A USB docking station resource allocation method

Abstract

The present application relates to USB docking station, specifically to a USB docking station resource allocation method, determining the search space of resource allocation; determining the objective function and constraint condition of resource allocation, constructing resource allocation model according to the objective function and constraint condition of resource allocation; the resource allocation model is solved by using aedes aegypti blood sucking reprogramming optimization algorithm, and the optimal resource allocation scheme is obtained; based on the optimal resource allocation scheme, the resource allocation of USB docking station is carried out; the technical scheme provided by the present application can effectively overcome the defects that the USB docking station resources cannot be reasonably and efficiently allocated in the prior art.

Description

Technical Field

[0001] This invention relates to USB docking stations, and more specifically to a method for allocating resources in a USB docking station. Background Technology

[0002] With the widespread use of electronic devices, USB docking stations, as key devices for connecting various devices and enabling data transfer and resource sharing, are increasingly important for the rational allocation of their resources. Rational resource allocation ensures the stable and efficient operation of all devices connected to the docking station, enhancing the user experience.

[0003] Currently, common resource allocation methods are mostly based on traditional optimization algorithms, such as linear programming and dynamic programming. However, traditional algorithms have certain limitations when dealing with complex USB docking station resource allocation problems: on the one hand, USB docking stations connect to a variety of devices with dynamic and uncertain resource requirements, making it difficult for traditional algorithms to adapt quickly to these changes, resulting in resource allocation failing to meet actual needs in a timely manner; on the other hand, traditional algorithms are prone to getting stuck in local optima when searching for the optimal solution, failing to find a globally optimal resource allocation scheme, leading to insufficient or excessive resource allocation for some devices, affecting device performance.

[0004] As a uniquely behaving organism, the Aedes aegypti mosquito reprograms its behavior after feeding through circadian rhythm gene regulation, shifting from diurnal host searching to nocturnal high-activity oviposition. Its flight range and energy decay characteristics provide new insights for optimizing algorithms. Based on these behavioral characteristics, the Aedes aegypti blood-feeding reprogramming optimization algorithm can simulate the behavior of female mosquitoes at different stages, achieving a balance between global exploration and local exploitation in the search space. This algorithm is expected to overcome the shortcomings of traditional algorithms and provide a more effective solution for USB docking station resource allocation. Summary of the Invention

[0005] (a) Technical problems to be solved In view of the above-mentioned shortcomings of the existing technology, the present invention provides a USB docking station resource allocation method, which can effectively overcome the defects of the existing technology in that it is difficult to allocate USB docking station resources reasonably and efficiently.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: A method for allocating resources in a USB docking station includes the following steps: S1. Determine the search space for resource allocation; S2. Determine the objective function and constraints for resource allocation, and construct a resource allocation model based on the objective function and constraints. S3. Solve the resource allocation model using the Aedes aegypti blood-sucking reprogramming optimization algorithm to obtain the optimal resource allocation scheme; S4. Allocate resources to the USB expansion dock based on the optimal resource allocation scheme; Among them, the Aedes aegypti blood-feeding reprogramming optimization algorithm is designed based on the Aedes aegypti mosquito's behavior pattern after feeding through diurnal rhythm gene regulation, realizing the state reprogramming from diurnal host search to nocturnal high-activity oviposition: The activity level of female mosquitoes is determined by the amplitude of their flight movement, and the activity level is quantified by the average amplitude of positional change. The simulation method simulates the natural decay of female mosquito energy over flight time, gradually reducing the activity threshold, and determines whether female mosquitoes enter the global exploration phase or the local development phase based on the relationship between activity level and activity threshold. During the global exploration phase, the behavior of female mosquitoes in a high-energy state, who fly with long strides and large ranges to search for distant freshwater oviposition sites, is simulated to traverse the entire search space and avoid getting trapped in local optima. During the local development phase, the behavior of female mosquitoes reducing their activity and finely adjusting their oviposition locations within a small range is simulated after finding a high-quality oviposition site. This fine-tuning search near the optimal solution improves the convergence speed.

[0007] Preferably, determining the search space for resource allocation in S1 includes: The resource allocation scheme includes the ports, bandwidth, and power allocated to each device. In other words, each solution vector in the search space contains the comprehensive encoded values ​​of the ports, bandwidth, and power allocated to each device.

[0008] Preferably, in S2, the objective function and constraints for resource allocation are determined, and a resource allocation model is constructed based on the objective function and constraints, including: S21. Determine the objective function for resource allocation: With maximizing total bandwidth utilization, optimizing power supply balance, and minimizing equipment conflicts as comprehensive optimization objectives, construct the objective function F(X): ; Among them, B m,real For the actual bandwidth allocated to the m-th device, B m,need P represents the minimum bandwidth required for the m-th device, where M is the number of devices connected to the USB docking station. n The output power of the nth port, Where N is the rated average output power of the USB expansion dock, and N is the number of ports on the USB expansion dock. c For bandwidth conflicts and power overload cycles, , , All are weighting coefficients, and ; S22. Determine the constraints for resource allocation; S23. Combine the objective function and constraints of resource allocation to construct a resource allocation model.

[0009] Preferably, in S3, the resource allocation model is solved using the Aedes aegypti blood-sucking reprogramming optimization algorithm to obtain the optimal resource allocation scheme, including: S31. Randomly generate an initial population in the search space. The position of each female mosquito in the population corresponds to a solution vector, and initialize the algorithm parameters. S32. Simulate the flight movement amplitude of female mosquitoes to determine their activity level, and quantify the activity level of female mosquitoes by the average position change amplitude. S33. Simulate the natural decay of female mosquito energy over flight time, gradually reduce the activity threshold, and determine whether the female mosquito enters the global exploration stage or the local development stage based on the relationship between activity level and activity threshold. S34. In the global exploration phase, simulate the behavior of female mosquitoes in a high-energy state, taking long strides and flying randomly over a wide area to search for distant freshwater oviposition sites, traversing the entire search space, avoiding getting trapped in local optima, and then proceeding to S36. S35. In the local development stage, simulate the behavior of female mosquitoes reducing their activity after finding a high-quality oviposition site and finely adjusting the oviposition location in a small range. Finely search near the optimal solution to improve the convergence speed and proceed to S36. S36. Use the objective function of resource allocation to evaluate all female mosquitoes in the current population, calculate the corresponding fitness value, and record and update the historical best solution. S37. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S32. Otherwise, take the historical best solution as the optimal resource allocation scheme.

[0010] Preferably, in S32, the activity level is determined by the simulated flight movement amplitude of the female mosquito, and the activity level of the female mosquito is quantified by the average change amplitude of position, including: The activity level of female mosquitoes is updated using the following formula: ; in, Let i be the activity level of the i-th female mosquito in the t-th iteration. , Let be the d-th dimension position of the i-th female mosquito in the t-th and t-1-th iterations, respectively, where D is the dimension of the search space.

[0011] Preferably, in S33, the simulated female mosquito's energy naturally decays with flight time, gradually lowering the activity threshold, and the relationship between activity level and the activity threshold determines whether the female mosquito enters the global exploration phase or the local development phase, including: S331. The activity threshold is updated using the following formula: ; in, The activity threshold at the t-th iteration. This is the initial activity threshold. The attenuation coefficient is... This controls the transition speed from global exploration to local development, where T is the maximum number of iterations. The activity threshold gradually decreases as the number of iterations increases, driving female mosquitoes into the global exploration phase in the early stage of the algorithm and into the local development phase in the later stage of the algorithm, thus achieving a natural transition from global exploration to local development. S332. In the t-th iteration, if the activity level of the i-th female mosquito... If the female mosquito enters the global exploration phase, then it enters the local development phase; otherwise, it enters the local development phase.

[0012] Preferably, in S34, during the global exploration phase, the behavior of female mosquitoes in a high-energy state—flying with long strides and over a wide area randomly to search for distant freshwater oviposition sites—is simulated, traversing the entire search space to avoid getting trapped in local optima, including: Simulating the behavior of female mosquitoes in a high-energy state, exhibiting long strides and wide-range random flight to search for distant freshwater oviposition sites, the following formula is used to update the position of female mosquitoes entering the global exploration phase: ; in, Let be the d-th dimension position of the i-th female mosquito in the (t+1)-th iteration. , v represents the upper and lower bounds of the assignment of the d-th dimension in the search space, respectively. max The maximum exploration step size is r1, which is a random number uniformly distributed in the range [0,1].

[0013] Preferably, in S35, during the local development phase, the behavior of simulating a female mosquito reducing its activity and finely adjusting its oviposition location within a small range after finding a high-quality oviposition site is simulated. This involves a fine-tuned search near the optimal solution to improve convergence speed, including: Simulating the behavior of female mosquitoes reducing activity and finely adjusting their oviposition locations after finding a prime oviposition site, the following formula is used to update the location of female mosquitoes that have entered the local development stage: ; in, Let be the d-th dimension position of the global optimal solution at the t-th iteration. is the local development step size coefficient, and r2 is a random number uniformly distributed in the range [0,1].

[0014] Preferably, in step S4, resource allocation is performed on the USB expansion dock based on the optimal resource allocation scheme, including: The historical best solution in the Aedes aegypti blood-sucking reprogramming optimization algorithm is decoded to obtain the optimal resource allocation scheme including the ports, bandwidth and power allocated to each device, and a resource allocation strategy is formulated to complete the optimized allocation of USB docking station resources.

[0015] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described USB docking station resource allocation method.

[0016] (III) Beneficial Effects Compared with the prior art, the USB docking station resource allocation method provided by the present invention has the following beneficial effects: 1) Precisely optimize allocation to improve resource utilization. The objective function is constructed with the comprehensive optimization goals of maximizing total bandwidth utilization, optimizing power supply balance, and minimizing device conflicts. The resource allocation model is solved using the Aedes aegypti blood-sucking reprogramming optimization algorithm. This multi-objective comprehensive optimization approach can fully consider the various resource requirements of the USB docking station connected devices, accurately allocate ports, bandwidth, and power to each device, avoid over-allocation or under-allocation of resources, achieve efficient resource utilization, and give full play to the performance of the USB docking station. 2) Intelligent algorithm solution improves allocation efficiency The resource allocation model is solved using the Aedes aegypti blood-sucking reprogramming optimization algorithm. This algorithm simulates the behavior pattern of Aedes aegypti and achieves a natural transition between global exploration and local development through dynamic adjustment of the activity threshold. In the global exploration phase, it avoids getting stuck in local optima through a large-scale search, and in the local development phase, it improves the convergence speed through a small-scale fine search. This intelligent search strategy can quickly find the optimal resource allocation scheme, greatly shorten the resource allocation time, improve allocation efficiency, and enable the USB docking station to respond to the resource needs of the device in a timely manner. 3) Dynamically adapt to changes and enhance system stability The resource requirements of devices connected to a USB docking station are dynamic and uncertain. However, this invention can adjust resource allocation in real time according to the actual situation. The Aedes aegypti blood-sucking reprogramming optimization algorithm can continuously evaluate and update the optimal solution during the iteration process, effectively adapt to changes in device resource requirements, and adjust resource allocation strategies in a timely manner to ensure that the USB docking station is always in a stable operating state. This avoids device failure or performance degradation caused by unreasonable resource allocation, thereby enhancing the stability and reliability of the entire system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram illustrating the process of solving the resource allocation model using the Aedes aegypti blood-sucking reprogramming optimization algorithm in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The core of this invention lies in addressing the problems of uneven resource allocation, low bandwidth utilization, device conflicts, and premature convergence and insufficient optimization accuracy of traditional optimization algorithms when multiple devices are accessed concurrently in a USB docking station. A USB docking station resource allocation method based on the Aedes aegypti blood-sucking reprogramming optimization algorithm is designed. By constructing a mechanism that allows female mosquito energy to naturally decay over flight time and a phase switching mechanism based on activity level, the algorithm can autonomously transition between global resource search and local fine-grained allocation based on individual activity levels, thereby obtaining the optimal resource allocation scheme and achieving balanced scheduling and stable supply of docking station resources under multiple devices.

[0021] In this invention, the Aedes aegypti blood-feeding reprogramming optimization algorithm is designed based on the Aedes aegypti mosquito's behavior pattern regulated by diurnal rhythm genes after blood-feeding, realizing the state reprogramming from diurnal host search to nocturnal high-activity oviposition. The core points of this algorithm include: The activity level of female mosquitoes is determined by the amplitude of their flight movement, and the activity level is quantified by the average amplitude of positional change. The simulation method simulates the natural decay of female mosquito energy over flight time, gradually reducing the activity threshold, and determines whether female mosquitoes enter the global exploration phase or the local development phase based on the relationship between activity level and activity threshold. During the global exploration phase, the behavior of female mosquitoes in a high-energy state, who fly with long strides and large ranges to search for distant freshwater oviposition sites, is simulated to traverse the entire search space and avoid getting trapped in local optima. During the local development phase, the behavior of female mosquitoes reducing their activity and finely adjusting their oviposition locations within a small range is simulated after finding a high-quality oviposition site. This fine-tuning search near the optimal solution improves the convergence speed.

[0022] The following describes the specific process of the USB docking station resource allocation method provided by this invention with specific examples (e.g.) Figure 1 (as shown) and technical effects.

[0023] S1. Determine the search space for resource allocation, including: The resource allocation scheme includes the ports, bandwidth, and power allocated to each device. In other words, each solution vector in the search space contains the comprehensive encoded values ​​of the ports, bandwidth, and power allocated to each device.

[0024] S2. Determine the objective function and constraints for resource allocation, and construct a resource allocation model based on the objective function and constraints, including: S21. Determine the objective function for resource allocation: With maximizing total bandwidth utilization, optimizing power supply balance, and minimizing equipment conflicts as comprehensive optimization objectives, construct the objective function F(X): ; Among them, B m,real For the actual bandwidth allocated to the m-th device, B m,need P represents the minimum bandwidth required for the m-th device, where M is the number of devices connected to the USB docking station. n The output power of the nth port, Where N is the rated average output power of the USB expansion dock, and N is the number of ports on the USB expansion dock. c For bandwidth conflicts and power overload cycles, , , All are weighting coefficients, and ; S22. Determine the constraints for resource allocation; S23. Combine the objective function and constraints of resource allocation to construct a resource allocation model.

[0025] In the technical solution of this application, an objective function is constructed with the comprehensive optimization objectives of maximizing total bandwidth utilization, optimizing power supply balance, and minimizing device conflicts. The resource allocation model is solved using the Aedes aegypti blood-sucking reprogramming optimization algorithm. This multi-objective comprehensive optimization approach can fully consider the various resource requirements of the USB docking station connected devices, accurately allocate ports, bandwidth, and power to each device, avoid over-allocation or under-allocation of resources, achieve efficient resource utilization, and fully leverage the performance of the USB docking station.

[0026] S3. The resource allocation model is solved using the Aedes aegypti blood-sucking reprogramming optimization algorithm to obtain the optimal resource allocation scheme, such as... Figure 2 As shown, it includes: S31. Randomly generate an initial population in the search space. The position of each female mosquito in the population corresponds to a solution vector, and initialize the algorithm parameters. S32. Simulating the flight movement amplitude of female mosquitoes determines their activity level. The activity level of female mosquitoes is quantified by the average change amplitude of their positions, specifically including: The activity level of female mosquitoes is updated using the following formula: ; in, Let i be the activity level of the i-th female mosquito in the t-th iteration. , Let be the d-th dimension position of the i-th female mosquito in the t-th and t-1-th iterations, respectively, where D is the dimension of the search space; S33. Simulate the natural decline of female mosquito energy over flight time, gradually lowering the activity threshold, and determine whether the female mosquito enters the global exploration phase or the local development phase based on the relationship between activity level and the activity threshold. Specifically, this includes: S331. The activity threshold is updated using the following formula: ; in, The activity threshold at the t-th iteration. This is the initial activity threshold. The attenuation coefficient is... This controls the transition speed from global exploration to local development, where T is the maximum number of iterations. The activity threshold gradually decreases as the number of iterations increases, driving female mosquitoes into the global exploration phase in the early stage of the algorithm and into the local development phase in the later stage of the algorithm, thus achieving a natural transition from global exploration to local development. S332. In the t-th iteration, if the activity level of the i-th female mosquito... If the female mosquito enters the global exploration phase, then it enters the local development phase; otherwise, it enters the local development phase. S34. In the global exploration phase, the behavior of female mosquitoes in a high-energy state, characterized by long strides and wide-range random flight to search for distant freshwater oviposition sites, is simulated. The entire search space is traversed to avoid getting trapped in local optima, and then the process proceeds to S36, which specifically includes: Simulating the behavior of female mosquitoes in a high-energy state, exhibiting long strides and wide-range random flight to search for distant freshwater oviposition sites, the following formula is used to update the position of female mosquitoes entering the global exploration phase: ; in, Let be the d-th dimension position of the i-th female mosquito in the (t+1)-th iteration. , v represents the upper and lower bounds of the assignment of the d-th dimension in the search space, respectively. max The maximum exploration step size is given by r1, which is a random number uniformly distributed in the range [0,1]. S35. In the local development phase, simulating the behavior of female mosquitoes reducing activity and finely adjusting their oviposition locations after finding a high-quality oviposition site, and conducting a fine search near the optimal solution to improve convergence speed, then proceeding to S36, specifically including: Simulating the behavior of female mosquitoes reducing activity and finely adjusting their oviposition locations after finding a prime oviposition site, the following formula is used to update the location of female mosquitoes that have entered the local development stage: ; in, Let be the d-th dimension position of the global optimal solution at the t-th iteration. r2 is the local development step size coefficient, and r2 is a random number uniformly distributed in the range [0,1]. S36. Use the objective function of resource allocation to evaluate all female mosquitoes in the current population, calculate the corresponding fitness value, and record and update the historical best solution. S37. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S32. Otherwise, take the historical best solution as the optimal resource allocation scheme.

[0027] In this application's technical solution, the Aedes aegypti blood-sucking reprogramming optimization algorithm is used to solve the resource allocation model. This algorithm simulates the behavioral patterns of Aedes aegypti and achieves a natural transition between global exploration and local development through dynamic adjustment of the activity threshold. At the same time, in the global exploration phase, it avoids getting stuck in local optima through a large-scale search, and in the local development phase, it improves the convergence speed through a small-scale fine search. This intelligent search strategy can quickly find the optimal resource allocation scheme, greatly shortening the resource allocation time, improving allocation efficiency, and enabling the USB docking station to respond to the resource needs of the devices in a timely manner.

[0028] Furthermore, the resource requirements of devices connected to the USB docking station are dynamic and uncertain. However, this invention can adjust resource allocation in real time according to the actual situation. The Aedes aegypti blood-sucking reprogramming optimization algorithm can continuously evaluate and update the optimal solution during the iteration process, effectively adapt to changes in device resource requirements, and adjust the resource allocation strategy in a timely manner to ensure that the USB docking station is always in a stable operating state. This avoids device failure or performance degradation caused by unreasonable resource allocation, thereby enhancing the stability and reliability of the entire system.

[0029] S4. Allocate resources to the USB docking station based on the optimal resource allocation scheme, including: The historical best solution in the Aedes aegypti blood-sucking reprogramming optimization algorithm is decoded to obtain the optimal resource allocation scheme including the ports, bandwidth and power allocated to each device, and a resource allocation strategy is formulated to complete the optimized allocation of USB docking station resources.

[0030] Meanwhile, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned USB docking station resource allocation method.

[0031] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for allocating resources of a USB docking station, characterized in that: Includes the following steps: S1. Determine the search space for resource allocation; S2. Determine the objective function and constraints for resource allocation, and construct a resource allocation model based on the objective function and constraints. S3. Solve the resource allocation model using the Aedes aegypti blood-sucking reprogramming optimization algorithm to obtain the optimal resource allocation scheme; S4. Allocate resources to the USB expansion dock based on the optimal resource allocation scheme; Among them, the Aedes aegypti blood-feeding reprogramming optimization algorithm is designed based on the Aedes aegypti mosquito's behavior pattern after feeding through diurnal rhythm gene regulation, realizing the state reprogramming from diurnal host search to nocturnal high-activity oviposition: The activity level of female mosquitoes is determined by the amplitude of their flight movement, and the activity level is quantified by the average amplitude of positional change. The simulation method simulates the natural decay of female mosquito energy over flight time, gradually reducing the activity threshold, and determines whether female mosquitoes enter the global exploration phase or the local development phase based on the relationship between activity level and activity threshold. During the global exploration phase, the behavior of female mosquitoes in a high-energy state, who fly with long strides and large ranges to search for distant freshwater oviposition sites, is simulated to traverse the entire search space and avoid getting trapped in local optima. During the local development phase, the behavior of female mosquitoes reducing their activity and finely adjusting their oviposition locations within a small range is simulated after finding a high-quality oviposition site. This fine-tuning search near the optimal solution improves the convergence speed.

2. The USB docking station resource allocation method of claim 1, wherein: The search space for determining resource allocation in S1 includes: The resource allocation scheme includes the ports, bandwidth, and power allocated to each device. In other words, each solution vector in the search space contains the comprehensive encoded values ​​of the ports, bandwidth, and power allocated to each device.

3. The USB docking station resource allocation method of claim 1, wherein: S2 defines the objective function and constraints for resource allocation. Based on these, a resource allocation model is constructed, including: S21. Determine the objective function for resource allocation: With maximizing total bandwidth utilization, optimizing power supply balance, and minimizing equipment conflicts as comprehensive optimization objectives, construct the objective function F(X): ； Wherein, B m,real is the actual allocated bandwidth of the mth device, B m,need is the minimum bandwidth required by the mth device, M is the number of devices connected to the USB docking station, P n is the output power of the nth port, is the rated average output power of the USB docking station, N is the number of ports of the USB docking station, N c is the number of bandwidth conflicts and power supply overloads, , , are all weight coefficients, and ; S22. Determine the constraints for resource allocation; S23. Combine the objective function and constraints of resource allocation to construct a resource allocation model.

4. The USB docking station resource allocation method according to claim 1, characterized in that: In S3, the resource allocation model is solved using the Aedes aegypti blood-sucking reprogramming optimization algorithm to obtain the optimal resource allocation scheme, including: S31. Randomly generate an initial population in the search space. The position of each female mosquito in the population corresponds to a solution vector, and initialize the algorithm parameters. S32. Simulate the flight movement amplitude of female mosquitoes to determine their activity level, and quantify the activity level of female mosquitoes by the average position change amplitude. S33. Simulate the natural decay of female mosquito energy over flight time, gradually reduce the activity threshold, and determine whether the female mosquito enters the global exploration stage or the local development stage based on the relationship between activity level and activity threshold. S34. In the global exploration phase, simulate the behavior of female mosquitoes in a high-energy state, taking long strides and flying randomly over a wide area to search for distant freshwater oviposition sites, traversing the entire search space, avoiding getting trapped in local optima, and then proceeding to S36. S35. In the local development stage, simulate the behavior of female mosquitoes reducing their activity after finding a high-quality oviposition site and finely adjusting the oviposition location in a small range. Finely search near the optimal solution to improve the convergence speed and proceed to S36. S36. Use the objective function of resource allocation to evaluate all female mosquitoes in the current population, calculate the corresponding fitness value, and record and update the historical best solution. S37. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S32. Otherwise, take the historical best solution as the optimal resource allocation scheme.

5. The USB docking station resource allocation method according to claim 4, characterized in that: In S32, the activity level of simulated female mosquitoes is determined by the amplitude of their flight movement. The activity level of female mosquitoes is quantified by the average change in position, including: The activity level of female mosquitoes is updated using the following formula: ； in, Let i be the activity level of the i-th female mosquito in the t-th iteration. , Let be the d-th dimension position of the i-th female mosquito in the t-th and t-1-th iterations, respectively, where D is the dimension of the search space.

6. The USB docking station resource allocation method according to claim 5, characterized in that: In S33, the simulated female mosquito's energy naturally decays over flight time, gradually lowering its activity threshold. Based on the relationship between activity level and the activity threshold, the system determines whether the female mosquito enters a global exploration phase or a local development phase, including: S331. The activity threshold is updated using the following formula: ； in, The activity threshold at the t-th iteration. This is the initial activity threshold. The attenuation coefficient is... This controls the transition speed from global exploration to local development, where T is the maximum number of iterations. The activity threshold gradually decreases as the number of iterations increases, driving female mosquitoes into the global exploration phase in the early stage of the algorithm and into the local development phase in the later stage of the algorithm, thus achieving a natural transition from global exploration to local development. S332. In the t-th iteration, if the activity level of the i-th female mosquito... If the female mosquito enters the global exploration phase, then it enters the local development phase; otherwise, it enters the local development phase.

7. The USB docking station resource allocation method according to claim 6, characterized in that: In S34, during the global exploration phase, the behavior of female mosquitoes in a high-energy state—flying with long strides and over a wide area randomly to search for distant freshwater oviposition sites—is simulated. This process traverses the entire search space to avoid getting trapped in local optima, including: Simulating the behavior of female mosquitoes in a high-energy state, exhibiting long strides and wide-range random flight to search for distant freshwater oviposition sites, the following formula is used to update the position of female mosquitoes entering the global exploration phase: ； in, Let be the d-th dimension position of the i-th female mosquito in the (t+1)-th iteration. , v represents the upper and lower bounds of the assignment of the d-th dimension in the search space, respectively. max The maximum exploration step size is r1, which is a random number uniformly distributed in the range [0,1].

8. The USB docking station resource allocation method according to claim 7, characterized in that: In S35, during the local development phase, the simulation demonstrates the behavior of female mosquitoes reducing activity and finely adjusting their oviposition locations after finding a high-quality oviposition site. This involves a meticulous search near the optimal solution to improve convergence speed, including: Simulating the behavior of female mosquitoes reducing activity and finely adjusting their oviposition locations after finding a prime oviposition site, the following formula is used to update the location of female mosquitoes that have entered the local development stage: ； in, Let be the d-th dimension position of the global optimal solution at the t-th iteration. is the local development step size coefficient, and r2 is a random number uniformly distributed in the range [0,1].

9. The USB docking station resource allocation method according to claim 1, characterized in that: S4 allocates resources to the USB docking station based on the optimal resource allocation scheme, including: The historical best solution in the Aedes aegypti blood-sucking reprogramming optimization algorithm is decoded to obtain the optimal resource allocation scheme including the ports, bandwidth and power allocated to each device, and a resource allocation strategy is formulated to complete the optimized allocation of USB docking station resources.

10. A computer device, characterized in that: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the USB docking station resource allocation method as described in any one of claims 1-9.

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