Unmanned aerial vehicle path planning method based on multi-base-station signal optimization

Abstract

The invention discloses an unmanned aerial vehicle path planning method based on multi-base-station signal optimization, and the method comprises the steps: constructing a communication system model of multiple base stations and an unmanned aerial vehicle, and determining communication parameters between each base station and the unmanned aerial vehicle; designing a signal power synthesis algorithm based on the communication parameters, and optimizing the received signal strength of the unmanned aerial vehicle by dynamically adjusting the power distribution of each base station; and designing an unmanned aerial vehicle flight path planning strategy according to the optimized signal intensity distribution, and dynamically adjusting a flight path to maintain optimal signal coverage. According to the method, the signal coverage condition, the receiving power and the interference factors among the base stations of the plurality of base stations can be comprehensively considered, the flight path of the unmanned aerial vehicle is dynamically optimized, and the maximization of the communication quality is ensured. By comprehensively considering multiple factors such as path loss, antenna gain and environmental factors, the receiving power in the flight path of the unmanned aerial vehicle is optimized, and the communication quality of the unmanned aerial vehicle in a complex environment can be remarkably improved, so that the high efficiency and stability of a flight task are ensured.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) path planning technology, and in particular to a UAV path planning method based on multi-base station received power optimization. Background Technology

[0002] With the widespread application of drones across various industries, especially the increasing demand in areas such as remote delivery, logistics, monitoring, and inspection, the communication requirements of drones are becoming increasingly important. During long-range flights, particularly under beyond-visual-range (BVR) conditions, drones require stable and efficient communication support to ensure real-time connectivity and safe control with the ground control center. However, in complex environments, traditional path planning methods often fail to adequately consider variations in base station signal coverage and receiving power, potentially leading to signal coverage blind spots during drone flight and impacting the reliability and stability of flight missions.

[0003] Currently, path planning based on the signal reception power of a single base station or a limited number of base stations has become a common practice. However, this method has limitations, neglecting the coordination and interference between base stations. In practical applications, signal interference and coverage overlap between multiple base stations are often not fully considered, causing UAVs to fail to achieve optimal path planning during flight. Therefore, how to dynamically optimize UAV path planning to ensure communication quality during flight has become a current technological challenge.

[0004] Most existing technologies assess signal strength based on the static positions of the drone and base station, simply calculating the distance between them for path planning. These methods typically ignore numerous critical factors, such as signal transmission loss, base station sector division, antenna directivity, and environmental influences. In real-world communication, signal transmission loss is not only related to the distance between the base station and the drone but also closely linked to environmental factors such as frequency, weather, buildings, and terrain. Furthermore, base station antennas usually have directional gain, especially in multi-sector antenna configurations, where variations in antenna gain significantly impact signal coverage. These overlooked factors can prevent drone path planning from providing optimal communication quality in dynamic flight environments. Therefore, comprehensively considering these complex factors and dynamically adjusting the drone's flight path by optimizing the received power of multiple base stations has become a key challenge for improving communication quality and flight mission efficiency. Summary of the Invention

[0005] The purpose of this invention is to propose a UAV path planning method based on multi-base station received power optimization, so as to overcome the limitation of existing technologies that only optimize by judging the distance to the base station without considering the actual model of the base station antenna.

[0006] A UAV path planning method based on multi-base station received power optimization includes:

[0007] A communication system model for multiple base stations and unmanned aerial vehicles (UAVs) is constructed. The model includes a spatial distribution model of base stations, a three-sector antenna model of base stations, and a spatial model of the feasible path area of ​​UAVs.

[0008] Determine the communication parameters between each base station and the UAV, including antenna gain, path loss, and channel characteristics;

[0009] Based on the aforementioned communication parameters, a signal power synthesis algorithm is designed to optimize the received signal strength of the UAV by dynamically adjusting the power allocation of each base station.

[0010] Based on the optimized signal strength distribution, a flight path planning strategy for the UAV is designed, and the flight route is dynamically adjusted to maintain optimal signal coverage.

[0011] In some preferred embodiments, the construction of the communication system model includes:

[0012] Establish a spatial distribution model of base stations, including the three-dimensional coordinates and altitude information of each base station, as well as the real-time three-dimensional dynamic coordinates of the UAV;

[0013] The base station is configured with a three-sector antenna. Each base station is divided into three sectors with a horizontal angle of 120° each. Each sector is equipped with an antenna array and each sector has independent directional gain.

[0014] A spatial model of the feasible path area for UAVs is constructed, and the model defines flight path constraints based on path maps and communication requirements.

[0015] A communication system model involving multiple base stations and unmanned aerial vehicles (UAVs) is constructed. This model includes multiple base stations, UAVs, and their flight path areas. During the construction process, the precise locations, antenna configurations, transmit power, and service coverage areas of the base stations are first determined. Simultaneously, the flight paths of the UAVs and their communication requirements at different locations are defined. Considering the sector configuration of the base stations, each base station's antenna is equipped with directional gain, and detailed modeling is performed based on its elevation and azimuth angles to ensure the accuracy of the signal propagation model.

[0016] In some preferred embodiments, determining the communication parameters includes:

[0017] A dual-mode path loss model (line-of-sight / non-line-of-sight) is used to calculate the path loss between the base station and the UAV.

[0018] The antenna gain is determined based on the relative position of the base station and the drone, as well as the antenna geometry.

[0019] The received power, path loss, and wireless channel characteristics of each base station are determined. The path loss is determined by factors such as the actual distance between the base station and the UAV, environmental factors (e.g., buildings, terrain), and operating frequency. The path loss between each base station and the UAV is calculated by applying line-of-sight (LoS) and non-line-of-sight (NLoS) models.

[0020] Preferably, the calculation of the line-of-sight / non-line-of-sight dual-mode path loss model includes:

[0021]

[0022] Loss LoS =28 + 22log 10 (D)+20log 10 (f)

[0023] Loss NLoS = -17.5 + (46 - 7·log) 10 (z BS ))·log 10 (D)+20·log 10 (40·π·f / 3)

[0024] Where PathLoss(Linear) is the linear value of path loss, Loss(dB) is the path loss, and z BS Here, D is the altitude coordinate of the base station, D is the distance between the base station and the drone, and f is the operating frequency. When the drone and the base station are in line-of-sight propagation, Loss(dB) is taken as Loss. LoS Otherwise, Loss(dB) is taken as Loss. NLoS value.

[0025] In some preferred embodiments, the antenna gain of each base station is the maximum antenna array gain among the three sectors of the corresponding base station, and the antenna array gain is achieved by dynamic adjustment of single-sub-array gain and array factor.

[0026] In some preferred embodiments, the design of the signal power synthesis algorithm includes:

[0027] The received power of each base station to the UAV is calculated based on path loss and antenna gain, and environmental factors are introduced to correct the received power.

[0028] The total received power of the UAV is synthesized by weighting the corrected received power and the weighting coefficients of each base station.

[0029] In some preferred embodiments, the formula for calculating the corrected received power of each base station is as follows:

[0030] P rx,adj =Prx ×α

[0031] P rx =P tx ×G i ×PathLoss(Linear)

[0032]

[0033] Among them, P rx,adj For the corrected received power, P rx For received power, P tx For received power, G i Here, α is the antenna gain, α is the environmental adjustment factor, and PathLoss(Linear) is the linear value of the path loss.

[0034] In some preferred embodiments, the total received power of the UAV is a function of:

[0035]

[0036] Among them, P rx,total It is the total received power, P rx,i w is the received power of the i-th base station. i It is the weight of the i-th base station, d i G is the distance from the drone to the i-th base station, β is the path loss exponent, and G is the distance from the drone to the i-th base station. BS,i Let be the maximum sector gain of the i-th base station.

[0037] In some preferred embodiments, the dynamic adjustment of power allocation for each base station includes:

[0038] The weighting coefficients are dynamically adjusted based on the real-time distance between the base station and the drone, antenna gain, path loss, and environmental adjustment factors.

[0039] The optimal power allocation combination for each base station is determined by an optimization algorithm to maximize the total received power of the UAV.

[0040] In some preferred embodiments, the generation of the path planning strategy includes:

[0041] Assess the current signal strength based on the received power calculation results of each base station;

[0042] Establish a signal strength change rate prediction model and dynamically adjust the flight path to ensure that the UAV is in the optimal signal reception area;

[0043] Maximize total received power and avoid signal coverage dead zones.

[0044] This invention provides a UAV path planning method based on multi-base station received power optimization. It comprehensively considers the signal coverage, received power, and inter-base station interference of multiple base stations to dynamically optimize the UAV flight path, ensuring maximum communication quality. By comprehensively considering path loss, antenna gain, and environmental factors, and optimizing the received power along the UAV flight path, this invention can significantly improve the communication quality of UAVs in complex environments, thereby ensuring the efficiency and stability of flight missions.

[0045] The key feature of this invention is that by optimizing the received power of multiple base stations and dynamically adjusting the UAV's flight path, the stability and reliability of the UAV communication system can be effectively improved. Through precise signal strength allocation and dynamic power control, high-quality communication can be consistently guaranteed under different flight environments and conditions. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of a UAV path planning method based on multi-base station received power optimization.

[0047] Figure 2 This is a schematic diagram of the main process of a UAV path planning method based on multi-base station received power optimization;

[0048] Figure 3 A detailed implementation flowchart of a UAV path planning method based on multi-base station received power optimization is shown below;

[0049] Figure 4 This is a table of path loss index β values. Detailed Implementation

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] See Figure 1 and Figure 2 A UAV path planning method based on multi-base station received power optimization includes:

[0052] Step 101: Construct a communication system model for multiple base stations and drones.

[0053] See Figure 3 First, a communication network model is constructed, which includes multiple base stations and drones. In this model, the location and altitude of each base station are known, where the location of base station i is (x... BS,i ,y BS,i,z BS,i The location of the drone is (x) UAV ,y UAV ,z UAV The drone's flight path area has been determined. Each base station is configured with a three-sector antenna, with each sector covering an angle of 120 degrees. The gain of the base station antenna varies with the elevation and azimuth angles of the drone relative to the base station antenna. The location and altitude of the base station have a significant impact on signal propagation and reception.

[0054] The angle calculation between the drone and multiple base stations is expressed by the following formula:

[0055] The elevation angle θ of the drone relative to base station i i :

[0056]

[0057] d xy,i The horizontal distance between base station i and the drone on the horizontal plane is calculated using the following formula:

[0058]

[0059] The azimuth angle φ of the UAV relative to base station i i :

[0060]

[0061] If the coordinate relationship between the drone and the base station is unusual (e.g., directly above or below), the angle for this unusual situation should be specified directly, with the elevation angle based on z. UAV -z BS,i The sign of the symbol is assigned a value of 0° or 180°, with the azimuth angle being 0°.

[0062] Secondly, the base station antenna gain is calculated based on the relative position between the base station and the UAV, as well as the antenna's geometric configuration. The base station antenna gain is affected by the elevation and azimuth angles; therefore, the gains in the vertical and horizontal directions need to be calculated separately. In the vertical direction, the antenna gain calculation considers the impact of the elevation angle on signal propagation; the specific formula is as follows:

[0063]

[0064] Where, θ 3dB It is a parameter related to the vertical gain, with a magnitude of 65°; A max This is the antenna's maximum gain, with a value of 30. This formula can be used to determine the antenna's gain at different elevation angles. In the horizontal direction, φ... 3dBThis is a parameter related to the horizontal gain, with a value of 65°. The horizontal antenna gain calculation takes into account the effect of the azimuth angle on signal propagation; the calculation formula is:

[0065]

[0066] Calculating the total gain requires considering the combined effects of gains in both the vertical and horizontal directions. Therefore, the total gain is given by the following formula:

[0067] A total =-min[A H +A V A max ]

[0068] This calculation combines the gains in the vertical and horizontal directions to obtain a comprehensive total gain. Further, the antenna gain is introduced, which is determined by the antenna's maximum gain G. max The sum of the total gain determines the value, where G is the sum of the total gain. max The size is 8dbi.

[0069] G elementdB =G max +A total

[0070] The antenna gain is converted to a linear gain for further calculations.

[0071]

[0072] The single-electrode gain F of the antenna element The single-element gain can be calculated using the following formula:

[0073]

[0074] Considering that an antenna array consists of multiple array elements, the spacing and number of each array element in the horizontal and vertical directions have a significant impact on the overall gain of the antenna array. In a three-sector linear array architecture, the overall gain of the antenna array is determined by the number of elements in the horizontal and vertical dimensions, the half-wavelength spacing configuration (0.5λ), the dual-polarization radiation characteristics, and the digital beamforming algorithm. The antenna gain of each sector needs to be dynamically adjusted by combining the gain of individual elements and the array factor. The array factor optimizes the beamforming capability in the horizontal / vertical dimensions through a weight matrix, and the horizontal and vertical element spacing must strictly adhere to the constraint that D / λ ≤ 0.5 to avoid grating lobe effects.

[0075] Assume the horizontal spacing is D H The vertical spacing is D. V The number of elements in the horizontal direction is N H The number of elements in the vertical direction is N VThe positions of the array elements can be calculated using the following formula:

[0076] x n = (n-1)×D H Where n = 1, 2, ..., N H

[0077] z m = (m-1)×D V Where m = 1, 2, ..., N V

[0078] These positions determine the spatial layout of each element in the array. The wave vector k is calculated, which describes the direction of signal propagation. The wave vector is related to the signal's wavelength λ, elevation angle θ, and azimuth angle φ, and is calculated using the following formula:

[0079]

[0080] Using the wave vector, the response of each array element to the signal can be further calculated, and this response can be represented by the beamforming pattern. The formula for calculating the beamforming vector of each array element is:

[0081] S i =exp(-jr i ·k)

[0082] Where, r i =(x n ,z m Let be the position of the i-th array element, and k be the wave vector. When calculating the array response, the weighting values ​​of different array elements also need to be considered. To optimize the array gain, given the tilt angles θ in the vertical and horizontal directions... V and θ H , where θ V =10°, θ H =0°. The weights of the array elements can be calculated.

[0083] The formula for calculating the weight in the vertical direction is:

[0084]

[0085] The formula for calculating the weight in the horizontal direction is:

[0086]

[0087] These weight vectors are combined into a two-dimensional weight vector w, which is calculated as follows:

[0088]

[0089] in, This represents the Kronecker product. Next, the array factor is calculated, which describes the response of the antenna array in different directions. The formula for calculating the array factor is:

[0090] AF = w H ·S

[0091] Among them, w H It is the conjugate transpose of the weight vector, and S is the beamforming vector. Finally, the total gain G of the antenna array... array It is the single-array gain F of each array element element The formula is obtained by combining the array factor AF:

[0092] F array =F element ·AF

[0093] The gain G of the antenna array array for:

[0094] G array =|F array | 2

[0095] Step 102: Determine the communication parameters between each base station and the UAV;

[0096] Determining the communication parameters between each base station and the UAV involves determining the received power, path loss, and wireless channel characteristics of each base station. Path loss is determined by factors such as the actual distance between the base station and the UAV, environmental factors (e.g., buildings, terrain), and operating frequency. By applying line-of-sight (LoS) and non-line-of-sight (NLoS) models, the path loss between each base station and the UAV is calculated, and the received power is derived from this. The formula for calculating path loss is as follows:

[0097] Loss LoS =28 + 22log 10 (D)+20log 10 (f)

[0098] Loss NLoS = -17.5 + (46 - 7·log) 10 (z BS ))·log 10 (D)+20·log 10 (40·π·f / 3)

[0099] The linear value of path loss can be transformed using the following formula:

[0100]

[0101] Combining path loss and channel gain, the formula for calculating received power is:

[0102] P rx =P tx ×G i ×PathLoss(Linear)

[0103] Where PathLoss(Linear) is the linear value of path loss, Loss(dB) is the path loss, and z BS Here, D is the altitude coordinate of the base station, D is the distance between the base station and the drone, and f is the operating frequency. When the drone and the base station are in line-of-sight propagation, Loss(dB) is taken as Loss. LoS Otherwise, Loss(dB) is taken as Loss. NLoS value.

[0104] P rx,adj For the corrected received power, P rx For received power, P tx For transmission power, G i G represents the antenna gain, α is the environmental adjustment factor, and PathLoss(Linear) is the linear value of the path loss. i Take the antenna array gain G in the three sectors of the selected base station. array Maximum value.

[0105] To adapt to signal attenuation under different environmental conditions, an environmental adjustment factor α needs to be introduced to correct the received power.

[0106] P rx,adj =P rx ×α

[0107] The value of α is adjusted according to environmental changes. In densely populated urban areas with tall buildings, α is less than 1, indicating strong signal attenuation; in open areas, α is greater than 1, indicating good signal propagation.

[0108] Step 103: Design a signal power synthesis algorithm based on the communication parameters, and optimize the received signal strength of the UAV by dynamically adjusting the power allocation of each base station;

[0109] In a multi-base station environment, the received power optimization objective is defined, and a weighted power synthesis algorithm is designed. This algorithm synthesizes the total received power based on the received power and antenna gain of each base station. The weight w for each base station is defined. i It can be seen through its distance d i The path loss exponent β and the maximum sector gain G of the i-th base station BS,i The path loss exponent β is calculated using empirical values. β is typically higher in urban environments and lower in suburban environments. In some specific application scenarios, β can be expressed as follows: Figure 4 The values ​​in the table. The gain G of the antenna array of the three sectors of the i-th base station is taken relative to the drone's location. array The largest one is set to G. BS,i Weight w i Calculated using the following formula:

[0110]

[0111] The formula for calculating the weighted total received power is:

[0112]

[0113] Where: P rx,total This is the total received power. P rx,i w is the received power of the i-th base station. i This represents the weight of the i-th base station. The weight w can be calculated through weighted power synthesis. i This formula obtains the overall received power of the UAV across multiple base stations, which is crucial for subsequent path planning and signal quality optimization. By weighting the received power, the formula ensures that base stations closer to the UAV and with higher antenna gain contribute more to the total received power, thereby optimizing signal distribution and improving the UAV's communication quality.

[0114] Step 104: Design a flight path planning strategy for the UAV based on the optimized signal strength distribution, and dynamically adjust the flight route to maintain optimal signal coverage.

[0115] Based on the optimization results of step 103, a path planning strategy for the UAV is designed to ensure that the UAV can always be in the optimal signal reception area and maximize the reception power.

[0116] Specifically, a spatial model of the feasible path area for the UAV is constructed, and the model defines flight path constraints based on a path map and communication requirements. The current signal strength is evaluated based on the received power calculation results of each base station.

[0117] A signal strength change rate prediction model is established to dynamically adjust the flight path to ensure the UAV is in the optimal signal reception area, maximizing total received power and avoiding signal coverage blind spots. During flight, the UAV monitors the signal strength of each base station in real time and dynamically adjusts its flight path to avoid entering signal blind spots. The system evaluates the current signal strength based on the received power calculation results of each base station and optimizes the flight path to ensure that the UAV is always within the signal coverage range of multiple base stations. The signal strength evaluation adopts a comparison mechanism between total received power and reference received power (RSRP), and makes a comprehensive judgment based on a preset RSRP threshold range and a quality evaluation matrix. The path planning not only considers the relative position between the UAV and the base stations, but also the impact of flight altitude and complex environment on signal propagation, dynamically adjusting the flight path to maximize received power and avoid signal coverage blind spots.

[0118] The UAV path planning method disclosed in this invention is mainly based on optimizing flight trajectory using multi-base station signal quality. After the user inputs the starting point and destination on the platform, the system combines preset base station locations, no-fly zone geographical information, and environmental parameters (such as path loss index and antenna directivity gain) to dynamically calculate the signal attenuation model between the UAV and each base station. Through multi-base station signal strength weighted synthesis technology, it avoids no-fly zones and signal blind spots, generating an optimal path that balances communication quality and flight efficiency. The planned path point trajectory is then imported into the UAV for execution via software. The core innovation of this invention lies in utilizing the signal distribution characteristics of base stations to optimize path decisions in real time, ensuring that the UAV is always within the optimal signal area covered by the collaborative coverage of multiple base stations.

[0119] The present invention has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A UAV path planning method based on multi-base station received power optimization, characterized in that, include: A communication system model for multiple base stations and unmanned aerial vehicles (UAVs) is constructed. The model includes a spatial distribution model of base stations, a three-sector antenna model of base stations, and a spatial model of the feasible path area of ​​UAVs. Determine the communication parameters between each base station and the UAV, including antenna gain, path loss, and channel characteristics; Based on the aforementioned communication parameters, a signal power synthesis algorithm is designed to optimize the received signal strength of the UAV by dynamically adjusting the power allocation of each base station. Based on the optimized signal strength distribution, a flight path planning strategy for the UAV is designed, and the flight route is dynamically adjusted to maintain optimal signal coverage.

2. The UAV path planning method based on multi-base station received power optimization according to claim 1, characterized in that, The construction of the communication system model includes: Establish a spatial distribution model of base stations, including the three-dimensional coordinates and altitude information of each base station, as well as the real-time three-dimensional dynamic coordinates of the UAV; The base station is configured with a three-sector antenna. Each base station is divided into three sectors with a horizontal angle of 120° each. Each sector is equipped with an antenna array and each sector has independent directional gain. A spatial model of the feasible path area for UAVs is constructed, and the model defines flight path constraints based on path maps and communication requirements.

3. The UAV path planning method based on multi-base station received power optimization according to claim 1, characterized in that, The determination of the communication parameters includes: A dual-mode path loss model (line-of-sight / non-line-of-sight) is used to calculate the path loss between the base station and the UAV. The antenna gain is determined based on the relative position of the base station and the drone, as well as the antenna geometry.

4. The UAV path planning method based on multi-base station received power optimization according to claim 3, characterized in that, The calculation of the line-of-sight / non-line-of-sight dual-mode path loss model includes: Loss LoS =28+22log 10 (D)+20log 10 (f) Loss NLoS N-17.5+(46-7·log 10 (z BS ))·log 10 (D)+20·log 10 (40·π·f / 3) Where PathLoss(Linear) is the linear value of path loss, Loss(dB) is the path loss, and z BS Here, D is the altitude coordinate of the base station, D is the distance between the base station and the drone, and f is the operating frequency. When the drone and the base station are in line-of-sight propagation, Loss(dB) is taken as Loss. LoS Otherwise, Loss(dB) is taken as Loss. NLoS value.

5. The UAV path planning method based on multi-base station received power optimization according to claim 3, characterized in that, The antenna gain of each base station is the maximum value of the antenna array gain in the three sectors of the corresponding base station. The antenna array gain is achieved by dynamic adjustment of single element gain and array factor.

6. The UAV path planning method based on multi-base station received power optimization according to claim 1, characterized in that, The design of the signal power synthesis algorithm includes: The received power of each base station to the UAV is calculated based on path loss and antenna gain, and environmental factors are introduced to correct the received power. The total received power of the UAV is synthesized by weighting the corrected received power and the weighting coefficients of each base station.

7. The UAV path planning method based on multi-base station received power optimization according to claim 6, characterized in that, The formula for calculating the corrected received power of each base station is as follows: P rx,adj =P rx ×α P rx =P tx ×G i ×PathLoss(Linear) Among them, P rx,adj For the corrected received power, P rx For received power, P tx For transmission power, G i Here, α is the antenna gain, α is the environmental adjustment factor, and PathLoss(Linear) is the linear value of the path loss.

8. The UAV path planning method based on multi-base station received power optimization according to claim 6, characterized in that, The function of the total received power of the UAV is: Among them, P rx,total It is the total received power, P rx,i w is the received power of the i-th base station. i It is the weight of the i-th base station, d i G is the distance from the drone to the i-th base station, β is the path loss exponent, and G is the distance from the drone to the i-th base station. BS,i This represents the maximum sector gain of the i-th base station.

9. The UAV path planning method based on multi-base station received power optimization according to claim 1, characterized in that, The dynamic adjustment of power allocation for each base station includes: The weighting coefficients are dynamically adjusted based on the real-time distance between the base station and the drone, antenna gain, path loss, and environmental adjustment factors. The optimal power allocation combination for each base station is determined by an optimization algorithm to maximize the total received power of the UAV.

10. The UAV path planning method based on multi-base station received power optimization according to claim 1, characterized in that, The generation of the path planning strategy includes: Assess the current signal strength based on the received power calculation results of each base station; Establish a signal strength change rate prediction model and dynamically adjust the flight path to ensure that the UAV is in the optimal signal reception area; Maximize total received power and avoid signal coverage dead zones.

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