Unmanned aerial vehicle resource allocation method and system based on auction mechanism and digital twinning

Abstract

The invention discloses an unmanned aerial vehicle resource allocation method and system based on an auction mechanism and digital twinning. The method comprises the following steps: constructing an auction-driven digital twinning framework of unmanned aerial vehicle resource collaboration; the application service provider submits a task request to the ground base station; the ground base station screens the task candidate set and starts an unmanned aerial vehicle resource auction framework; the unmanned aerial vehicle resource auction framework submits bidding information, and the ground base station calculates scores and allocates tasks; the ground base station calculates a payment amount, and the application service provider pays a corresponding reward; the unmanned aerial vehicle resource auction framework executes the task and synchronously updates the digital twin model; and dynamically feeding back the updated information of the digital twin model to the next task time slot. According to the invention, efficient and adaptive large-scale industrial scene resource allocation is realized.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) resource allocation technology, and in particular to a UAV resource allocation method and system based on auction mechanisms and digital twins. Background Technology

[0002] The rapid development of drones has led to their widespread adoption across various sectors, with the Industrial Internet being a key application area. Drones are increasingly used in industrial environments for purposes such as infrastructure inspection, logistics delivery, disaster response, and security monitoring. For example, in industrial safety applications, drones are deployed for real-time surveillance of hazardous areas, perimeter patrols, and emergency response coordination. These applications require robust frameworks to support effective resource management, integration with multiple application service providers, and the implementation of effective incentive mechanisms to ensure optimal performance and scalability.

[0003] Digital twin technology, a virtual representation of physical assets, has gained significant appeal in industrial applications due to its ability to provide real-time synchronization, predictive analytics, and enhanced decision-making capabilities. In the context of drones, digital twins can create a dynamic virtual model that reflects the physical drone environment, facilitating real-time data collection, task optimization, and resource allocation. The integration of digital twins with the Industrial Internet of Things (IIoT) has become a major technological trend, demonstrating enormous potential in improving operational efficiency and the performance of adaptive systems in complex industrial environments.

[0004] However, several challenges remain in realizing the full potential of drone systems with digital twin technology in Industrial Internet applications. First, traditional resource allocation methods often focus on a single objective, such as minimizing energy consumption or maximizing mission value, neglecting the need to balance trade-offs between multiple competing factors. For example, in industrial safety monitoring, drones may need to simultaneously optimize energy efficiency, mission coverage, and mission value—often conflicting objectives. Second, the dynamic nature of the mission environment, including fluctuating mission demands, spatial coverage requirements, and resource constraints, can lead to inefficient allocation or even mission failure. Existing methods often fail to adapt to these dynamic changes, resulting in suboptimal resource utilization and reduced system performance. Furthermore, ensuring fairness and incentive compatibility in resource allocation remains a significant challenge, as drones may have varying energy costs and operational constraints, making it difficult to design a mechanism that incentivizes cost reporting while maximizing social welfare. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for allocating drone resources based on an auction mechanism and digital twins, which ensures the fairness of task allocation while considering system energy efficiency and task execution performance.

[0006] The technical solution adopted in this invention is:

[0007] The drone resource allocation system based on auction mechanism and digital twin includes a ground base station and at least one application service provider. The ground base station acts as a central hub and has a mission command center, a digital twin model builder, and a drone resource auction framework. The ground base station is used to receive mission requests from application service providers, allocate drone resources through the auction mechanism, and continuously update the digital twin model based on real-time data from drones.

[0008] At least one drone participates in mission bidding, mission evaluation, and compensation through the auction mechanism provided by the drone resource auction framework. The drone resource auction framework includes a mission candidate set selection module, an auction winner determination module, and an auction payment determination module. The mission candidate set selection module is used to select the optimal mission set based on a composite value function of mission value, coverage, and time constraints. The auction winner determination module is used to determine the winning drone for each mission based on a scoring rule that balances energy efficiency and mission value. The auction payment determination module is used to ensure fairness and incentive compatibility by implementing a second-highest price payment rule.

[0009] The drone resource allocation method based on auction mechanisms and digital twins includes the following steps:

[0010] Step 1: Construct an auction-driven digital twin framework for drone resource collaboration; including a ground base station and at least one application service provider; the ground base station has a mission command center, a digital twin model builder, and a drone resource auction framework;

[0011] Step 2: The application service provider submits a task request to the ground base station;

[0012] Step 3: The ground base station filters the candidate set of tasks and initiates the UAV resource auction framework;

[0013] Step 4: The drone resource auction framework submits bidding information, and the ground base station calculates scores and assigns tasks;

[0014] Step 5: The ground base station calculates the payment amount, and the application service provider pays the corresponding fee.

[0015] Step 6: The drone resource auction framework executes its tasks and simultaneously updates the digital twin model;

[0016] Step 7: Dynamically feed back the updated information from the digital twin model to the next task time slot.

[0017] Furthermore, the parameters for the task request in step 2 include the budget b. k Time constraint t kValue v k Coverage range c k .

[0018] Furthermore, step 3 selects an optimal subset of application service providers by maximizing the total value of the tasks, based on the request parameters of each task k and under the constraints of total budget and resources. K represents the set of all tasks.

[0019] Furthermore, step 3 specifically includes the following steps:

[0020] Step 3-1: Calculate the value of the candidate task subset S based on the request parameters. Eliminating those exceeding the energy constraints of drones Or flight distance constraints The task; among them, This represents the farthest flight distance of drone i between two adjacent time slots; This represents the total energy consumption cost of executing any task k in time slot t. This represents the total energy budget for each drone i per operating cycle.

[0021] Step 3-2: Iteratively select the task that maximizes the increment of V(S) using a greedy algorithm until the sum of the budgets of all tasks ∑b k ≤B exhaustion yields the set of candidate tasks with the maximum value, where the budget constraint is B;

[0022] Specifically, the task candidate set selection module: receiving task requests and filtering candidate sets (outputting the set of tasks with the highest value S).

[0023] 1. Task Parameter Definition: Request parameters submitted by the application service provider for task k: Budget b k Time constraint t k Value v k Coverage range c k .

[0024] 2. Composite value function screening:

[0025] (1) Calculate the value of candidate task subset S Eliminating those exceeding the energy constraints of drones Or flight distance constraints The task.

[0026] (2) Iteratively select the task that maximizes the increment of V(S) using a greedy algorithm until the budget ∑b is reached. k ≤B is exhausted.

[0027] Furthermore, step 4 specifically includes the following steps:

[0028] Step 4-1: Calculate the total energy consumption cost of each drone performing task k in time slot t. The set of all drones is represented as I = {1,…,i,…,I};

[0029] Total energy consumption cost in This represents the hovering cost incurred by drone i in time slot t; These represent the computational cost and propulsion cost incurred by UAV i in performing task k in time slot t, respectively.

[0030] Furthermore, hovering costs It is a constant; computational cost in It is the size of the data generated by task k in time slot t. This refers to the unit energy cost calculated by the drone; propulsion cost. in It is the unit energy cost of propulsion. The drone i is performing task k at time slot t. This refers to drone i at time slot t-1.

[0031] Step 4-2, the drone submits a two-dimensional bid b for task k. i,k =(e i,k ,p i,k ),in For total energy consumption, p i,k For quotations; t k This represents the task time required by the application service provider for task k, i.e., the time constraint t in the task request parameters. k ;

[0032] Step 4-3: Calculate the effective score for each drone's two-dimensional bid. The effective score function is in the form of: in, This represents the energy cost of drone i when performing task k;

[0033] Step 4-4: Select the bidding drone with the highest valid score as the receiver σ of task k. i ,Right now

[0034] Steps 4-5: Summarize the recipients of all tasks to form the winning drone set;

[0035] Specifically, the auction winner determination module includes: score calculation and winner determination (outputting the set of winning drones A).

[0036] 1. Drone Bidding: Drone i submits a two-dimensional bid b for task k. i,k =(ei,k ,p i,k ),in For total energy consumption, p i,k This is a quote.

[0037] 2. Efficient scoring rules:

[0038] (1) The effective scoring function has the following form: in, This represents the energy cost of drone i when performing task k.

[0039] (2) Allocation rule: The receiver σ of task k i It is the highest-scoring bidding drone, namely

[0040] Furthermore, in step 5, the energy consumption e of each drone performing its respective task is used as the unit of measurement. i,k The price that yields the second-highest effective score is the reward π to be paid to the corresponding drone. i Indicates, that is Where s(·) is a component-form non-decreasing function of s(0) = 0, and s(·) actually represents the energy cost of UAV i when performing task k.

[0041] Specifically, the auction payment determination module calculates the payment amount (outputs the payment set P).

[0042] Payment Rules: Winner's Payout π i It is in his energy consumption e i,k The price that yields the second highest score is π. i - This rule ensures that the actual price quoted for drones is used to maximize their effectiveness and avoids malicious bidding.

[0043] Specifically, in a two-price auction, the winner is the highest bidder, but they only need to pay the second-highest price. Here, however, the price is replaced with a score; the winner is the person with the highest score, but they only need to pay the price offered by the person with the second-highest score. For example, A has a score of 9 and bids 6, B has a score of 8 and bids 5, and C has a score of 7 and bids 6; A, with the highest score, wins the bid and pays the second-highest scorer B's bid of 5.

[0044] Furthermore, in step 7, digital twin model maintenance is performed when the drone resource auction framework is idle.

[0045] This invention employs the above technical solutions, combining digital twins and auction mechanisms to construct a dynamic resource allocation system by real-time updating of UAV status and mission environment information. This not only optimizes the accuracy of task allocation but also improves the overall efficiency of the system. This invention addresses the problems of insufficient multi-objective optimization, poor dynamic adaptability, lack of incentive compatibility, and weak real-time decision-making capabilities in the dynamic allocation of UAV resources in industrial IoT scenarios. By integrating auction mechanisms and digital twin technology, it achieves synergistic optimization of task value, energy consumption, and spatial coverage in complex industrial environments. Simultaneously, it utilizes two-dimensional scoring rules and a second-price payment mechanism to improve the fairness and energy efficiency of resource allocation. Furthermore, through digital twin-driven dynamic model synchronization and a distributed architecture, it ensures real-time decision-making efficiency and system scalability, ultimately achieving the goal of efficient, fair, and dynamically adaptive UAV resource allocation. This invention ensures the fairness of task allocation while considering system energy efficiency and task execution performance. Attached Figure Description

[0046] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments;

[0047] Figure 1 This is a flowchart illustrating the drone resource allocation method based on auction mechanism and digital twin of the present invention.

[0048] Figure 2 This is a schematic diagram of the drone resource allocation system architecture based on auction mechanism and digital twin of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0050] This invention aims to address the problems of insufficient multi-objective optimization, poor dynamic adaptability, lack of incentive compatibility, and weak real-time decision-making capabilities in the dynamic allocation of drone resources in industrial IoT scenarios. By integrating auction mechanisms and digital twin technology, it achieves synergistic optimization of task value, energy consumption, and spatial coverage in complex industrial environments. At the same time, it utilizes two-dimensional scoring rules and a second-price payment mechanism to improve the fairness and energy efficiency of resource allocation. Furthermore, through digital twin-driven dynamic model synchronization and distributed architecture, it ensures real-time decision-making efficiency and system scalability, ultimately achieving the goal of efficient, fair, and dynamically adaptive drone resource allocation.

[0051] like Figure 1 and 2As shown, this invention discloses a drone resource allocation system based on an auction mechanism and digital twins, which includes a ground base station and at least one application service provider. The ground base station acts as a central hub and has a mission command center, a digital twin model builder, and a drone resource auction framework. The ground base station is used to receive mission requests from application service providers, allocate drone resources through an auction mechanism, and continuously update the digital twin model based on real-time data from the drones.

[0052] At least one drone participates in mission bidding, mission evaluation, and compensation through the auction mechanism provided by the drone resource auction framework. The drone resource auction framework includes a mission candidate set selection module, an auction winner determination module, and an auction payment determination module. The mission candidate set selection module is used to select the optimal mission set based on a composite value function of mission value, coverage, and time constraints. The auction winner determination module is used to determine the winning drone for each mission based on a scoring rule that balances energy efficiency and mission value. The auction payment determination module is used to ensure fairness and incentive compatibility by implementing a second-highest price payment rule.

[0053] The drone resource allocation method based on auction mechanisms and digital twins includes the following steps:

[0054] Step 1: Construct an auction-driven digital twin framework for drone resource collaboration; including a ground base station and at least one application service provider; the ground base station has a mission command center, a digital twin model builder, and a drone resource auction framework;

[0055] Step 2: The application service provider submits a task request to the ground base station;

[0056] Step 3: The ground base station filters the candidate set of tasks and initiates the UAV resource auction framework;

[0057] Step 4: The drone resource auction framework submits bidding information, and the ground base station calculates scores and assigns tasks;

[0058] Step 5: The ground base station calculates the payment amount, and the application service provider pays the corresponding fee.

[0059] Step 6: The drone resource auction framework executes its tasks and simultaneously updates the digital twin model;

[0060] Step 7: Dynamically feed back the updated information from the digital twin model to the next task time slot.

[0061] Furthermore, the parameters for the task request in step 2 include the budget b. k Time constraint t k Value v k Coverage range c k .

[0062] Furthermore, step 3 selects an optimal subset of application service providers by maximizing the total value of the tasks, based on the request parameters of each task k and under the constraints of total budget and resources. K represents the set of all tasks.

[0063] Furthermore, step 3 specifically includes the following steps:

[0064] Step 3-1: Calculate the value of the candidate task subset S based on the request parameters. Eliminating those exceeding the energy constraints of drones Or flight distance constraints The task; among them, This represents the farthest flight distance of drone i between two adjacent time slots; This represents the total energy consumption cost of executing any task k in time slot t. This represents the total energy budget for each drone i per operating cycle.

[0065] Step 3-2: Iteratively select the task that maximizes the increment of V(S) using a greedy algorithm until the sum of the budgets of all tasks ∑b k ≤B exhaustion yields the set of candidate tasks with the maximum value, where the budget constraint is B;

[0066] Specifically, the task candidate set selection module: receiving task requests and filtering candidate sets (outputting the set of tasks with the highest value S).

[0067] 1. Task Parameter Definition: Request parameters submitted by the application service provider for task k: Budget b k Time constraint t k Value v k Coverage range c k .

[0068] 2. Composite value function screening:

[0069] (1) Calculate the value of candidate task subset S Eliminating those exceeding the energy constraints of drones Or flight distance constraints The task.

[0070] (2) Iteratively select the task that maximizes the increment of V(S) using a greedy algorithm until the budget ∑b is reached. k ≤B is exhausted.

[0071] Furthermore, step 4 specifically includes the following steps:

[0072] Step 4-1: Calculate the total energy consumption cost of each drone performing task k in time slot t. The set of all drones is represented as I = {1,…,i,…,I};

[0073] Total energy consumption cost in This represents the hovering cost incurred by drone i in time slot t; These represent the computational cost and propulsion cost incurred by UAV i in performing task k in time slot t, respectively.

[0074] Furthermore, hovering costs It is a constant; computational cost in It is the size of the data generated by task k in time slot t. This refers to the unit energy cost calculated by the drone; propulsion cost. in It is the unit energy cost of propulsion. The drone i is performing task k at time slot t. This refers to drone i at time slot t-1.

[0075] Step 4-2, the drone submits a two-dimensional bid b for task k. i,k =(e i,k ,p i,k ),in For total energy consumption, p i,k For quotations; t k This represents the task time required by the application service provider for task k, i.e., the time constraint t in the task request parameters. k ;

[0076] Step 4-3: Calculate the effective score for each drone's two-dimensional bid. The effective score function is in the form of: in, This represents the energy cost of drone i when performing task k;

[0077] Step 4-4: Select the bidding drone with the highest valid score as the receiver σ of task k. i ,Right now

[0078] Steps 4-5: Summarize the recipients of all tasks to form the winning drone set;

[0079] Specifically, the auction winner determination module includes: score calculation and winner determination (outputting the set of winning drones A).

[0080] 1. Drone Bidding: Drone i submits a two-dimensional bid b for task k. i,k =(e i,k ,p i,k ),in For total energy consumption, p i,k This is a quote.

[0081] 2. Efficient scoring rules:

[0082] (1) The effective scoring function has the following form: in, This represents the energy cost of drone i when performing task k.

[0083] (2) Allocation rule: The receiver σ of task k i It is the highest-scoring bidding drone, namely

[0084] Furthermore, in step 5, the energy consumption e of each drone performing its respective task is used as the unit of measurement. i,k The price that yields the second-highest effective score is the reward π to be paid to the corresponding drone. i Indicates, that is Where s(·) is a component-form non-decreasing function of s(0) = 0, and s(·) actually represents the energy cost of UAV i when performing task k.

[0085] Specifically, the auction payment determination module calculates the payment amount (outputs the payment set P).

[0086] Payment Rules: Winner's Payout π i It is in his energy consumption e i,k The price that yielded the second-highest score was... This rule ensures that the actual price quoted for drones is used to maximize their effectiveness and avoids malicious bidding.

[0087] Furthermore, in step 7, digital twin model maintenance is performed when the drone resource auction framework is idle.

[0088] The specific principles of this invention will be explained in detail below:

[0089] The core of this invention is an auction-driven digital twin framework for UAV resource collaboration, coordinating task allocation and digital twin model updates with a ground base station at its center. It dynamically filters task subsets using a composite value function V(S), combining task value, time constraints, and spatial coverage for multi-objective optimization. A two-dimensional scoring rule φ and a second-highest-price payment mechanism incentivize UAVs to accurately declare costs and optimize energy efficiency. Simultaneously, it utilizes real-time synchronization of UAV status and environmental data via digital twins to support dynamic task allocation and high-fidelity simulation. The system filters tasks through a task candidate set selection module, determines the winner through scoring in an auction winner determination module, calculates the payment amount through an auction payment determination module, and updates the virtual model after task execution. Idle UAVs synchronously maintain digital twin consistency, thereby achieving efficient and adaptive resource allocation in large-scale industrial scenarios.

[0090] Constructing an Auction-Driven Digital Twin Framework for UAV Resource Collaboration: This invention proposes an auction-driven digital twin framework for UAV resource collaboration, integrating digital twin technology with an auction-based resource allocation mechanism. The framework aims to optimize UAV resource allocation in complex, multi-efficiency environments by dynamically balancing mission value, energy consumption, and spatial coverage. The system uses a ground base station as a central hub, serving as both a mission command center and a digital twin model builder. The ground base station receives mission requests from application service providers, allocates UAV resources through an auction mechanism, and continuously updates the digital twin model based on real-time data from the UAVs.

[0091] The core of the auction-driven digital twin framework for drone resource collaboration is the drone resource auction framework, which consists of three main modules: task candidate set selection, auction winner determination, and auction payment determination. The task candidate set selection module chooses the optimal task set based on a composite value function considering task value, coverage, and time constraints. The auction winner determination module uses an effective scoring rule to determine the winning drone for each task, balancing energy efficiency and task value. Finally, the auction payment determination module ensures fairness and incentive compatibility by implementing a second-highest price payment rule.

[0092] This invention considers an auction-driven digital twin framework for UAV resource collaboration, consisting of I UAVs, one ground base station, and K application service providers, operating on discrete time slots (time intervals) T. In this system, the ground base station assumes the dual role of mission command center and digital twin model builder.

[0093] Calculating drone service costs: The set of drones with different functions is represented as I = {1,…,i,…,I}. The auction-driven digital twin framework for drone resource collaboration includes two different operating modes: a digital twin model maintenance task k = 0, which continuously updates the model to ensure virtual-physical synchronization during drone idle periods; and application service provider tasks 1,…,K triggered by external service requests. Each drone can autonomously switch between these modes, prioritizing application service provider tasks while maintaining the fidelity of the digital twin model during inactivity.

[0094] First, based on the specifications of lithium polymer batteries, the limited battery capacity restricts the total energy budget for each drone per operating cycle. Secondly, the speed of UAV i is limited, therefore its flight distance between two adjacent time slots is limited. These constraints collectively define the drone's action space. in This represents the spatial coordinates of UAV i in time slot t.

[0095] Total energy consumption cost of drone i performing task k in time slot t It consists of three parts: in These represent the three energy costs incurred by drone i in time slot t: hovering cost, computation cost, and propulsion cost. The specific calculation methods for these three costs are as follows:

[0096] 1) Hovering cost: The hovering cost of drone i at each location. It is a constant.

[0097] 2) Computational cost: The computational cost of UAV i performing task k in time slot t. The definition is as follows: in It is the size of the data generated by task k in time slot t. This is the unit energy cost calculated by the drone.

[0098] 3) Propulsion cost: The propulsion cost of UAV i performing mission k in time slot t. as follows: in It is the unit energy cost of propulsion. This refers to drone i at time slot t-1.

[0099] Construct an application service provider task model: The set of application service providers is represented as K = {1,…,k,…,K}. Each application service provider submits a task k, which has four parameters: task budget b. k Task time t k Task value v k spatial coverage c k Based on these parameters, the system will select an optimal subset of application service providers. Maximize the total value of the task within budget and resource constraints.

[0100] Let V(S) denote the value function for a selected subset S of tasks provided by the application service provider. The application service provider expects to obtain the maximum value from the services provided by the selected tasks under budget constraint B. In this paper, the selected subset of tasks... The value function V(S) is expressed as a non-negative monotonic modulus function, that is: Where S is the task set, v k It is the value of task k, t k It is the time for task k, c kIt represents the coverage of task k, and T is the maximum total time.

[0101] Drone Resource Auction Framework: This invention proposes a drone resource auction framework that maximizes social welfare in an asynchronous distributed environment by integrating task evaluation and allocation mechanisms. The framework uses a ground base station as the core auction center. After receiving task requests from application service providers in each decision-making time slot, it dynamically initiates resource bidding: First, a task candidate set selection module filters a subset S of tasks that meet spatiotemporal and budget constraints, maximizing the composite value function V(S); then, using the scoring rules of the auction winner determination module, the optimal executor set A is selected from the bidding drones; finally, the second-highest price rule of the auction payment determination module generates a payment set P, ensuring the fairness and incentive compatibility of the auction. During this process, idle drones without assigned tasks synchronously execute digital twin model maintenance tasks, updating the virtual model in real time to ensure dynamic consistency between the physical environment and the digital twin. Meanwhile, application service provider tasks trigger the auction process in a completely asynchronous and decentralized manner, avoiding the latency and resource conflicts caused by centralized scheduling.

[0102] In the drone resource auction framework, multiple drones participate in the bidding, each submitting a two-dimensional bid that includes its energy consumption for completing the mission and its expected compensation. The ground base station, acting as the auctioneer, selects the winning drones based on the bidding results and determines the amount to be paid to them. Specifically, the auction process includes the following steps:

[0103] 1) Publication of Auction Rules: The auctioneer's ground base station publishes the auction rules, including the allocation rules Γ(·) and the payment rules Π(·);

[0104] 2) Drone Bidding Submission: Each participating drone i (i∈I) submits a two-dimensional bid b during the bidding process. i,k =(e i,k ,p i,k ), where e i,k p represents the energy consumption required for drone i to complete mission k. i,k Let $\begin{bmatrix} \ ... i,k |i∈I}. To better describe this bidding information, this invention defines the following matrix:

[0105] Energy Matrix:

[0106] Price matrix:

[0107] 3) Auctioneer's Decision: After receiving all bidding information, the auctioneer's ground base station determines the task allocation results and payment amount according to the pre-set allocation rule Γ(·) and payment rule Π(·). Specifically, the ground base station will determine the winning drone set based on the bidding information. and the corresponding payment amount set According to the rules: σ = Γ(b), π = Π(b), where σ represents the final set of winning drones, and π represents the reward paid to each winning drone. Furthermore, the energy consumption of the winning drones in completing the mission can be expressed as...

[0108] To effectively select drones for the mission, this invention utilizes an efficient scoring rule to quantify the impact of energy consumption on the score. The efficient scoring rule is defined as follows: given the energy consumption E submitted by bidding drone i... i,k and price P i,k For any assigned drone i, the efficient scoring rule φ(e) i,k ,p i,k The equation gives φ(e) i,k ,p i,k ) = p i,k -s(e i,k ), where s(·) is a component non-decreasing function of s(0)=0.

[0109] The scoring mechanism reflects both the price submitted by the drone and incorporates a penalty for energy consumption, thus encouraging more energy-efficient drones to achieve higher scores. In practical applications, s(e i,k Generally, the definition needs to be based on the actual situation. This invention further defines an efficient scoring rule φ(e) that comprehensively considers the energy cost of drones. i,k ,p i,k This comprehensive evaluation of the drone's overall performance is conducted. Based on the scoring results, the auctioneer further refines the task allocation and payment decisions under the drone resource auction mechanism through allocation and payment rules.

[0110] The following are the specific steps for implementing the auction mechanism:

[0111] 4.1) Task Candidate Set Selection Module: Task Request Reception and Candidate Set Filtering (Outputting the Set of Tasks with Maximum Value S)

[0112] 1. Task Parameter Definition: Request parameters submitted by the application service provider for task k: Budget b k Time constraint t k Value v k Coverage range c k .

[0113] 2. Composite value function screening:

[0114] (1) Calculate the value of candidate task subset S Eliminating those exceeding the energy constraints of drones Or flight distance constraints The task.

[0115] (2) Iteratively select the task that maximizes the increment of V(S) using a greedy algorithm until the budget ∑b is reached. k ≤B is exhausted.

[0116] 4.2) Auction Winner Determination Module: Score Calculation and Winner Determination (Output the set of winning drones A)

[0117] 1. Drone Bidding: Drone i submits a two-dimensional bid b for task k. i,k =(e i,k ,p i,k ),in For total energy consumption, p i,k This is a quote.

[0118] 2. Efficient scoring rules:

[0119] (1) The effective scoring function has the following form: in, This represents the energy cost of drone i when performing task k.

[0120] (2) Allocation rule: The receiver σ of task k i It is the highest-scoring bidding drone, namely

[0121] 4.3) Auction Payment Determination Module: Payment Amount Calculation (Output Payment Set P)

[0122] Payment Rules: Winner's Payout π i It is in his energy consumption e i,k The price that yielded the second-highest score was... This rule ensures that the actual price quoted for drones is used to maximize their effectiveness and avoids malicious bidding.

[0123] The present invention has the following technical advantages: (1) Multi-objective dynamic optimization capability: By designing a composite value function V(S), and combining nonlinear weights of task value, time constraints and spatial coverage, multi-objective collaborative optimization is achieved. It breaks through the limitations of traditional single-objective optimization, adapts to the dynamic task requirements in industrial scenarios (such as sudden tasks and resource fluctuations), and supports the rapid solution of greedy algorithms through the characteristics of submodular functions, meeting real-time requirements. (2) High-efficiency energy utilization and incentive compatibility: A two-dimensional bidding scoring rule φ is introduced, combined with a second price payment mechanism (the second highest bidder wins). Energy consumption penalty items encourage low-energy drones to participate in tasks, reducing the overall energy consumption of the system; at the same time, it ensures the real reporting cost of drones, prevents malicious bidding, and improves the fairness and efficiency of resource allocation. (3) Deep integration of digital twin and auction mechanism: An auction-driven digital twin framework is constructed, and the task environment and resource constraints are dynamically updated by synchronizing the physical drone status and virtual model in real time. It supports high-fidelity simulation and real-time decision optimization, quickly responds to sudden tasks or energy shortages, and reduces trial and error costs; the distributed architecture avoids the performance bottleneck of centralized scheduling and adapts to large-scale industrial Internet of Things scenarios. (4) Dynamic adaptability and scalability: An asynchronous distributed task triggering mechanism is adopted, in which idle UAVs perform digital twin model maintenance tasks, ensuring the consistency of the virtual-physical environment. It adapts to the dynamic changes in task requirements in complex industrial environments, supports parallel processing of multiple UAVs and multiple tasks, and improves the scalability and robustness of the system.

[0124] Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. Without conflict, the embodiments and features in the embodiments of this application can be combined with each other. The components of the embodiments of this application described and illustrated herein can generally be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of this application is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

Claims

1. A drone resource allocation system based on auction mechanism and digital twin, characterized in that: It includes a ground base station and at least one application service provider. The ground base station acts as a central hub and has a mission command center, a digital twin model builder, and a drone resource auction framework. The ground base station is used to receive mission requests from application service providers, allocate drone resources through an auction mechanism, and continuously update the digital twin model based on real-time data from drones. At least one drone participates in mission bidding, mission evaluation, and remuneration through the auction mechanism provided by the drone resource auction framework; the drone resource auction framework includes a mission candidate set selection module, an auction winner determination module, and an auction payment determination module. The task candidate set selection module is used to select the optimal task set based on a composite value function of task value, coverage and time constraints; the auction winner determination module is used to determine the winning drone for each task based on a scoring rule that balances energy efficiency and task value; the auction payment determination module is used to ensure fairness and incentive compatibility by implementing a second-highest price payment rule.

2. A drone resource allocation method based on auction mechanism and digital twin, employing the drone resource allocation system based on auction mechanism and digital twin as described in claim 1, characterized in that: The method includes the following steps: Step 1: Construct an auction-driven digital twin framework for drone resource collaboration; including a ground base station and at least one application service provider; the ground base station has a mission command center, a digital twin model builder, and a drone resource auction framework; Step 2: The application service provider submits a task request to the ground base station; Step 3: The ground base station filters the candidate set of tasks and initiates the UAV resource auction framework; Step 4: The drone resource auction framework submits bidding information, and the ground base station calculates scores and assigns tasks; Step 5: The ground base station calculates the payment amount, and the application service provider pays the corresponding fee. Step 6: The drone resource auction framework executes its tasks and simultaneously updates the digital twin model; Step 7: Dynamically feed back the updated information from the digital twin model to the next task time slot.

3. The drone resource allocation method based on auction mechanism and digital twin as described in claim 2, characterized in that: The parameters for the task request in step 2 include the budget b. k Time constraint t k Value v k Coverage range c k .

4. The drone resource allocation method based on auction mechanism and digital twin as described in claim 2, characterized in that: Step 3: Based on the request parameters of each task k, select an optimal subset of application service providers by maximizing the total value of the tasks within the constraints of total budget and resources. K represents the set of all tasks.

5. The drone resource allocation method based on auction mechanism and digital twin as described in claim 4, characterized in that: Step 3 specifically includes the following steps: Step 3-1: Calculate the value of the candidate task subset S based on the request parameters. Eliminating those exceeding the energy constraints of drones Or flight distance constraints The task, among which This represents the farthest flight distance of drone i between two adjacent time slots; This represents the total energy consumption cost of executing any task k in time slot t. This represents the total energy budget for each drone i per operating cycle; Step 3-2: Iteratively select the task that maximizes the increment of V(S) using a greedy algorithm until the sum of the budgets of all tasks ∑b k ≤B exhaustion yields the set of candidate tasks with the maximum value, where the budget constraint is B.

6. The drone resource allocation method based on auction mechanism and digital twin as described in claim 2, characterized in that: Step 4 specifically includes the following steps: Step 4-1: Calculate the total energy consumption cost of each drone performing task k in time slot t. The set of all drones is represented as I = {1,…,i,…,I}; Step 4-2, the drone submits a two-dimensional bid b for task k. i,k =(e i,k ,p i,k ),in For total energy consumption, p i,k For quotations; t k This represents the task time required by the application service provider for task k, i.e., the time constraint t in the task request parameters. k Step 4-3: Calculate the effective score for each drone's two-dimensional bid. The effective score function is in the form of: in, This represents the energy cost of drone i when performing task k; Step 4-4: Select the bidding drone with the highest valid score as the receiver σ of task k. i ,Right now Steps 4-5: Summarize the recipients of all tasks to form the winning drone set.

7. The drone resource allocation method based on auction mechanism and digital twin as described in claim 6, characterized in that: Total energy consumption cost in This represents the hovering cost incurred by drone i in time slot t; Let $t$ represent the computational cost and propulsion cost incurred by UAV i in performing task $k$ in time slot $t$, respectively.

8. The drone resource allocation method based on auction mechanism and digital twin as described in claim 7, characterized in that: Hovering costs It is a constant; computational cost in It is the size of the data generated by task k in time slot t. This is the unit energy cost calculated by the drone i; Promotion costs in It is the unit energy cost of propulsion. The drone i is performing task k at time slot t. This refers to drone i at time slot t-1.

9. The drone resource allocation method based on auction mechanism and digital twin as described in claim 2, characterized in that: In step 5, the energy consumption e of each drone performing its respective task is used as the unit of measurement. i,k The price that yields the second-highest effective score is the reward π to be paid to the corresponding drone. i Indicates, that is Where s(·) is a component-form non-decreasing function of s(0) = 0, and s(·) actually represents the energy cost of UAV i when performing task k.

10. The method for allocating drone resources based on auction mechanism and digital twin as described in claim 2, characterized in that: In step 7, digital twin model maintenance is performed when the drone resource auction framework is idle.

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