UAV and USV network resource allocation system and method based on improved whale optimization algorithm

Abstract

The application discloses a UAV and USV network resource allocation system and method based on an improved whale optimization algorithm, a constraint condition construction module is used to take the correlation coefficient between a USV and a UAV, and a service time length of the UAV providing services to the UAV and a processor calculation frequency of the USV as constraint conditions; an optimization problem construction module is used to construct an optimization problem of minimizing USV task processing energy consumption and maximizing USV data processing amount; and a resource allocation module is used to obtain resource allocation between the UAV and the USV network by iteratively solving the optimization problem by using the improved whale optimization algorithm. The application fuses the traditional whale optimization algorithm with a novel coding mode and crossover mutation operation, solves the problems of poor feasible solution performance, poor convergence and high time complexity in the existing resource allocation, and meets the QoS demand in intensive tasks.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of UAV and USV cooperative network resource allocation, and particularly refers to a UAV and USV network resource allocation system and method based on an improved whale algorithm. BACKGROUND

[0002] In recent years, unmanned surface vehicles (USVs) have made a series of landmark achievements in environmental monitoring, water rescue, and space resource exploration due to their low cost, small size, high flexibility, and strong operability. Current inland USVs integrate advanced sensor communication systems, computing systems, and sensor systems, and have certain autonomous perception and intelligent decision-making capabilities, which are expected to improve the informatization level of inland water areas and are widely regarded as one of the key technologies of the new generation of shipping systems. However, USVs are limited by limited computing resources and battery capacity, and cannot effectively guarantee the Quality of Service (QoS) requirements of computationally intensive tasks such as intelligent navigation and path planning. In addition, the inland shipping environment is complex and lacks telecommunication infrastructure, so how to effectively guarantee the QoS requirements of USV computing tasks is a problem that needs to be solved.

[0003] Under this background, the emergence of digital twin (DT) driven UAV and USV networks provides a feasible solution to this technical problem. Unmanned aerial vehicles (UAVs) can provide flexible data offloading services for USVs, and DTs can dynamically allocate computing and communication resources according to the operating states of UAVs and USVs. However, existing resource allocation methods generally have poor convergence, high time complexity, and other problems, inevitably causing high energy consumption and serious waste of computing resources.

[0004] Therefore, it is urgent to propose a digital twin driven UAV and USV network resource allocation method that can effectively meet the special requirements of the inland shipping environment, i.e., to ensure the QoS of processing computationally intensive tasks under the premise of meeting the constraints of computing resources and energy consumption; at the same time, to solve the problems of slow convergence, high computational complexity, and low performance in the resource allocation process. Finally, a low-complexity and high-efficiency UAV and USV network resource allocation mechanism is realized. SUMMARY

[0005] The purpose of the present invention is to provide a UAV and USV network resource allocation system and method based on an improved whale algorithm. The present invention dynamically allocates computing and communication resources according to the operating status of UAV and USV through a USV and UAV collaborative network driven by data twins, and uses the improved whale algorithm to solve the problems of poor feasible solution performance, poor convergence and high time complexity in existing resource allocation methods, so as to obtain a low-complexity UAV and USV network resource allocation method based on the improved whale optimization algorithm.

[0006] To achieve this purpose, the present invention designs a UAV and USV network resource allocation system based on an improved whale algorithm, comprising:

[0007] The constraint condition construction module is used to use the correlation coefficient between the USV and the UAV, the service duration of the service provided by the UAV to the UAV, and the processor calculation frequency of the USV as constraint conditions;

[0008] The optimization problem construction module is used to construct the optimization problem of minimizing the USV task processing energy consumption and maximizing the USV data processing volume;

[0009] The resource allocation module is used to obtain an improved whale algorithm by defining auxiliary variable optimization coding and adding crossover mutation operations in the whale algorithm. The improved whale algorithm is used to iteratively solve the optimization problem to obtain the global optimal whale individual position, and the resources between the UAV and USV networks are allocated according to the global optimal whale individual position.

[0010] Among them, the individual position of the whale represents a feasible solution to the optimization problem.

[0011] The beneficial effects of the present invention are:

[0012] 1) This invention integrates the traditional whale optimization algorithm with a new encoding method and cross-mutation operation to allocate UAV and USV network resources. While meeting the QoS requirements of USV computationally intensive tasks, it effectively reduces the algorithm complexity and improves operational efficiency. In addition, this invention can effectively reduce the energy consumption of UAVs and USVs and extend service time.

[0013] 2) This paper proposes an improved whale optimization algorithm encoding mechanism, which converts the traditional matrix encoding method into auxiliary variable form, avoiding the complex 0-1 constraints in the matrix and improving search efficiency. Traditional heuristic algorithms such as genetic algorithms have a very large search space when dealing with high-dimensional problems with complex constraints, and the solution search process is prone to falling into local optimality. By converting to auxiliary variable form, the search space is effectively reduced, ensuring that the algorithm can converge to the global optimal solution or a high-quality approximate solution more quickly.

[0014] 3) The present application aims at the poor convergence of traditional whale optimization algorithm for non-continuous variable optimization problem, and proposes a new type of constraint analysis and cross variation operation fused with cross compilation operation, introduces randomness and diversity in the training process, enhances the search ability of whale optimization algorithm, and effectively avoids the premature convergence of the algorithm to local optimal solution, and improves the global search ability. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 An information interaction schematic diagram of the UAV and USV network resource allocation system based on the improved whale optimization algorithm of the present application;

[0016] Figure 2 A DT-driven USV and UAV cooperative network system diagram in the embodiment of the present application;

[0017] Figure 3 A coding mechanism schematic diagram of the improved whale optimization algorithm of the present application;

[0018] Figure 4 A cross variation mechanism schematic diagram of the improved whale optimization algorithm of the present application;

[0019] Figure 5 A data processing amount schematic diagram of each method realized under different noises in the embodiment of the present application;

[0020] Figure 6 A USV processing energy consumption schematic diagram of the EWOA and each method in the embodiment of the present application. DETAILED DESCRIPTION

[0021] The present application will be further described in detail below in combination with the drawings and specific embodiments:

[0022] Embodiment 1:

[0023] As shown in a UAV and USV network resource allocation system based on an improved whale optimization algorithm, comprising: Figures 1-6

[0024] The constraint condition construction module is used to take the correlation coefficient between the USV and the UAV, the service time length of the UAV providing service to the UAV and the processor calculation frequency of the USV as constraint conditions;

[0025] The optimization problem construction module is used to construct an optimization problem of minimizing the USV task processing energy consumption and maximizing the USV data processing amount;

[0026] ​The resource allocation module is configured to obtain an improved whale optimization algorithm by defining auxiliary variables to optimize coding and adding cross variation operations in the whale optimization algorithm, iteratively solve the optimization problem by using the improved whale optimization algorithm to obtain a global best whale individual position, and allocate resources between the UAV and USV network according to the global best whale individual position.

[0027] The whale individual position represents a feasible solution to the optimization problem.

[0028] In the above technical solution, the specific method for obtaining the improved whale optimization algorithm by defining auxiliary variables to optimize coding and adding cross variation operations in the whale optimization algorithm is as follows: a correlation coefficient matrix composed of correlation coefficients between USVs and UAVs is obtained, and the coding mode of the elements in the correlation coefficient matrix is converted into an auxiliary variable composed of the UAV number that provides services for the USV; and cross variation operations are performed on the randomly selected whale individual positions by using the cross variation operation method, thereby obtaining the improved whale optimization algorithm.

[0029] In the above technical solution, the constraint condition and optimization problem construction include: taking the correlation coefficient between the USV and the UAV, the service duration of the UAV to the UAV, and the processor computing frequency of the USV as constraints, and the construction method is as follows:

[0030] In the above technical solution, the constraint condition and optimization problem construction include: taking the correlation coefficient between the USV and the UAV, the service duration of the UAV to the UAV, and the processor computing frequency of the USV as constraints, and the construction method is as follows: Each UAV provides services for the selected USV by using time division multiplexing, and the service duration is defined as β; the processor computing frequency of the USV is defined as f.

[0031] The constraint conditions of the correlation coefficient α between the USV and the UAV, the service duration β of the UAV to the UAV, and the processor computing frequency f of the USV are as follows: Specifically:

[0032]

[0033]

[0034] wherein α m,n represents whether the mth USV selects to unload data to the nth UAV in the fixed time slot; α m,n = 1 represents that the mth USV selects to unload data to the nth UAV in the time slot; otherwise, α m,n = 0. USV and UAV, respectively; in each time slot, the ith USV can only offload data to the nth UAV; β m denotes the service duration of the mth USV to the UAV, and δ is the time slot length; m is the processor computing frequency of the mth USV, and The core voltage of the USV processor can be dynamically adjusted. denotes the maximum frequency of the processor of the mth USV;

[0035] The specific method for constructing the optimization problem of minimizing the USV task processing energy consumption and maximizing the USV data processing amount is as follows:

[0036]

[0037] wherein, is the processor power consumption and transmission power consumption of the USV, is the sum of the processor power consumption and transmission power consumption of all USVs, is the data processing amount of the mth USV, and η is a weight factor, η < 0.

[0038] The specific process of constructing the optimization problem of minimizing the USV task processing energy consumption and maximizing the USV data processing amount and the constraint condition is as follows:

[0039] The M USVs and N UAVs in the USV and UAV network are driven by DT. Define the USV and UAV set as and In each fixed time slot, the USV can offload the collected data to a UAV for processing. Define the correlation coefficient between the USV and the UAV as For any α m,n = 1 indicates that the mth USV selects to offload data to the nth UAV in the time slot; otherwise, α m,n = 0. Since α m,n is a binary variable, it needs to satisfy:

[0040]

[0041] At the same time, in each time slot, the USV i can only offload data to a UAV, so it needs to satisfy

[0042]

[0043] Each UAV provides services to the selected USV through time division multiplexing, and the service duration is defined as wherein β mrepresents the service time of the mth USV accessing the UAV, β m Needs to be satisfied

[0044]

[0045] Among them, δ represents the time slot length. At the same time, the time slot length allocated to the USV by any n-th UAV must satisfy

[0046]

[0047] Assume that in this time slot, the mth USV chooses to unload data to the nth UAV, and the amount of unloaded data is It can be expressed as

[0048]

[0049] Among them, B m,n represents the access bandwidth allocated by the nth UAV to the mth USV, h0 is the channel gain, is the transmission power of the mth USV, d m,n represents the distance between the mth USV and the nth UAV, σ 2 Represents the ambient noise power. The UAV is equipped with a high-performance processor, so the data processing delay is negligible.

[0050] In any time slot, the USV processor calculates the frequency is controlled by piezoelectricity, so f m Need to meet

[0051]

[0052] in, Indicates the maximum frequency of the processor of the mth USV. The local data processing capacity of the mth USV It can be expressed as

[0053]

[0054] Where ε represents the amount of data that can be processed per processor cycle. Define the energy consumption of the mth USV task processing as is the power consumption of the USV processor and the transmission power consumption. Therefore, it can be expressed as

[0055]

[0056] in, Indicates the effective capacitance switch of the USV processor. In any time slot, the data processing capacity of the mth USV can be expressed as

[0057]

[0058] To prolong the service cycle of the USV and reduce the data backlog, an optimization problem is constructed to minimize the energy consumption of the USV task processing and maximize the data processing capacity of the USV, and the correlation coefficient α between the USV and the UAV, the service time β and the processor computing frequency f of the USV are jointly optimized. The optimization problem can be constructed as follows:

[0059]

[0060] wherein η<0 is a weight factor. The DT can obtain a high-quality feasible solution by solving the optimization problem, and then be used for controlling the USV and the UAV.

[0061] In the above technical solution, the resource allocation module can set an initialization process, initialize the number of UAVs as N, the number of USVs as M, randomly determine the initial position of the population, and set the initial iteration number r EWOA to 0, and set the maximum iteration number.

[0062] The initialization process lays the foundation for the entire optimization process. By setting the number of UAVs as N and the number of USVs as M, and randomly determining the initial position of the population, it is ensured that the algorithm starts exploring the solution space from a diversified starting point, avoiding the algorithm from searching into a local optimum or converging too early. The initial iteration number is set to 0, indicating that the algorithm is in the starting state, and the setting of the control coefficient helps to balance the relationship between global search and local development, so that the algorithm can effectively search in a wide solution space while focusing on potential areas. The maximum iteration number as a termination condition prevents the algorithm from falling into an infinite loop, and the result can be obtained within a limited time.

[0063] In the resource allocation module in the above technical solution, the method for coding the improved whale optimization algorithm is as follows: a conversion relationship between the correlation coefficient matrix A and the auxiliary variable θ is established, and the correlation coefficient matrix A is converted into the auxiliary variable θ formed by the combination of the UAV numbers served by each USV, wherein, θ m represents the UAV number served by the mth USV and is an integer with a value range of [1, N], and the correlation coefficient matrix A is an M×N matrix composed of variables α m,n ;

[0064] The whale group coordinates are coded by the improved whale optimization algorithm coding mechanism, specifically as follows:

[0065] If there are I whales in the group, the whale group coordinates are represented as:

[0066]

[0067] wherein, represents the rth iteration of the whale i.EWOA The coordinates after iterations, Specifically expressed as:

[0068]

[0069] Among them, the coordinates of each whale individual are composed of the auxiliary variables θ feasible for M USVs, the service time β and the processor calculation frequency f. Indicates the rth EWOA A set of feasible solutions corresponding to the mth USV in whale individual i after iterations.

[0070] Because the correlation coefficient matrix A is a matrix composed of 0-1 variables α m,n The above solution converts the correlation coefficient matrix A into auxiliary variables. Among them, θ m The number of the UAV serving the mth USV is an integer in the range [1, N]. This form not only simplifies the representation, reduces the encoding dimensionality, and avoids the complex 0-1 constraints in the matrix, but also improves search efficiency. Traditional heuristic algorithms, such as genetic algorithms, have a very large search space when dealing with high-dimensional problems with complex constraints and are prone to getting stuck in local optimal solutions. By converting to θ, the search space is greatly reduced, allowing the algorithm to more quickly find the global optimal solution or a high-quality approximate solution.

[0071] Where A represents an M×N matrix consisting of the correlation coefficient α, and α represents the general term for the correlation coefficient between USV and UAV.

[0072] In the above technical solution, the specific method of using the improved whale algorithm to iteratively solve the optimization problem to obtain the best individual whale position in the current iteration is:

[0073] Calculate the fitness of each whale individual position, select the one with the largest fitness as the best whale individual position for the current iteration, and the rth whale position of whale i EWOA The fitness function of the coordinates after iterations for:

[0074]

[0075] in, is the sum of the power consumption of all USV processors and the transmission power consumption, is the power consumption and transmission power consumption of the mth USV processor; is the data processing amount of the mth USV, is the set of USVs in the USV and UAV network, η is the weight factor, η<0, a rth EWOA a set of feasible solutions corresponding to the mth USV in the rth iteration of the whale individual i.

[0076] The fitness evaluation analyzes the fitness value of each whale individual position through a specific fitness function, achieving the purpose of dynamically evaluating the quality of the solution. The core objective of the optimization problem, minimizing energy consumption and maximizing data processing capacity, is directly considered, ensuring the consistency of the search direction; by introducing a weight factor, the priority between the two objectives can be flexibly adjusted in different application scenarios; selecting the whale individual with the maximum fitness as the current best solution can ensure that the algorithm evolves in a better direction at each step, improving the possibility of finding the global optimal solution. Not only does it improve the efficiency and accuracy of the algorithm, but it also enhances its ability to cope with complex optimization problems, especially in task allocation and resource management scenarios involving USV and UAV collaboration, effectively promoting the overall performance of the system.

[0077] In the above technical solution, the specific method for solving the optimization problem using the improved whale algorithm to obtain the global best whale individual position is:

[0078] The improved whale algorithm uses a fitness function to iteratively solve the optimization problem to obtain the best whale individual position at this iteration, and according to the best whale individual position at this iteration and the control coefficients a and p1, selects the positions of other whale individuals in the whale group except the current best whale individual, and again solves the optimization problem using the improved whale algorithm, until the maximum number of iterations is reached to obtain the global best whale individual position.

[0079] The specific method for determining the positions of other whale individuals in the whale group except the current best whale individual according to the best whale individual position at this iteration and the control coefficients a and p1 is:

[0080] When a ≥ 1 and p1 ≥ 0.5, the whale individual position is updated in a way that surrounds the prey, and the positions of other individuals in the group are updated to The position update method of surrounding the prey can be represented as:

[0081]

[0082] where, is the position of other individuals in the whale group in the next iteration, a rth EWOA iteration of the whale group, in the process of surrounding the prey r1 and r2 are random numbers between 0 and 1; represents the xth element in , where A1 represents the whale individual the direction of position update of the whale, D x represents the update step size;

[0083] When a≥1 and p1<0.5, during the whale hunting process, the whales in the group emit bubbles to surround the prey through spiral motion. Simulating spiral motion to update the position can effectively increase the search ability of individuals, and other individuals in the group will update their positions through spiral motion around the center of the current optimal solution. The position update method can be represented as:

[0084]

[0085] where, is the position of other individuals in the whale group in the next iteration, b is the constant of the logarithmic spiral shape, and l is a random number with a value range of-1 to 1;

[0086] When a<1, to prevent the group search from falling into local optimum, the individuals in the group no longer approach , but randomly select an individual in the group to approach, and randomly select the r EWOA th iteration of a whale individual position Through the position update formula of surrounding the prey, the position update position of other whale individuals to the randomly selected whale individual is calculated,

[0087] where a decreases from 2 to 0 with the change of iteration number, and p1 is a random number between 0 and 1.

[0088] This position update strategy effectively improves the performance of the optimization algorithm by dynamically adjusting the control coefficients a and p1, combining the mechanisms of surrounding prey, spiral motion and random selection. In the early iterations, when a is large, a≥1, when p1≥0.5, the surrounding prey method is used to make the group approach the current optimal solution, speeding up the convergence; when p1<0.5, the spiral motion simulation hunting behavior is used to enhance the local search ability and avoid premature convergence into local optimum. As the iteration proceeds, a gradually decreases to 0, when a<1, the group no longer relies on the current optimal solution, but randomly selects individuals as reference points, preventing the search process from converging too early and increasing the global exploration ability.

[0089] Not only improves the probability of finding the global optimal solution, but also enhances its robustness and adaptability, especially suitable for complex and variable optimization problems. In the task allocation of USV and UAV cooperation, it can efficiently balance the two goals of minimizing energy consumption and maximizing data processing, ensuring the best performance of unmanned systems in executing tasks. At the same time, this method also ensures that the algorithm has good exploration ability in different environments, thereby improving the overall optimization efficiency. ​

[0090] In the above technical solution, the improved whale optimization algorithm adds the following cross mutation operation:

[0091] The probability of cross mutation occurring according to the position of each whale individual is CR. When p2≤CR, the position of the whale individual is subjected to cross mutation operation, and two cross mutation methods are randomly selected for cross mutation:

[0092] Cross mutation method a: randomly select an auxiliary variable θ in the whale individual i corresponding to the UAV number serving a certain mth USV m , select an integer in [1, N] that is not equal to θ m as the new position of the whale individual i;

[0093] Cross mutation method b: randomly select two groups of resource allocation feasible solutions in the whale individual i corresponding to two mth USVs and m'th USVs and exchange them to obtain a new whale individual position.

[0094] Cross mutation enhances the exploration ability of the whale optimization algorithm by introducing randomness and diversity. The two methods effectively avoid premature convergence to local optimal solutions and improve global search ability. In this way, cross mutation not only increases the position diversity of the population, but also promotes the combination of excellent features of different feasible solutions, making the optimization process more flexible and efficient. It helps to find better USV and UAV collaboration configuration, significantly reduces the task processing energy consumption while improving data processing capacity.

[0095] To enhance the global search ability, the probability of cross mutation occurring for each whale individual i is CR, that is, when p2≤CR, the whale is subjected to cross mutation operation, and p2 is a random number between 0 and 1. The two cross mutation operation methods used in this patent are shown in Figure 3 , a new whale position is generated by randomly selecting one of the two cross mutation operations. The first cross mutation operation is to randomly select a UAV number θ m in the whale individual i corresponding to a certain mth USV providing service, and select an integer in [1, N] that is not equal to θ m as the new position of the whale individual i. The second cross mutation operation is to randomly select two groups of USV resource allocation feasible solutions in the whale individual i and exchange them to obtain a new whale individual position.

[0096] In the above technical solution, in the resource allocation module, the method for repairing the position of the whale individual that does not meet the constraint condition optimization problem by analyzing the constraint violation of the whale individual corresponding to the USV resource allocation scheme is:

[0097] The individual positions generated during the execution of the encirclement of prey, search for prey and bubble attack updates may violate the constraints. Therefore, the non-continuous auxiliary variable θ is analyzed and the distance The nearest integer in the range [1, N] is used as the coordinate of the repaired whale i;

[0098] For constraint problems Analyze and find out what is not satisfied The feasible solution of processor calculation frequency corresponding to the whale individual can be repaired by the following operation

[0099]

[0100] After completing the cross-compilation operation, the constraint problem and Analyze, if any Then order like Then order

[0101] Among them, θ m,i , β m,i 、f m,i They represent the auxiliary variables, service duration and processor calculation frequency of the i-th feasible solution of the m-th USV, Indicates the rth EWOA The feasible solution corresponding to the individual whale after updating in the iteration, Indicates the rth EWOA The feasible solution corresponding to the updated whale individual constraint processing in the iteration, where Indicates the rth EWOA The processor calculation frequency of the i-th feasible solution of the m-th USV of the whale individual after the update in the iteration, Indicates the rth EWOA The processor calculation frequency of the i-th feasible solution for the m-th USV after processing the updated whale individual constraints in the iteration, Indicates the rth EWOA The service time of the i-th feasible solution of the m-th USV of the updated whale in the iteration, Indicates the rth EWOA The service time of the i-th feasible solution of the m-th USV of the updated whale in the iteration, Indicates the rth EWOA The auxiliary variable of the i-th feasible solution of the m-th USV of the whale individual after the update in the iteration, represents the maximum frequency of the processor of the mth USV, and δ is the time slot length.

[0102] The repair operation enhances the robustness of the algorithm, avoids the exploration of invalid areas in the solution space, and improves the search efficiency. Finally, through strict constraint processing, the quality of the obtained solution is guaranteed, and the reliability and performance of the unmanned system cooperative task are significantly improved.

[0103] In the technical solution, the resource allocation module further includes a decoding process, which is the inverse process of the encoding mechanism. The decoding process decodes the auxiliary variable θ in the global best whale individual position into the correlation coefficient α between the USV and the UAV, and combines it with the service duration β and the processor calculation frequency f of the USV to obtain the global best feasible solution.

[0104] The decoding process is the inverse operation of the encoding process. In the above technical solution, the auxiliary variable θ in the global best whale individual position found by the optimization algorithm is decoded into the correlation coefficient α between the USV and the UAV, and is combined with the service duration β between the USV and the UAV and the processor calculation frequency f of the USV to output the global best feasible solution. This ensures that the optimization result can be directly applied to the actual system configuration, realizing seamless conversion from abstract mathematical model to specific parameter setting. In this way, not only the practicality and operability of the solution are improved, but also the optimized parameter configuration can effectively reduce energy consumption and improve data processing efficiency, thereby significantly enhancing the cooperation performance and task execution capability of the UAV and the USV.

[0105] Iterate the above scheme, and if the number of iterations reaches the maximum number of iterations, the iteration training ends.

[0106] The correlation coefficient α between the USV and the UAV, the service duration β, and the processor calculation frequency f of the USV are outputted, and the correlation coefficient α between the USV and the UAV, the service duration β, and the processor calculation frequency f of the USV are used to interact and control the resource allocation of the USV and the UAV in the optimization physical layer, as shown in the following figure. Figure 2 As shown in the figure, the DT runs in the remote cloud service center, and the DT layer and the physical layer can interact in real time through a wireless communication link, so the DT layer can obtain the coordinates and related physical parameters of the USV and the UAV in real time and update the twin individual state. In addition, the DT layer can issue operation instructions to the USV and the UAV according to the demand state of the twin individual, and allocate resources. In each time slot, each USV will collect and process inland hydrological data.

[0107] In order to better prove the effectiveness and practicability of the improved whale optimization algorithm (EWOA) model proposed in the application, the performance of the EWOA algorithm is tested by using MATLAB software, and the superiority of the proposed method is verified by comparing it with other advanced algorithms such as genetic algorithm (GA), differential evolution algorithm (DE) and fixed service duration algorithm (FSD). It is assumed that the network coverage is 150m x 150m, and 10 USVs are randomly distributed. Each service period is divided into 500 time slots, and the length of each time slot is 0.2s. The performance comparison results of the proposed EWOA and other algorithms are shown in Figure 5 Figure 5 The data processing amount of each method implemented under different noise environments is described. As the noise power increases, the USV data processing amount of each method implemented gradually decreases. This is because the increase of noise power will significantly reduce the signal-to-noise ratio of the USV-to-UAV transmission link, resulting in a decrease in data transmission amount. Specifically, when the noise power is-80dBm, the FSD, GA and EWOA respectively implement about 4x10 5 bits, 9x10 5 bits, 11x10 5 bits and 1x10 6 bits. EWOA can achieve the maximum USV data processing amount, significantly reducing the backlog of USV data queue.

[0108] As Figure 6 The USV processing energy consumption corresponding to each method is described, and it can be seen that EWOA can significantly reduce the USV processing energy consumption (about 50%) compared with other methods.

[0109] Embodiment 2:

[0110] A UAV and USV network resource allocation method based on an improved whale optimization algorithm, comprising the following steps:

[0111] Step 1, taking the correlation coefficient between USV and UAV, the service duration of UAV providing service to UAV and the processor calculation frequency of USV as constraint conditions;

[0112] Step 2, constructing an optimization problem of minimizing USV task processing energy consumption and maximizing USV data processing amount;

[0113] ​Step 3, in the whale algorithm, the coding is optimized by defining auxiliary variables and adding cross mutation operation, an improved whale algorithm is obtained, the global best whale individual position is obtained by iterative solving of the optimization problem using the improved whale algorithm, and the resource allocation between the UAV and the USV network is performed according to the global best whale individual position;

[0114] Wherein, the whale individual position represents a feasible solution of the optimization problem.

[0115] Step 3.1, initialize the number of UAVs, the number of USVs, the population position, the initial iteration number and the control coefficient, and set the maximum iteration number. The number of UAVs is N, the number of USVs is M, the initial population position is randomly determined, the initial iteration number r EWOA is 0, the control coefficient is a and p1, and the maximum iteration number is set.

[0116] Step 3.2, encode the coordinates of the whale group after each iteration of the whale individual position by the coding mechanism of the improved whale optimization algorithm.

[0117] Step 3.3, calculate the current iteration best whale individual position of the whale individual position by the fitness function.

[0118] Step 3.4, according to the current iteration best whale individual position, combined with the control coefficient, different stages are divided, and the surrounding prey operation, bubble net attack and search predation are respectively adopted to update other whale individual positions.

[0119] Step 3.5, according to the probability of cross mutation of each whale individual, the selected cross mutation whale individual is screened by using random number, the cross mutation operation is performed on the selected whale individual position corresponding to the different USV resource allocation feasible solution of interest, and the cross mutation whale individual position is obtained.

[0120] Step 3.6, analyze the constraint violation of the whale individual corresponding to the USV resource allocation scheme, and repair the whale individual position of the optimization problem which does not meet the constraint condition.

[0121] Step 3.7, repeat steps 3.3 to 3.6 for iterative training, when the iteration number reaches the maximum iteration number, the iterative training is ended, and the global best whale individual position is obtained;

[0122] Step 3.8, decode the global best whale individual position into the correlation coefficient between the USV and the UAV, the service time and the processor calculation frequency of the USV.

[0123] In steps 1 and 2, the construction of the above constraints and optimization problem ensures the clarity and operability of the problem by explicitly defining the optimization objective (minimizing USV task processing energy consumption and maximizing data processing capacity) and constraints (correlation coefficients, service duration, processor computing frequency). This provides a clear direction and basis for subsequent algorithm design, ensuring the effectiveness and feasibility of the optimization process, thereby improving the quality of the solution and the convergence speed of the algorithm.

[0124] In step 3.1, the number of UAVs, the number of USVs, the population position, the initial iteration number, and the control coefficient are initialized, and the maximum iteration number is set, providing the necessary starting point and boundary for algorithm operation. This ensures that the algorithm starts from a reasonable starting point and finds a satisfactory solution within a limited number of iterations, avoiding infinite loops and improving the efficiency and practicality of the algorithm.

[0125] In step 3.2, the whale population coordinates after each iteration are encoded using the improved whale optimization algorithm (EWOA), simplifying the problem representation and reducing the search space. This improves search efficiency, allowing the algorithm to converge to high-quality solutions more quickly, while reducing computational complexity and enhancing the scalability and applicability of the algorithm.

[0126] In step 3.3, the fitness of each whale individual position is calculated using the fitness function, and the best whale individual in the current iteration is selected to ensure that high-quality solutions exist in each generation of the population. This guides the algorithm to evolve towards the optimal solution, improving the quality of the solution and the convergence speed of the algorithm, and helps to quickly find the global optimal solution or a high-quality approximate solution.

[0127] In step 3.4, the positions of other whale individuals are updated using the surround prey, bubble net attack, and search predation strategies based on the position of the best whale individual in the current iteration and the control coefficient. This enhances the global search ability and local search ability of the algorithm, balances exploration and development, and improves the robustness of the algorithm and the quality of the solution by finding new solutions in the global range and refining local optimal solutions.

[0128] In step 3.5, the probability of crossover and mutation for each whale individual is determined, and random numbers are used to select the crossover and mutation of whale individuals. This introduces diversity by exchanging the feasible solutions of different USVs corresponding to whale individuals, increasing the diversity of the population, preventing the algorithm from falling into local optima, improving the global search ability of the algorithm, enhancing the robustness and adaptability of the algorithm, and helping to discover more potential high-quality solutions.

[0129] In step 3.6, the positions of whale individuals that do not meet the constraints are repaired to ensure that all solutions meet the constraints. This ensures that the solutions generated by the algorithm are feasible, improves the quality of the solution and the robustness of the algorithm, avoids the generation of invalid solutions, and ensures the practical application value of the optimization results.

[0130] In step 3.7, the fitness function, position update module, crossover mutation and position repair are sequentially performed, and iterative training is performed. After the number of iterations reaches the maximum number of iterations, the iterative training is completed, and the correlation coefficient between the USV and the UAV, the service time and the processor calculation frequency of the USV are output. This systematic iterative training ensures the stability and reliability of the algorithm and improves the overall optimization effect.

[0131] In step 3.8, the decoding operation is the inverse operation of the encoding operation, and the auxiliary variable θ in the global best whale individual position can be decoded into the correlation coefficient α between the USV and the UAV, and combined with the service time β between the USV and the UAV and the processor calculation frequency f of the USV to output the global best feasible solution. This ensures that the optimization result can be directly applied to the actual system configuration, improving the practicality and operability of the solution, and also ensures that the optimized parameter configuration can effectively reduce energy consumption and improve data processing efficiency, thereby significantly enhancing the collaboration performance and task execution capability of the unmanned system.

[0132] The above scheme solves the multi-objective optimization problem of UAV and USV network resource allocation by using the improved whale optimization algorithm (EWOA), aiming to minimize the USV task processing energy consumption and maximize the data processing amount.

[0133] The resource allocation method based on the improved whale optimization algorithm is as follows:

[0134] S1, input the initial number of UAVs N, the number of USVs M,

[0135] S2, initialize the population position The initial number of iterations r EWOA = 0 and the control coefficient a = 2, and set the maximum number of iterations;

[0136] S3, determine whether it is true, if it is true, go to step 4, if it is not true, go to S11;

[0137] S4, determine whether a ≥ 1 is true, if it is true, go to S5, if it is not true, go to S6;

[0138] S5, determine p1, if p1 ≥ 0.5, update the whale individual position by the surrounding prey operation, if p1 < 0.5, update the whale individual position by the bubble net attack;

[0139] S6, update so that the whale individual position is updated by searching for prey;

[0140] S7, for the whale individual that violates , repair it;

[0141] S8, updating p2, cross mutation operation is performed on the whale individual of p2

[0142] S9, repairing the individual violating ;

[0143] S10, updating r EWOA , a, and looping S3 to S10;

[0144] S11, outputting θ, β, f.

[0145] Embodiment 3:

[0146] A computer program product including a computer program, which, when executed by a processor, implements the steps of the above method.

[0147] The contents not described in detail in the specification belong to the prior art known to those skilled in the art. Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams Figure 1 The functions specified in the flowcharts and / or block diagrams

[0149] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams Figure 1 The functions specified in the flowcharts and / or block diagrams

[0150] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0151] Finally, it should be noted that the above examples are merely used to illustrate the technical solutions of the present application, but not to limit the protection scope thereof. Although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that, after reading the present application, the specific implementation manners of the present application can be changed, modified or replaced by equivalents by those skilled in the art, and these changes, modifications or replacements all fall within the protection scope of the claims of the present application to be filed.

[0152] The contents not described in detail in the specification belong to the prior art known to those skilled in the art.​​

Claims

1. A UAV and USV network resource allocation system based on an improved whale algorithm, characterized in that: include: The constraint condition construction module is used to use the correlation coefficient between the USV and the UAV, the service duration of the service provided by the UAV to the UAV, and the processor calculation frequency of the USV as constraint conditions; The optimization problem construction module is used to construct the optimization problem of minimizing the USV task processing energy consumption and maximizing the USV data processing volume; The resource allocation module is used to obtain an improved whale algorithm by defining auxiliary variable optimization coding and adding crossover mutation operations in the whale algorithm. The improved whale algorithm is used to iteratively solve the optimization problem to obtain the global optimal whale individual position, and the resources between the UAV and USV networks are allocated according to the global optimal whale individual position. Among them, the individual whale position represents a feasible solution to the optimization problem; A conversion relationship between the correlation coefficient matrix and the auxiliary variables is established, and the correlation coefficient matrix is ​​converted into a combination of UAV numbers provided by each USV to form the auxiliary variables.

2. The UAV and USV network resource allocation system based on the improved whale algorithm according to claim 1 is characterized by: In the whale algorithm, the improved whale algorithm is obtained by defining auxiliary variables to optimize the coding and adding crossover mutation operations. The specific method is as follows: the correlation coefficients between USVs and UAVs constitute a correlation coefficient matrix, and the coding method of the elements in the correlation coefficient matrix is ​​converted into auxiliary variables composed of the UAV numbers provided by the USV; and the crossover mutation operation method is used to randomly select the individual positions of whales for crossover mutation operations.

3. The UAV and USV network resource allocation system based on the improved whale algorithm according to claim 1 is characterized in that: In the constraint condition construction module and the optimization problem construction module, the correlation coefficient between USV and UAV, the service duration of the UAV providing services to UAV, and the processor calculation frequency of the USV are used as constraints. The construction method is: The UAV and USV network is set to include M USVs and N UAVs, all driven by DT. In each fixed time slot, the USV can offload the collected data to a UAV for processing. The correlation coefficient between the USV and UAV is defined as Each UAV provides services to the USVs that choose to access it through time division multiplexing, and defines the service duration β; the processor calculation frequency of the USV is defined as f; The constraints for the correlation coefficient α between USV and UAV, the service duration β of UAV providing services to UAV, and the processor calculation frequency f of USV are: Specifically: Among them, α m,n Indicates whether the mth USV chooses to offload data to the nth UAV within a fixed time slot; α m,n =1 means that the mth USV chooses to unload data to the nth UAV in this time slot; otherwise, α m,n =0, are the sets of USVs and UAVs respectively; in each time slot, the i-th USV can only offload data to one n-th UAV; β m represents the service duration of the mth USV accessing the UAV, δ is the time slot length; f m Calculate the frequency for the processor of the mth USV, and Dynamically adjust the USV processor core voltage; Indicates the maximum processor frequency of the mth USV; The specific method for constructing the optimization problem of minimizing USV task processing energy consumption and maximizing USV data processing volume is as follows: in, is the processor power consumption and transmission power consumption of USV, is the sum of the power consumption of all USV processors and the transmission power consumption, is the data processing amount of the mth USV, and η is the weight factor.

4. The UAV and USV network resource allocation system based on the improved whale algorithm according to claim 1 is characterized in that: In the resource allocation module, the improved whale optimization algorithm encoding method is as follows: establish the conversion relationship between the correlation coefficient matrix A and the auxiliary variable θ, convert the correlation coefficient matrix A into the UAV number combination provided by each USV to form the auxiliary variable θ, where, θ m It represents the UAV number that provides service for the mth USV and is an integer in the range [1, N]. The correlation coefficient matrix A is composed of the variable α m,n The M×N matrix composed of The whale group coordinates are encoded using the improved whale optimization algorithm encoding mechanism, specifically: Assume there are I whales in the group, then the coordinates of the whale group are expressed as: in, Denotes the rth whale i EWOA The coordinates after iterations, Specifically expressed as: Among them, the coordinates of each whale individual are composed of the auxiliary variables θ feasible for M USVs, the service time β and the processor calculation frequency f. Indicates the rth EWOA A set of feasible solutions corresponding to the mth USV in whale individual i after iterations.

5. The UAV and USV network resource allocation system based on the improved whale algorithm according to claim 1 is characterized in that: The specific method of iteratively solving the optimization problem using the improved whale algorithm to obtain the optimal individual whale position in the current iteration is: Calculate the fitness of each whale individual position, select the one with the largest fitness as the best whale individual position for the current iteration, and the rth whale position of whale i eWOA The fitness function of the coordinates after iterations for: in, is the power consumption of the processor and the transmission power consumption in the mth USV, is the sum of the power consumption of all USV processors and the transmission power consumption, is the data processing amount of the mth USV, is the set of USVs in the USV and UAV network, η is the weight factor, Indicates the rth EWOA After iterations, a set of feasible solutions corresponding to the mth USV in whale individual i, where θ m,i , β m,i 、f m,i They represent the auxiliary variables, service duration, and processor calculation frequency of the i-th feasible solution of the m-th USV respectively.

6. The UAV and USV network resource allocation system based on the improved whale algorithm according to claim 5 is characterized in that: The specific method of using the improved whale algorithm to solve the optimization problem and obtain the global optimal whale individual position is: The improved whale algorithm uses the fitness function to iteratively solve the optimization problem to obtain the best individual whale position in the iteration. According to the best individual whale position in the iteration and the control coefficients a and p1, the positions of other individual whales in the whale group except the current best individual whale are selected, and the improved whale algorithm is used again to solve the optimization problem until the maximum number of iterations is reached to obtain the global best individual whale position. According to the best individual whale position under iteration and the control coefficients a and p1, the specific method for determining the positions of other whale individuals in the whale group except the current best individual whale is as follows: When a≥1, p1≥0.5, the position of the whale individual is updated by surrounding the prey, and the other individuals in the group move towards the best whale individual. The way to update the surrounding position can be expressed as: in, is the position of other individuals in the whale group in the next iteration, Indicates the rth EWOA In the iteration, the whale group selects the individual that is close to them, and in the process of surrounding the prey r1 and r2 are random numbers between 0 and 1; express The xth element in , where A1 represents the individual whale Position update direction, D x represents the update step size; When a≥1, p1<0.5, other individuals in the group will The position is updated by spiral motion as the center. The position update method is expressed as: in, is the position of other individuals in the whale group in the next iteration, b is the logarithmic spiral shape constant, and l is a random number ranging from -1 to 1; When a<1, randomly select the rth EWOA The position of an individual whale in the iteration By updating the position formula of the prey, the position of other whales is calculated to move towards the random whale. The surrounding position updates the position, where 7. The UAV and USV network resource allocation system based on the improved whale algorithm according to claim 6 is characterized in that: In the improved whale algorithm, the added crossover mutation operation is: Using the probability of crossover mutation for each whale individual position as CR, we perform crossover mutation on the whale individual positions with p2≤CR, where p2 is a random number between 0 and 1, and randomly select the following two crossover mutation methods for crossover mutation: Cross-mutation method a: Randomly select the auxiliary variable θ of the UAV number corresponding to the mth USV in the whale individual i m , select [1,N] not equal to θ m The integer is the new position of whale individual i; Crossover mutation method b: Randomly select two feasible resource allocation solutions corresponding to the m-th USV and the m′-th USV in whale individual i and Swap the two to get the new individual whale positions.

8. The UAV and USV network resource allocation system based on the improved whale algorithm according to claim 7 is characterized in that: In the resource allocation module, the constraint violations of the USV resource allocation scheme corresponding to the whale individuals are analyzed, and the method for repairing the positions of the whale individuals that do not meet the constraint optimization problem is as follows: Analyze the non-continuous auxiliary variable θ and take the distance The nearest integer in the range [1, N] is used as the coordinate of the repaired whale i; For constraint problems Analyze and find out what is not satisfied The feasible solution of processor calculation frequency corresponding to the whale individual can be repaired by the following operation After completing the cross-compilation operation, the constraint problem and Analyze, if any Then order like Then order Among them, θ m,i , β m,i 、f m,i They represent the auxiliary variables, service duration and processor calculation frequency of the i-th feasible solution of the m-th USV, Indicates the rth EWOA The feasible solution corresponding to the individual whale after updating in the iteration, Indicates the rth EWOA The feasible solution corresponding to the updated whale individual constraint processing in the iteration, represents the maximum frequency of the processor of the mth USV, and δ is the time slot length.

9. A UAV and USV network resource allocation method based on an improved whale algorithm, characterized in that: The following steps are involved: The constraints are the correlation coefficient between USV and UAV, the service duration of the service provided by UAV to UAV, and the processor computing frequency of USV; Construct an optimization problem to minimize the energy consumption of USV task processing and maximize the amount of USV data processing; In the whale algorithm, an improved whale algorithm is obtained by defining auxiliary variable optimization coding and adding crossover mutation operations. The improved whale algorithm is used to iteratively solve the optimization problem to obtain the global optimal whale individual position, and resources are allocated between the UAV and USV networks based on the global optimal whale individual position; Among them, the individual whale position represents a feasible solution to the optimization problem; A conversion relationship between the correlation coefficient matrix and the auxiliary variables is established, and the correlation coefficient matrix is ​​converted into a combination of UAV numbers provided by each USV to form the auxiliary variables.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to claim 9 are implemented.

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