Base station field angle adjusting method and device, communication equipment and storage medium

Abstract

The invention relates to a base station field angle adjusting method and device, communication equipment and a computer readable storage medium. The method comprises the following steps: acquiring the flight height of an unmanned aerial vehicle under the condition that the unmanned aerial vehicle is sensed by a low-altitude sensing network; according to the distance between the base stations in the low-altitude sensing network, the base station height of the target base station and the flight height of the unmanned aerial vehicle, obtaining an optimal field angle of the unmanned aerial vehicle sensed by the target base station; the target base station is a base station sensing the unmanned aerial vehicle in the low-altitude sensing network; and adjusting the field angle range of the target base station based on the optimal field angle. Compared with the prior art that the field angle of the base station is set to be a fixed value, the technical scheme of the invention realizes the self-adaptive adjustment of the field angle range of the target base station according to the flight height of the unmanned aerial vehicle, thereby improving the intelligent degree of the field angle setting of the base station.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, apparatus, communication equipment, computer-readable storage medium and computer program product for adjusting the viewing angle of a base station. Background Art

[0002] With the development of communication technology, a base station device has emerged that can be used to realize the perception function. The perception function can be used for airspace supervision. For example, millimeter waves can be transmitted through base station equipment. Since millimeter waves have the characteristics of high frequency and wide bandwidth, they can be used to realize the perception of drones.

[0003] At present, the field of view angle of the base station is usually set to a fixed value. Since millimeter-wave perception requires main lobe coverage and side lobes cannot achieve perception capabilities, if the field of view angle of the millimeter-wave base station is too small or deviates from the position of the drone, the millimeter-wave base station will not be able to perceive the drone. In this regard, in order to ensure the coverage area of ​​the perception capability, it is usually necessary to set a larger field of view angle range for the base station.

[0004] However, if a larger field of view angle range is set for the base station, the energy consumption of the millimeter wave base station will increase and the perception accuracy will also be reduced. Therefore, the current field of view angle setting of the millimeter wave base station is not smart enough. Summary of the invention

[0005] Based on this, it is necessary to provide a base station field of view angle adjustment method, device, communication equipment, computer-readable storage medium and computer program product that can improve the intelligence level of the field of view angle setting of the millimeter wave base station in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for adjusting a base station viewing angle, comprising:

[0007] When the low-altitude sensing network senses a drone, obtaining the flight altitude of the drone;

[0008] According to the spacing between base stations in the low-altitude sensing network, the base station height of the target base station, and the flight altitude of the drone, an optimal field of view angle of the target base station sensing the drone is obtained; the target base station is a base station in the low-altitude sensing network that senses the drone;

[0009] Based on the optimal viewing angle, the viewing angle range of the target base station is adjusted.

[0010] In one of the embodiments, adjusting the field of view angle range of the target base station based on the optimal field of view angle includes: obtaining a field of view angle deflection angle of the target base station for sensing the drone; and adjusting the field of view angle range of the target base station according to the optimal field of view angle and the field of view angle deflection angle.

[0011] In one of the embodiments, the step of obtaining the target base station for sensing the field of view deflection angle of the drone includes: obtaining the vertical plane beam width of the target base station; and obtaining the field of view deflection angle according to the vertical plane beam width.

[0012] In one of the embodiments, obtaining the field of view angle deflection angle includes: obtaining real-time flight data of the UAV; inputting the real-time flight data into a pre-trained UAV flight trajectory prediction model, and obtaining a predicted flight trajectory of the UAV through the UAV flight trajectory prediction model; and obtaining the field of view angle deflection angle according to the predicted flight trajectory.

[0013] In one of the embodiments, obtaining the field of view angle deflection angle according to the predicted flight trajectory includes: obtaining the predicted flight speed of the UAV and the predicted distance between the UAV and the target base station according to the predicted flight trajectory; and obtaining the field of view angle deflection angle based on the predicted flight speed and the predicted distance.

[0014] In one of the embodiments, before inputting the real-time flight data into a pre-trained UAV flight trajectory prediction model, it also includes: obtaining historical flight data of a sample UAV and the actual flight trajectory of the sample UAV; inputting the historical flight data into the UAV flight trajectory prediction model to be trained, and obtaining the predicted flight trajectory of the sample UAV through the UAV flight trajectory prediction model; training the UAV flight trajectory prediction model based on the difference between the predicted flight trajectory of the sample UAV and the actual flight trajectory of the sample UAV, and obtaining the pre-trained UAV flight trajectory prediction model.

[0015] In one of the embodiments, adjusting the field of view angle range of the target base station according to the optimal field of view angle and the field of view angle deflection angle includes: using the sum of the optimal field of view angle and the field of view angle deflection angle as the first field of view angle used by the target base station to perceive the drone, and using the difference between the optimal field of view angle and the field of view angle deflection angle as the second field of view angle used by the target base station to perceive the drone; using the angle range between the first field of view angle and the second field of view angle as the field of view angle range used by the target base station to perceive the drone; and generating a control instruction based on the field of view angle range used by the target base station to perceive the drone to adjust the field of view angle range of the target base station.

[0016] In one of the embodiments, the number of drones sensed by the target base station is multiple; the target base station has multiple field of view angle ranges for sensing the drones, corresponding to different drones respectively; the generating of control instructions based on the field of view angle range used by the target base station to sense the drones to adjust the field of view angle range of the target base station includes: obtaining the maximum first field of view angle and the minimum second field of view angle from the field of view angle range used by the target base station to sense each drone; generating control instructions using the maximum first field of view angle and the minimum second field of view angle to adjust the field of view angle range of the target base station to the angle range between the maximum first field of view angle and the minimum second field of view angle.

[0017] In one of the embodiments, after adjusting the field of view angle range of the target base station based on the optimal field of view angle, it also includes: when it is detected that the flight altitude of the UAV has changed, obtaining the changed flight altitude of the UAV; using the changed flight altitude of the UAV as the new flight altitude of the UAV, and returning to execute the step of obtaining the optimal field of view angle of the target base station for sensing the UAV based on the spacing between the base stations in the low-altitude perception network, the base station height of the target base station, and the flight altitude of the UAV.

[0018] In one of the embodiments, the base station field of view angle adjustment method may also include: when any current base station in the low-altitude perception network does not perceive the drone, obtaining the default perception height of the current base station; and setting the default field of view angle range of the current base station according to the spacing between the base stations in the low-altitude perception network, the base station height of the current base station, and the default perception height.

[0019] In a second aspect, the present application also provides a base station viewing angle adjustment device, comprising:

[0020] A flight altitude acquisition module, used to acquire the flight altitude of the UAV when the low-altitude sensing network senses the UAV;

[0021] A field of view angle acquisition module, used to obtain the optimal field of view angle of the target base station sensing the drone according to the spacing between the base stations in the low-altitude sensing network, the base station height of the target base station, and the flight altitude of the drone; the target base station is the base station that senses the drone in the low-altitude sensing network;

[0022] A viewing angle adjustment module is used to adjust the viewing angle range of the target base station based on the optimal viewing angle.

[0023] In a third aspect, the present application further provides a communication device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in any one of the embodiments of the first aspect when executing the computer program.

[0024] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the embodiments of the first aspect.

[0025] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method described in any one of the embodiments of the first aspect.

[0026] The above-mentioned base station field of view angle adjustment method, device, communication equipment, computer-readable storage medium and computer program product obtain the flight altitude of the drone when the low-altitude sensing network senses the drone; obtain the optimal field of view angle of the target base station sensing the drone according to the spacing between the base stations in the low-altitude sensing network, the base station height of the target base station, and the flight altitude of the drone; the target base station is the base station that senses the drone in the low-altitude sensing network; based on the optimal field of view angle, adjust the field of view angle range of the target base station. The present application can obtain the flight altitude of the drone when the target base station of the low-altitude sensing network senses the drone, so that the field of view angle range of the target base station can be adjusted according to the spacing between the base stations in the low-altitude sensing network, the height of the target base station and the flight altitude of the drone. Compared with the traditional technology in which the field of view angle of the base station is set to a fixed value, the technical solution of the present application realizes adaptive adjustment of the field of view angle range of the target base station according to the flight altitude of the drone, thereby improving the intelligence of the field of view angle setting of the base station. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0028] FIG1 is a diagram showing an application environment of a method for adjusting a viewing angle of a base station in one embodiment;

[0029] FIG2 is a schematic diagram of a flow chart of a method for adjusting a viewing angle of a base station in one embodiment;

[0030] FIG3 is a schematic diagram of a process of adjusting the viewing angle range of a target base station in one embodiment;

[0031] FIG4 is a schematic diagram of a process for obtaining a field of view deflection angle in one embodiment;

[0032] FIG5 is a schematic diagram of a process of adjusting the viewing angle range of a target base station in another embodiment;

[0033] FIG6 is a schematic diagram of the calculation principle of the optimal viewing angle in one embodiment;

[0034] FIG7 is a schematic diagram of calculating the optimal viewing angle in one embodiment;

[0035] FIG8 is a structural block diagram of a base station viewing angle adjustment device in one embodiment;

[0036] FIG. 9 is a diagram showing the internal structure of a communication device in one embodiment. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] The base station field of view angle adjustment method provided in the embodiment of the present application can be applied to the application environment as shown in Figure 1. Among them, the drone 102 communicates with the target base station 101 in the low-altitude perception network through the interoception signal. Specifically, when the drone is sensed in the low-altitude perception network, that is, when the target base station 101 senses the drone 102, the flight altitude of the drone 102 can be obtained, and the optimal field of view angle of the target base station 101 sensing the drone 102 can also be calculated based on the spacing between the base stations in the low-altitude perception network, the base station height of the target base station 101 and the flight altitude of the drone 102, and then the field of view angle range of the target base station 101 can be adjusted based on the optimal field of view angle. Among them, the target base station 101 can be any base station device that can be used to form a low-altitude perception network, and the drone 102 can be, but is not limited to, various types of drone devices.

[0039] In one embodiment, as shown in FIG. 2 , a method for adjusting the viewing angle of a base station is provided. The method is described by taking the controller for controlling the viewing angle of a target base station 101 as an example. The controller may be set in the target base station 101 or implemented by a communication device. The communication device may be used to control the viewing angle of each base station of a low-altitude sensing network, and includes the following steps:

[0040] Step S201, when the low-altitude sensing network senses the drone 102, the flight altitude of the drone 102 is obtained.

[0041] Among them, the low-altitude perception network refers to a network system that integrates multiple functions such as communication, perception, and computing to achieve efficient operation and safe control of low-altitude airspace. This network system mainly relies on information infrastructure such as communication, navigation, monitoring, and perception. It realizes intelligent management and monitoring of low-altitude airspace through multi-network communication, integrated navigation, multi-source monitoring, and intelligent services. The communication function is mainly used to support the communication needs of low-altitude aircraft, such as drones, to ensure smooth communication of aircraft, and the perception function refers to the use of radar perception and other technologies to achieve tracking, positioning, ranging, speed measurement, and imaging recognition of low-altitude flying objects. The low-altitude perception network can be composed of multiple base stations. The above base stations can complete the perception of drones through the perception function, and the flight altitude of the drone refers to the flight altitude of the drone perceived by the base station.

[0042] Specifically, when a base station in the low-altitude sensing network senses the drone 102, the flight altitude of the drone 102 can be obtained first.

[0043] Step S202, according to the spacing between base stations in the low-altitude sensing network, the base station height of the target base station 101, and the flight altitude of the drone, the optimal field of view of the target base station 101 sensing the drone 102 is obtained; the target base station 101 is the base station that senses the drone 102 in the low-altitude sensing network;

[0044] Step S203: adjusting the viewing angle range of the target base station 101 based on the optimal viewing angle.

[0045] The target base station 101 refers to the base station that senses the drone 102 in the low-altitude sensing network, the field of view (FOV) refers to the angle that the sensor or antenna of the base station can cover, the optimal field of view refers to the best field of view used by the target base station 101 to sense the drone 102, and the field of view range refers to the angle range that the sensor or antenna of the target base station 101 can cover. The base station height refers to the height at which the target base station 101 is set.

[0046] Specifically, after obtaining the flight altitude of the drone 102, the controller that performs field of view angle control can also determine the base station altitude of the target base station 101 that senses the drone 102, as well as the spacing between the base stations in the low-altitude sensing network, thereby using the spacing between the base stations, the base station altitude of the target base station 101, and the flight altitude of the drone 102 to calculate the optimal field of view angle of the target base station 101 for sensing the drone 102, and based on the optimal field of view angle, adjust the field of view angle range of the target base station 101 to ensure that the target base station 101 can complete the perception of the drone 102 within a relatively small field of view angle range.

[0047] Among them, the calculation of the optimal field of view angle can be obtained by the following formula:

[0048]

[0049] Among them, Fov represents the optimal field of view angle of the target base station 101 for sensing the drone 102, ISD represents the station spacing of the base stations in the low-altitude sensing network, H represents the flight altitude of the drone 102, and h represents the base station height of the target base station 101.

[0050] In the above-mentioned base station field of view adjustment method, when the low-altitude sensing network senses the drone 102, the flight altitude of the drone 102 is obtained; according to the spacing between the base stations in the low-altitude sensing network, the base station height of the target base station 101, and the flight altitude of the drone 102, the optimal field of view of the target base station 101 sensing the drone 102 is obtained; the target base station 101 is the base station that senses the drone 102 in the low-altitude sensing network; based on the optimal field of view, the field of view range of the target base station 101 is adjusted. The present application can obtain the flight altitude of the drone 102 when the target base station 101 of the low-altitude sensing network senses the drone, so that the field of view range of the target base station 101 can be adjusted according to the spacing between the base stations in the low-altitude sensing network, the height of the target base station 101 and the flight altitude of the drone 102. Compared with the conventional technology in which the field of view of the base station is set to a fixed value, the technical solution of the present application realizes adaptive adjustment of the field of view range of the target base station 101 according to the flight altitude of the drone 102, thereby improving the intelligence of the field of view setting of the base station.

[0051] In one embodiment, as shown in FIG3 , step S202 may further include:

[0052] Step S301, obtaining the field of view deflection angle used by the target base station 101 to perceive the drone 102.

[0053] The field of view deflection angle refers to the deflection angle of the field of view set by the target base station 101 to ensure that the drone 102 can be perceived. Since the flying altitude of the drone 102 will continue to change during the flight, if the target base station 101 only perceives the drone 102 through the optimal field of view angle, it is possible that when the flying altitude of the drone 102 changes slightly, the target base station 101 will not be able to perceive it. Therefore, in order to avoid this situation, it is usually necessary to deflect the optimal field of view angle to a certain extent to form the field of view angle range of the target base station 101, and the deflection angle is the field of view deflection angle.

[0054] Step S302: adjusting the viewing angle range of the target base station 101 according to the optimal viewing angle and the viewing angle deflection angle.

[0055] After determining the optimal field of view angle and the field of view angle deflection angle, the field of view angle range of the target base station 101 can be adjusted according to the optimal field of view angle and the field of view angle deflection angle. For example, the optimal field of view angle of the target base station 101 for sensing the drone is 40°. If the field of view angle range of the target base station 101 is directly set to 40°, when the flight altitude of the drone 102 changes slightly, the drone 102 may be lost. Therefore, it is necessary to set a field of view angle deflection angle to ensure that the drone 102 can be sensed even when the flight altitude of the drone 102 changes slightly. The field of view angle deflection angle can be 5°, and the field of view angle range of the target base station 101 can be set to (35°, 40°).

[0056] In this embodiment, the field of view angle deflection angle can also be set to utilize the optimal field of view angle and the field of view angle deflection angle to adjust the field of view angle range of the target base station 101. In this way, the loss caused by slight changes in the flight altitude of the drone 102 can be avoided, thereby further improving the stability of the drone 102's perception.

[0057] Furthermore, step S301 may further include: acquiring a vertical beam width of the target base station 101; and obtaining a field of view deflection angle according to the vertical beam width.

[0058] Among them, the vertical plane beam width refers to the angle between the two directions in which the radiation power of the target base station 101 drops by 3dB on both sides of the maximum radiation direction in the vertical direction of the target base station 101. In this embodiment, the setting of the field of view angle deflection angle can be obtained based on the vertical plane beam width of the target base station 101. For example, the vertical plane beam width of the target base station 101 can be used as the field of view angle deflection angle. In this way, the efficiency of obtaining the field of view angle deflection angle can be improved while ensuring uniform distribution of signal strength within the coverage range.

[0059] In this embodiment, the field of view deflection angle of the target base station 101 for sensing the drone 102 can be obtained according to the vertical plane beam width of the target base station 101. In this way, the efficiency of obtaining the field of view deflection angle can be improved while ensuring uniform distribution of signal strength within the coverage area.

[0060] In addition, as shown in FIG. 4 , step S301 may further include:

[0061] Step S401, obtaining real-time flight data of the UAV 102;

[0062] Step S402 , inputting the real-time flight data into a pre-trained UAV flight trajectory prediction model, and obtaining the predicted flight trajectory of the UAV 102 through the UAV flight trajectory prediction model.

[0063] Real-time flight data refers to the real-time flight information data of the drone 102, which may include, for example, the real-time flight altitude, real-time position, and real-time flight speed of the drone 102, etc. The drone flight trajectory prediction model is a pre-trained artificial intelligence model used to predict the flight trajectory of the drone, and the predicted flight trajectory is the flight trajectory of the drone 102 output by the model.

[0064] Specifically, when the low-altitude sensing network senses the drone 102, it can also collect real-time flight data of the drone 102, such as the drone's position, altitude, and movement trajectory data. The real-time flight data can then be input into a pre-trained drone flight trajectory prediction model. Alternatively, the real-time flight data can be pre-processed first, and the pre-processed data can be input into a pre-trained drone flight trajectory prediction model. The predicted flight trajectory of the drone 102 can then be output through the drone flight trajectory prediction model.

[0065] Step S403, obtaining the field of view deflection angle according to the predicted flight trajectory.

[0066] After obtaining the predicted flight trajectory of the drone 102, the field of view deflection angle can be calculated according to the predicted flight trajectory. In this way, the field of view deflection angle can be adjusted in advance according to the predicted flight trajectory, thereby further ensuring the perception of the drone 102 and further improving the stability of the perception of the drone 102.

[0067] In this embodiment, the flight trajectory of the drone 102 can also be predicted through the drone flight trajectory prediction model, and the prediction of the flight trajectory can be used to obtain the field of view angle deflection angle. In this way, the field of view angle deflection angle can be adjusted in advance, thereby further ensuring the perception of the drone 102 and further improving the stability of the perception of the drone 102.

[0068] Furthermore, step S403 may further include: obtaining a predicted flight speed of the drone 102 and a predicted distance between the drone 102 and the target base station 101 according to the predicted flight trajectory; and obtaining a field of view deflection angle based on the predicted flight speed and the predicted distance.

[0069] The predicted flight speed refers to the predicted flight speed of the drone 102, which may be the predicted flight linear speed of the drone 102, and the predicted distance refers to the distance between the drone 102 and the target base station 101 obtained by predicting the flight. In this embodiment, the predicted flight speed of the drone 102 and the predicted distance between the drone 102 and the target base station 101 can be obtained according to the predicted flight trajectory. Then, the predicted flight angular velocity of the drone 102 can be calculated according to the predicted flight speed and the predicted distance, so as to obtain the field of view deflection angle using the predicted flight angular velocity.

[0070] In this embodiment, the predicted flight speed of the drone 102 and the predicted distance between the drone 102 and the target base station can also be obtained by predicting the flight trajectory, so that the field of view deflection angle can be obtained according to the predicted flight speed and the predicted distance. In this way, the accuracy of obtaining the field of view deflection angle can be improved.

[0071] In addition, before step S402, it can also include: obtaining historical flight data of the sample UAV and the actual flight trajectory of the sample UAV; inputting the historical flight data into the UAV flight trajectory prediction model to be trained, and obtaining the predicted flight trajectory of the sample UAV through the UAV flight trajectory prediction model; training the UAV flight trajectory prediction model according to the difference between the predicted flight trajectory of the sample UAV and the actual flight trajectory of the sample UAV, and obtaining a pre-trained UAV flight trajectory prediction model.

[0072] The sample UAV refers to the UAV used to train the UAV flight trajectory prediction model, and the historical flight data refers to the flight data of the sample UAV during the historical flight, for example, it may include the position and altitude of the sample UAV in the historical flight, etc., and the actual flight trajectory refers to the actual flight trajectory of the sample UAV in the historical flight.

[0073] The UAV flight trajectory prediction model to be trained refers to the UAV flight trajectory prediction model that needs to be trained. Specifically, the historical flight data of the sample UAV during the historical flight process and the actual flight trajectory of the sample UAV during the historical flight process can be collected, and the above historical flight data can be input into the UAV flight trajectory prediction model to be trained, and the predicted flight trajectory of the sample UAV can be obtained by the output of the UAV flight trajectory prediction model. After that, the difference between the actual flight data of the sample UAV and the predicted result can be used to train the UAV flight trajectory prediction model, thereby obtaining a pre-trained UAV flight trajectory prediction model, which can be used to realize the prediction of the UAV flight trajectory.

[0074] In this embodiment, the historical flight data of the sample drone can also be used to complete the training of the drone flight trajectory prediction model. In this way, the accuracy of the drone flight trajectory prediction model in predicting the flight trajectory can be improved.

[0075] In one embodiment, as shown in FIG5 , step S302 may further include:

[0076] Step S501, using the sum of the optimal field of view angle and the field of view angle deflection angle as the first field of view angle used by the target base station 101 to perceive the drone 102, and using the difference between the optimal field of view angle and the field of view angle deflection angle as the second field of view angle used by the target base station 101 to perceive the drone 102;

[0077] Step S502: Using the angle range between the first field of view angle and the second field of view angle as the field of view angle range of the target base station for sensing the drone.

[0078] The first field of view angle refers to the angle obtained by summing the optimal field of view angle and the field of view angle deflection angle, which can be the maximum field of view angle used by the target base station 101 to perceive the drone 102, and the second field of view angle refers to the angle obtained by subtracting the optimal field of view angle from the field of view angle deflection angle, which can be the minimum field of view angle used by the target base station 101 to perceive the drone 102.

[0079] Specifically, after obtaining the optimal field of view angle and the field of view angle deflection angle, the optimal field of view angle and the field of view angle deflection angle can be summed to obtain the first field of view angle, and the optimal field of view angle and the field of view angle deflection angle can be subtracted to obtain the second field of view angle. In this way, the angle range between the first field of view angle and the second field of view angle can be used as the field of view angle range for the target base station 101 to perceive the drone 102.

[0080] For example, the field of view angle range of the target base station 101 for sensing the drone 102 can be expressed as FoV Range, and the FoV Range can be (FOV-Δ, FOV+Δ), where FOV represents the optimal field of view, Δ represents the field of view deflection angle, FOV-Δ represents the second field of view, and FOV+Δ represents the first field of view.

[0081] Step S503, based on the field of view angle range of the target base station 101 for sensing the drone, a control instruction is generated to adjust the field of view angle range of the target base station 101.

[0082] The control instruction is an instruction for adjusting the field of view angle range of the target base station 101. In this embodiment, after obtaining the field of view angle range of the target base station 101 for sensing the drone 102, the corresponding control instruction can be further generated based on the above field of view angle range, thereby adjusting the field of view angle range of the target base station 101 through the control instruction.

[0083] In this embodiment, the first field of view angle and the second field of view angle can also be calculated by using the optimal field of view angle and the field of view angle deflection angle, so as to use the angle range between the first field of view angle and the second field of view angle to generate a control instruction to complete the field of view angle range adjustment of the target base station 101. In this way, the accuracy of the field of view angle range adjustment of the target base station 101 can be improved.

[0084] In one embodiment, the number of drones 102 sensed by the target base station 101 is multiple; the target base station 101 has multiple field of view angle ranges for sensing the drones 102, corresponding to different drones respectively; step S503 may further include: obtaining the maximum first field of view angle and the minimum second field of view angle from the field of view angle ranges used by the target base station 101 to sense each drone 102; using the maximum first field of view angle and the minimum second field of view angle to generate control instructions to adjust the field of view angle range of the target base station 101 to the angle range between the maximum first field of view angle and the minimum second field of view angle.

[0085] In this embodiment, the number of drones 102 sensed by the target base station 101 may be multiple, so the field of view angle ranges used by the target base station 101 to sense the drones 102 may also be multiple, corresponding to different drones 102. For example, the field of view angle ranges used by the target base station 101 to sense the drones 102 may include range a, range b, and range c, corresponding to drone a, drone b, and drone c, respectively. The largest first field of view angle refers to the maximum value of the first field of view angle of the multiple field of view angle ranges, and similarly, the smallest second field of view angle refers to the minimum value of the second field of view angle of the multiple field of view angle ranges.

[0086] For example, range a refers to the angle range between the first field of view angle a and the second field of view angle a, range b refers to the angle range between the first field of view angle b and the second field of view angle b, and range c refers to the angle range between the first field of view angle c and the second field of view angle c. Then the maximum first field of view angle is the maximum angle value between the first field of view angle a, the first field of view angle b and the first field of view angle c. Similarly, the minimum second field of view angle is the minimum angle value between the second field of view angle a, the second field of view angle b and the second field of view angle c.

[0087] After determining the maximum first field of view angle and the minimum second field of view angle, the corresponding control instructions can be generated to adjust the field of view angle range of the target base station 101 to the angle range between the maximum first field of view angle and the minimum second field of view angle. For example, if range a is (30°, 40°), range b is (45°, 55°), and range c is (35°, 45°), then the minimum second field of view angle is 30°, and the maximum first field of view angle is 55°. Therefore, the field of view angle range of the target base station 101 can be adjusted to (30°, 55°). The target base station 101 adjusted in this way can ensure that drone a, drone b, and drone c are perceived at the same time with the minimum field of view angle range.

[0088] In this embodiment, when the number of drones 102 sensed by the target base station 101 is multiple, multiple field of view angle ranges for the target base station 101 to sense the above-mentioned drones can be obtained, and the largest first field of view angle and the smallest second field of view angle in the above-mentioned multiple field of view angle ranges are used to generate control instructions, so that the field of view angle range of the target base station is adjusted to the largest first field of view angle and the smallest second field of view angle. The angle range, in this way, can ensure that the target base station 101 can sense multiple drones 102 at the same time with the smallest field of view angle range, further improving the intelligence of the field of view angle setting.

[0089] In one embodiment, after step S203, the process may further include: when detecting that the flight altitude of the drone 102 changes, obtaining the changed flight altitude of the drone 102; using the changed flight altitude of the drone 102 as the new flight altitude of the drone 102, and returning to execute step S202.

[0090] In this embodiment, if the flight altitude of the drone 102 changes, it is also necessary to timely adjust the optimal field of view of the target base station 101 to perceive the drone 102, so as to adjust the field of view range of the target base station 101. Specifically, if it is detected that the flight altitude of the drone 102 has changed, the changed flight altitude of the drone 102 can be further obtained, and the changed flight altitude can be used as the new flight altitude of the drone 102, so as to again use the spacing between the base stations in the low-altitude perception network, the base station height of the target base station 101 and the new flight altitude to calculate the new optimal field of view of the drone 102, so as to adjust the field of view range of the target base station 101 based on the new optimal field of view. In this way, when the altitude of the drone 102 is sensed to have changed, the field of view range of the target base station 101 can be adaptively optimized, so as to further ensure that the target base station 101 can perceive the drone 102.

[0091] In this embodiment, when the height of the drone 102 changes, the field of view angle range of the target base station 101 can be adaptively optimized to further ensure that the target base station 101 can perceive the drone 102. In this way, the intelligence of the field of view angle range adjustment of the target base station is further improved.

[0092] In one embodiment, the base station field of view angle adjustment method may also include: when any current base station in the low-altitude perception network does not perceive the drone, obtaining the default perception height of the current base station; setting the default field of view angle range of the current base station according to the spacing between the base stations in the low-altitude perception network, the base station height of the current base station, and the default perception height.

[0093] The current base station refers to any base station in the low-altitude perception network that has not sensed the drone. If a base station does not sense the drone, then the field of view angle range set by it can be a default value, that is, the default field of view angle range, and the default perception altitude is the pre-set default flight altitude of the drone. In this embodiment, if the current base station does not sense the drone, the default perception altitude of the current base station can be used to replace the flight altitude of the drone, thereby combining the spacing between base stations in the low-altitude perception network and the base station height of the current base station to set the default field of view angle range of the current base station.

[0094] In this embodiment, if any current base station in the low-altitude sensing network does not sense the drone, the default sensing height of the current base station can be used to set the default field of view angle range of the current base station. The default field of view angle range set in this way can ensure reasonable signal coverage even in the absence of a drone, thereby further improving the accuracy of the field of view angle range setting.

[0095] In one embodiment, a solution for determining the FOV of a low-altitude millimeter wave base station is also provided, which can ensure the continuity of air coverage. Through the location information reported by the drone, the constraints of the field of view of the low-altitude millimeter wave interawareness integrated base station are adjusted to improve the coverage of air drones and enhance the overall communication and perception performance of the network. Specifically, the following steps may be included:

[0096] Step 1: Confirm the low-altitude millimeter wave base station spacing ISD and station height h:

[0097] By deploying multiple millimeter wave base stations in the low-altitude sensing network, the signal coverage areas between base stations can be seamlessly connected. High-gain directional antennas are used to ensure that the signal propagation distance and coverage range at low altitudes can achieve wireless coverage.

[0098] Step 2: Confirm the low-altitude millimeter wave base station's FoV:

[0099] The inter-station spacing ISD and station height h of the millimeter wave base station determine the optimal field of view FoV of the millimeter wave antenna according to the perceived drone height H. The calculation principle of FoV can be shown in Figure 6, and the calculation method is:

[0100]

[0101] Among them, Fov represents the optimal field of view angle of the base station for sensing the drone, ISD represents the station spacing of the base stations in the low-altitude sensing network, H represents the flight altitude of the drone, and h represents the base station height of base station 101.

[0102] Step 3: Confirm the FOV Range of the low-altitude millimeter wave base station:

[0103] Using an antenna device with adjustable field of view, considering the main lobe scanning range, the 3db width of the vertical beam is Δ, and the calculated FOV Range of the millimeter wave antenna is adjusted to (FOV-Δ, FOV+Δ) to ensure uniform distribution of signal strength within the coverage area.

[0104] Step 4: Adaptively optimize the millimeter wave base station FoV angle:

[0105] When the height H of the drone is sensed to change, steps 2 and 3 are repeated to calculate the FoV Range. When the millimeter-wave intersensory base station senses multiple drones and different heights h_1, h_2, h_3,…, substitute the formula in step 2 to calculate FoV Range1, FoV Range2, FoV Range3,… respectively and take the maximum range millimeter-wave base station FoV angle of FoV Range1, FoV Range2, FoV Range3,….

[0106] Step 5: FoV angle when there is no drone in the sensing area:

[0107] When there is no drone in the sensing area, since the drone height h cannot be obtained, the FoV Range is calculated by the base station's default sensing height. Taking ISD 1km, low-altitude default height H=300m, and station height h=30m as an example, the air FoV is calculated to be 40°.

[0108] Step 6: Introduce AI technology for optimization:

[0109] The base station collects the drone's location, altitude, and movement trajectory data in real time. The AI ​​algorithm is used to analyze the existing real-time data in combination with historical data to predict the possible movement path and altitude of the drone. Based on the AI ​​prediction results, the base station's field of view angle range is dynamically adjusted to optimize the antenna pointing in advance to improve the response speed and signal coverage efficiency. AI technology is used to monitor the signal interference between base stations, dynamically adjust the field of view angle and transmission power, reduce interference, and improve communication performance. The base station uses AI algorithm self-learning to continuously optimize the field of view angle setting strategy to improve the overall communication performance and stability of the network.

[0110] For specific implementation, please refer to the following examples:

[0111] As shown in Figure 7, when the inter-station spacing ISD is 1 km, 1 / 3 ISD = 333 m is taken as the cell radius. When the coverage height is 300 m, subtracting the base station height, h1 is equal to 270 m, and arctan 270 / 333 = 40° can be obtained.

[0112] When the drone's altitude drops, for example to 200 meters, the FOV can also be calculated to be arctan 170 / 333 = 27°. When there are two drones in the air at the same time, the drone at an altitude of 300 meters is used as the FOV of the millimeter wave base station. When there is only one drone, the angle calculated from the drone's altitude is used as the FOV. The drone's altitude data is calculated by the drone's altitude sensor fed back to the base station through a communication link.

[0113] AI's decision-making on signal detection and response to problems is not the core invention of this patent. The core invention of this patent is to adjust the field of view of the millimeter wave base station using the drone trajectory predicted by AI. The specific implementation scheme of the millimeter wave base station using AI to predict the trajectory of the drone can be implemented in the following steps: First, the millimeter wave telepathic integrated base station is used to continuously scan and track the drone to obtain the flight data of the drone. The sensing network element Sensing Function pre-processes the collected sensing data, including denoising, filtering, feature extraction and other steps to improve the quality and availability of the data to identify the drone. The intelligent processing network element of the base station core network (such as NWDAF) uses a large amount of drone flight data to train the AI ​​algorithm so that it can accurately identify the flight mode and characteristics of the drone. The accuracy and real-time performance of drone prediction are improved by optimizing the algorithm parameters and model structure. The trained model is deployed in the base station. When the millimeter wave base station detects and obtains the flight data of the drone, the trained AI algorithm is used to predict the trajectory of the drone and the prediction results are updated in real time. According to the difference between the actual flight data of the UAV and the predicted results, the algorithm parameters and prediction model are dynamically adjusted to improve the accuracy and robustness of the prediction. In addition, multi-base station collaboration can be used to achieve multi-base station perception and prediction data sharing and collaborative work, thereby improving the accuracy of UAV prediction in a networking environment. Finally, the predicted UAV trajectory can be used for early tracking and adjustment of the base station FOV field of view. For example, the change in the flight altitude of the UAV can be predicted in advance, and the field of view of the millimeter wave base station can be dynamically adjusted to ensure that the UAV is always within the main lobe coverage of the millimeter wave base station, ensuring the communication and perception of the millimeter wave base station to the UAV.

[0114] The advantage of this embodiment is that the FOV of the millimeter wave base station is determined according to the millimeter wave base station and combined with the height and number of drones in the area, and the AI ​​capabilities of B5G and 6G base stations are taken into account. The drone's historical data is used to predict the drone's motion trajectory, and the field of view of the millimeter wave base station is optimized and adjusted in advance. The transmission power of the millimeter wave base station is dynamically adjusted to reduce interference while improving communication and perception performance. Compared with the current existing solution (fixed millimeter wave base station FOV), the millimeter wave perception accuracy of the air base station can be improved, and the energy consumption of the millimeter wave base station can be reduced. Since millimeter wave perception requires main lobe coverage and side lobes cannot achieve perception capabilities, if the millimeter wave base station field of view is too small, or deviates from the position of the drone, the millimeter wave base station will not be able to achieve drone perception, and the performance of the millimeter wave side lobes for communication will also be significantly reduced compared to the main board. Therefore, the size of the millimeter wave field of view is very critical to achieving millimeter wave synaesthesia integrated coverage.

[0115] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0116] Based on the same inventive concept, the embodiment of the present application also provides a base station field of view angle adjustment device for implementing the base station field of view angle adjustment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more base station field of view angle adjustment devices provided below can refer to the limitations of the base station field of view angle adjustment method above, and will not be repeated here.

[0117] In one embodiment, as shown in FIG8 , a base station viewing angle adjustment device is provided, comprising: a flight altitude acquisition module 801, a viewing angle acquisition module 802, and a viewing angle adjustment module 803, wherein:

[0118] The flight altitude acquisition module 801 is used to acquire the flight altitude of the UAV when the low-altitude sensing network senses the UAV;

[0119] The viewing angle acquisition module 802 is used to obtain the optimal viewing angle of the target base station sensing the drone according to the spacing between the base stations in the low-altitude sensing network, the base station height of the target base station, and the flight altitude of the drone; the target base station is the base station that senses the drone in the low-altitude sensing network;

[0120] The viewing angle adjustment module 803 is used to adjust the viewing angle range of the target base station based on the optimal viewing angle.

[0121] In one embodiment, the field of view angle adjustment module 803 is further used to obtain the field of view angle deflection angle of the target base station for sensing the drone; and adjust the field of view angle range of the target base station according to the optimal field of view angle and the field of view angle deflection angle.

[0122] In one embodiment, the viewing angle adjustment module 803 is further used to obtain the vertical plane beam width of the target base station; and obtain the viewing angle deflection angle according to the vertical plane beam width.

[0123] In one embodiment, the field of view angle adjustment module 803 is further used to obtain real-time flight data of the UAV; input the real-time flight data into a pre-trained UAV flight trajectory prediction model, and obtain the predicted flight trajectory of the UAV through the UAV flight trajectory prediction model; and obtain the field of view angle deflection angle according to the predicted flight trajectory.

[0124] In one embodiment, the field of view angle adjustment module 803 is further used to obtain the predicted flight speed of the drone and the predicted distance between the drone and the target base station according to the predicted flight trajectory; based on the predicted flight speed and the predicted distance, the field of view angle deflection angle is obtained.

[0125] In one embodiment, the base station field of view angle adjustment device also includes: a prediction model training module, which is used to obtain historical flight data of the sample UAV and the actual flight trajectory of the sample UAV; input the historical flight data into the UAV flight trajectory prediction model to be trained, and obtain the predicted flight trajectory of the sample UAV through the UAV flight trajectory prediction model; according to the difference between the predicted flight trajectory of the sample UAV and the actual flight trajectory of the sample UAV, the UAV flight trajectory prediction model is trained to obtain a pre-trained UAV flight trajectory prediction model.

[0126] In one embodiment, the field of view angle adjustment module 803 is further used to use the sum of the optimal field of view angle and the field of view angle deflection angle as the first field of view angle used by the target base station to perceive the drone, and use the difference between the optimal field of view angle and the field of view angle deflection angle as the second field of view angle used by the target base station to perceive the drone; use the angle range between the first field of view angle and the second field of view angle as the field of view angle range used by the target base station to perceive the drone; and generate control instructions based on the field of view angle range used by the target base station to perceive the drone to adjust the field of view angle range of the target base station.

[0127] In one embodiment, the number of drones sensed by the target base station is multiple; the target base station has multiple field of view angle ranges for sensing drones, corresponding to different drones respectively; the field of view angle adjustment module 803 is further used to obtain the maximum first field of view angle and the minimum second field of view angle from the field of view angle range used by the target base station to sense each drone; and generate control instructions using the maximum first field of view angle and the minimum second field of view angle to adjust the field of view angle range of the target base station to the angle range between the maximum first field of view angle and the minimum second field of view angle.

[0128] In one embodiment, the base station field of view angle adjustment device also includes: a field of view angle update module, which is used to obtain the changed flight altitude of the drone when it is detected that the flight altitude of the drone has changed; use the changed flight altitude of the drone as the new flight altitude of the drone, and return to execute the step of obtaining the optimal field of view angle of the target base station for sensing the drone based on the spacing between base stations in the low-altitude perception network, the base station height of the target base station, and the flight altitude of the drone.

[0129] In one embodiment, the base station field of view angle adjustment device also includes: a default field of view angle setting module, which is used to obtain the default perception height of the current base station when any current base station in the low-altitude perception network does not perceive the drone; and set the default field of view angle range of the current base station according to the spacing between the base stations in the low-altitude perception network, the base station height of the current base station, and the default perception height.

[0130] Each module in the above-mentioned base station viewing angle adjustment device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the base station device in the form of hardware, or can be stored in the memory in the base station device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0131] In one embodiment, a communication device is provided, which may be a base station, and its internal structure diagram may be shown in FIG9. The base station device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the base station device is used to provide computing and control capabilities. The memory of the base station device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the base station device is used to exchange information between the processor and an external device. The communication interface of the base station device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a base station field of view angle adjustment method is implemented.

[0132] Those skilled in the art will understand that the structure shown in FIG. 9 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the base station device to which the scheme of the present application is applied. The specific base station device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] In one embodiment, a communication device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0134] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0135] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0137] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0139] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for adjusting a viewing angle of a base station, characterized in that: The method comprises: When the low-altitude sensing network senses a drone, obtaining the flight altitude of the drone; According to the spacing between base stations in the low-altitude sensing network, the base station height of the target base station, and the flight altitude of the drone, an optimal field of view angle of the target base station sensing the drone is obtained; the target base station is a base station in the low-altitude sensing network that senses the drone; Based on the optimal viewing angle, the viewing angle range of the target base station is adjusted.

2. The method according to claim 1, characterized in that The adjusting the viewing angle range of the target base station based on the optimal viewing angle includes: Obtaining a deflection angle of the field of view used by the target base station to sense the drone; The field of view angle range of the target base station is adjusted according to the optimal field of view angle and the field of view angle deflection angle.

3. The method according to claim 2, characterized in that The obtaining of the deflection angle of the field of view of the target base station for sensing the drone includes: Obtaining the vertical beam width of the target base station; The field of view deflection angle is obtained according to the vertical beam width.

4. The method according to claim 2, characterized in that: The obtaining of the field of view deflection angle comprises: Acquiring real-time flight data of the UAV; Inputting the real-time flight data into a pre-trained UAV flight trajectory prediction model, and obtaining a predicted flight trajectory of the UAV through the UAV flight trajectory prediction model; The field of view deflection angle is obtained according to the predicted flight trajectory.

5. The method according to claim 4, characterized in that The obtaining the field of view angle deflection angle according to the predicted flight trajectory comprises: According to the predicted flight trajectory, obtaining a predicted flight speed of the UAV and a predicted distance between the UAV and the target base station; Based on the predicted flight speed and the predicted distance, the field of view angle deflection angle is obtained.

6. The method according to claim 4, characterized in that Before inputting the real-time flight data into the pre-trained UAV flight trajectory prediction model, the method further includes: Acquire historical flight data of a sample UAV and an actual flight trajectory of the sample UAV; Inputting the historical flight data into the UAV flight trajectory prediction model to be trained, and obtaining the predicted flight trajectory of the sample UAV through the UAV flight trajectory prediction model; According to the predicted flight trajectory of the sample UAV and the difference between the actual flight trajectory of the sample UAV, the UAV flight trajectory prediction model is trained to obtain the pre-trained UAV flight trajectory prediction model.

7. The method according to claim 2, characterized in that: The adjusting the viewing angle range of the target base station according to the optimal viewing angle and the viewing angle deflection angle includes: The sum of the optimal field of view angle and the field of view angle deflection angle is used as a first field of view angle for the target base station to perceive the drone, and the difference between the optimal field of view angle and the field of view angle deflection angle is used as a second field of view angle for the target base station to perceive the drone; Using the angle range between the first field of view angle and the second field of view angle as the field of view angle range used by the target base station to perceive the drone; Based on the field of view angle range of the target base station for sensing the drone, a control instruction is generated to adjust the field of view angle range of the target base station.

8. The method according to claim 7, characterized in that The number of drones sensed by the target base station is multiple; the field of view angle ranges of the target base station for sensing the drones are multiple, corresponding to different drones respectively; The generating a control instruction based on the field of view angle range of the target base station for sensing the field of view angle range of the drone to adjust the field of view angle range of the target base station includes: Acquire a maximum first field of view angle and a minimum second field of view angle from a field of view angle range used by the target base station to perceive each of the drones; A control instruction is generated using the maximum first field of view angle and the minimum second field of view angle to adjust the field of view angle range of the target base station to an angle range between the maximum first field of view angle and the minimum second field of view angle.

9. The method according to claim 1, characterized in that: After adjusting the viewing angle range of the target base station based on the optimal viewing angle, the method further includes: When detecting that the flight altitude of the UAV changes, obtaining the changed flight altitude of the UAV; The changed flight altitude of the UAV is used as the new flight altitude of the UAV, and the step of obtaining the optimal field of view angle of the target base station for sensing the UAV is returned to based on the spacing between base stations in the low-altitude sensing network, the base station height of the target base station, and the flight altitude of the UAV.

10. The method according to claim 1, characterized in that The method further comprises: When any current base station in the low-altitude sensing network fails to sense the drone, obtaining a default sensing height of the current base station; The default field of view angle range of the current base station is set according to the spacing between base stations in the low-altitude perception network, the base station height of the current base station, and the default perception height.

11. A base station viewing angle adjustment device, characterized in that: The device comprises: A flight altitude acquisition module, used to acquire the flight altitude of the UAV when the low-altitude sensing network senses the UAV; A field of view angle acquisition module, used to obtain the optimal field of view angle of the target base station sensing the drone according to the spacing between the base stations in the low-altitude sensing network, the base station height of the target base station, and the flight altitude of the drone; the target base station is the base station that senses the drone in the low-altitude sensing network; A viewing angle adjustment module is used to adjust the viewing angle range of the target base station based on the optimal viewing angle.

12. A communication device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

14. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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