Data collection method and system for unmanned aerial vehicle relay-assisted overwater scene

Abstract

The invention discloses a data collection method and system for an unmanned aerial vehicle relay-assisted overwater scene, the scene comprises L water surface users, for each water surface user, an unmanned aerial vehicle collects water surface user data at a data collection point ql and transmits the water surface user data to a data center at a relay point pl, and by establishing a time-minimized objective function, the data collection point ql and the relay point pl are calculated. And carrying out iterative optimization on the unmanned aerial vehicle speed, the access sequence and the hovering point position to obtain an optimal solution of T, and obtaining an optimal unmanned aerial vehicle speed set, an access sequence set and a hovering point position set. According to the method, the influence of the water scene, the unmanned aerial vehicle flight energy, the water user energy and the position change on the task objective function and constraint is fully considered, the user data freshness is ensured, and the optimal solution is obtained by using the joint optimization strategy.

Description

Technical Field

[0001] The present invention relates to unmanned aerial vehicle (UAV) assisted communication technology, and in particular to a data collection method and system for UAV relay assisted water scenarios. Background Art

[0002] With the increasing demand for delay-sensitive applications in data collection in complex environments, UAV assisted communication technology has emerged, which can especially solve the contradiction between limited device resources and good wireless communication performance in complex environments. To quantify the timeliness of information, the Age of Information (AoI) is introduced as an indicator. The traditional definition of AoI is a destination-centered indicator to measure the freshness of information. The Peak Age of Information (PAoI) is defined as the longest time elapsed since the last task was received at the destination, which measures the worst-case scenario of the age of information.

[0003] Existing methods usually consider minimizing the average AoI as the goal when collecting and transmitting data, and plan the UAV flight trajectory. However, in water scenarios, such as water emergency communication scenarios, etc., water surface users are more energy-constrained than ground users, and the communication link is easily affected by the water surface environment. Existing methods do not consider the disadvantage of limited UAV energy resources, and also ignore the impact of the changes in the positions and energies of UAVs and water surface users on the freshness of information. Therefore, how to consider the impact of the actual water scenario and the deployment position of UAVs to meet the needs of water surface users is still a very challenging problem. Summary of the Invention

[0004] Object of the Invention: The object of the present 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 position, water surface user energy and position changes on the freshness of tasks, and thus ensure the freshness of tasks of water surface users in the collection area to face different application scenarios (such as water emergency scenarios, marine Internet of Things, etc.).

[0005] Technical Solution: The data collection method for UAV relay assisted water scenarios according to the present invention, the UAV relay assisted water scenario includes L water surface users. For each water surface user, the UAV collects the data of the water surface user at the data collection point q l and transmits the data of the water surface user to the data center at the relay point p l , 1 ≤ l ≤ L; the data collection method includes the following steps:

[0006] (1) Construct the objective function:

[0007]

[0008] where t c,l is the time for the UAV to collect data at the data collection point q l t l,s is the time for the UAV to transmit data at the relay point p l ; is the flight time for the UAV to fly from q l to p l ; is the flight time for the UAV to fly from p l to q l+1 t 0 represents the flight time from the starting position to the data collection point q of the first user 1 t 1 represents the time for the UAV to fly back to the starting position from the relay transmission point p of the L-th user l ;

[0009] (2) Iteratively optimize the UAV speed, access sequence, and hover point position with the goal of minimizing the time T to obtain the optimal solution of T, and obtain the optimal UAV speed set, access sequence set, and hover point position set; the UAV speed set V = {v l , l = 1,..., L} represents the speed from the data collection point to the relay point, the access sequence set W = {κ l , l = 1,..., L} represents the data collection point position index, and the hover point position set R = {q l , p l , l = 1,..., L} represents the data collection point and relay point positions;

[0010] (2.1) Set the initial V, W, and R, and use the convex optimization algorithm to optimize v l , and update the UAV speed set V * ;

[0011] (2.2) Set the initial V = V * , W, and R, and use the intelligent optimization algorithm to optimize κ l , and update the access sequence set W * ;

[0012] (2.3) Set the initial V = V * , W = W * , and R, and use the convex optimization algorithm to optimize q l and p l , and 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 iterative convergence is completed, minimize the objective function, and output the optimal set of UAV speeds, the set of access sequences, and the set of hovering point positions.

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

[0015]

[0016] where is the distance between the relay point p l and the data center c 0 , d max is the distance that satisfies the minimum signal-to-noise ratio between the relay point and the data center.

[0017] Further, in step (2), when , the relay point p l in the iterative optimization process includes the following two cases:

[0018] (a) When the flight speed from q l to p l cannot reach its maximum value, let p l coincide with q l , that is, the UAV collects and transmits data at the data collection point q l ;

[0019] (b) When the flight speed from q l to p l reaches its maximum value, after the UAV collects data at the data collection point q l , it goes to the relay point p l to transmit data.

[0020] Further, in step (1), the objective function further includes a peak age-of-information constraint:

[0021] Γ l ≤ AOI limit , 1 ≤ l ≤ L;

[0022] where is the peak age-of-information of the l-th surface user, and AOI limit represents the threshold of the peak age-of-information of the surface user.

[0023] Further, in step (1), the objective function further includes a total UAV energy consumption constraint:

[0024]

[0025] Calculate the total UAV energy consumption E uav , is the total energy consumption threshold of the UAV.

[0026] Further, in step (1), the objective function further includes the energy consumption constraint of the water surface users:

[0027]

[0028] where is the energy consumption of the l-th water surface user, and P n is the transmission power of the water surface user, is the energy consumption threshold of the l-th water surface user.

[0029] Further, in step (2), the convex optimization algorithms include the gradient descent algorithm, the Lagrangian dual algorithm, and the successive convex approximation algorithm; the intelligent optimization algorithms include the ant colony algorithm, the genetic algorithm, and the particle swarm algorithm.

[0030] In the data collection system assisted by the UAV relay for the water surface scenario of the present invention, the UAV relay assisted water surface scenario includes L water surface users. For each water surface user, the UAV collects the data of the water surface user at the data collection point q l and transmits the data of the water surface user to the data center at the relay point p l , 1 ≤ l ≤ L;

[0031] The system includes:

[0032] An objective function construction unit for constructing an objective function;

[0033]

[0034] where t c,l is the time for the UAV to collect data at the data collection point q l and t l,s is the time for the UAV to transmit data at the relay point p l , is the flight time of the UAV from q l to p l , is the flight time of the UAV from p l to q l+1 , t 0 represents the flight time from the starting position to the data collection point q of the first user 1 and t 1 represents the time for the UAV to fly back to the starting position from the relay transmission point p of the L-th user l ;

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

[0036] The UAV speed set V = {v l , l = 1,..., L} represents the speed from the data collection point to the relay point, the access sequence set W = {κ l , l = 1,..., L} represents the data collection point position index, and the hovering point position set R = {q l , p l , l = 1,..., L} represents the data collection point and relay point positions.

[0037] The iterative optimization includes the following steps:

[0038] (2.1) Set the initial V, W, and R, and use the convex optimization algorithm to optimize v l , and update the UAV speed set V * ;

[0039] (2.2) Set the initial V = V * , W, and R, and use the intelligent optimization algorithm to optimize κ l , and update the access sequence set W * ;

[0040] (2.3) Set the initial V = V * , W = W * , and R, and use the convex optimization algorithm to optimize q l and p l , and update the hovering point position set R * ;

[0041] (2.4) Set the initial V = V * , W = W * , and R, and repeat steps (2.1) to (2.3) until the iterative convergence is completed, achieve the minimization of the objective function, and output the optimal UAV speed set, access sequence set, and hovering point position set.

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

[0043] The computer-readable storage medium described in the present invention stores a computer program. When the computer program is executed by a processor, it implements the data collection method for the UAV relay-assisted water scenario.

[0044] Beneficial effects: Compared with the prior art, the advantages of the present invention are as follows: The present invention takes advantage of the drone as an aerial vehicle, fully considers the impacts of the water scenario, the flight energy of the drone, the energy and position changes of the water users on the mission objective function and constraints, and defines the PAoI suitable for this scenario as a constraint condition to ensure the freshness of user data. At the same time, the optimal solution is obtained by using the joint optimization strategy. The present invention not only has high stability but also can flexibly respond to the changes in the actual scenario; specifically, by modifying different constraint conditions, it can adaptively balance the total mission time, the age of redundant information of users, and energy consumption, etc., and update the flight speed, access sequence, and hovering position in different regions to meet the constraint requirements of different users, so as to cope with the changes in the actual scenario. Brief Description of the Drawings

[0045] Figure 1 It is a schematic diagram of the drone relay-assisted water scenario of the present invention.

[0046] Figure 2 It is a timing diagram of the age of information of water surface users of the present invention.

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

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

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

[0050] Step 1: Initialize the water surface scenario.

[0051] As Figure 1 shown, the drone is equipped with various types of collection devices (such as temperature sensors, humidity sensors, cameras, etc.) to collect different water surface user information. The number of water surface users is L, the position of the data center is c 0 position u c0 , the hovering point position where the drone collects user data is defined as the data collection point q l , 1 ≤ l ≤ L, the hovering point position where the drone transmits user data is defined as the relay point p l , 1 ≤ l ≤ L. The present invention defines the relay point optimized by the strategy as the best relay point, which is essentially the calculation and update of the relay point p l under different constraints, so it is also represented by p l to represent the best relay point. The drone speed set V = {v l , l = 1,..., L} represents the speed from the data collection point to the relay point, and the access sequence set W = {κ l, l = 1, ..., L} represents the data collection point location index, and the hovering point location set R = {q l , p l , l = 1, ..., L} represents the data collection point and relay point locations.

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

[0053] The communication methods between the water surface users and the UAV, and between the UAV and the data center c 0 simultaneously consider line of sight (LOS) and non-line of sight (NLOS) communications. Then, the channel gains between the l-th water surface user and the UAV, and between the UAV and the data center c 0 are respectively expressed as:

[0054]

[0055] where, β 0 is the channel power gain at a reference distance of 1m, and k represents the additional attenuation factor of the link due to the complex water surface scenario. are respectively the distances between the l-th water surface user and the l-th data collection point q l , and the distance between the l-th relay transmission point p l and the data center c 0 ; respectively represent the outage probabilities between the water surface user and the UAV, and between the UAV and the data center c 0 . The parameter a determines the center position of the LOS probability curve and is related to characteristics such as the distribution density of obstacles in the environment. The parameter b reflects the attenuation rate of the LOS probability with respect to angle or distance and can describe the impact of environmental complexity on the communication link. θ l,q = 180 / π sin -1 (H u / d l,q ), represents the communication elevation angle, and in this embodiment, the values are as follows: H u = 100m, H B = 10m, a = 10, b = 0.6. According to Shannon's formula, the data transmission rates between the l-th water surface user and the UAV, and between the UAV and the data center c 0 can be respectively expressed as:

[0056]

[0057] where, P n and P u are respectively the transmission powers of the water surface user and the UAV, which are equal to 10dBm and 20dBm respectively in this embodiment, B is the system bandwidth equal to 1MHz, σ2 The noise power is equal to -110 dBm.

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

[0059] Combined with Figure 2 it can be known that the total time of the objective function task is calculated as follows:

[0060]

[0061] where t c,l represents the time for the UAV to collect data from the l-th user, and t l,s represents the time for the UAV to transmit data from the l-th user at the relay point p l The flight time from the data collection point q of the l-th user to the relay transmission point p l is l The flight time from the relay transmission point p of the l-th user to the data collection point q l of the (l + 1)-th user is l t 0 represents the flight time from the starting position to the data collection point q 1 of the first user, and t 1 represents the time for the UAV to fly back to the starting position from the relay transmission point p l of the L-th user.

[0062] where t c,l is the time for the UAV to collect data from the l-th surface user, and is calculated as follows:

[0063]

[0064] where w l is the size of the transmitted data packet. Similarly, the data transmission time t l at the l-th relay point p l,s is calculated as follows:

[0065]

[0066] The flight time for the UAV to go from the data collection point q l of the l-th user to the relay transmission point p l is The flight time for the UAV to go from the relay transmission point p l of the l-th user to the data collection point q l+1 of the (l + 1)-th user is They are calculated respectively as follows:

[0067]

[0068] wherein v l is respectively the distance and speed of the UAV from the data collection point q of the l-th user l to the relay transmission point p l . V 0 = 20 m / s is respectively the distance and speed from the relay transmission point p of the l-th user l to the data collection point q of the (l + 1)-th user l+1 .

[0069] Furthermore, considering the problem of weak signals between the UAV and the data center caused by the complex environment of the actual scenario (such as weather, humidity, surface evaporation duct, etc.), in order to ensure the integrity of data transmission, the position of the relay point p l should satisfy the following constraints:

[0070]

[0071] wherein is the distance between the relay point and the data center, d max is the distance that satisfies the minimum signal-to-noise ratio between the relay point UAV and the data center wherein P u is the transmission power of the UAV, σ 2 is the noise power, β 0 is the channel power gain when the reference distance is 1 m represents the outage probability between the UAV and the data center, H u is the flight altitude of the UAV, H B is the height of the data center. In this embodiment, SNR min = 5 dB

[0072] It should be noted that during the algorithm iteration process, the position of the relay point p l is directly related to the data collection point q l . Specifically:

[0073] When the relay point p calculated by the algorithm iteration l will have the following two situations: (a) During iteration, when the speed from the data collection point q l to the relay point p l cannot reach the maximum value, let p l coincide with q l , and the UAV collects and transmits data at the data collection point q l ; (b) During iteration, when the speed from the data collection point q l to the relay point p lWhen the speed can reach the maximum value, the UAV is at the data collection point q l After collecting the data, it flies to the optimal relay point p l For data transmission.

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

[0075]

[0076] where AOI limit represents the threshold of the PAoI of the surface user.

[0077] The UAV propulsion power consumption model adopted in the present invention is expressed as:

[0078]

[0079] where P 0 and P 1 are the blade profile power and the induced power of the UAV in the hovering state respectively; U tip represents the tip speed of the UAV rotor blade; v 0 represents the average rotor induced speed of the UAV in hovering; d 0 and d s represent the UAV fuselage drag ratio and rotor robustness respectively, ρ and A disc represent the air density and the rotor disk area respectively; in the present invention, the corresponding values are d 0 = 0.6, U tip = 120, A disc = 0.503, ρ = 1.225, s 0 = 0.05, P 0 = 79.86, P 1 = 88.63, v 0 = 4.03. Then the total energy consumption of the UAV mission satisfies the following constraints:

[0080]

[0081] where, is the total energy consumption threshold of the UAV. The energy consumption threshold value of the present invention is 180 KJ - 240 KJ. At this time, the energy consumption of the l-th surface user satisfies the following constraints:

[0082]

[0083] where is the energy consumption threshold of the l-th surface user, and P n is the transmission power of the surface user.

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

[0085] min T (15)

[0086] s.t. 0 < v l < v max , 1 ≤ l ≤ L (15a)

[0087] Γ l ≤ AOI limit , 1 ≤ l ≤ L (15b)

[0088]

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

[0090] Use the coordinate descent method to decompose the problem into three sub - problems, respectively optimize the flight speed set V of the UAV, the access sequence set W, and the hovering point position set R, and jointly iterate to obtain the optimal solution of T.

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

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

[0093] Step 4.2, Optimize the flight speed of the UAV. Set the initial flight speed set V, the access sequence set W, and the hovering point position set R, and use, such as: gradient descent algorithm, Lagrangian dual algorithm, successive convex approximation algorithm, etc. In this embodiment, the successive convex approximation algorithm is used to optimize the flight speed, update the flight speed set and denote it as V * , and the implementation is as follows:

[0094] According to equations (12)(13), there is an expansion Due to its non - convex property, introduce slack variables φ = {φ l > 0, l ∈ L}, where Then we can get:[[]]

[0095]

[0096] Furthermore, in each iteration of using the successive convex approximation algorithm, by optimizing the flight speed increment of the UAV, the objective function is successively minimized. Let v n (q l ) represent the lower - bound expression at the nth iteration. Applying the first - order Taylor expansion, we can get:[[]]

[0097] v n (q l ) = 4(vl ) 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 the UAV is described as follows:

[0099]

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

[0101] Step 4.3, optimize the access sequence of the UAV, set V * , W, and R, and use intelligent optimization algorithms such as: ant colony algorithm, genetic algorithm, particle swarm algorithm, etc. In this embodiment, the genetic algorithm is used to update the access sequence set and is denoted as W * , and the implementation is as follows:

[0102]

[0103] Step 4.4, optimize the hovering point positions of the UAV, including the data collection point and relay transmission point positions, set V * , W * , and R, and use such as: gradient descent algorithm, Lagrangian dual algorithm, successive convex approximation algorithm, etc. In this embodiment, the successive convex approximation algorithm is used to optimize the hovering point positions, update the hovering point position set and is denoted as R * , and the implementation is as follows:

[0104] Process the channel gain:

[0105]

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

[0107]

[0108] Among them Use the homogeneous approximation for the LOS probability to obtain Set and to the values corresponding to the elevation angle.

[0109] Furthermore, due to the non-convex property, introduce a set of slack variables Z = {α l , β l , γ l, ω l}, where Let R n (q l ), R n (p l ) represent the local points obtained at the n-th iteration, respectively. Then we can get:

[0110]

[0111] The first-order Taylor expansion can be obtained as follows:

[0112]

[0113] Where:

[0114]

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

[0116]

[0117] s.t. γ 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), (15e)

[0121] Step 4.5, substitute the updated V * , W * and R * into Step 4.2, and repeat Step 4.2, Step 4.3, and Step 4.4 until the iteration converges to minimize the objective function.

[0122] Step 4.6, output the optimal strategy at this time, the flight speed set the access sequence set the hovering point position set and the optimal solution of the mission time T.

[0123] For the UAV relay-assisted data collection system in the water scenario described in the present invention, the UAV relay-assisted water scenario includes L water surface users. For each water surface user, the UAV is at the data collection point q lCollect water surface user data at relay point p l Transmit water surface user data to the data center, 1 ≤ l ≤ L;

[0124] The system includes:

[0125] Objective function construction unit for constructing the objective function;

[0126]

[0127] where t c,l is the time for the UAV to collect data at data collection point q l t l,s is the time for the UAV to transmit data at relay point p l is the flight time for the UAV to go from q l to p l is the flight time for the UAV to go from p l to q l+1 t 0 represents the flight time from the starting position to the data collection point q of the first user 1 t 1 represents the time for the UAV to fly back to the starting position from the relay transmission point p of the Lth user l ;

[0128] Iterative optimization unit for iteratively optimizing the UAV speed, access sequence, and hovering point position with the goal of minimizing time T to obtain the optimal solution of T, and obtaining the optimal UAV speed set, access sequence set, and hovering point position set;

[0129] The UAV speed set V = {v l , l = 1,..., L} represents the speed from the data collection point to the relay point, the access sequence set W = {κ l , l = 1,..., L} represents the data collection point position index, and the hovering point position set R = {q l , p l , l = 1,..., L} represents the data collection point and relay point positions;

[0130] The iterative optimization includes the following steps:

[0131] (2.1) Set the initial V, W, and R, and use the convex optimization algorithm to optimize v l , and update the UAV speed set V * ;

[0132] (2.2) Set the initial V = V * , W, and R, and use the intelligent optimization algorithm to optimize κ l ​​, update the access sequence set \(W\) * ;

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

[0134] (2.4) Set the initial \(V = V\) * , \(W = W\) * and \(R\), repeat steps (2.1) to (2.3) until the iteration converges, minimize the objective function, and output the optimal UAV speed set, access sequence set, and hover point position set.

[0135] The electronic device according to the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is loaded into the processor, it implements the data collection method for the UAV relay-assisted water scenario described above.

[0136] The computer-readable storage medium according to the present invention stores a computer program. When the computer program is executed by a processor, it implements the data collection method for the UAV relay-assisted water scenario described above.

[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 store program code in the form of instructions or data structures and can be accessed by a computer.

[0138] The processor is used to execute the computer program stored in the memory to implement each step in the method described in the above embodiments.

Claims

1. A method for collecting data on water scenes using drone relay assistance, characterized in that: The UAV relay-assisted water scenario includes L surface users. For each surface user, the UAV is at the data collection point q l Collect surface user data at relay point p l Transmitting surface user data to a data center, 1≤l≤L; the data collection method comprises the following steps: (1) Construct the objective function: where t c,l For drones at data collection points l The time of data collection, t l,s For the drone at the relay point p l The time to transfer data, For drones from q l Go to p l Flight time, For drones from p l Go to q l+1 The flight time of t0 is the flight time from the starting point to the data collection point q1 of the first user, and t1 is the flight time of the drone from the relay transmission point p of the Lth user. l The time to fly back to the starting point; (2) With the goal of minimizing the time T, the UAV speed, access sequence, and hovering point position are iteratively optimized to obtain the optimal solution of T, and the optimal set of UAV speeds, access sequences, and hovering point positions are obtained; the UAV speed set V = {v l , l=1,...,L} represents the speed from the data collection point to the relay point, and the access sequence set W={κ l , l=1,...,L} represents the data collection point location index, and the hovering point location set R={q l ,p l ,l=1,...,L} represents the location of data collection point and relay point; (2.1) Set the initial V, W and R, and use the convex optimization algorithm to optimize v l , update the drone speed set V * ; (2.2) Set the initial V = V * , W and R, and use intelligent optimization algorithm to optimize κ l , update the access sequence set W * ; (2.3) Set the initial V = V * 、W=W * and R, using convex optimization algorithm to optimize q 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, the objective function is minimized, and the optimal set of drone speeds, access sequences, and hovering point positions are output.

2. The method for collecting data of water scenes using drone relay assistance according to claim 1, characterized in that: In step (1), the objective function also includes a distance constraint: in Relay point p l Distance from data center c0, d max The distance between the relay point and the data center that meets the minimum signal-to-noise ratio.

3. The method for collecting data of water scenes using drone relay assistance according to claim 2, characterized in that: In step (2), when When 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 coincidence, that is, the drone is at the data collection point q l Collect data and transmit data; (b) From q l Go to p l When the flight speed reaches its maximum value, the drone is at the data collection point q l After collecting data, go to the relay point p l Transfer data.

4. The method for collecting data of water scenes using drone relay assistance 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 is the peak information age of the lth surface user, AOI limit Threshold indicating the age of peak information of surface users.

5. The method for collecting data of water scenes using drone relay assistance according to claim 1, characterized in that: In step (1), the objective function also includes the total energy consumption constraint of the UAV: Calculate the total energy consumption E of the UAV using the UAV propulsion power consumption model uav , is the total energy consumption threshold of the drone.

6. The method for collecting data of water scenes using drone relay assistance according to claim 1, characterized in that: In step (1), the objective function also includes the water surface user energy consumption constraint: in is the energy consumption of the lth water surface user, P n Transmit power for surface users, is the energy consumption threshold of the lth water surface user.

7. The method for collecting data of water scenes using drone relay assistance according to claim 1, characterized in that: In step (2), the convex optimization algorithm includes a gradient descent algorithm, a Lagrange dual algorithm, and a successive convex approximation algorithm; the intelligent optimization algorithm includes an ant colony algorithm, a genetic algorithm, and a particle swarm algorithm.

8. A data collection system for water scenes assisted by drone relay, characterized in that: The UAV relay-assisted water scenario includes L surface users. For each surface user, the UAV is at the data collection point q l Collect surface user data at relay point p l Transmit surface user data to the data center, 1≤l≤L; The system includes: An objective function building unit, used for building an objective function; where t c,l For drones at data collection points l The time of data collection, t l,s For the drone at the relay point p l The time to transfer data, For drones from q l Go to p l Flight time, For drones from p l Go to q l+1 The flight time of t0 is the flight time from the starting point to the data collection point q1 of the first user, and t1 is the flight time of the drone from the relay transmission point p of the Lth user. l The time to fly back to the starting point; An iterative optimization unit, used for iteratively optimizing the UAV speed, access sequence and hovering point position with the goal of minimizing the time T, obtaining the optimal solution of T, and obtaining the optimal UAV speed set, access sequence set and hovering point position set; UAV speed set V = {v l , l=1,...,L} represents the speed from the data collection point to the relay point, and the access sequence set W={κ l , l=1,...,L} represents the data collection point location index, and the hovering point location set R={q l ,p l ,l=1,...,L} represents the location of data collection point and relay point; Iterative optimization includes the following steps: (2.1) Set the initial V, W and R, and use the convex optimization algorithm to optimize v l , update the drone speed set V * ; (2.2) Set the initial V = V * , W and R, and use intelligent optimization algorithm to optimize κ l , update the access sequence set W * ; (2.3) Set the initial V = V * 、W=W * and R, using convex optimization algorithm to optimize q 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, the objective function is minimized, and the optimal set of drone speeds, access sequences, and hovering point positions are output.

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, the method for collecting data of water scenes assisted by drone relay is implemented according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the method for collecting data of water scenes assisted by drone relay is implemented according to any one of claims 1 to 7.

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