An adaptive power allocation method and system for unmanned aerial vehicle assisted internet of things at sea in the face of sea wave disturbance

Abstract

The application discloses a kind of unmanned aerial vehicle auxiliary sea surface internet of things oriented sea wave disturbance adaptive power distribution method and system, belong to wireless communication resource optimization technical field.For the problem that sea wave disturbance leads to time-varying of sea surface communication channel, poor adaptability of traditional power distribution, with system energy efficiency maximization as goal to construct optimization problem, through layered decoupling strategy, based on the worst channel user's quality of service constraint closed solution common information flow power;After transforming fractional optimization problem by using Dinkelbach method, the global optimization of private information flow power is carried out, and the adaptive power distribution is realized.The application has fast convergence speed and strong global optimization ability, can effectively improve the communication reliability and resource utilization efficiency of sea surface internet of things under dynamic marine environment, and is suitable for unmanned aerial vehicle auxiliary sea surface internet of things communication scene.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication resource optimization and intelligent optimization algorithm technology, specifically relating to an adaptive power allocation method for UAV-assisted marine Internet of Things in a wave-turbulent environment. Background Technology

[0002] Ocean wave disturbances cause the positions of buoys to change continuously, resulting in significant time-varying characteristics in the communication link channel quality. Existing maritime IoT systems typically employ fixed power allocation strategies or optimization algorithms based on Weighted Minimum Mean Squared Error (WMMSE) for resource allocation. However, fixed power allocation cannot adapt to dynamic channel changes, while WMMSE relies on gradient iteration, leading to high computational complexity. On the other hand, intelligent optimization algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) suffer from drawbacks such as parameter sensitivity, susceptibility to local optima, and slow convergence speed. Therefore, it is necessary to propose an adaptive power allocation method that can adjust the transmit power of each user in real time according to ocean wave disturbances. Summary of the Invention

[0003] This invention provides an adaptive power allocation method and system for UAV-assisted marine Internet of Things (IoT) in the face of ocean wave disturbances. The purpose is to solve the problem that ocean wave disturbances cause rapid changes in the channel state of communication links, and that fixed power allocation is difficult to adapt to the dynamic marine environment.

[0004] Firstly, the present invention aims to provide an adaptive power allocation method for unmanned aerial vehicles (UAVs) assisted in marine IoT applications amidst wave disturbances, comprising the following steps:

[0005] S1. Obtain the real-time position of each buoy under the influence of ocean wave disturbance, update the communication link channel status information between the UAV and the buoy and between the base station and the buoy based on the real-time position, and calculate the instantaneous signal-to-interference-plus-noise ratio corresponding to each buoy;

[0006] S2. With the goal of maximizing system energy efficiency, construct a power allocation optimization problem. The constraints of the power allocation optimization problem include at least signal-to-interference-plus-noise ratio constraints, power robustness constraints, and constant modulus constraints.

[0007] S3. Based on the position of the array elements and the direction of the signal target in the uniform planar array, calculate the continuous phase values ​​of each array element in the UAV-buoy link and the base station-buoy link respectively, and stack them in column priority to form a continuous analog precoding matrix; determine the discrete phase set according to the phase shifter bit width, quantize the continuous phase of each array element to the nearest discrete phase value, and obtain the analog precoding matrix that satisfies the constant modulus constraint.

[0008] S4. Under the alternating optimization framework, the digital beamforming vectors of the public information flow and the private information flow are solved alternately and iteratively to obtain the optimal digital beamforming matrix.

[0009] S5. Perform hierarchical decoupling processing on optimization variables: Based on the service quality constraints of the common information flow of the buoy with the worst channel quality among all buoys, calculate the transmission power of the common information flow in a closed-loop solution.

[0010] S6. The power allocation optimization problem is transformed into a subtractive optimization problem using the Dinkelbach fractional programming method.

[0011] S7. Map the buoy position to the Harris Hawk position vector and the launch power to the prey position. Initialize the population and calculate the initial fitness. Based on the escape probability and contraction energy, select a power update strategy. Calculate the fitness function, update the optimal solution according to the fitness, iterate until convergence, and output the optimal power allocation scheme.

[0012] Furthermore, a preferred solution is provided: In S1, the real-time position of the buoy is obtained by superimposing the ideal position with the horizontal position offset caused by wave disturbance; the link propagation distance, azimuth angle and pitch angle are recalculated based on the real-time position of the buoy, and then the channel matrix and the instantaneous signal-to-interference-plus-noise ratio of each buoy are updated.

[0013] Furthermore, a preferred solution is provided: In S2, the constraints of the power allocation optimization model also include a robust signal-to-interference-plus-noise ratio (SIR) margin constraint, which is used to compensate for the fluctuations in the SIR caused by the buoy position shift due to wave disturbances.

[0014] Furthermore, a preferred solution is provided: In S4, the process of alternately iteratively solving for the digital beamforming vectors of the public information flow and the private information flow is specifically as follows:

[0015] The digital beamforming matrix of the fixed private information flow is used to transform the beamforming subproblem of the public information flow into a convex semidefinite programming problem by relaxing the rank-one constraint in a semidefinite manner, and the digital beamforming vector of the public information flow is obtained.

[0016] The digital beamforming vector of the fixed public information flow is obtained by introducing auxiliary variables, using continuous convex approximation to process non-convex constraints and combining semidefinite relaxation to remove rank-one constraints, and iteratively solving the convex semidefinite programming problem to obtain the digital beamforming vector of the private information flow.

[0017] The process is iterated alternately until convergence, yielding the final optimal digital beamforming matrix.

[0018] Furthermore, a preferred solution is provided: in S5, the transmission power of the public information flow is taken as the maximum power value required to satisfy the minimum signal-to-interference-plus-noise ratio constraint of the public information flow among all buoys, so that the public information flow just meets the minimum service quality requirements of all buoys.

[0019] Furthermore, a preferred solution is provided: in S7, the power update strategy includes: soft encirclement, hard encirclement, soft encirclement with progressive rapid dive, and hard encirclement with progressive rapid dive.

[0020] Furthermore, a preferred solution is provided: In S7, the fitness function introduces a penalty factor weight and an indicator function. When the power allocation vector violates the constraints, the indicator function takes the value of 1 and triggers the penalty term to penalize infeasible solutions.

[0021] Secondly, the purpose of this invention is to propose an adaptive power allocation system for unmanned aerial vehicles (UAVs) assisted in marine IoT operations amidst sea disturbances, used to implement the adaptive power allocation method for unmanned aerial vehicles assisted in marine IoT operations amidst sea disturbances as described in any one or more of the above-mentioned schemes. The system includes:

[0022] Channel update module: used to obtain the real-time position of each buoy under the influence of sea wave disturbance, update the channel status information of communication links between UAV and buoy, and between base station and buoy based on the real-time position, and calculate the instantaneous signal-to-interference-plus-noise ratio corresponding to each buoy;

[0023] Optimization Modeling Module: Used to construct a power allocation optimization problem with the goal of maximizing system energy efficiency. The constraints of the power allocation optimization problem include at least signal-to-interference-plus-noise ratio constraints, power robustness constraints, and constant modulus constraints.

[0024] Analog precoding matrix quantization module: Based on the position of array elements and the direction of signal target based on uniform planar array, it calculates the continuous phase values ​​of each array element in the UAV-buoy link and base station-buoy link respectively, and stacks them in column priority to form a continuous analog precoding matrix; it determines the discrete phase set according to the phase shifter bit width, quantizes the continuous phase of each array element to the nearest discrete phase value, and obtains an analog precoding matrix that satisfies the constant modulus constraint;

[0025] Beamforming matrix solving module: Used to iteratively solve for the digital beamforming vectors of public information flow and private information flow under an alternating optimization framework, so as to obtain the optimal digital beamforming matrix;

[0026] Layered decoupling module: used to perform layered decoupling processing on optimization variables: based on the service quality constraints of the common information flow of the buoy with the worst channel quality among all buoys, the transmit power of the common information flow is calculated in a closed-loop solution manner;

[0027] Fractional Programming Transformation Module: Used to transform the power allocation optimization problem into a subtractive optimization problem using the Dinkelbach fractional programming method;

[0028] Intelligent optimization output module: used to map buoy position to Harris Hawk position vector, launch power to prey position, initialize population and calculate initial fitness; select power update strategy based on escape probability and contraction energy; calculate fitness function, update optimal solution according to fitness, iterate until convergence, and output optimal power allocation scheme.

[0029] Thirdly, the present invention aims to provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, and when the processor runs the computer program stored in the memory, the processor executes the adaptive power allocation method for unmanned aerial vehicle-assisted marine Internet of Things under wave disturbance according to any one or more of the above-described schemes.

[0030] Fourthly, the present invention aims to provide a computer-readable storage medium for storing a computer program that executes the adaptive power allocation method for unmanned aerial vehicle-assisted marine Internet of Things under wave disturbance described in any one or more of the above-described schemes.

[0031] Compared with the prior art, the advantages of the present invention are:

[0032] (1) This invention introduces rate split multiple access technology into the UAV-assisted maritime Internet of Things scenario and establishes a power allocation model for joint optimization of public and private information flows. In view of the time-varying channel state caused by wave disturbance, with the goal of maximizing system energy efficiency, and under the conditions of satisfying the minimum service quality constraints of each user and the total transmit power constraints, the adaptive dynamic allocation of public flow power and private flow power is realized, overcoming the defect of poor adaptability of traditional power allocation strategies in dynamic marine environments.

[0033] (2) This invention adopts a "layered decoupling" power optimization approach, separating the public power from the high-dimensional optimization variables. Utilizing the transmission characteristic that the public signal must be decoded by all users, the public power value is directly calculated based on the QoS requirements of the user with the worst channel conditions, avoiding power redundancy while ensuring reliable transmission of public information. The private power allocation problem is transformed into an independent sub-problem, and the Harris Eagle optimization algorithm is used for global optimization. This strategy effectively reduces the solution dimension and computational complexity of the optimization problem.

[0034] (3) This invention applies the Harris Eagle optimization algorithm to the power allocation problem of the Internet of Things at sea. By simulating four encirclement strategies of Harris Eagles' coordinated hunting—soft encirclement, hard encirclement, progressive soft encirclement dive, and progressive hard encirclement dive—the power solution space is explored globally and locally. This algorithm does not depend on the convexity and gradient information of the objective function, and is suitable for non-convex and nonlinear power optimization problems caused by wave disturbances. It has the advantages of fast convergence speed and strong global search capability, and can quickly adapt to the dynamically changing marine channel environment.

[0035] This invention is applicable to unmanned aerial vehicle (UAV)-assisted maritime Internet of Things (IoT) communication scenarios. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the system model of the present invention;

[0038] Figure 2 The graphs show the system and rate of change with maximum transmit power under different algorithms;

[0039] Figure 3 The graph shows the system energy efficiency as a function of maximum transmit power under different algorithms. Detailed Implementation

[0040] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0041] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0043] Implementation Method 1

[0044] This embodiment proposes an adaptive power allocation method for UAV-assisted marine Internet of Things (IoT) systems operating under wave disturbances. The method is applied to a marine IoT communication system consisting of a shore-based base station, UAV relay nodes, and multiple marine buoy terminals. The system employs a rate-split multiple access (RSA) transmission mechanism, dividing the signal into a public information stream and a private information stream. The method aims to maximize system energy efficiency. Under the conditions of satisfying user quality of service (QoS) constraints and total transmit power constraints, it adaptively adjusts the transmit power of the public and private information streams based on channel state changes caused by wave disturbances, thereby improving the system's communication reliability and resource utilization efficiency in dynamic marine environments.

[0045] Adaptive power distribution system for UAV-assisted marine IoT in the face of wave disturbances, such as Figure 1 As shown. Compared with traditional maritime wireless communication systems, this invention addresses the problems of rapid changes in communication link channel state caused by wave disturbances and the difficulty of fixed power allocation adapting to the dynamic marine environment. It introduces wave disturbance dynamic channel modeling, adaptive power optimization model, and Harris Hawks Optimization (HHO) algorithm to achieve dynamic optimization allocation of transmission power for public and private information flows, thereby improving system energy efficiency, communication reliability, and resource utilization.

[0046] The system comprises a shore-based base station, UAV relay nodes, and multiple offshore buoy terminals. The shore-based base station is equipped with a large-scale antenna array, while the UAVs act as aerial relay nodes, providing wireless communication services to the offshore buoys. The system employs a Rate Split Multiple Access (RSMA) transmission mechanism to jointly transmit public and private information flows. Offshore buoys experience random positional drift due to factors such as wind, waves, currents, and tides, causing continuous changes in the propagation distance, channel gain, and signal-to-interference-plus-noise ratio between the UAVs and the buoys. This makes it difficult to guarantee system communication performance using traditional fixed power allocation strategies.

[0047] In this invention, to address the time-varying channel characteristics caused by wave disturbances, the communication link status information of each user is acquired in real time. A system energy efficiency optimization model considering the impact of wave disturbances is established. The power of the public information flow and the power of each user's private information flow are used as optimization variables. Under the conditions of satisfying the system's total transmit power constraint, minimum service quality constraint, and user power constraint, the Dinkelbach fractional programming method is used to transform the original non-convex energy efficiency optimization problem into an equivalent optimization problem. Then, the Harris Eagle optimization algorithm is used to perform a global search on the power allocation vector to achieve adaptive adjustment of the transmit power of the public and private information flows under different wave disturbance conditions, thereby improving the system's energy efficiency and communication stability.

[0048] The method includes the following steps:

[0049] Step 1: Establish a drone-assisted maritime IoT communication system consisting of a shore-based base station, drone relay nodes, and multiple maritime buoys. The shore-based base station is equipped with a large-scale uniform planar array antenna. The drones act as airborne relay nodes and are configured with a hybrid beamforming structure. Multiple maritime buoys serve as communication terminals, randomly distributed across the sea surface. The system employs the RSMA (RateSplitting Multiple Access) transmission mechanism. The information transmitted by the base station includes a common information stream and multiple private information streams. The common information is shared by all buoys for decoding, while the private information is sent to the corresponding buoys. The system's transmitted signal is represented as follows:

[0050] ,

[0051] in, Indicates the power of public information transmission. Indicates the first Power for sending private information to individual users , These represent the beam directions of the public and private flows, respectively. , These represent public and private information, respectively. The beam direction is obtained from existing beamforming modules; this invention only optimizes the power distribution section.

[0052] Considering the effects of ocean waves, currents, and tides, the position of a marine buoy continuously and randomly drifts. A three-dimensional Cartesian coordinate system is established, with the base station located at the origin and the UAV hovering at a fixed altitude. The ideal position of each buoy is represented as: The actual position after wave disturbance is represented as ,in and Indicates buoy Positional shift caused by drift.

[0053] Based on the buoy's real-time position, the distance, azimuth, and elevation angles between the UAV and the buoy are recalculated, and the UAV-buoy channel, base station-buoy channel, and equivalent channel matrix are updated. The updated channel matrix is ​​then used to calculate the instantaneous SINR for each user, providing real-time channel information for subsequent power optimization.

[0054] Step 2: With the goal of maximizing system energy efficiency, the problem of hybrid beamforming and power distribution is designed as follows:

[0055] ,

[0056] The optimization variable is the analog precoding matrix. Normalized digital precoding matrix and power allocation set . For signal-to-interference-plus-noise ratio (SIR) constraints. and These are the minimum SINR thresholds for private and public flows, respectively. This is a robust SINR margin used to compensate for the impact of buoy position shifts caused by wave disturbances on SINR. For power robust constraints, This represents the maximum power allocated to any buoy. Indicates constant modulus constraint. This indicates the circuit power consumption of the system.

[0057] Step 3: Based on the position of the array elements and the target direction of the uniform planar array, calculate the continuous phase value of each array element under the UAV-buoy link and the base station-buoy link respectively, and stack them in column priority order to form a continuous analog precoding matrix.

[0058] Based on the phase shifter bit width Determine the discrete phase set, and perform modulo operation on the continuous phases of each array element. After taking the remainder, the matrix is ​​quantized to the nearest discrete phase value to obtain the quantized analog precoding matrix. Its elements satisfy the constant modulus constraint and can be directly configured by a finite bit-width phase shifter.

[0059] Step 4: Fix the private beamforming matrix within the alternating optimization framework. Define user Equivalent interference plus noise power Public SINR is The common subproblem is transformed into a convex SDP solution by relaxing the rank-one constraints using SDR. Let the solution be... Reconstruct the eigenvector corresponding to its largest eigenvalue. .

[0060] Fixed common beamforming vector ,definition Private SINR is Introducing auxiliary variables SCA is used to handle non-convex constraints, and SDR is combined with the removal of rank-one constraints. The convex SDP is solved iteratively, and after convergence, the results are obtained. Perform eigenvalue decomposition and reconstruct the principal eigenvectors. Alternately optimize public and private variables until convergence is achieved, yielding the optimal solution. and .

[0061] Step 5: Based on the analog beamforming matrix and digital beamforming matrix obtained in Steps 3 and 4, further optimize the power. This includes the common power. Instead of being used as an optimization variable, it is obtained from the closed-form solution when the QoS constraints of the public signal are taken as equal, that is, the minimum power corresponding to the worst channel among all users:

[0062] ,

[0063] This setting ensures that the public signal exactly meets the minimum speed requirements of all users, while avoiding interference. Over-allocation, private power As an optimization variable, the problem can be represented as:

[0064] ,

[0065] Step Six: Using the Dinkelbach method, for a given The value problem can be transformed into:

[0066] ,

[0067] Map the buoy position to the Harris Hawk position vector Transmission power Mapped to prey location Initialize the population and calculate the initial fitness.

[0068] Step 7: Based on the probability of escape and contraction energy Choose one of four power update strategies:

[0069] Soft siege :

[0070] , .

[0071] hard siege :

[0072] .

[0073] A gradual, rapid, and soft encirclement :

[0074] .

[0075] Gradual, rapid dive for hard encirclement :

[0076] .

[0077] Step 8: Calculate the fitness function:

[0078] ,

[0079] in For the penalty factor weight, This is an indicator function (1 for constraint violation, 0 otherwise). The optimal solution is updated based on fitness. Iterate until convergence, and output the optimal power allocation scheme.

[0080] Implementation Method 2

[0081] This embodiment is a further illustrative example of the adaptive power allocation method for unmanned aerial vehicles (UAVs) assisted in marine IoT under wave disturbances described in Embodiment 1.

[0082] To verify the effectiveness of the beamforming method proposed in this invention, this embodiment establishes the following... Figure 1 The diagram illustrates a maritime IoT communication scenario. The system includes a shore-based base station, a drone relay node, and six offshore buoys. The shore-based base station is configured with a 12×12 uniform planar array, containing 144 transmitting antennas. The drones are equipped with six radio frequency chains. The total system bandwidth is set to 5 MHz, and the noise power is [not specified]. The maximum transmit power varies between 5 W and 25 W. The UAV hovers at coordinates (100, 200, 100) m, with ideally distributed buoys around the base station, and wave disturbances with a standard deviation of 5 m are superimposed to simulate the random drift of buoy positions in a real marine environment. The energy efficiency and sum rate performance of the proposed HHO power allocation algorithm are compared with those of three benchmark schemes: PSO, GA, and WMMSE, under the same scenario. Each scheme is subjected to multiple independent experiments, and the average value is taken.

[0083] Figure 2The results comparing the sum rate of different algorithms with the maximum transmit power are presented. As shown in the figure, the sum rate of all algorithms exhibits a monotonically increasing trend as the transmit power increases from 5 W to 25 W. Among them, the HHO algorithm proposed in this invention achieves the best sum rate performance at all power points, reaching approximately 215 Mbps at the maximum power of 25 W, which is an improvement of approximately 3.3%, 8.6%, and 4.8% compared to PSO, GA, and WMMSE, respectively.

[0084] Figure 3 The comparison results of system energy efficiency with maximum transmit power under different algorithms are presented. As shown in the figure, the energy efficiency of all algorithms exhibits a monotonically decreasing trend with increasing transmit power. This is because the rate of power consumption growth in the high-power region exceeds the rate gain. The HHO algorithm proposed in this invention achieves the highest energy efficiency at all power points, reaching approximately 21.5 Mbps / W at a maximum power of 5 W, which is significantly superior to the three benchmark schemes. These results demonstrate that the method of this invention can achieve a better trade-off between system power consumption and rate performance, verifying the effectiveness and superiority of the HHO algorithm in resource allocation for maritime IoT.

[0085] Implementation Method 3

[0086] This embodiment proposes an adaptive power allocation system for UAV-assisted marine IoT in the face of wave disturbance, used to implement the adaptive power allocation method for UAV-assisted marine IoT in the face of wave disturbance described in Embodiment 1. The system includes:

[0087] Channel update module: used to obtain the real-time position of each buoy under the influence of sea wave disturbance, update the channel status information of communication links between UAV and buoy, and between base station and buoy based on the real-time position, and calculate the instantaneous signal-to-interference-plus-noise ratio corresponding to each buoy;

[0088] Optimization Modeling Module: Used to construct a power allocation optimization problem with the goal of maximizing system energy efficiency. The constraints of the power allocation optimization problem include at least signal-to-interference-plus-noise ratio constraints, power robustness constraints, and constant modulus constraints.

[0089] Analog precoding matrix quantization module: Based on the position of array elements and the direction of signal target based on uniform planar array, it calculates the continuous phase values ​​of each array element in the UAV-buoy link and base station-buoy link respectively, and stacks them in column priority to form a continuous analog precoding matrix; it determines the discrete phase set according to the phase shifter bit width, quantizes the continuous phase of each array element to the nearest discrete phase value, and obtains an analog precoding matrix that satisfies the constant modulus constraint;

[0090] Beamforming matrix solving module: Used to iteratively solve for the digital beamforming vectors of public information flow and private information flow under an alternating optimization framework, so as to obtain the optimal digital beamforming matrix;

[0091] Layered decoupling module: used to perform layered decoupling processing on optimization variables: based on the service quality constraints of the common information flow of the buoy with the worst channel quality among all buoys, the transmit power of the common information flow is calculated in a closed-loop solution manner;

[0092] Fractional Programming Transformation Module: Used to transform the power allocation optimization problem into a subtractive optimization problem using the Dinkelbach fractional programming method;

[0093] Intelligent optimization output module: used to map buoy position to Harris Hawk position vector, launch power to prey position, initialize population and calculate initial fitness; select power update strategy based on escape probability and contraction energy; calculate fitness function, update optimal solution according to fitness, iterate until convergence, and output optimal power allocation scheme.

[0094] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. An adaptive power allocation method for UAV-assisted marine IoT under wave disturbance, characterized in that, Includes the following steps: S1. Obtain the real-time position of each buoy under the influence of ocean wave disturbance, update the communication link channel status information between the UAV and the buoy and between the base station and the buoy based on the real-time position, and calculate the instantaneous signal-to-interference-plus-noise ratio corresponding to each buoy; S2. With the goal of maximizing system energy efficiency, construct a power allocation optimization problem. The constraints of the power allocation optimization problem include at least signal-to-interference-plus-noise ratio constraints, power robustness constraints, and constant modulus constraints. S3. Based on the position of the array elements and the direction of the signal target in a uniform planar array, calculate the continuous phase values ​​of each array element in the UAV-buoy link and the base station-buoy link respectively, and stack them in column priority to form a continuous analog precoding matrix; The discrete phase set is determined based on the phase shifter bit width. The continuous phase of each array element is quantized to the nearest discrete phase value to obtain an analog precoding matrix that satisfies the constant modulus constraint. S4. Under the alternating optimization framework, the digital beamforming vectors of the public information flow and the private information flow are solved alternately and iteratively to obtain the optimal digital beamforming matrix. S5. Perform hierarchical decoupling processing on optimization variables: Based on the service quality constraints of the common information flow of the buoy with the worst channel quality among all buoys, calculate the transmission power of the common information flow in a closed-loop solution. S6. The power allocation optimization problem is transformed into a subtractive optimization problem using the Dinkelbach fractional programming method. S7. Map the buoy position to the Harris Hawk position vector, and the launch power to the prey position. Initialize the population and calculate the initial fitness. Select a power update strategy based on the escape probability and shrinkage energy. Calculate the fitness function, update the optimal solution based on the fitness, iterate until convergence, and output the optimal power allocation scheme.

2. The adaptive power allocation method for UAV-assisted marine IoT under wave disturbance as described in claim 1, characterized in that, In S1, the real-time position of the buoy is obtained by superimposing the ideal position with the horizontal position offset caused by wave disturbance; the link propagation distance, azimuth angle and pitch angle are recalculated based on the real-time position of the buoy, and then the channel matrix and the instantaneous signal-to-interference-plus-noise ratio of each buoy are updated.

3. The adaptive power allocation method for UAV-assisted marine IoT under wave disturbance as described in claim 1, characterized in that, In S2, the constraints of the power allocation optimization model also include a robust signal-to-interference-plus-noise ratio (SIR) margin constraint, which is used to compensate for the fluctuations in the SIR caused by the buoy position shift due to wave disturbances.

4. The adaptive power allocation method for UAV-assisted marine IoT under wave disturbance as described in claim 1, characterized in that, In S4, the process of alternately iteratively solving for the digital beamforming vectors of the public information flow and the private information flow is specifically as follows: The digital beamforming matrix of the fixed private information flow is used to transform the beamforming subproblem of the public information flow into a convex semidefinite programming problem by relaxing the rank-one constraint in a semidefinite manner, and the digital beamforming vector of the public information flow is obtained. The digital beamforming vector of the fixed public information flow is obtained by introducing auxiliary variables, using continuous convex approximation to process non-convex constraints and combining semidefinite relaxation to remove rank-one constraints, and iteratively solving the convex semidefinite programming problem to obtain the digital beamforming vector of the private information flow. The process is iterated alternately until convergence, yielding the final optimal digital beamforming matrix.

5. The adaptive power allocation method for UAV-assisted marine IoT under wave disturbance as described in claim 1, characterized in that, In step S5, the transmission power of the public information flow is taken as the maximum power value required to satisfy the minimum signal-to-interference-plus-noise ratio constraint of the public information flow among all buoys, so that the public information flow just meets the minimum service quality requirements of all buoys.

6. The adaptive power allocation method for UAV-assisted marine IoT under wave disturbance as described in claim 1, characterized in that, In S7, the power update strategy includes: soft encirclement, hard encirclement, soft encirclement with gradual rapid dive, and hard encirclement with gradual rapid dive.

7. The adaptive power allocation method for UAV-assisted marine IoT under wave disturbance as described in claim 1, characterized in that, In S7, the fitness function introduces a penalty factor weight and an indicator function. When the power allocation vector violates the constraints, the indicator function takes the value of 1 and triggers the penalty term to penalize infeasible solutions.

8. An adaptive power allocation system for unmanned aerial vehicles (UAVs) assisted in marine IoT applications amidst wave disturbances, characterized in that: An adaptive power allocation method for implementing any one of claims 1-7 for unmanned aerial vehicle-assisted marine Internet of Things (UAV) systems resistant to wave disturbances, the system comprising: Channel update module: used to obtain the real-time position of each buoy under the influence of sea wave disturbance, update the channel status information of communication links between UAV and buoy, and between base station and buoy based on the real-time position, and calculate the instantaneous signal-to-interference-plus-noise ratio corresponding to each buoy; Optimization Modeling Module: Used to construct a power allocation optimization problem with the goal of maximizing system energy efficiency. The constraints of the power allocation optimization problem include at least signal-to-interference-plus-noise ratio constraints, power robustness constraints, and constant modulus constraints. Analog precoding matrix quantization module: Based on the position of array elements and the direction of signal target based on uniform planar array, it calculates the continuous phase values ​​of each array element in the UAV-buoy link and base station-buoy link respectively, and stacks them in column priority to form a continuous analog precoding matrix; it determines the discrete phase set according to the phase shifter bit width, quantizes the continuous phase of each array element to the nearest discrete phase value, and obtains an analog precoding matrix that satisfies the constant modulus constraint; Beamforming matrix solving module: Used to iteratively solve for the digital beamforming vectors of public information flow and private information flow under an alternating optimization framework, so as to obtain the optimal digital beamforming matrix; Layered decoupling module: used to perform layered decoupling processing on optimization variables: based on the service quality constraints of the common information flow of the buoy with the worst channel quality among all buoys, the transmit power of the common information flow is calculated in a closed-loop solution manner; Fractional Programming Transformation Module: Used to transform the power allocation optimization problem into a subtractive optimization problem using the Dinkelbach fractional programming method; Intelligent optimization output module: used to map buoy position to Harris Hawk position vector, launch power to prey position, initialize population and calculate initial fitness; select power update strategy based on escape probability and contraction energy; calculate fitness function, update optimal solution according to fitness, iterate until convergence, and output optimal power allocation scheme.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the adaptive power allocation method for unmanned aerial vehicle-assisted marine Internet of Things (UAV) oriented to wave disturbance, as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program that executes the adaptive power allocation method for unmanned aerial vehicle-assisted marine Internet of Things under wave disturbance as described in any one of claims 1-7.

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