attitude change information.

[0064] Specifically, based on the existing multiple flight trajectories, they are segmented according to the pre-set flight plan of the target UAV. The flight plan contains key information such as the various stages of the UAV flight and mission points. Based on this information, the continuous flight trajectory is split into independent flight trajectory points. These trajectory points are the basis for subsequent calculations and analysis, and they accurately define the key positions of the UAV during the flight.

[0065] The inertial navigation module of the target drone is used to perform in-depth calculations on the multiple flight trajectory points obtained by segmentation. The accelerometer and gyroscope in the inertial navigation module play an important role. The accelerometer continuously measures the acceleration of the drone at each trajectory point, thus forming an acceleration data set; the gyroscope measures the angular velocity of the drone around each axis, thus obtaining an angular velocity data set. These two data sets record in detail the dynamic motion characteristics of the drone at different trajectory points.

[0066] After obtaining the acceleration data set, angular velocity data set and multiple flight trajectory points, a real-time fusion operation is performed to obtain the flight state parameter set. The acceleration data set is composed of the acceleration data measured by the accelerometer in the inertial navigation module of the target UAV for multiple flight trajectory points, which reflects the speed change of the UAV at each trajectory point; the angular velocity data set is the angular velocity data set of the UAV around each axis measured by the gyroscope, which reflects the speed of the UAV's attitude rotation. According to the order of the flight trajectory points, the acceleration data and angular velocity data corresponding to the same trajectory point are associated, and then a fusion algorithm, such as the Kalman filter algorithm or the complementary filter algorithm, is used to process these associated data. In the fusion process, the algorithm will comprehensively consider the characteristics and change trends of the acceleration and angular velocity data, and assign different weights according to their influence on the flight attitude. Through continuous iterative calculations, the data of the two are deeply fused, and finally a series of parameters that can comprehensively and accurately reflect the flight state of the UAV are obtained, which constitute the flight state parameter set. This parameter set includes multiple flight attitude change values, such as the change values ​​of the pitch angle, roll angle and yaw angle at each flight trajectory point. They provide a key basis for accurately grasping the attitude adjustment of the UAV during flight, and are of great significance for further analyzing the flight status of the UAV and adjusting the antenna to ensure stable communication.

[0067] Multiple flight attitude change values ​​and multiple flight trajectories are used for complementary filtering to obtain flight attitude change information. Multiple flight attitude change values ​​are obtained by fusing acceleration data sets and angular velocity data sets. They can accurately reflect the instantaneous change of the attitude of the drone at each flight trajectory point, such as the dynamic changes of pitch angle, roll angle and yaw angle, but may be affected by sensor errors, external interference and other factors and have certain noise. Multiple flight trajectories depict the flight path of the drone from a macro level, provide the overall framework and trend of the flight, and have high stability and reliability, but are not accurate enough in reflecting subtle changes in attitude. The complementary filtering algorithm is used to combine the dynamic detail information of the flight attitude change value with the global stability information of the flight trajectory. In the filtering process, the high-frequency noise interference in the flight attitude change value is removed by the algorithm to retain the real and effective attitude change characteristics; for the flight trajectory, the attitude change trend information contained in it is mined to assist in correcting the flight attitude change value. By constantly complementing and correcting each other, the final output result can not only reflect the subtle changes in the drone's attitude, but also conform to the overall flight path rules, thereby obtaining accurate and comprehensive flight attitude change information, providing solid data support for the subsequent precise adjustment of the drone antenna, and ensuring that the drone can achieve stable and efficient communication during flight.

[0068] In a possible implementation, step S400 further includes:

[0069] Step S410: performing multipath suppression analysis on the signal transmission and reception record log based on the flight attitude change information to generate a multipath pseudo signal.

[0070] Step S420: using the multipath false signal as an index, traversing and matching the signal receiving and sending record log, removing and updating the signal receiving and sending record log according to the matching result, and determining the signal receiving and sending record update log.

[0071] Step S430: performing regression calculation on the antenna module according to the signal transmission and reception record update log in combination with the signal transmission strength value and the signal reception quality value to obtain an antenna dynamic operation coefficient.

[0072] Step S440: Perform weight calculation according to the antenna dynamic operation coefficient in combination with the real-time flight status of the target UAV to obtain multiple weight coefficients, control and adjust the antenna module based on the multiple weight coefficients, and formulate the antenna adjustment strategy.

[0073] Specifically, the flight attitude change information is a series of real-time data generated by the attitude of the drone (such as changes in pitch, roll, yaw and other angles) during flight. These data reflect the dynamic behavior of the drone in the air and have a significant impact on the propagation of the signal. The signal transmission and reception log records in detail the signal transmission and reception of the drone at each moment, including key information such as signal strength, frequency, and quality. Taking the flight attitude change information as an important basis, the multi-path suppression analysis work is started. First, based on the flight attitude change information, the changes in the signal propagation path of the drone in different attitudes are analyzed. Due to the change of the drone's attitude, the signal may reach the receiving end through different paths. For example, in an environment with complex terrain or multiple obstacles, the signal will be reflected, refracted, and other phenomena will occur, thereby generating multiple propagation paths. With the help of signal processing algorithms, each piece of data in the signal transmission and reception log is deeply analyzed, and by comparing the characteristics of the signal at different times and different attitudes, such as signal intensity fluctuations, phase changes, delay differences, etc., the abnormal signal characteristics caused by multi-path propagation can be identified. These abnormal signal features are the identifiers of multipath false signals. Based on these identifiers, multipath false signals are extracted and integrated to generate multipath false signals. The generation of these multipath false signals provides key basic data for subsequent operations such as distinguishing the true from the false in signal transmission and reception record logs and improving signal quality.

[0074] The multipath pseudo signal is set as the index, and the signal transceiver record log is traversed and matched comprehensively and carefully based on this clue. During the traversal process, each signal transceiver record in the log is checked one by one and compared with the various features of the multipath pseudo signal. These features include key information such as signal strength, frequency, phase, and waveform. Once the features of a certain record are found to be highly consistent with the features of the multipath pseudo signal, the record is determined to be invalid data interfered by the multipath effect. Based on the matching results, quick action is taken to remove these invalid records that are determined to be interfered from the signal transceiver record log. After the elimination operation is completed, the remaining valid records are reorganized and sorted, and the various information in the log is updated to ensure the consistency and accuracy of the log. After this series of elimination and update operations, the signal transceiver record log originally mixed with interference data has been purified, and finally a more accurate and reliable signal transceiver record update log is determined. This updated log can truly reflect the actual signal reception and transmission of the UAV during flight, providing a solid data foundation for subsequent antenna performance analysis and adjustment strategy formulation based on the log, and helping to improve the stability and reliability of the UAV communication system.

[0075] According to the signal transmission and reception record update log, combined with the signal transmission strength value and signal reception quality value obtained from the previous analysis, regression calculation is performed on the antenna module. Regression calculation is a method of analyzing the relationship between variables by establishing a mathematical model. The data in the signal transmission and reception record update log, the signal transmission strength value and the signal reception quality value are used to find the relationship between them and the operating status of the antenna module, thereby obtaining the antenna dynamic operation coefficient, which can reflect the working performance of the antenna under different flight conditions.

[0076] Real-time flight status information such as the speed, direction and altitude of the target drone is obtained in real time. For speed, the current flight rate is obtained through the drone's speed sensor. This rate will directly affect the relative speed of signal propagation and the timeliness of signal reception. The direction information is determined by the heading data of the electronic compass or satellite positioning system, which determines the relative position relationship between the drone and the signal source. The altitude information is obtained through devices such as barometers or laser altimeters. The signal propagation environment at different altitudes is quite different. For example, the atmospheric density and the degree of electromagnetic interference will change with altitude. Then, these real-time flight status information are combined with the existing antenna dynamic operation coefficient. The antenna dynamic operation coefficient is calculated based on multiple factors such as the signal transmission and reception record update log, the signal transmission strength value and the signal reception quality value, reflecting the current working performance of the antenna. A weight calculation model is established, which will assign corresponding calculation formulas according to the different degrees of influence of speed, direction and altitude on the antenna signal reception effect. For example, when the speed is fast, in order to ensure stable signal reception, the antenna direction needs to be adjusted more quickly to keep up with the relative position change of the signal source. At this time, the weight calculation formula corresponding to the speed will tend to produce a larger weight value, thereby increasing the intensity of antenna adjustment; when the direction changes, the weight value corresponding to the direction will be calculated through the trigonometric function relationship based on the angle between the new direction and the direction of the signal source; when the height is different, the weight value corresponding to the height will be determined based on the signal attenuation model at different heights. Through calculation, multiple weight coefficients corresponding to speed, direction, and height can be obtained. Subsequently, the antenna module is controlled and adjusted according to these weight coefficients, and the weight coefficients are converted into specific control instructions, which contain the angle and attitude information that the antenna needs to adjust. For example, through the calculated weight coefficient, it is determined that the antenna needs to rotate a certain angle in the horizontal direction to better align with the signal source; in the vertical direction, the pitch angle of the antenna is adjusted according to the weight coefficient to ensure the best signal reception effect. At the same time, the real-time flight status changes of the drone are continuously monitored. Once the flight status changes, the weight calculation and antenna adjustment will be re-performed to achieve dynamic control of the antenna, ensuring that the drone can obtain the best signal reception effect during the entire flight process, and finally formulate a set of real-time and accurate antenna adjustment strategies.

[0077] In a possible implementation, step S500 further includes:

[0078] Step S510: constructing simulation operation scenario parameters of the antenna module, executing the antenna adjustment strategy according to the simulation operation scenario parameters to perform simulation analysis, and generating antenna simulation adjustment results.

[0079] Step S520: Perform signal evaluation based on the antenna simulation adjustment result to generate multiple signal scores, perform gain analysis based on the multiple signal scores, and determine multiple parameters to be gained, wherein the multiple parameters to be gained include antenna angle parameters to be gained and antenna direction parameters to be gained.

[0080] Step S530: performing gain calculation on the antenna adjustment strategy according to the antenna gain angle parameter, the antenna gain direction parameter and the multiple weight coefficients, determining multiple gain fitnesses, marking the antenna adjustment strategy for optimization according to the multiple gain fitnesses, and generating the gain instruction.

[0081] Specifically, in order to construct the simulation operation scenario parameters of the antenna module and perform simulation analysis, the specific implementation means are as follows. First, collect data from various aspects to build a comprehensive simulation scenario. In terms of the environment, use the geographic information system (GIS) to obtain the topographic information of the target area, such as the distribution of mountains, plains, and waters. At the same time, combined with meteorological data, consider the impact of different weather conditions (such as sunny days, rainy days, and foggy days) on signal propagation. For signal characteristics, set signal sources of different strengths, frequencies, and directions according to the actual application scenario. Then, according to the characteristics of the antenna module, such as antenna type (dipole antenna, phased array antenna, etc.) and its radiation pattern, gain and other parameters, these data are integrated into the simulation system. When performing simulation analysis, use professional electromagnetic simulation software such as FEKO and CST. These softwares have built-in multiple propagation models (such as free space propagation model, ray tracing model) and antenna models, which can accurately simulate the propagation process of signals in complex environments. Input the constructed simulation operation scenario parameters into the simulation software, and set the angle and direction adjustment rules of the antenna under different flight states according to the established antenna adjustment strategy. The simulation software simulates the operation of the antenna at various time points based on the preset algorithms and models, and calculates the signal reception strength, pattern changes, interference conditions and other information in real time. During the simulation process, the various performance indicators of the antenna are continuously recorded, such as signal strength, signal-to-noise ratio, bit error rate, etc. After the simulation, the recorded data is sorted and analyzed to generate detailed antenna simulation adjustment results. The results are presented in the form of charts, reports, etc., which intuitively show the performance of the antenna in the simulation scenario, including the direction of the antenna at different times, the changing trend of the signal reception effect, etc., providing a strong basis for the subsequent evaluation and optimization of the antenna adjustment strategy.

[0082] After obtaining the antenna simulation adjustment results, the signals are comprehensively evaluated. First, multiple signal scores are generated based on a series of scientific and reasonable evaluation indicators. The evaluation indicators mainly cover signal strength, which is judged by measuring the received signal power. Stronger signal strength often means better reception effect; signal stability, which is measured by observing the fluctuation amplitude of the signal over a period of time. The smaller the fluctuation, the higher the stability; signal quality, which is measured by analyzing key parameters such as bit error rate and signal-to-noise ratio. Low bit error rate and high signal-to-noise ratio represent high-quality signals. According to the preset scoring criteria, the signal performance under each evaluation indicator is scored to generate multiple signal scores. Gain analysis is carried out based on these signal scores. By comparing the signal scores under different simulation scenarios, in-depth analysis is conducted on which scenarios have poor signal reception effects. If it is found that the weak signal strength in a certain scenario is due to an inappropriate antenna angle, then the antenna gain angle parameters that can enhance the signal will be analyzed in detail and determined, that is, the optimal angle that the antenna needs to be adjusted to; if the poor signal reception is due to a large deviation between the antenna direction and the signal source direction, the antenna gain direction parameters will be accurately calculated, that is, the optimal direction that the antenna should point to. After such a detailed gain analysis, multiple parameters to be gained are finally determined. These parameters include the antenna gain angle parameters and the antenna gain direction parameters. They provide a key basis for further optimizing the antenna adjustment strategy and improving the antenna performance.

[0083] The multi-layer perceptron algorithm is used to integrate the antenna gain angle parameter, antenna gain direction parameter and multiple weight coefficients into the input feature vector. At the same time, the signal gain data is extracted from the antenna simulation adjustment result as the output label, and these data are divided into training set, validation set and test set according to a certain ratio. Then, the multi-layer perceptron model is constructed. The number of neurons in the input layer is consistent with the dimension of the input feature vector. 2-3 hidden layers are set. The number of neurons in each layer is adjusted as needed. The hidden layer uses the ReLU activation function to enhance the nonlinear expression ability. One neuron in the output layer performs linear output. Then, the training set is used to train the model, with the mean square error as the loss function. The model parameters are updated with the help of stochastic gradient descent or Adam optimizer. The validation set is used to prevent overfitting during training. After the model is trained, the input feature vector composed of the new relevant parameters is input to obtain the gain fitness prediction value. Then, the gain fitness threshold is set, and the adjustment strategy below the threshold is marked as the situation to be optimized. Then, the influence of each parameter on the gain fitness is analyzed by interpretable methods such as feature importance analysis. According to the analysis results, specific gain instructions are generated to clarify the angle range, amplitude and other contents of the antenna adjustment, thereby improving the antenna signal gain and communication performance.

[0084] Embodiment 2: Figure 2 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing an implementation of the present invention. Figure 2 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. The electronic device is in the form of a general computing device, and its components may include but are not limited to an input device 201, a processor 202, a memory 203, and an output device 204. Among them, the processor 202 may be one or more; the processor 202 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 203, that is, realizing the above-mentioned method for dynamic adjustment of the antenna of a drone supported by a single Beidou.

[0085] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0086] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0087] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for dynamically adjusting the antenna of a UAV supported by a single Beidou, characterized in that: The method comprises: The target UAV is positioned through the Beidou satellite navigation system to obtain the spatial positioning data signal of the target UAV; Based on the spatial positioning data signal, an antenna module of the target UAV is activated to perform signal transmission and reception, and a signal transmission and reception record log is obtained; Analyze the flight area of ​​the target UAV to obtain terrain environment parameters, perform flight analysis on the target UAV based on the terrain environment parameters, and generate flight attitude change information; Performing operation calculation on the antenna module of the target UAV according to the flight attitude change information combined with the signal transmission and reception record log, generating an antenna dynamic operation coefficient, performing operation analysis on the antenna module according to the antenna dynamic operation coefficient, and formulating an antenna adjustment strategy; Performing multipath suppression analysis on the signal receiving and sending record log based on the flight attitude change information to generate a multipath pseudo signal; Using the multipath false signal as an index, traversing and matching the signal receiving and sending record log, removing and updating the signal receiving and sending record log according to the matching result, and determining the signal receiving and sending record update log; Perform regression calculation on the antenna module according to the signal transmission and reception record update log combined with the signal transmission strength value and the signal reception quality value to obtain the antenna dynamic operation coefficient; Perform weight calculation according to the antenna dynamic operation coefficient and the real-time flight status of the target UAV to obtain multiple weight coefficients, control and adjust the antenna module based on the multiple weight coefficients, and formulate the antenna adjustment strategy; Simulate and execute the antenna adjustment strategy to perform gain control, generate a gain instruction, optimize the antenna adjustment strategy through the gain instruction, generate an antenna adjustment optimization strategy, and dynamically adjust the antenna module of the target UAV through the antenna adjustment optimization strategy; Constructing simulation operation scenario parameters of the antenna module, executing the antenna adjustment strategy for simulation analysis according to the simulation operation scenario parameters, and generating antenna simulation adjustment results; Perform signal evaluation based on the antenna simulation adjustment result to generate multiple signal scores, perform gain analysis according to the multiple signal scores, and determine multiple parameters to be gained, wherein the multiple parameters to be gained include antenna angle parameters to be gained and antenna direction parameters to be gained; The antenna adjustment strategy is gain-calculated according to the antenna gain angle parameter, the antenna gain direction parameter and the multiple weight coefficients, and multiple gain fitnesses are determined. The antenna adjustment strategy is marked as to be optimized according to the multiple gain fitnesses, and the gain instruction is generated.

2. The method for dynamically adjusting the antenna of a UAV supported by a single Beidou as claimed in claim 1, characterized in that: The target UAV is positioned by the Beidou satellite navigation system to obtain the spatial positioning data signal of the target UAV, and the method includes: Start the signal receiving module of the Beidou satellite navigation system to perform initialization analysis on the target UAV, determine the prior knowledge of the target UAV, perform positioning analysis based on the prior knowledge, and obtain multiple particles; Obtaining a time step, performing propagation prediction on the plurality of particles according to the time step, obtaining particle propagation prediction information, performing resampling according to the particle propagation prediction information, and generating a particle sampling data set; Performing differential correction on the plurality of particles based on the particle sampling data set to determine a plurality of particle positioning data; The plurality of particle positioning data are added to the spatial positioning data signal.

3. The method for dynamically adjusting the antenna of a UAV supported by a single Beidou as claimed in claim 2, characterized in that: Based on the spatial positioning data signal, an antenna module of the target UAV is activated to perform signal transmission and reception, and a signal transmission and reception record log is obtained, the method comprising: Performing real-time analysis on the plurality of particle positioning data, extracting signal noise parameters, performing signal interference analysis based on the signal noise parameters, and determining a plurality of interference factors; Performing Kalman filtering on the spatial positioning data signal according to the multiple interference factors to generate a spatial positioning standard data signal; Activate the antenna module of the target UAV to determine the antenna transmission and reception direction according to the spatial positioning standard data signal; Perform two-way communication recording on the target UAV through the antenna transceiver direction to obtain a plurality of two-way communication data, wherein the plurality of two-way communication data includes signal transmission communication data and signal reception communication data; Perform signal analysis according to the signal sending communication data to obtain a signal sending strength value, and perform signal analysis according to the signal receiving communication data to obtain a signal receiving quality value; The signal transmission strength value is associated and integrated with the signal reception quality value to construct the signal transmission and reception record log.

4. The method for dynamically adjusting the antenna of a UAV supported by a single Beidou as claimed in claim 1, characterized in that: Analyze the flight area of ​​the target UAV to obtain terrain environment parameters, perform flight analysis on the target UAV based on the terrain environment parameters, and generate flight attitude change information, the method comprising: The target UAV traverses the flight area to collect data and construct three-dimensional terrain data of the flight area; Perform terrain parameter analysis based on the three-dimensional terrain data to obtain a plurality of terrain features, and construct terrain environment parameters according to the plurality of terrain features, wherein the terrain environment parameters include a terrain environment model; Retrieving multiple flight paths of the target UAV in the flight area, mapping the multiple flight paths to the terrain environment model for flight simulation, and generating multiple flight trajectories; The inertial navigation module of the target UAV is started according to the multiple flight trajectories to perform flight estimation on the target UAV and generate the flight attitude change information.

5. The method for dynamically adjusting the antenna of a UAV supported by a single Beidou as claimed in claim 4, characterized in that: The inertial navigation module of the target UAV is started according to the multiple flight trajectories to perform flight estimation on the target UAV and generate the flight attitude change information, the method comprising: Segmenting the multiple flight trajectories according to the flight plan of the target UAV to determine multiple flight trajectory points; Calculating the multiple flight trajectory points using the inertial navigation module to obtain an acceleration data set and an angular velocity data set of the target UAV; The acceleration data set and the angular velocity data set are fused in real time according to the multiple flight trajectory points to obtain a flight state parameter set, wherein the flight state parameter set includes multiple flight attitude change values; The flight attitude change information is obtained by performing complementary filtering on the multiple flight attitude change values ​​and the multiple flight trajectories.

6. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; The processor is used to implement the method for dynamic adjustment of the antenna of a UAV supported by a single Beidou as described in any one of claims 1 to 5 when executing the executable instructions stored in the memory.

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