A method and system for unmanned aerial vehicle relay assisted data collection for overwater scenarios

Abstract

This invention discloses a method and system for data collection in a waterborne scenario assisted by a drone relay. The scenario includes L water surface users, and for each user, the drone relays data at point q. l Collect water surface user data at relay point p l The invention transmits surface user data to a data center. By establishing a time-minimizing objective function, iterative optimization is performed on the drone speed, access sequence, and hovering point position to obtain the optimal solution for T, and the optimal sets of drone speed, access sequence, and hovering point positions are obtained. This invention fully considers the impact of changes in the water scene, drone flight energy, and the energy and position of surface users on the mission objective function and constraints, ensuring the freshness of user data, and using a joint optimization strategy to obtain the optimal solution.

Description

Technical Field

[0001] This invention relates to unmanned aerial vehicle (UAV) assisted communication technology, and in particular to a UAV relay-assisted data collection method and system for waterborne scenarios. Background Technology

[0002] With the increasing demand for latency-sensitive applications in complex environments, unmanned aerial vehicle (UAV)-assisted communication technology has emerged, particularly in resolving the contradiction between limited equipment resources and achieving good wireless communication performance in complex environments. To quantify the timeliness of information, Age of Information (AoI) is introduced as an indicator. Traditionally, AoI is defined as a destination-centric metric to measure the freshness of information. Peak Age of Information (PAoI) is defined as the longest elapsed time since the last mission was received at the destination, representing the worst-case scenario for AoI.

[0003] Existing methods typically aim to minimize the average Area of ​​Interest (AoI) when collecting and transmitting data to plan drone flight trajectories. However, in aquatic scenarios, such as emergency communication scenarios, surface users have more limited energy compared to ground users, and communication links are easily affected by the aquatic environment. Existing methods do not consider the limited energy resources of drones and ignore the impact of drone and surface user position and energy changes on information freshness. Therefore, how to consider the impact of actual aquatic scenarios and the deployment location of drones to meet the needs of surface users remains a very challenging problem. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a data collection method and system for UAV relay-assisted water scenarios, which can fully consider the impact of UAV flight speed, flight energy consumption, transmission location, and changes in the energy and location of water users on mission freshness, thereby ensuring the freshness of missions for water users in the collection area, in order to meet different application scenarios (such as water emergency scenarios, marine Internet of Things, etc.).

[0005] Technical Solution: The present invention describes a data collection method for UAV relay-assisted water scenarios, wherein the UAV relay-assisted water scenarios include L water surface users, and for each water surface user, the UAV at data collection point q l Collect water surface user data at relay point p l Transmitting surface user data to the data center, where 1 ≤ l ≤ L; the data collection method includes the following steps:

[0006] (1) Construct the objective function:

[0007]

[0008] Where t c,l For drones at data collection point q l The time for data collection, t l,s For drones at relay point p l Data transmission time For drones from q l Go to p l Flight time, For drones from p l Go to q l+1 The flight time is t0, where t0 represents the flight time from the starting point to the data collection point q1 of the first user, and t1 represents the flight time from the relay point p of the Lth user. l The time it takes to fly back to the starting point;

[0009] (2) Iterative optimization of UAV speed, access sequence, and hovering position is performed with the goal of minimizing time T to obtain the optimal solution for T, and the optimal set of UAV speed, access sequence, and hovering position are obtained; UAV speed set V = {v l The sequence W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point, and the access sequence set W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point. l {1, ..., L} represents the index of the data collection point location, and the set of hover point locations R = {q} l ,p l The numbers l = 1, ..., L represent the locations of data collection points and relay points;

[0010] (2.1) Set initial values ​​V, W, and R, and optimize v using a convex optimization algorithm. l Update drone speed set V * ;

[0011] (2.2) Set the initial V = V * W and R are optimized using intelligent optimization algorithms. l Update the access sequence set W * ;

[0012] (2.3) Set the initial V = V * W = W * And R, optimize q using a convex optimization algorithm l and p l Update the hover point position set R * ;

[0013] (2.4) Set the initial V = V * W = W * and R = R *Repeat steps (2.1) to (2.3) until the iteration converges, minimize the objective function, and output the optimal set of UAV speeds, access sequences, and hovering point positions.

[0014] Furthermore, in step (1), the objective function also includes a distance constraint:

[0015]

[0016] in For relay point p l Distance from data center c0, d max To meet the minimum signal-to-noise ratio requirement between the relay point and the data center.

[0017] Furthermore, in step (2), when At that time, the relay point p in the iterative optimization process l This includes the following two situations:

[0018] (a) From q l Go to p l When the flight speed cannot reach its maximum value, let p l With q l Overlap, meaning the drone is at data collection point q l Collect and transmit data;

[0019] (b) From q l Go to p l When the drone reaches its maximum flight speed, it is at the data collection point q. l After collecting the data, proceed to relay point p. l Transmitting data.

[0020] Furthermore, in step (1), the objective function also includes a peak information age constraint:

[0021] Γ l ≤AOI limit ,1≤l≤L;

[0022] in For the peak information age of the l-th water surface user, AOI limit The threshold representing the peak age of users on the water surface.

[0023] Furthermore, in step (1), the objective function also includes a constraint on the total energy consumption of the UAV:

[0024]

[0025] Calculate the total energy consumption E of the UAV using a UAV propulsion power consumption model. uav , This represents the total energy consumption threshold for drones.

[0026] Furthermore, in step (1), the objective function also includes energy consumption constraints for surface users:

[0027]

[0028] in For the energy consumption of the l-th water surface user, P n For water surface users, Let be the energy consumption threshold for the l-th water surface user.

[0029] Furthermore, in step (2), the convex optimization algorithm includes gradient descent algorithm, Lagrange dual algorithm, and successive convex approximation algorithm; the intelligent optimization algorithm includes ant colony algorithm, genetic algorithm, and particle swarm optimization algorithm.

[0030] The data collection system for unmanned aerial vehicle (UAV) relay-assisted water scenarios described in this invention includes L water surface users. For each water surface user, the UAV relays data at data collection point q. l Collect water surface user data at relay point p l Transmit surface user data to the data center, where 1 ≤ l ≤ L;

[0031] The system includes:

[0032] Objective function construction unit, used to construct the objective function;

[0033]

[0034] Where t c,l For drones at data collection point q l The time for data collection, t l,s For drones at relay point p l Data transmission time For drones from q l Go to p l Flight time, For drones from p l Go to q l+1 The flight time is t0, where t0 represents the flight time from the starting point to the data collection point q1 of the first user, and t1 represents the flight time from the relay point p of the Lth user. l The time it takes to fly back to the starting point;

[0035] The iterative optimization unit is used to iteratively optimize the UAV speed, access sequence, and hovering position with the goal of minimizing time T, to obtain the optimal solution for T, and to obtain the optimal set of UAV speed, access sequence, and hovering position.

[0036] The set of drone velocities V = {v l The sequence W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point, and the access sequence set W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point. l {1, ..., L} represents the index of the data collection point location, and the set of hover point locations R = {q} l ,p l The numbers l = 1, ..., L represent the locations of data collection points and relay points;

[0037] Iterative optimization includes the following steps:

[0038] (2.1) Set initial values ​​V, W, and R, and optimize v using a convex optimization algorithm. l Update drone speed set V * ;

[0039] (2.2) Set the initial V = V * W and R are optimized using intelligent optimization algorithms. l Update the access sequence set W * ;

[0040] (2.3) Set the initial V = V * W = W * And R, optimize q using a convex optimization algorithm l and p l Update the hover point position set R * ;

[0041] (2.4) Set the initial V = V * W = W * For R, repeat steps (2.1) to (2.3) until the iteration converges, minimize the objective function, and output the optimal set of UAV speeds, access sequences, and hovering point positions.

[0042] The electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the data collection method for UAV relay-assisted water scenarios.

[0043] The computer-readable storage medium of the present invention stores a computer program, which, when executed by a processor, implements the data collection method for UAV relay-assisted water scenarios.

[0044] Beneficial Effects: Compared with existing technologies, the advantages of this invention are as follows: This invention utilizes the advantages of UAVs as aerial carriers, fully considers the impact of water-based scenarios, UAV flight energy, water user energy, and positional changes on the mission objective function and constraints, and defines a PAoI suitable for this scenario as a constraint condition to ensure user data freshness. Simultaneously, it uses a joint optimization strategy to obtain the optimal solution. This invention not only has high stability but can also flexibly respond to changes in actual scenarios. Specifically, by modifying different constraint conditions, it can adaptively balance the total mission time, the age of user redundancy information, and energy consumption, and update flight speeds, access sequences, and hovering positions in different areas to meet the constraint requirements of different users, thereby adapting to changes in actual scenarios. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of a drone relay-assisted waterborne scenario according to the present invention.

[0046] Figure 2 This is a time-series diagram of age information for users on the water surface, as presented in this invention.

[0047] Figure 3 This is a flowchart of the iterative optimization algorithm of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0049] like Figure 1 As shown, the data collection method for drone relay-assisted water scenarios includes the following steps.

[0050] Step 1: Initialize the water scene.

[0051] like Figure 1 As shown, the drone is equipped with various types of data collection devices (such as temperature sensors, humidity sensors, and cameras) to collect information from users on different water surfaces. The number of users on the water surface is L, and the data center c0 is located at u. c0 The hovering position of the drone collecting user data is defined as the data collection point q. l , 1≤l≤L, the hovering point position of the UAV transmitting user data is defined as the relay point p. l 1≤l≤L, this invention defines the relay point optimized through strategy as the optimal relay point, which is essentially the optimal relay point p under different constraints. l The calculation update is therefore also used with p. l To represent the optimal relay point, define the set of drone velocities V = {v l The sequence W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point, and the access sequence set W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point. l{1, ..., L} represents the index of the data collection point location, and the set of hover point locations R = {q} l ,p l ,l=1,...,L} represents the locations of data collection points and relay points.

[0052] Step 2: Build a communication link and establish a communication system.

[0053] Considering both line-of-sight (LOS) and non-line-of-sight (NLOS) communication methods between surface users and drones, and between drones and data center c0, the channel gains between surface users l and drones, and between drones and data center c0, are expressed as follows:

[0054]

[0055] Where β0 is the channel power gain when the reference distance is 1m, and k represents the additional attenuation factor caused by the complex water surface scenario. These represent the l-th surface user and the l-th data collection point q, respectively. l The distance and the l-th relay point p l Distance from data center c0; θ represents the interruption probability between the water surface user and the drone, and the interruption probability between the drone and the data center c0, respectively. Parameter a determines the center position of the line-of-sight probability curve and is related to characteristics such as the distribution density of obstacles in the environment. Parameter b reflects the decay rate of the line-of-sight probability with changes in angle or distance, and can describe the impact of environmental complexity on the communication link. l,q =180 / πsin -1 (H u / d l,q ), The value of H represents the communication angle, and in this embodiment, it is taken as follows: u =100m, H B =10m, a=10, b=0.6. According to Shannon's formula, the data transmission rates between the water surface user l and the drone, and between the drone and the data center c0, can be expressed as:

[0056]

[0057] Among them, P n and P u σ represents the transmit power for the surface user and the drone, respectively, which in this embodiment are equal to 10dBm and 20dBm, where B is the system bandwidth equal to 1MHz. 2 The noise power is -110dBm.

[0058] Step 3: Construct the objective function and constraints.

[0059] Combination Figure 2 The total time for the objective function task can be calculated as follows:

[0060]

[0061] Among them, t c,l t represents the time it takes for the drone to collect data from the l-th user. l,s This indicates that the drone is at relay point p. l The time to transmit the l-th user data This indicates that the drone collects data from the data collection point q of the l-th user. l Heading to relay point p l Flight time, This indicates that the drone originates from the relay point p of the l-th user. l Head to the data collection point q of the (l+1)th user l The flight time is t0, where t0 represents the flight time from the starting point to the data collection point q1 of the first user, and t1 represents the flight time from the relay point p of the Lth user. l The time it takes to fly back to the starting point.

[0062] Where t c,l The time required for the drone to collect data from the l-th surface user is calculated as follows:

[0063]

[0064] Where w l Similarly, for the size of the transmitted data packet, the l-th relay point p... l Data transmission time t l,s The calculation is as follows:

[0065]

[0066] The drone collects data from the l-th user's data collection point q. l Heading to relay point p l Flight time The drone starts from the relay point p of the l-th user. l Head to the data collection point q of the (l+1)th user l+1 Flight time The calculations are as follows:

[0067]

[0068] in v l The data collection point q of the drone from the l-th user is respectively l Heading to relay point p lDistance and speed, V0 = 20m / s represents the relay transmission point p from the l-th user. l Head to the data collection point q of the (l+1)th user l+1 Distance and speed.

[0069] Furthermore, considering the complex environment of real-world scenarios (such as weather, humidity, and water surface evaporation waveguides) leading to weak signals between drones and data centers, relay point p is used to ensure data transmission integrity. l The location should satisfy the following constraints:

[0070]

[0071] in d represents the distance between the relay point and the data center. max To meet the minimum signal-to-noise ratio distance between relay point drones and data centers, in P u For the UAV's transmit power, σ 2 β is the noise power, and β0 is the channel power gain at a reference distance of 1m. H represents the probability of an outage between drones and data centers. u H is the flight altitude of the drone. B For data center height, in this embodiment, SNR min =5dB.

[0072] It is worth noting that during the algorithm iteration process, the relay point p l Location and data collection point q l Directly related, specifically:

[0073] when relay point p in the algorithm's iterative computation l The following two situations may occur: (a) During iteration, from data collection point q l Fly to relay point p l When the speed cannot reach its maximum value, let p l With q l Overlap, the drone is at data collection point q l (a) Collect and transmit data; (b) During iteration, from data collection point q l Fly to relay point p l When the speed reaches its maximum, the drone is at the data collection point q l After collecting the data, fly to the optimal relay point p. l To transmit data.

[0074] Furthermore, the PaoI of the l-th surface user satisfies the following constraints:

[0075]

[0076] AOI limit This represents the threshold for the PAoI of surface users.

[0077] The UAV propulsion power consumption model used in this invention is expressed as follows:

[0078]

[0079] Where P0 and P1 are the blade profile power and induced power of the UAV in hovering state, respectively; U tip The blade tip velocity of the UAV rotor blades is represented by v0; the average rotor induced velocity of the UAV when hovering is represented by d0 and d... s ρ and A represent the airframe drag ratio and rotor rigidity of the UAV, respectively. disc These represent air density and rotor disk area, respectively; in this invention, the corresponding values ​​are d0 = 0.6 and U. tip =120, A disc =0.503, ρ=1.225, s0=0.05, P0=79.86, P1=88.63, v0=4.03. Therefore, the total energy consumption of the UAV mission satisfies the following constraints:

[0080]

[0081] in, The total energy consumption threshold for the drone is set at 180KJ-240KJ. Under these conditions, the energy consumption of the l-th surface user satisfies the following constraints:

[0082]

[0083] in Let P be the energy consumption threshold for the l-th water surface user. n Transmission power for surface users.

[0084] In summary, the objective function minimization problem can be formulated as follows:

[0085] min T (15)

[0086] st0 < v l <v max ,1≤l≤L (15a)

[0087] Γ l ≤AOI limit ,1≤l≤L (15b)

[0088]

[0089] Step 4: Solve for the optimal joint optimization strategy.

[0090] The problem is decomposed into three subproblems using the coordinate descent method. The flight speed set V, the visit sequence set W, and the hovering point position set R of the UAV are optimized respectively. The optimal solution of T is obtained by joint iteration.

[0091] like Figure 3 As shown, the specific implementation method is as follows:

[0092] Step 4.1, Initialization: System parameters; Assume the maximum flight speed of the UAV is v. max =40m / s, initial flight speed set V, visit sequence set W, and hovering point position set R;

[0093] Step 4.2: Optimize the UAV's flight speed. Set an initial flight speed set V, a visit sequence set W, and a hovering point position set R. Utilize algorithms such as gradient descent, Lagrange duality, and successive convex approximation. In this embodiment, the successive convex approximation algorithm is used to optimize the flight speed, updating the flight speed set and representing it as V. * The implementation is as follows:

[0094] According to equations (12) and (13), the existence exists. Due to its non-convex nature, a slack variable φ = {φ} is introduced. l >0, l∈L}, where Then we can obtain:

[0095]

[0096] Furthermore, using a successive convex approximation algorithm, in each iteration, the objective function is minimized by optimizing the UAV's flight speed increment, where v is... n (q l () represents the lower bound expression for the nth iteration, which can be obtained by applying the first-order Taylor expansion:

[0097] v n (q l ) = 4(v l ) n +4(v l ) n (v l -(v l ) n )≤(v l ) 4 ,1≤l≤L(17)

[0098] The problem of optimizing the flight speed of a drone is described as follows:

[0099]

[0100] Equations (15a), (15b), and (15c)

[0101] Step 4.3, optimize the drone's access sequence and set V * W and R are used to update the set of access sequences using intelligent optimization algorithms, such as ant colony optimization, genetic algorithm, and particle swarm optimization. In this embodiment, a genetic algorithm is used to update the set of access sequences and represent it as W. * The implementation is as follows:

[0102]

[0103] Step 4.4, optimize the drone hovering point location, including the data collection point and relay transmission point location, and set V. * W * Given R, and using algorithms such as gradient descent, Lagrange duality, and successive convex approximation, this embodiment uses the successive convex approximation algorithm to optimize the hovering point position, update the hovering point position set, and represent it as R. * The implementation is as follows:

[0104] Process the channel gain:

[0105]

[0106] in, According to the Jensen inequality approximation, then:

[0107]

[0108] in Using a homogeneous approximation of the LOS probability, we get... Will and Set to the value corresponding to the elevation angle.

[0109] Furthermore, due to the non-convex nature, a set of slack variables Z = {α} is introduced. l ,β l ,γ l ,ω l},in Let R n (q l ), R n (p l Let ) represent the local points obtained in the nth iteration, then we can obtain:

[0110]

[0111] The first-order Taylor expansion yields:

[0112]

[0113] in:

[0114]

[0115] The problem of optimizing the hovering point position of a drone is described as follows:

[0116]

[0117] stγ l ≤R n (q l ), 1≤l≤L (30a)

[0118] ω l ≤R n (p l ), 1≤l≤L (30b)

[0119]

[0120] Equations (15b), (15c), (15d), and (15e)

[0121] Step 4.5, update V each time. * W * and R * Substitute into step 4.2, and repeat steps 4.2, 4.3, and 4.4 until the iteration converges and the objective function is minimized.

[0122] Step 4.6: Output the optimal strategy at this point, and the set of flight speeds. Access sequence set Hover point location set The optimal solution for the task time T.

[0123] The data collection system for unmanned aerial vehicle (UAV) relay-assisted water scenarios described in this invention includes L water surface users. For each water surface user, the UAV relays data at data collection point q. l Collect water surface user data at relay point p l Transmit surface user data to the data center, where 1 ≤ l ≤ L;

[0124] The system includes:

[0125] Objective function construction unit, used to construct the objective function;

[0126]

[0127] Where t c,l For drones at data collection point ql The time for data collection, t l,s For drones at relay point p l Data transmission time For drones from q l Go to p l Flight time, For drones from p l Go to q l+1 The flight time is t0, where t0 represents the flight time from the starting point to the data collection point q1 of the first user, and t1 represents the flight time from the relay point p of the Lth user. l The time it takes to fly back to the starting point;

[0128] The iterative optimization unit is used to iteratively optimize the UAV speed, access sequence, and hovering position with the goal of minimizing time T, to obtain the optimal solution for T, and to obtain the optimal set of UAV speed, access sequence, and hovering position.

[0129] The set of drone velocities V = {v l The sequence W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point, and the access sequence set W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point. l {1, ..., L} represents the index of the data collection point location, and the set of hover point locations R = {q} l ,p l The numbers l = 1, ..., L represent the locations of data collection points and relay points;

[0130] Iterative optimization includes the following steps:

[0131] (2.1) Set initial values ​​V, W, and R, and optimize v using a convex optimization algorithm. l Update drone speed set V * ;

[0132] (2.2) Set the initial V = V * W and R are optimized using intelligent optimization algorithms. l Update the access sequence set W * ;

[0133] (2.3) Set the initial V = V * W = W * And R, optimize q using a convex optimization algorithm l and p l Update the hover point position set R * ;

[0134] (2.4) Set the initial V = V * W = W *For R, repeat steps (2.1) to (2.3) until the iteration converges, minimize the objective function, and output the optimal set of UAV speeds, access sequences, and hovering point positions.

[0135] The electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the data collection method for UAV relay-assisted water scenarios.

[0136] The computer-readable storage medium of the present invention stores a computer program, which, when executed by a processor, implements the data collection method for UAV relay-assisted water scenarios.

[0137] The computer-readable storage medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, flash memory or any other medium that can be used to store program code in the form of instructions or data structures and is accessible by a computer.

[0138] The processor is used to execute a computer program stored in memory to implement the various steps in the methods described in the above embodiments.

Claims

1. A method for data collection in a waterborne scenario using UAV relay assistance, characterized in that, The UAV relay-assisted waterborne scenario includes L surface users. For each surface user, the UAV is at data collection point q. l Collect water surface user data at relay point p l Transmitting surface user data to the data center, where 1 ≤ l ≤ L; the data collection method includes the following steps: (1) Construct the objective function: Where t c,l For drones at data collection point q l The time for data collection, t l,s For drones at relay point p l Data transmission time For drones from q l Go to p l Flight time, For drones from p l Go to q l+1 The flight time is t0, where t0 represents the flight time from the starting point to the data collection point q1 of the first user, and t1 represents the flight time from the relay point p of the Lth user. l The time it takes to fly back to the starting point; (2) Iterative optimization of UAV speed, access sequence, and hovering position is performed with the goal of minimizing time T to obtain the optimal solution for T, and the optimal set of UAV speed, access sequence, and hovering position are obtained; UAV speed set V = {v l The sequence W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point, and the access sequence set W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point. l {1, ..., L} represents the index of the data collection point location, and the set of hover point locations R = {q} l ,p l The numbers l = 1, ..., L represent the locations of data collection points and relay points; (2.1) Set initial values ​​V, W, and R, and optimize v using a convex optimization algorithm. l Update drone speed set V * ; (2.2) Set the initial V = V * W and R are optimized using intelligent optimization algorithms. l Update the access sequence set W * ; (2.3) Set the initial V = V * W = W * And R, optimize q using a convex optimization algorithm l and p l Update the hover point position set R * ; (2.4) Set the initial V = V * W = W * and R = R * Repeat steps (2.1) to (2.3) until the iteration converges, minimize the objective function, and output the optimal set of UAV speeds, access sequences, and hovering point positions.

2. The data collection method for unmanned aerial vehicle (UAV) relay-assisted waterborne scenarios according to claim 1, characterized in that, In step (1), the objective function further includes a distance constraint: in For relay point p l Distance from data center c0, d max To meet the minimum signal-to-noise ratio requirement between the relay point and the data center.

3. The data collection method for unmanned aerial vehicle (UAV) relay-assisted waterborne scenarios according to claim 2, characterized in that, In step (2), when At that time, the relay point p in the iterative optimization process l This includes the following two situations: (a) From q l Go to p l When the flight speed cannot reach its maximum value, let p l With q l Overlap, meaning the drone is at data collection point q l Collect and transmit data; (b) From q l Go to p l When the drone reaches its maximum flight speed, it is at the data collection point q. l After collecting the data, proceed to relay point p. l Transmitting data.

4. The data collection method for unmanned aerial vehicle (UAV) relay-assisted waterborne scenarios according to claim 1, characterized in that, In step (1), the objective function also includes a peak information age constraint: C l ≤AOI limlt ,1≤l≤L; in For the peak information age of the l-th water surface user, AOI limit The threshold representing the peak age of users on the water surface.

5. The data collection method for unmanned aerial vehicle (UAV) relay-assisted waterborne scenarios according to claim 1, characterized in that, In step (1), the objective function also includes a total energy consumption constraint for the UAV: Calculate the total energy consumption E of the UAV using a UAV propulsion power consumption model. uav , This represents the total energy consumption threshold for drones.

6. The data collection method for unmanned aerial vehicle (UAV) relay-assisted waterborne scenarios according to claim 1, characterized in that, In step (1), the objective function also includes energy consumption constraints for surface users: in For the energy consumption of the l-th water surface user, P n For water surface users, Let be the energy consumption threshold for the l-th water surface user.

7. The data collection method for unmanned aerial vehicle (UAV) relay-assisted waterborne scenarios according to claim 1, characterized in that, In step (2), the convex optimization algorithm includes gradient descent algorithm, Lagrange dual algorithm, and successive convex approximation algorithm; the intelligent optimization algorithm includes ant colony algorithm, genetic algorithm, and particle swarm optimization algorithm.

8. A data collection system for unmanned aerial vehicle (UAV) relay-assisted waterborne scenarios, characterized in that, The UAV relay-assisted waterborne scenario includes L surface users. For each surface user, the UAV is at data collection point q. l Collect water surface user data at relay point p l Transmit surface user data to the data center, where 1 ≤ l ≤ L; The system includes: Objective function construction unit, used to construct the objective function; Where t c,l For drones at data collection point q l The time for data collection, t l,s For drones at relay point p l Data transmission time For drones from q l Go to p l Flight time, For drones from p l Go to q l+1 The flight time is t0, where t0 represents the flight time from the starting point to the data collection point q1 of the first user, and t1 represents the flight time from the relay point p of the Lth user. l The time it takes to fly back to the starting point; The iterative optimization unit is used to iteratively optimize the UAV speed, access sequence, and hovering position with the goal of minimizing time T, to obtain the optimal solution for T, and to obtain the optimal set of UAV speed, access sequence, and hovering position. The set of drone velocities V = {v l The sequence W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point, and the access sequence set W = {k, l = 1, ..., L} represents the speed at which data travels from the data collection point to the relay point. l {1, ..., L} represents the index of the data collection point location, and the set of hover point locations R = {q} l ,p l The numbers l = 1, ..., L represent the locations of data collection points and relay points; Iterative optimization includes the following steps: (2.1) Set initial values ​​V, W, and R, and optimize v using a convex optimization algorithm. l Update drone speed set V * ; (2.2) Set the initial V = V * W and R are optimized using intelligent optimization algorithms. l Update the access sequence set W * ; (2.3) Set the initial V = V * W = W * And R, optimize q using a convex optimization algorithm l and p l Update the hover point position set R * ; (2.4) Set the initial V = V * W = W * and R = R * Repeat steps (2.1) to (2.3) until the iteration converges, minimize the objective function, and output the optimal set of UAV speeds, access sequences, and hovering point positions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the data collection method for UAV relay-assisted water scenarios according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the data collection method for UAV relay-assisted water scenarios according to any one of claims 1-7.

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