Unmanned aerial vehicle communication trajectory optimization method and system based on graph theory and geometry

Abstract

The application discloses a kind of unmanned plane communication track optimization method and system based on graph theory and geometry, method includes the following steps: step 1, obtain user data, and set unmanned plane flight data;Step 2, calculate user aggregation point maximum receiving range radius, adjacent aggregation point distance and each aggregation point weight;Step 3, whether the receiving range is overlapped and the intersection coordinates are calculated;Step 4, initialize unmanned plane flight track;Step 5, optimize unmanned plane flight track.The flight track of the unmanned plane is optimized, the operation efficiency of the unmanned plane can be significantly improved, unnecessary time waste is avoided, valuable time is saved for disaster area communication and rescue, and the post-disaster rescue efficiency is improved.

Description

Technical Field

[0001] This invention belongs to the field of drone flight trajectory optimization technology, specifically a trajectory optimization method based on geometric inequalities and graph theory. This method considers the drone's flight time, path length, and system energy consumption to optimize an efficient and safe flight path for the drone. In emergency communication scenarios following natural disasters, drones can serve as temporary mobile airborne base stations, providing emergency communications to affected areas. Background Art

[0002] Due to their high mobility, low operating costs, and flexible deployment, drones are considered cost-effective, on-demand aerial communications platforms and are widely used in various communications scenarios, with emergency communications being a key application. In post-disaster emergency communications, drones can be equipped with 5G micro-base stations. Leveraging their flexibility and rapid deployment, they can quickly ensure communications within a small area of ​​the disaster area. This helps address issues such as damaged communications infrastructure and communication difficulties between emergency rescue commanders and frontline personnel during emergency rescue operations.

[0003] Drones can leverage their high mobility to ensure communications in some disaster areas, but this inevitably consumes a lot of energy, and currently, their limited battery capacity remains a drawback. When drones assist in emergency communications, energy consumption primarily comes from flight and communication, both of which are directly related to the flight time and distance. Therefore, optimizing efficient and safe drone trajectories while ensuring ground communications and improving drone energy efficiency are crucial to achieving emergency communications goals.

[0004] Existing technologies use only graph theory or dynamic optimization methods to optimize drone flight paths, resulting in high algorithm complexity and potentially suboptimal paths. This invention, based on graph theory and applying geometric inequalities, can further optimize paths and improve drone energy efficiency. Summary of the Invention

[0005] To address the aforementioned challenges of the existing technology, this paper proposes a trajectory optimization method and system for drone-assisted emergency communications based on geometry and graph theory. This method addresses the issue of battery energy limitations during drone trajectory optimization by transforming the energy problem into a distance function problem for minimizing the drone's trajectory, based on the energy loss during drone operation. Graph theory search algorithms are used to find the shortest path and ensure the connectivity of the drone's trajectory. Based on this, geometric methods are applied to further optimize the drone's trajectory, shortening the drone's flight distance and improving its energy efficiency while still meeting the emergency communication needs of users in disaster areas.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] An optimization method for the communication trajectory of an unmanned aerial vehicle based on graph theory and geometry, comprising the following steps:

[0008] Step 1, obtain user data and set the flight data of the unmanned aerial vehicle;

[0009] Step 2, calculate the maximum reception range radius of the user aggregation point, the distance between adjacent aggregation points, and the weight of each aggregation point;

[0010] Step 3, determine whether the reception ranges overlap and calculate the intersection coordinates;

[0011] Step 4, initialize the flight trajectory of the unmanned aerial vehicle;

[0012] Step 5, optimize the flight trajectory of the unmanned aerial vehicle.

[0013] Preferably, in step 1, the unmanned aerial vehicle data includes: the flight height of the unmanned aerial vehicle is constantly H, the flight speed of the unmanned aerial vehicle is constantly V, the service range radius of the unmanned aerial vehicle is R, the initial position L I =(x I , y I , H), the termination position L F =(x F , y F , H), the real-time position (x(t), y(t), H) of the unmanned aerial vehicle, where 0 < t < T, T is the total time before the energy of the unmanned aerial vehicle is exhausted, the total energy E max of the on-board battery of the unmanned aerial vehicle, the average moving energy consumption W h of the unmanned aerial vehicle, the average hovering energy consumption W m of the unmanned aerial vehicle, and the average hovering time τ of the unmanned aerial vehicle.

[0014] The user data includes: the number of user aggregation points is L, the number of users N l at the l-th aggregation point, the average altitude h l at the l-th user aggregation point, the coordinates (x l , y l , h l ) of the l-th user aggregation point, the maximum reception range radius R l at the l-th user aggregation point, the weight w l at the l-th user aggregation point, where

[0015] As a preferred solution, in step 2, calculating the maximum reception range radius R l at the l-th user aggregation point specifically includes:

[0016] The range of signal reception for users at the gathering point is not only related to the radius of the drone's service range, but also to the height difference between the drone and the gathering point;

[0017] Calculate the height difference Δh between the drone and the gathering point and the maximum receiving range radius R l :

[0018] Δh=Hh l (1)

[0019]

[0020] Among them, R is the maximum radius of the drone's service range, H is the drone's flight altitude, and h l is the average altitude of the lth user gathering point.

[0021] As a preferred solution, in step 2, calculating the distance between adjacent cluster points specifically includes:

[0022] Considering the distance between cluster points in a two-dimensional coordinate system, calculate the distance between the l1th and l2th cluster points:

[0023]

[0024] in is the two-dimensional coordinate of the gathering point l1, is the two-dimensional coordinate of the gathering point l2.

[0025] As a preferred solution, in step 2, the weight w of the clustering point is calculated. l ,include:

[0026] Introduce the weight factor to calculate the weight w of each cluster point l , where the distance weight coefficient α is assumed to be i , user quantity weight coefficient β:

[0027]

[0028] in represents the distance between the lth gathering point on the two-dimensional plane and each gathering point served by the drone, Represents the lth gathering point and the terminal point L on the two-dimensional plane F The distance between i represents the distance weight coefficient of the two gathering points, and α4 represents the weight coefficient of the distance between the gathering point and the UAV termination point.

[0029] As a preferred solution, in step 3, calculating the receiving range coordinates of the lth user gathering point includes:

[0030] The receiving range of the user gathering point is determined by the center coordinates of the gathering point and the maximum receiving range radius. The receiving range coordinates of the lth user gathering point are calculated as follows:

[0031] (x(t)-x l ) 2 +(y(t)-y l ) 2 ≤R l 2 (5)

[0032] Among them, (x(t), y(t)) is the real-time coordinate of the UAV flight.

[0033] As a preferred solution, in step 3, calculating the overlapping portion of adjacent receiving ranges specifically includes:

[0034] Determine whether there is an overlapping part between two adjacent receiving ranges;

[0035] Consider the distance between the l1th and l2th cluster points on a two-dimensional plane The sum of the receiving range radius of the l1th and l2th gathering points Relationship:

[0036] when When , there is an overlap between the two receiving ranges;

[0037] Calculate the coordinates of the intersection of two receiving ranges:

[0038]

[0039] when When , there is no overlapping part between the two receiving ranges;

[0040] To simplify the problem, we only consider the ideal case where the receptions along the flight trajectory have overlapping parts.

[0041] As a preferred solution, in step 4, during the initialization of the flight trajectory, only the scenario where the drone departs from the starting point, flies to the center of each gathering point, hovers over it to provide service, and finally reaches the end point is considered. In this case, only the weight of each gathering point needs to be considered, without considering the drone battery energy, that is, it is assumed that the energy is sufficient to complete the entire flight mission. The initialization process includes:

[0042] Step 41: The drone is at the initial position, i.e. L I =(x I ,y I ,H),At this time, the drone calculates the weight of each gathering point according to the distance between all nearby gathering points and the starting position and the number of users, and selects the gathering point with the largest weight as the first service target;

[0043] Step 42: The drone flies in a straight line at a constant speed V to the center of the first gathering point and hovers above it to provide emergency communication services. At this time, the drone updates the distance from all nearby gathering points to the first gathering point, updates the weight of each gathering point based on the distance and the number of users, and selects the gathering point with the largest weight as the next service target. This process continues until the drone has served all users and flies to the end position to complete the mission.

[0044] As a preferred solution, in step 5, the trajectory initialized in step 4 is optimized to optimize the flight distance of the UAV. The flight distance optimization process includes:

[0045] Step 51: Based on the flight trajectory initialized in step 4, calculate the intersection of the flight trajectory and each receiving range boundary. Each time the drone switches the service object, two new intersection points will be generated, namely the intersection of the flight trajectory and the current nth receiving range boundary. and the intersection with the next service area boundary This results in two point sets:

[0046]

[0047]

[0048] Among them, (x C ,y C ) represents the center coordinate of the current receiving range, R C Indicates the radius of the current receiving range; (x L ,y L ) represents the center coordinate of the next receiving range, R L Indicates the radius of the next receiving range;

[0049] Step 52: Optimize the drone trajectory for the first time and select the points to concentrate on. Each point is used as the switching point of the drone's service range. At this time, the drone no longer flies through the center of the gathering point, but starts directly from the starting point and flies to It hovers over each switching point in the flight to provide service, and finally reaches the end point to end the flight mission;

[0050] Step 53: Based on the new flight trajectory optimized in step 52, update the intersection point of the flight trajectory and the next service range boundary to obtain

[0051] Step 54: Optimize the drone trajectory for the second time and select the points to concentrate on. Each point is used as the service range switching point of the drone. At this time, the drone no longer flies through Above, but directly from the starting point, fly to The switch point in the process is hovered over and served, and finally reaches the end point;

[0052] Step 55: Based on the flight trajectory optimized in step 54, update the intersection of the flight trajectory and the current service range boundary to obtain

[0053] Step 56: Repeat steps 52 to 55 in sequence, and repeat the iteration until the number of iterations reaches a threshold or the update result converges to a minimum value, thereby obtaining the global optimal flight trajectory and minimum flight distance.

[0054] As a preferred solution, in step 5, a step of optimizing the energy problem of the UAV flight process is performed.

[0055] The energy consumption of a drone consists of two parts: flight energy loss and hovering energy loss. The flight energy loss is related to the flight time of the drone. Since the flight speed of a drone is constant, the flight energy loss of the drone is related to the total flight distance X of the drone:

[0056]

[0057] The energy loss of drone hovering is related to the number of user gathering points l served by the drone. Since the drone hovers at the switching point of the receiving range, and the hovering time of the drone is constant each time, that is:

[0058] E m =W m ·τ·(l-1) (10)

[0059] Drone Energy Limits:

[0060] E h +E m ≤E max (11)

[0061] The optimization process of the drone energy problem is as follows:

[0062] Assuming that the battery energy of the drone is sufficient to complete all emergency communication tasks from the starting point to the end point of L user gathering points, the flight trajectory is optimized on this basis and the final total energy loss is calculated. Compare With E max ;

[0063] like This indicates that the assumption is not true, the drone cannot complete all communication tasks, and the number of served gathering points needs to be reduced; on the contrary, it indicates that the drone has enough energy to complete all communication tasks;

[0064] like At this time, reduce i service points, where i=1,2,3,……L, and recalculate the total energy loss of the drone at this time Compare With E max Size, keep increasing the value of i until At this time, Li is the maximum number of gathering points that the drone can serve, that is, the optimal trajectory of the drone under the energy limit of the drone battery.

[0065] The present invention also discloses a system based on the above optimization method, which includes the following modules:

[0066] Data preparation module: obtain user data and set drone flight data;

[0067] Calculation module: calculates the maximum receiving range radius of the user gathering point, the distance between adjacent gathering points and the weight of each gathering point;

[0068] Determination module: determines whether the receiving ranges overlap and calculates the intersection coordinates;

[0069] Flight trajectory initialization module: initializes the UAV flight trajectory;

[0070] Flight trajectory optimization module: optimizes the flight trajectory of the drone.

[0071] Compared with the prior art, the present invention has the following technical effects:

[0072] (1) The present invention proposes a UAV trajectory optimization method and system, which can optimize the flight trajectory of the UAV according to the post-disaster emergency communication needs, significantly improve the operating efficiency of the UAV, avoid unnecessary time waste, save precious time for communication and rescue in the disaster area, and improve the efficiency of post-disaster rescue.

[0073] (2) The present invention relates to an algorithm that combines graph theory and geometric inequalities. Based on the current mainstream graph theory algorithm, the algorithm uses geometric inequalities to further optimize the trajectory of the UAV, thereby reducing the complexity of the algorithm and improving the efficiency of the algorithm. At the same time, the algorithm takes into account the energy limitations of the UAV, and by optimizing the route and reducing the flight time of the UAV, the energy loss of the UAV flight is reduced, the energy utilization efficiency is improved, and ultimately the overall performance of the UAV is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a flow chart of a method for optimizing UAV communication trajectories based on graph theory and geometry in Example 1.

[0075] Figure 2 This is a block diagram of a UAV communication trajectory optimization system based on graph theory and geometry in Example 2. DETAILED DESCRIPTION

[0076] The following describes the embodiments of the present invention through specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0077] Example 1:

[0078] This embodiment provides a method for optimizing drone communication trajectories based on geometric graph theory. This method addresses the issue of drone battery energy limitations, transforms energy issues into distance issues, utilizes geometric graph theory, and comprehensively considers user communication quality and drone energy loss to derive the optimal flight trajectory.

[0079] like Figure 1 As shown, this embodiment provides a trajectory optimization method for UAV-assisted emergency communication based on graph theory and geometric inequalities, which specifically includes the following steps:

[0080] S1. Get user data in the current scenario.

[0081] S2. Set the drone flight data.

[0082] S3. Calculate the maximum receiving range radius of the lth user gathering point as R l .

[0083] S4. Calculate the distance between adjacent cluster points.

[0084] S5. Determine the weight w of each cluster point l .

[0085] S6. Determine the coordinates of the reception range of the first user gathering point and the overlapping parts of adjacent reception ranges;

[0086] In this embodiment, step S6 further includes the following steps:

[0087] S6.1. Calculate the receiving range coordinates of the lth user gathering point;

[0088] S6.2. Determine whether the receiving ranges overlap and calculate the intersection coordinates.

[0089] S7. Initialize the UAV flight trajectory.

[0090] S8. Optimize the flight trajectory of the drone;

[0091] In this embodiment, step S8 further includes the following steps:

[0092] S8.1. Obtain the intersection point set based on the flight trajectory initialized in step S7 and

[0093] S8.2. Select the intersection set To serve the switching point, optimize the flight trajectory once and update the intersection set at the same time

[0094] S8.3. Select the intersection set To serve the switching point, optimize the flight trajectory once and update the intersection set at the same time

[0095] S8.4. Repeat steps S8.2 and S8.3 until the global optimal flight trajectory and minimum flight distance are obtained;

[0096] S8.5. Based on the flight trajectory optimized in step S8.4, the energy problem of the drone is considered. The energy required for flight is compared with the total battery energy. If the battery energy is insufficient, the number of service gathering points is reduced in sequence until the drone energy is sufficient to complete the mission.

[0097] This example makes the following assumptions:

[0098] (1) The number of users at the gathering point is within the maximum number of users that the drone can serve.

[0099] (2) The height of all gathering points shall not be higher than the flight altitude of the UAV, and the height difference between the two shall not be less than the radius of the UAV service range.

[0100] (3) There are overlapping parts in the receiving ranges of adjacent gathering points that the flight trajectory passes through.

[0101] More specifically, this embodiment is based on a geometric graph theory UAV communication trajectory optimization method, which includes the following steps:

[0102] S1. Get user data in the current scenario.

[0103] User data includes: the number of user gathering points is L, the number of users at the lth gathering point is N l , the average altitude of the lth user gathering point is h l , the coordinates of the lth user gathering point (x l ,y l ,h l ), the maximum receiving range radius of the lth user gathering point is R l , the weight of the lth user gathering point w l ,in

[0104] S2. Set the drone flight data.

[0105] The UAV data includes: the UAV flight altitude is constantly H, the UAV flight speed is constantly V, the UAV service range radius is R, and the UAV initial position L I =(x I ,y I ,H), the UAV termination position L F =(x F ,y F ,H), the UAV real-time position (x(t), y(t), H), where 0 < t < T, T is the total time before the UAV's energy is exhausted, the total energy E of the UAV on-board battery max , the UAV moving average energy consumption W h , the UAV hovering average energy consumption W m , the UAV hovering average time τ.

[0106] S3. Calculate the maximum reception range radius R of the l-th user aggregation point l .

[0107] The range size of the signal received by the users at the aggregation point is not only related to the UAV service range radius but also related to the height difference between the UAV and the aggregation point;

[0108] First, calculate the height difference Δh between the UAV and the aggregation point and the maximum reception range radius R l :

[0109] Δh = H - h l (12)

[0110]

[0111] where R is the maximum radius of the UAV service range, H is the UAV flight altitude, and h l is the average altitude of the l-th user aggregation point;

[0112] S4. Calculate the distance between adjacent aggregation points.

[0113] Considering the distance between the two in the two-dimensional plane, calculate the distance between the l1-th and l2-th aggregation points in the two-dimensional plane

[0114]

[0115] S5. Determine the weight w of each aggregation point l .

[0116] Introduce a weight factor to calculate the weight w of each aggregation point l , where, assuming the distance weight coefficient α i , and the user number weight coefficient β:

[0117]

[0118] in represents the distance between the lth gathering point on the two-dimensional plane and each gathering point served by the drone, Represents the lth gathering point and the terminal point L on the two-dimensional plane F The distance between i represents the distance weight coefficient of the two gathering points, and α4 represents the weight coefficient of the distance between the gathering point and the UAV termination point.

[0119] S6. Determine the coordinates of the reception range of the first user gathering point and the overlapping parts of adjacent reception ranges;

[0120] In this embodiment, step S6 further includes the following steps:

[0121] S6.1. Calculate the receiving range coordinates of the lth user gathering point;

[0122] The receiving range of the user gathering point is determined by the center coordinates of the gathering point and the maximum receiving range radius. The receiving range coordinates of the lth user gathering point are calculated as follows:

[0123] (x(t)-x l ) 2 +(y(t)-y l ) 2 ≤R l 2 (16)

[0124] Among them, (x(t), y(t)) is the real-time coordinate of the UAV flight.

[0125] S6.2. Determine whether the receiving ranges overlap and calculate the intersection coordinates.

[0126] Consider the distance between the l1th and l2th cluster points on a two-dimensional plane The sum of the receiving range radius of the l1th and l2th gathering points relationship;

[0127] when When , there is an overlap between the two receiving ranges;

[0128] Calculate the coordinates of the intersection of two receiving ranges:

[0129]

[0130] when There is no overlap between the two receiving ranges.

[0131] S7. Initialize the UAV flight trajectory.

[0132] During the initialization of the flight trajectory, we only consider the scenario where the drone departs from the starting point, flies to the center of each gathering point, hovers over it to provide service, and finally reaches the end point. In this case, we only need to consider the weight of each gathering point, without considering the drone's battery energy, that is, we assume that the energy is sufficient to complete the entire flight mission. The initialization process includes:

[0133] S7.1. The drone is at the initial position, i.e. L I =(x I ,y I ,H),At this time, the drone calculates the weight of each gathering point according to the distance between all nearby gathering points and the starting position and the number of users, and selects the gathering point with the largest weight as the first service target;

[0134] S7.2. The UAV flies in a straight line at a constant speed V to the center of the first gathering point and hovers above it to provide emergency communication service. At this time, the UAV updates the distance from all nearby gathering points to the first gathering point, updates the weight of each gathering point based on the distance and the number of users, and selects the gathering point with the largest weight as the next service target. This process continues until the UAV has served all users and reaches the end position to complete the mission.

[0135] S8. Optimize the flight trajectory of the drone;

[0136] In this embodiment, step S8 further includes the following steps:

[0137] S8.1. Obtain the intersection point set based on the flight trajectory initialized in step S7 and

[0138] Based on the flight trajectory initialized in step S7, the intersection of the flight trajectory and each receiving range boundary is calculated. Each time the UAV switches the service object, two new intersection points are generated, namely the intersection of the flight trajectory and the current nth receiving range boundary. and the intersection with the next service area boundary This results in two point sets:

[0139]

[0140]

[0141] Among them, (x C ,y C ) represents the center coordinate of the current receiving range, R C Indicates the radius of the current receiving range; (x L ,y L ) represents the center coordinate of the next receiving range, R L Indicates the radius of the next receiving range;

[0142] S8.2. Select the intersection set To serve the switching point, optimize the flight trajectory once and update the intersection set at the same time

[0143] Optimize the UAV trajectory for the first time and select the points to concentrate Each point is used as the switching point of the drone's service range. At this time, the drone no longer flies through the center of the gathering point, but starts directly from the starting point and flies to Hover over each switching point in the service, and finally reach the end point to end the flight mission. According to the optimized new flight trajectory, update the intersection of the flight trajectory and the next service range boundary to obtain

[0144] S8.3. Select the intersection set To serve the switching point, optimize the flight trajectory once and update the intersection set at the same time

[0145] Perform a second optimization on the drone trajectory and select points to concentrate Each point is used as the service range switching point of the drone. At this time, the drone no longer flies through Above, but directly from the starting point, fly to The flight path is optimized and the intersection of the flight path and the boundary of the current service range is updated to obtain

[0146] S8.4. Repeat steps S8.2 and S8.3 in sequence, and repeat the iterations until the number of iterations reaches a threshold or the update result converges to a minimum value, thereby obtaining the global optimal flight trajectory and minimum flight distance.

[0147] S8.5. Based on the flight trajectory optimized in step S8.4, consider the drone's energy problem and compare the energy required for flight with the total battery energy. If the battery energy is insufficient, reduce the number of service gathering points in sequence until the drone has enough energy to complete the mission:

[0148] The energy consumption of a drone consists of two parts: flight energy loss and hovering energy loss. The flight energy is related to the flight time of the drone. Since the flight speed of a drone is constant, the flight energy loss of the drone is related to the total flight distance X of the drone:

[0149]

[0150] The energy loss of drone hovering is related to the number of user gathering points l served by the drone. Since the drone hovers at the switching point of the receiving range, and the hovering time of the drone is constant each time, that is:

[0151] E m =W m ·τ·(l-1) (21)

[0152] Drone Energy Limits:

[0153] E h +E m ≤E max (twenty two)

[0154] Assuming that the battery energy of the drone is sufficient to complete all emergency communication tasks from the starting point to the end point of L user gathering points, the flight trajectory is optimized on this basis and the final total energy loss is calculated. Compare With E max :

[0155] like This indicates that the assumption is not true, the drone cannot complete all communication tasks, and the number of served gathering points needs to be reduced; on the contrary, it indicates that the drone has enough energy to complete all communication tasks;

[0156] like At this time, reduce i service points, where i=1,2,3,……L, and recalculate the total energy loss of the drone at this time Compare With E max Size, keep increasing the value of i until At this time, Li is the maximum number of gathering points that the drone can serve, that is, the optimal trajectory of the drone under the energy limit of the drone battery.

[0157] Example 2:

[0158] like Figure 2 As shown, this embodiment discloses a system based on the optimization method described in Example 1, which includes the following modules:

[0159] Data preparation module: obtain user data and set drone flight data;

[0160] Calculation module: calculates the maximum receiving range radius of the user gathering point, the distance between adjacent gathering points and the weight of each gathering point;

[0161] Determination module: determines whether the receiving ranges overlap and calculates the intersection coordinates;

[0162] Flight trajectory initialization module: initializes the UAV flight trajectory;

[0163] Flight trajectory optimization module: optimizes the flight trajectory of the drone.

[0164] For other contents of this embodiment, please refer to Example 1.

[0165] The above description is only a detailed description of the preferred embodiments and principles of the present invention. For ordinary technicians in this field, based on the ideas provided by the present invention, there may be changes in the specific implementation methods, and these changes should also be considered as the scope of protection of the present invention.

Claims

1. A UAV communication trajectory optimization method based on graph theory and geometry, characterized by The following steps are involved: Step 1: Get user data and set drone flight data; Step 2: Calculate the maximum receiving range radius of the user gathering point, the distance between adjacent gathering points, and the weight of each gathering point; Step 3: Determine whether the receiving ranges overlap and calculate the intersection coordinates; Step 4: Initialize the drone flight trajectory; Step 5: Optimize the drone’s flight trajectory; Step 5 specifically includes: Step 51: Based on the flight trajectory of the drone initialized in step 4, the intersection of the flight trajectory and each receiving range boundary is calculated. Each time the drone switches the service object, two new intersection points will be generated, namely the intersection of the flight trajectory and the current nth receiving range boundary. and the intersection with the next service area boundary , which results in two point sets: （7） （8） in, Indicates the center coordinates of the current receiving range. Indicates the radius of the current receiving range; Indicates the center coordinates of the next receiving range, Indicates the radius of the next receiving range; Step 52: Optimize the drone trajectory for the first time and select the points to concentrate on. Each point is used as the switching point of the drone's service range. At this time, the drone no longer flies through the center of the gathering point, but starts directly from the starting point and flies to Hover above each switching point in the game, reach the end point, and end the flight mission; Step 53: Based on the new flight trajectory optimized in step 52, update the intersection of the flight trajectory and the next service range boundary to obtain ; Step 54: Optimize the drone trajectory for the second time and select the points to concentrate on. Each point is used as the service range switching point of the drone. At this time, the drone no longer flies through Above, but directly from the starting point, fly to Hover over the switch point in the dialog box and reach the end point; Step 55: Based on the flight trajectory optimized in step 54, update the intersection of the flight trajectory and the current service range boundary to obtain ; Step 56: Repeat steps 52 to 55 until the number of iterations reaches a threshold or the update result converges to a minimum value, thereby obtaining the global optimal flight trajectory and minimum flight distance. After step 5, the energy problem step of optimizing the UAV flight process is carried out; In the energy problem step of optimizing the UAV flight process, the UAV energy consumption includes flight energy loss and hovering energy loss. Among them, the flight energy loss is related to the UAV flight time. Since the UAV flight speed is constant, the UAV flight energy loss is related to the total flight distance X of the UAV: （9） Hovering energy loss and the number of user gathering points served by drones Since the drone hovers at the receiving range switching point and the drone hovers for a constant time each time, that is: （10） Drone Energy Limits: （11） The energy optimization process of the UAV flight process is as follows: Assuming that the drone's battery energy is sufficient to complete all communication tasks from the starting point to the end point of L user gathering points, optimize the flight trajectory on this basis and calculate the final total energy loss ,Compare and : like , it means that the assumption is not true, the drone cannot complete all communication tasks, and the number of gathering points to be served needs to be reduced. service points, including , recalculate the total energy loss of the drone at this time ,Compare and Size, growing value until ,at this time This is the maximum number of gathering points that the drone can serve, which is the optimal trajectory of the drone under the drone battery energy limit; On the contrary, it indicates that the drone has enough energy to complete all communication tasks.

2. The UAV communication trajectory optimization method based on graph theory and geometry as claimed in claim 1, characterized in that: Step 1: UAV flight data includes: UAV flight altitude is constant , the UAV flight speed is constant , the drone service range radius is , the initial position of the drone , the drone's termination position , real-time location of the drone ,in, , is the total time before the drone runs out of energy, the total energy of the drone's onboard battery , average energy consumption of drone movement , average energy consumption of drone hovering , average hovering time of the drone ; User data includes: the number of user gathering points is L, Number of users per gathering point , No. The average altitude of user gathering points is , No. User gathering point coordinates , No. The maximum receiving range radius of a user gathering point is , No. User gathering point weight ,in .

3. The UAV communication trajectory optimization method based on graph theory and geometry as claimed in claim 2, characterized in that: In step 2, calculate the The maximum receiving range radius of a user gathering point is , specifically including: Calculate the height difference between the drone and the gathering point and the maximum receiving range radius is : （1） （2） in, is the maximum radius of the drone’s service range, is the flight altitude of the drone, For the The average altitude of user gathering points.

4. The UAV communication trajectory optimization method based on graph theory and geometry as claimed in claim 3, characterized in that: In step 2, the distance between adjacent cluster points is calculated, specifically including: Consider the distance between the cluster points in the two-dimensional coordinate system and calculate the and The distance between the gathering points: （3） in As a gathering point The 2D coordinates of the point, As a gathering point The 2D coordinates of a point.

5. The UAV communication trajectory optimization method based on graph theory and geometry as claimed in claim 4, characterized in that: In step 2, calculate the weight of each cluster point , specifically including: Introduce weight factors to calculate the weight of each cluster point , where the distance weight coefficient is assumed to be , user quantity weight coefficient : （4） in, Represents the first The distance between each gathering point and each gathering point that the drone has served, Represents the first gathering points and termination points The distance between Represents the distance weight coefficient between two cluster points, The weight coefficient representing the distance between the gathering point and the drone’s end point; In step 3, determine The coordinates of the receiving range of the user gathering point are as follows: （5） in, The real-time coordinates of the drone flight; In step 3, the overlapping parts of adjacent receiving ranges are determined, which specifically includes: Consider the first and The distance between the cluster points With the and The sum of the receiving range radii of the gathering points Relationship: when When , there is an overlap between the two receiving ranges; Calculate the coordinates of the intersection of two receiving ranges: （6） when There is no overlap between the two receiving ranges.

6. The UAV communication trajectory optimization method based on graph theory and geometry as claimed in claim 5, characterized in that: In step 4, it is assumed that the energy is sufficient to complete the entire flight mission; the initialization process specifically includes: Step 41: The drone is at the initial position, i.e. At this time, the drone calculates the weight of each gathering point based on the distance between all nearby gathering points and the starting position and the number of users, and selects the gathering point with the largest weight as the first service target; Step 42: The drone moves at a constant speed , fly in a straight line to the center of the first gathering point and hover. At this time, the drone updates the distance from all nearby gathering points to the first gathering point, updates the weight of each gathering point according to the distance and the number of users, and selects the gathering point with the largest weight as the next service target, and so on, until the drone has served all users and flies to the end position to end the mission.

7. A system based on the optimization method according to any one of claims 1 to 6, characterized in that Includes the following modules: Data preparation module: obtain user data and set drone flight data; Calculation module: calculates the maximum receiving range radius of the user gathering point, the distance between adjacent gathering points and the weight of each gathering point; Determination module: determines whether the receiving ranges overlap and calculates the intersection coordinates; Flight trajectory initialization module: initializes the UAV flight trajectory; Flight trajectory optimization module: optimizes the flight trajectory of the drone.

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