Wind power blade multi-robot task scheduling method based on multi-layer stackelberg game model

Abstract

This invention discloses a multi-robot task scheduling method and apparatus for wind turbine blades based on a multi-level Stackelberg game model, relating to the field of wind turbine blade manufacturing technology. The method includes: constructing a Stackelberg game framework, assigning roles according to process dominance, with the edge-cutting robot as the first-level leader, the grinding robot as the second-level leader, and the roller-coating robot as the follower; the edge-cutting robot formulates an initial scheduling strategy based on an auction mechanism, and the grinding robot optimizes the strategy through a same-level auction, the combination of these two strategies forming the first-level game; the roller-coating robot determines the optimal solution based on the combined strategy of the first two, forming a second-level game with the grinding robot; a multi-level Stackelberg game model is established based on work efficiency, energy consumption, load balance, and conflict penalty terms; the game equilibrium is solved using backward induction, finally obtaining the optimal scheduling scheme for each robot. Using this invention can improve the overall work efficiency of wind turbine blade post-processing.

Description

Technical Field

[0001] This invention relates to the field of wind turbine blade manufacturing technology, and in particular to a method and apparatus for scheduling multi-robot tasks for wind turbine blades based on a multi-level Stackelberg game model. Background Technology

[0002] As a core component of wind power generation systems, the efficiency and quality of post-processing steps (edge ​​trimming, deburring, roller coating, or spraying, etc.) of wind turbine blades directly determine their service life and power generation efficiency. With the increasing size and precision of wind turbine blades, traditional manual post-processing methods can no longer meet production demands. The mainstream solution is to use large industrial robots (such as heavy-duty cutting robots, large grinding robots, or spraying robots) for multi-machine collaborative operations.

[0003] In existing technologies, multi-robot scheduling for wind turbine blade post-processing mainly employs centralized control, offline programming, genetic algorithms, and particle swarm optimization. Current technologies primarily focus on single-process (e.g., grinding) robot path planning or simple multi-robot collaboration, lacking a scheduling method based on a master-slave game structure built using Stackelberg theory. Publicly available patents such as CN105538095B ("A Multi-Robot Collaborative Grinding System and Method for Large Wind Turbine Blades") and CN121032163B ("Dynamic Task Allocation Method for Multi-Robot Collaborative Operation") only address multi-robot collaborative grinding or pipeline layout, failing to utilize game theory for hierarchical decision-making and master-slave collaboration. Furthermore, they primarily target fast, small-scale, and large-scale robot clusters, with scheduling strategies often employing flat or single-layer game structures. While existing multi-robot scheduling research explores Stackelberg game theory, it focuses on general-purpose robots or non-wind turbine blade scenarios, failing to adapt to the unique working conditions of wind turbine blade post-processing, characterized by "strict process sequence, limited robot numbers, large size, limited working space, and prone to load imbalance."

[0004] Current multi-robot scheduling methods for wind turbine blade post-processing still have significant drawbacks: First, centralized scheduling relies on the computing power of the central controller, which is prone to communication delays and computing bottlenecks, making it difficult to respond to dynamic operating conditions in real time. Second, traditional deterministic algorithms cannot handle uncertainties such as time, energy consumption, and location, easily leading to operation delays and resource waste. Third, scheduling strategies are not designed for the process hierarchy of edge trimming, grinding, and roller coating, resulting in poor process connection or uneven robot load. Fourth, the lack of a hierarchical game-based collaborative mechanism leads to unclear robot strategy priorities, insufficient collaboration, and easy path and process conflicts, resulting in low overall operation efficiency. To address these issues, a multi-layer Stackelberg game-based multi-robot task scheduling method for wind turbine blade post-processing is proposed, a hierarchical collaborative scheduling method adapted to the characteristics of the process hierarchy, to improve the robustness, real-time performance, and global efficiency of the scheduling system. Summary of the Invention

[0005] To address the technical problems of existing technologies, such as difficulty in real-time response to dynamic working conditions, uneven robot load due to the lack of a scheduling strategy, and unclear robot strategy priorities and insufficient coordination due to the lack of a hierarchical game theory collaboration mechanism, leading to path and process conflicts and low overall work efficiency, this invention provides a multi-robot task scheduling method and device for wind turbine blades based on a multi-layer Stackelberg game model. The technical solution is as follows: On the one hand, a multi-robot task scheduling method for wind turbine blades based on a multi-level Stackelberg game model is provided. This method is implemented by a multi-robot task scheduling device for wind turbine blades based on a multi-level Stackelberg game model. The method includes: S1. Identify the game participants, construct the Stackelberg game framework, and divide the game into leaders and followers based on process dominance; among them, the game participants include: edge cutting robot, grinding robot, and roller coating robot; S2. The edge-cutting robot is designated as the primary leader, and an auction mechanism is used to determine the initial task scheduling strategy for the edge-cutting robot, including: work sub-area, work sequence and resource allocation; S3. The polishing robot is designated as the second-level leader and a follower of the edge-cutting robot. Within the same level, the optimal task scheduling strategy of the polishing robot is determined through an auction mechanism. Based on the optimal task scheduling strategy of the polishing robot, the initial task scheduling strategy of the edge-cutting robot is modified, and the optimal task scheduling strategy of the edge-cutting robot is output. The optimal task scheduling strategies of the edge-cutting robot and the optimal task scheduling strategies of the polishing robot are combined to form the first-level Stackelberg game. S4. Treat the roller coating robot as a follower, and simultaneously as a follower of the edge-cutting robot and the grinding robot. Determine the optimal task scheduling strategy for the roller coating robot based on the common strategy of the edge-cutting robot and the grinding robot. Combine the optimal task scheduling strategy of the grinding robot and the optimal task scheduling strategy of the roller coating robot to form a second-level Stackelberg game. S5. Construct the profit functions for the edge-cutting robot, the grinding robot, and the roller coating robot respectively. Based on the operating efficiency, energy consumption, load balance, and conflict penalty term, establish a multi-level Stackelberg game model. S6. Based on the first-layer Stackelberg game and the second-layer Stackelberg game, solve the multi-layer Stackelberg game model to obtain the global Stackelberg equilibrium strategy; the edge-cutting robot, grinding robot and roller coating robot execute the global Stackelberg equilibrium strategy, output the scheduling scheme, and complete the collaborative operation of wind turbine blade processing.

[0006] On the other hand, a multi-robot task scheduling device for wind turbine blades based on a multi-level Stackelberg game model is provided. This device is applied to a multi-robot task scheduling method for wind turbine blades based on a multi-level Stackelberg game model. The device includes: The game element construction module is used to build the Stackelberg game framework and classify the leaders and followers of the game based on the dominance of the process; among them, the game participants include: edge cutting robot, grinding robot and roller coating robot; The first-level scheduling strategy generation module is used to determine the initial task scheduling strategy of the edge-cutting robot as the first-level leader using an auction mechanism, including: work sub-region, work sequence and resource allocation; The second-level scheduling strategy optimization module is used to determine the optimal task scheduling strategy for the grinding robot as the second-level leader and the edge-cutting robot as the follower within the same level through an auction mechanism. Based on the optimal task scheduling strategy of the grinding robot, the initial task scheduling strategy of the edge-cutting robot is modified, and the optimal task scheduling strategy of the edge-cutting robot is output. The optimal task scheduling strategies of the edge-cutting robot and the optimal task scheduling strategies of the grinding robot are combined to form the first-level Stackelberg game. The follower strategy matching module is used to treat the roller coating robot as a follower, and simultaneously as a follower of the edge-cutting robot and the grinding robot. It determines the optimal task scheduling strategy for the roller coating robot based on the common strategy of the edge-cutting robot and the grinding robot. The optimal task scheduling strategy of the grinding robot and the optimal task scheduling strategy of the roller coating robot are combined to form a second-level Stackelberg game. The game model construction module is used to construct the profit functions of the edge-cutting robot, the grinding robot, and the roller coating robot, respectively. Based on the operation efficiency, energy consumption, load balance degree, and conflict penalty term, a multi-level Stackelberg game model is established. The equilibrium solution and execution module is used to solve the multi-layer Stackelberg game model based on the first-layer Stackelberg game and the second-layer Stackelberg game to obtain the global Stackelberg equilibrium strategy. The edge-cutting robot, grinding robot and roller coating robot execute the global Stackelberg equilibrium strategy, output the scheduling scheme, and complete the collaborative operation of wind turbine blade processing.

[0007] On the other hand, a multi-robot task scheduling device for wind turbine blades based on a multi-level Stackelberg game model is provided. The multi-robot task scheduling device for wind turbine blades based on a multi-level Stackelberg game model includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the methods in the multi-robot task scheduling method for wind turbine blades based on the multi-level Stackelberg game model described above is implemented.

[0008] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, the at least one instruction being loaded and executed by a processor to implement any of the above-described methods of multi-robot task scheduling for wind turbine blades based on a multi-layer Stackelberg game model.

[0009] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: This invention focuses on constructing a multi-layer Stackelberg game model to schedule multi-robot tasks for the process hierarchy characteristics of edge trimming, grinding, and roller coating in wind turbine blade post-processing. This invention constructs a dedicated game model adapted to the process hierarchy, and for the first time applies a multi-layer Stackelberg game model to the scheduling of multi-robots in wind turbine blade post-processing. Based on the process dependencies of edge trimming → grinding → roller coating, a multi-layer game structure is constructed, accurately matching the hierarchical characteristics of the processes. This solves the problem of poor process connection in traditional scheduling methods and achieves a deep integration of process constraints and game strategies. This invention clarifies the strategic dominance and constraint relationships of robots in each process by dividing them into primary / secondary leaders and followers, forming a hierarchical scheduling mode of "leader-led strategy selection - follower-driven optimization". This effectively solves the problem of disorder in strategy selection among multiple robots and improves the overall coordination of scheduling. This invention achieves load balancing and conflict-free scheduling through a non-cooperative game-theoretic collaborative mechanism among leaders. The follower strategy strictly follows the leader's constraints, avoiding process and path conflicts at the model level. This solves the problems of load imbalance and process and path conflicts, improves the overall operational efficiency and scheduling intelligence level of wind turbine blade post-processing, and has strong adaptability and scalability. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of a multi-robot task scheduling method for wind turbine blades based on a multi-layer Stackelberg game model provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of the participant role division and hierarchical structure of a multi-level Stackelberg game provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of a process for determining an initial task scheduling strategy using an auction mechanism, provided by an embodiment of the present invention; Figure 4 This is a schematic diagram of the strategy interaction and equilibrium formation between the edge-cutting robot and the polishing robot in the first-layer Stackelberg game provided by the embodiment of the present invention; Figure 5 This is a schematic diagram of the strategy interaction and equilibrium formation between the grinding robot and the roller coating robot in the second-layer Stackelberg game provided in this embodiment of the invention; Figure 6 This is a schematic diagram of the process for solving the equilibrium of a multi-level Stackelberg game using reverse induction, provided in an embodiment of the present invention. Figure 7 This is a block diagram of a multi-robot task scheduling device for wind turbine blades based on a multi-layer Stackelberg game model provided in an embodiment of the present invention. Figure 8 This is a schematic diagram of the structure of a multi-robot task scheduling device for wind turbine blades based on a multi-layer Stackelberg game model, provided in an embodiment of the present invention. Detailed Implementation

[0012] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0013] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0014] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0015] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0016] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0017] This invention provides a method for scheduling multi-robot tasks on wind turbine blades based on a multi-level Stackelberg game model. This method can be implemented by a multi-robot task scheduling device for wind turbine blades based on a multi-level Stackelberg game model, which can be a terminal or a server. Figure 1 The flowchart shown is for a multi-robot task scheduling method for wind turbine blades based on a multi-level Stackelberg game model. The processing flow of this method may include the following steps: S1. Determine the game participants, construct the Stackelberg game framework, and divide the game into leaders and followers based on process dominance; among them, the game participants include: edge cutting robot, grinding robot and roller coating robot.

[0018] This invention can be applied to collaborative operation scenarios involving large industrial robots (≤10 robots per workshop, ≤50 robots across the entire plant) in post-processing of wind turbine blades, including grinding, trimming, and roller coating. This embodiment uses the post-processing of a 150m wind turbine blade as an example, configuring 3 trimming robots, 4 grinding robots, and 2 roller coating robots for collaborative operation. The blade surface is discretized into five sub-regions: the blade root, the blade body, and the blade tip. The overall implementation progresses step-by-step, involving game theory model construction, role division, strategy selection optimization, and operation execution.

[0019] Among them, such as Figure 2 The diagram shown is a schematic diagram of the participant role division and hierarchical structure of a multi-level Stackelberg game according to an embodiment of the present invention.

[0020] Optionally, the specific implementation process of S1 includes S11-S13: S11. Based on the type of post-processing procedure for wind turbine blades, define the game participants and divide them into a set of edge-cutting robots, a set of grinding robots, and a set of roller coating robots. The set of edge-cutting robots is represented as follows: (m=3); the set of polishing robots is represented as (n=4); the set of roller coating robots is represented as (k=2), where m is the number of edge-cutting robots; n is the number of grinding robots; k is the number of roller coating robots; and m+n+k≤10 (workshop level) or m+n+k≤50 (factory level).

[0021] S12. Define the strategy space for the edge cutting robot, the strategy space for the grinding robot, and the strategy space for the roll coating robot. Wherein, the strategy space of the edge-cutting robot is represented as ,in, Let be the work area number of the i-th edge-cutting robot. Let i be the execution state of the i-th edge-cutting robot; let the strategy space of the grinding robot be represented as... ,in, Let j be the work area number for the j-th grinding robot. Let j be the operation state of the j-th grinding robot; the strategy space of the roller coating robot is represented as: ,in, This is the work area number for the z-th roller coating robot. This represents the operational status of the z-th roller coating robot.

[0022] S13. Divide the game roles according to the dominance of the process, with the edge-cutting robot as the first-level leader, the grinding robot as the second-level leader and follower of the edge-cutting robot, and the roller coating robot as a follower of both the edge-cutting robot and the grinding robot.

[0023] S2. The edge-cutting robot is designated as the primary leader, and an auction mechanism is used to determine the initial task scheduling strategy for the edge-cutting robot, including: work sub-area, work sequence, and resource allocation.

[0024] Among them, such as Figure 3 The diagram shown is a flowchart illustrating an auction mechanism for determining an initial task scheduling strategy according to an embodiment of the present invention.

[0025] Optionally, the specific implementation process of S2 includes S21-S23: S21. Using the edge-cutting robot as the primary leader, construct the edge-cutting robot evaluation function to determine the initial scheduling strategy; whereby the evaluation function of the i-th edge-cutting robot for the m-th work area is expressed by the following formula (1): (1) in, This is the distance from the cutting robot to the next target point; The total time for the edge-cutting robot to perform the target point operation; The total energy consumption for the edge-cutting robot to perform target point operations; Indicates the first weighting coefficient; This represents the second weighting coefficient; This represents the third weighting coefficient, and ; S22. Set the set of cutting operation areas for each auction item and the corresponding quantitative indicators associated with each auction item, including: the distance from the cutting robot to the next target point, the total time for the cutting robot to perform the target point operation, and the total energy consumption of the cutting robot to perform the target point operation. S23. Use the evaluation function value of the edge-cutting robot as the bid price to participate in the bidding. Allocate the work area according to the lowest bid rule. Even if the evaluation function of the i-th edge-cutting robot for the m-th work area is the smallest, determine the initial task scheduling strategy of the edge-cutting robot.

[0026] The initial task scheduling strategy for the edge-cutting robot is expressed as follows: ,in, This indicates the sub-region number to which the i-th edge-cutting robot is assigned; This represents the execution state of the i-th edge-cutting robot, for example, =1 indicates that the robot is performing tasks in that area. =0, no job is performed.

[0027] S3. The polishing robot is designated as the second-level leader and a follower of the edge-cutting robot. Within the same level, the optimal task scheduling strategy for the polishing robot is determined through an auction mechanism. Based on the optimal task scheduling strategy of the polishing robot, the initial task scheduling strategy of the edge-cutting robot is modified, and the optimal task scheduling strategy of the edge-cutting robot is output. The optimal task scheduling strategies of the edge-cutting robot and the optimal task scheduling strategies of the polishing robot are combined to form the first-level Stackelberg game.

[0028] Among them, such as Figure 4 The diagram shown is a schematic representation of the strategy interaction and equilibrium formation between the edge-cutting robot and the polishing robot in the first-layer Stackelberg game provided by an embodiment of the present invention.

[0029] Optionally, the specific implementation process of S3 includes S31-S35: S31. The polishing robot is designated as a secondary leader. After the task scheduling strategy of the edge cutting robot is announced, the robot adopts a same-level auction mechanism to determine its own strategy, and sets the set of polishing operation areas for the auction targets and the completion sequence constraints of the corresponding edge cutting areas associated with each target. S32. Calculate the quotation submitted by the polishing robot; whereby the quotation of the j-th polishing robot for the n-th work area is expressed by the following formula (2): (2) in, This represents the price quoted by the j-th polishing robot for the n-th work area; To estimate the distance; To estimate the time when the next work session will begin; The total energy required is projected. S33. Allocate work areas based on the lowest bid as the core rule to determine the optimal task scheduling strategy for the grinding robot; wherein, the optimal task scheduling strategy for the grinding robot is expressed as: ;in, This represents the optimal sub-region number to which the i-th grinding robot is assigned; This represents the optimal execution state of the i-th grinding robot.

[0030] S34. Based on the optimal task scheduling strategy of the grinding robot, the initial task scheduling strategy of the edge cutting robot is modified to meet the following requirements. That is, given the known grinding robot's strategy based on every possible strategy of the edge-cutting robot. Give the optimal response Under the premise that the edge-cutting robot can maximize its own benefits The strategy to maximize The optimal task scheduling strategy for the edge-cutting robot is obtained. S35. The optimal task scheduling strategy of the edge-cutting robot and the optimal task scheduling strategy of the grinding robot form the first-level Stackelberg game equilibrium.

[0031] The first-level Stackelberg game equilibrium is represented as ( , ),in, This represents the optimal task scheduling strategy for the edge-cutting robot; This represents the optimal task scheduling strategy for the polishing robot.

[0032] S4. Treat the roller coating robot as a follower, and simultaneously as a follower of the edge-cutting robot and the grinding robot. Determine the optimal task scheduling strategy for the roller coating robot based on the common strategy of the edge-cutting robot and the grinding robot. Combine the optimal task scheduling strategy of the grinding robot and the optimal task scheduling strategy of the roller coating robot to form a second-level Stackelberg game.

[0033] Optionally, the specific implementation process of S4 includes S41-S42: S41. Taking the roller coating robot as a follower, based on the first-level Stackelberg game equilibrium, and with the goal of maximizing its own profit, determine the optimal task scheduling strategy for the roller coating robot; wherein, the process of determining the optimal task scheduling strategy for the roller coating robot is represented by the following formula (3): (3) in, This represents the optimal task scheduling strategy for the roller coating robot; This represents the optimal task scheduling strategy for the edge-cutting robot; This indicates the optimal task scheduling strategy for the polishing robot; This refers to a cutting robot; Among them, by determining the optimal task scheduling strategy for the roller coating robot, the allocation of the roller coating operation area satisfies the completion sequence constraints of the grinding operation, the non-conflict constraints of the robot path, and the load balancing constraints.

[0034] S42. Combine the optimal task scheduling strategy of the grinding robot and the optimal task scheduling strategy of the roller coating robot to form a second-layer Stackelberg game.

[0035] Among them, such as Figure 5 The diagram shown is a schematic representation of the strategy interaction and equilibrium formation between the grinding robot and the coating robot in the second-layer Stackelberg game provided in this embodiment of the invention.

[0036] The second-level Stackelberg game is represented as ( ), This represents the optimal task scheduling strategy for the roller coating robot; This represents the optimal task scheduling strategy for the polishing robot.

[0037] S5. Construct the profit functions for the edge-cutting robot, the grinding robot, and the roller coating robot respectively. Based on the operating efficiency, energy consumption, load balance degree, and conflict penalty term, establish a multi-level Stackelberg game model.

[0038] Optionally, the specific implementation process of S5 includes S51-S54: S51. Construct the profit function of the edge-cutting robot, which is expressed by the following formula (4): (4) in, This represents the revenue of the edge-cutting robot. For roller coating robots; To polish the robot; For edge-cutting robots; To improve the working efficiency of the edge-cutting robot itself; This represents the total energy consumption of the edge-cutting robot. ∈[0,1] represents the load balancing degree of the edge-cutting robot; This is a penalty item for collisions in the edge-cutting robot. This indicates the importance that the edge-cutting robot places on operational efficiency; This indicates the penalty weight for total energy consumption; Weights indicating load balancing effectiveness; The penalty weight for path or process conflicts is represented, and + + =1, and dynamically adjusted according to process characteristics (generally, the penalty weight for total energy consumption in edge trimming and grinding processes is higher, and the weight for operational efficiency in roller coating processes is higher); among them, when path / process conflicts occur... =1, when there is no conflict =0; where, , and depending on A combined strategy among the three.

[0039] S52. Construct the profit function of the polishing robot, expressed by the following formula (5): (5) in, This represents the revenue generated by the polishing robot; To improve the robot's own operational efficiency, To optimize the robot's total energy consumption, ∈[0,1] represents the load balancing degree of the grinding robot. To refine the robot's conflict penalty system, when a path / process conflict occurs... =1, when there is no conflict =0; This indicates the importance that the polishing robot places on its own operational efficiency; The penalty weight represents the total energy consumption. Weights indicating load balancing effectiveness; This represents the penalty weight for path or process conflicts; where, , and It depends on the combination of the grinding robot and the roller coating robot strategy.

[0040] S53. Construct the profit function of the roller coating robot, which is expressed by the following formula (6): (6) in, This represents the revenue value of the roller coating robot; To improve the operating efficiency of the roller coating robot itself. The total energy consumption of the roller coating robot. ∈[0,1] represents the load balance of the roller coating robot; For roller coating robots, a conflict penalty is applied when a path / process conflict occurs. =1, when there is no conflict =0; This indicates the importance that the roller coating robot places on its own operational efficiency; The penalty weight represents the total energy consumption. Weights indicating load balancing effectiveness; This represents the penalty weight for path or process conflicts; where, , and It depends solely on the strategy of the roller coating robot itself; S54. Construct a multi-level Stackelberg game model based on the profit functions of the edge-cutting robot, the grinding robot, and the roller coating robot.

[0041] S6. Based on the first-layer Stackelberg game and the second-layer Stackelberg game, solve the multi-layer Stackelberg game model to obtain the global Stackelberg equilibrium strategy; the edge-cutting robot, grinding robot and roller coating robot execute the global Stackelberg equilibrium strategy, output the scheduling scheme, and complete the collaborative operation of wind turbine blade processing.

[0042] Among them, such as Figure 6 The diagram shown is a schematic diagram of the process for solving the equilibrium of a multi-level Stackelberg game using reverse induction, provided in an embodiment of the present invention.

[0043] Optionally, the specific implementation process of S6 includes S61-S65: S61. Based on the first-level and second-level Stackelberg games, the multi-level Stackelberg game model is solved using backward induction. According to the payoff function of the edge-cutting robot, the combination of arbitrary edge-cutting and grinding strategies for the roller coating robot is solved. The optimal response; Among them, the roller coating robot can handle any... The optimal response is expressed by the following formula (7): (7) in, This indicates that the roller coating robot can handle any... The optimal response; The revenue function of the roller coating robot depends on the three-party strategy.

[0044] S62, The roller coating robot will be used on any... Substitute the optimal response into the profit function of the grinding robot to solve for the optimal response of the grinding robot to any roll coating robot. Among them, the grinding robot is comparable to the arbitrary roller coating robot. The optimal response is expressed by the following formula (8): (8) in, This indicates that the grinding robot is compatible with any roller coating robot. The optimal response; The revenue function representing the polishing robot; S63, The roller coating robot will be used on any... Substituting the optimal response of the grinding robot and the optimal response of the grinding robot to any roller coating robot into the profit function of the edge cutting robot, we can solve for the optimal response of the edge cutting robot. The optimal response of the edge-cutting robot is expressed by the following formula (9): (9) in, This represents the optimal response of the edge-cutting robot; This represents the revenue function of the edge-cutting robot.

[0045] S64. Based on the optimal response of the grinding robot to any roller coating robot, the optimal response of the grinding robot to any roller coating robot, and the optimal response of the edge cutting robot, a global Stackelberg equilibrium strategy is constructed. S65. Each robot executes the global Stackelberg load balancing strategy and outputs the scheduling scheme for each robot. The scheduling scheme includes: the work area allocation result for each robot, the work sequence table, the path planning scheme, energy consumption, and load balancing indicators.

[0046] The method and system provided in this invention are applicable to multi-robot collaborative operation scenarios for the post-processing of wind turbine blades of various specifications. They can dynamically adjust the parameters and weights of the game model according to the number of robots, blade structure and operating conditions, and have strong adaptability and scalability, effectively improving the operation efficiency, quality and scheduling intelligence level of wind turbine blade post-processing.

[0047] This invention focuses on constructing a multi-layer Stackelberg game model to schedule multi-robot tasks for the process hierarchy characteristics of edge trimming, grinding, and roller coating in wind turbine blade post-processing. This invention constructs a dedicated game model adapted to the process hierarchy, and for the first time applies a multi-layer Stackelberg game model to the scheduling of multi-robots in wind turbine blade post-processing. Based on the process dependencies of edge trimming → grinding → roller coating, a multi-layer game structure is constructed, accurately matching the hierarchical characteristics of the processes. This solves the problem of poor process connection in traditional scheduling methods and achieves a deep integration of process constraints and game strategies. This invention clarifies the strategic dominance and constraint relationships of robots in each process by dividing them into primary / secondary leaders and followers, forming a hierarchical scheduling mode of "leader-led strategy selection - follower-driven optimization". This effectively solves the problem of disorder in strategy selection among multiple robots and improves the overall coordination of scheduling. This invention achieves load balancing and conflict-free scheduling through a non-cooperative game-theoretic collaborative mechanism among leaders. The follower strategy strictly follows the leader's constraints, avoiding process and path conflicts at the model level. This solves the problems of load imbalance and process and path conflicts, improves the overall operational efficiency and scheduling intelligence level of wind turbine blade post-processing, and has strong adaptability and scalability.

[0048] Figure 7 This is a block diagram of a multi-robot task scheduling device for wind turbine blades based on a multi-level Stackelberg game model, provided by an embodiment of the present invention. This device is used for a multi-robot task scheduling method for wind turbine blades based on a multi-level Stackelberg game model. (Refer to...) Figure 7 The device includes a game element construction module 710, a first-level scheduling strategy generation module 720, a second-level scheduling strategy optimization module 730, a follower strategy matching module 740, a game model construction module 750, and an equilibrium solution and execution module 760. Among them: Game element construction module 710 is used to construct the Stackelberg game framework and divide the game into leaders and followers based on process dominance; among them, the game participants include: edge cutting robot, grinding robot and roller coating robot; The first-level scheduling strategy generation module 720 is used to determine the initial task scheduling strategy of the edge-cutting robot as the first-level leader using an auction mechanism, including: work sub-region, work sequence and resource allocation; The secondary scheduling strategy optimization module 730 is used to determine the optimal task scheduling strategy for the grinding robot as the secondary leader and the follower of the edge-cutting robot within the same level through an auction mechanism; based on the optimal task scheduling strategy of the grinding robot, the initial task scheduling strategy of the edge-cutting robot is modified, and the optimal task scheduling strategy of the edge-cutting robot is output; the optimal task scheduling strategy of the edge-cutting robot and the optimal task scheduling strategy of the grinding robot are combined to form the first layer of Stackelberg game. The follower strategy matching module 740 is used to treat the roller coating robot as a follower, and simultaneously as a follower of the edge cutting robot and the grinding robot. It determines the optimal task scheduling strategy of the roller coating robot based on the common strategy of the edge cutting robot and the grinding robot. The optimal task scheduling strategy of the grinding robot and the optimal task scheduling strategy of the roller coating robot are combined to form a second-level Stackelberg game. The game model construction module 750 is used to construct the profit functions of the edge cutting robot, the grinding robot, and the roller coating robot respectively. Based on the operation efficiency, energy consumption, load balance degree and conflict penalty term, a multi-layer Stackelberg game model is established. The equilibrium solution and execution module 760 is used to solve the multi-layer Stackelberg game model based on the first-layer Stackelberg game and the second-layer Stackelberg game to obtain the global Stackelberg equilibrium strategy. The edge-cutting robot, grinding robot and roller coating robot execute the global Stackelberg equilibrium strategy, output the scheduling scheme, and complete the collaborative operation of wind turbine blade processing.

[0049] Optionally, the game element construction module 710 is used for: Based on the type of post-processing procedure for wind turbine blades, game participants are defined and divided into a set of edge-cutting robots, a set of grinding robots, and a set of roller coating robots. Define the strategy space for the edge-cutting robot, the strategy space for the grinding robot, and the strategy space for the roll coating robot; The roles in the game are divided according to the dominance of the process. The edge-cutting robot is the first-level leader, the grinding robot is the second-level leader and a follower of the edge-cutting robot, and the roller coating robot is a follower of both the edge-cutting robot and the grinding robot.

[0050] Optionally, the first-level scheduling strategy generation module 720 is used for: The edge-cutting robot is used as the primary leader, and an evaluation function for the edge-cutting robot is constructed to determine the initial scheduling strategy; wherein, the evaluation function of the i-th edge-cutting robot for the m-th work area is expressed by the following formula (10): (10) in, This is the distance from the cutting robot to the next target point; The total time for the edge-cutting robot to perform the target point operation; The total energy consumption for the edge-cutting robot to perform target point operations; Indicates the first weighting coefficient; This represents the second weighting coefficient; This represents the third weighting coefficient, and ; Set the set of edge-cutting operation areas for each auction item and the corresponding quantitative indicators associated with each auction item, including: the distance from the edge-cutting robot to the next target point, the total time for the edge-cutting robot to perform the target point operation, and the total energy consumption of the edge-cutting robot to perform the target point operation. The evaluation function value of the edge-cutting robot is used as the bid price to participate in the bidding. The work area is allocated according to the lowest bid rule. Even if the evaluation function of the i-th edge-cutting robot for the m-th work area is the smallest, the initial task scheduling strategy of the edge-cutting robot is determined.

[0051] Optionally, the secondary scheduling strategy optimization module 730 is used for: The grinding robot is designated as a secondary leader. After the task scheduling strategy of the edge cutting robot is announced, a same-level auction mechanism is used to determine its own strategy, and the set of grinding operation areas for the auction items and the completion sequence constraints of the corresponding edge cutting areas associated with each item are set. Calculate the price submitted by the polishing robot; whereby the price quoted by the j-th polishing robot for the n-th work area is expressed by the following formula (11): (11) in, This represents the price quoted by the j-th polishing robot for the n-th work area; To estimate the distance; To estimate the time when the next work session will begin; The total energy required is projected. The work area is allocated based on the lowest bid as the core rule, and the optimal task scheduling strategy for the polishing robot is determined. Based on the optimal task scheduling strategy for the grinding robot, the initial task scheduling strategy for the edge-cutting robot is modified to meet the following requirements. The optimal task scheduling strategy for the edge-cutting robot is obtained. The optimal task scheduling strategy of the edge-cutting robot and the optimal task scheduling strategy of the grinding robot form the first-level Stackelberg game equilibrium.

[0052] Optionally, the follower strategy matching module 740 is used for: Taking the roller coating robot as a follower, based on the first-level Stackelberg game equilibrium, and with the goal of maximizing its own profit, the optimal task scheduling strategy for the roller coating robot is determined; the process of determining the optimal task scheduling strategy for the roller coating robot is expressed by the following formula (12): (12) in, This represents the optimal task scheduling strategy for the roller coating robot; This represents the optimal task scheduling strategy for the edge-cutting robot; This indicates the optimal task scheduling strategy for the polishing robot; This refers to a cutting robot; The optimal task scheduling strategy of the grinding robot and the optimal task scheduling strategy of the roller coating robot are combined to form a second-level Stackelberg game.

[0053] Optionally, the game model construction module 750 is used for: The profit function of the edge-cutting robot is expressed by the following formula (13): (13) in, This represents the revenue of the edge-cutting robot. For roller coating robots; To polish the robot; For edge-cutting robots; To improve the working efficiency of the edge-cutting robot itself; This represents the total energy consumption of the edge-cutting robot. ∈[0,1] represents the load balancing degree of the edge-cutting robot; This is a conflict penalty term for the edge-cutting robot, applied when a path / process conflict occurs. =1, when there is no conflict =0; This indicates the importance that the edge-cutting robot places on operational efficiency; This indicates the penalty weight for total energy consumption; Weights indicating load balancing effectiveness; The penalty weight for path or process conflicts is represented, and + + =1; The profit function of the polishing robot is constructed and expressed by the following formula (14): (14) in, This represents the revenue generated by the polishing robot; To improve the robot's own operational efficiency, To optimize the robot's total energy consumption, ∈[0,1] represents the load balancing degree of the grinding robot. To refine the robot's conflict penalty system, when a path / process conflict occurs... =1, when there is no conflict =0; This indicates the importance that the polishing robot places on its own operational efficiency; The penalty weight represents the total energy consumption. Weights indicating load balancing effectiveness; Indicates the penalty weight for path or process conflicts; The profit function of the roller coating robot is constructed and expressed by the following formula (15): (15) in, This represents the revenue value of the roller coating robot; To improve the operating efficiency of the roller coating robot itself. The total energy consumption of the roller coating robot. ∈[0,1] represents the load balance of the roller coating robot; For roller coating robots, a conflict penalty is applied when a path / process conflict occurs. =1, when there is no conflict =0; This indicates the importance that the roller coating robot places on its own operational efficiency; The penalty weight represents the total energy consumption. Weights indicating load balancing effectiveness; Indicates the penalty weight for path or process conflicts; A multi-level Stackelberg game model is constructed based on the profit functions of the edge-cutting robot, the grinding robot, and the roll coating robot.

[0054] Optionally, the equilibrium solution and execution module 760 is used for: Based on the first and second layer Stackelberg games, a backward induction method is used to solve the multi-layer Stackelberg game model. According to the payoff function of the edge-cutting robot, the combination of arbitrary edge-cutting and grinding strategies for the roller coating robot is solved. The optimal response; The roller coating robot is used for any Substitute the optimal response into the profit function of the grinding robot to solve for the optimal response of the grinding robot to any roll coating robot. The roller coating robot is used for any Substituting the optimal response of the grinding robot and the optimal response of the grinding robot to any roller coating robot into the profit function of the edge cutting robot, we can solve for the optimal response of the edge cutting robot. Based on the optimal response of the grinding robot to any roller coating robot, the optimal response of the grinding robot to any roller coating robot, and the optimal response of the edge cutting robot, a global Stackelberg equilibrium strategy is constructed. Each robot executes a global Stackelberg load balancing strategy and outputs a scheduling scheme for each robot; wherein, the scheduling scheme includes: the work area allocation result of each robot, the work sequence table, the path planning scheme, energy consumption, and load balancing indicators.

[0055] This invention focuses on constructing a multi-layer Stackelberg game model to schedule multi-robot tasks for the process hierarchy characteristics of edge trimming, grinding, and roller coating in wind turbine blade post-processing. This invention constructs a dedicated game model adapted to the process hierarchy, and for the first time applies a multi-layer Stackelberg game model to the scheduling of multi-robots in wind turbine blade post-processing. Based on the process dependencies of edge trimming → grinding → roller coating, a multi-layer game structure is constructed, accurately matching the hierarchical characteristics of the processes. This solves the problem of poor process connection in traditional scheduling methods and achieves a deep integration of process constraints and game strategies. This invention clarifies the strategic dominance and constraint relationships of robots in each process by dividing them into primary / secondary leaders and followers, forming a hierarchical scheduling mode of "leader-led strategy selection - follower-driven optimization". This effectively solves the problem of disorder in strategy selection among multiple robots and improves the overall coordination of scheduling. This invention achieves load balancing and conflict-free scheduling through a non-cooperative game-theoretic collaborative mechanism among leaders. The follower strategy strictly follows the leader's constraints, avoiding process and path conflicts at the model level. This solves the problems of load imbalance and process and path conflicts, improves the overall operational efficiency and scheduling intelligence level of wind turbine blade post-processing, and has strong adaptability and scalability.

[0056] Figure 8 This is a schematic diagram of a multi-robot task scheduling device for wind turbine blades based on a multi-layer Stackelberg game model, provided by an embodiment of the present invention. Figure 8 As shown, the wind turbine blade multi-robot task scheduling device based on a multi-level Stackelberg game model can include the above-mentioned... Figure 7 The illustrated wind turbine blade multi-robot task scheduling device is based on a multi-level Stackelberg game model. Optionally, the wind turbine blade multi-robot task scheduling device 810 based on the multi-level Stackelberg game model may include a first processor 2001.

[0057] Optionally, the wind turbine blade multi-robot task scheduling device 810 based on the multi-layer Stackelberg game model may also include a memory 2002 and a transceiver 2003.

[0058] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0059] The following is combined with Figure 8 A detailed introduction to each component of the wind turbine blade multi-robot task scheduling device 810 based on a multi-layer Stackelberg game model is provided below: The first processor 2001 is the control center of the wind turbine blade multi-robot task scheduling device 810 based on a multi-layer Stackelberg game model. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0060] Optionally, the first processor 2001 can execute various functions of the wind turbine blade multi-robot task scheduling device 810 based on the multi-level Stackelberg game model by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0061] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 8 CPU0 and CPU1 are shown in the diagram.

[0062] In a specific implementation, as one example, the wind turbine blade multi-robot task scheduling device 810 based on a multi-layer Stackelberg game model can also include multiple processors, for example... Figure 8 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0063] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0064] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via the interface circuit of the wind turbine blade multi-robot task scheduling device 810 based on a multi-layer Stackelberg game model. Figure 8 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0065] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0066] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 8 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0067] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently, and it can be connected to the interface circuit of the wind turbine blade multi-robot task scheduling device 810 based on the multi-layer Stackelberg game model. Figure 8 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0068] It should be noted that, Figure 8The structure of the wind turbine blade multi-robot task scheduling device 810 based on the multi-level Stackelberg game model shown in the figure does not constitute a limitation on the router. The actual wind turbine blade multi-robot task scheduling device based on the multi-level Stackelberg game model may include more or fewer components than shown, or combine some components, or have different component arrangements.

[0069] Furthermore, the technical effects of the wind turbine blade multi-robot task scheduling device 810 based on the multi-layer Stackelberg game model can be referred to the technical effects of the wind turbine blade multi-robot task scheduling method based on the multi-layer Stackelberg game model described in the above method embodiments, and will not be repeated here.

[0070] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or it may be any conventional processor, etc.

[0071] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0072] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0073] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0074] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0075] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0076] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0077] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0078] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0079] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0080] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0081] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-robot task scheduling method for wind turbine blades based on a multi-level Stackelberg game model, characterized in that, The method includes: S1. Identify the game participants, construct the Stackelberg game framework, and divide the game into leaders and followers based on process dominance; among them, the game participants include: edge cutting robot, grinding robot, and roller coating robot; S2. The edge-cutting robot is designated as the primary leader, and an auction mechanism is used to determine the initial task scheduling strategy for the edge-cutting robot, including: work sub-area, work sequence, and resource allocation. S3. The polishing robot is designated as the second-level leader and a follower of the edge-cutting robot. Within the same level, the optimal task scheduling strategy of the polishing robot is determined through an auction mechanism. Based on the optimal task scheduling strategy of the polishing robot, the initial task scheduling strategy of the edge-cutting robot is modified, and the optimal task scheduling strategy of the edge-cutting robot is output. The optimal task scheduling strategies of the edge-cutting robot and the optimal task scheduling strategies of the polishing robot are combined to form the first-level Stackelberg game. S4. Treat the roller coating robot as a follower, and simultaneously as a follower of the edge-cutting robot and the grinding robot. Determine the optimal task scheduling strategy for the roller coating robot based on the common strategy of the edge-cutting robot and the grinding robot. Combine the optimal task scheduling strategy of the grinding robot and the optimal task scheduling strategy of the roller coating robot to form a second-level Stackelberg game. S5. Construct the profit functions for the edge-cutting robot, the grinding robot, and the roller coating robot respectively. Based on the operating efficiency, energy consumption, load balance, and conflict penalty term, establish a multi-level Stackelberg game model. S6. Based on the first-layer Stackelberg game and the second-layer Stackelberg game, solve the multi-layer Stackelberg game model to obtain the global Stackelberg equilibrium strategy; the edge-cutting robot, grinding robot and roller coating robot execute the global Stackelberg equilibrium strategy, output the scheduling scheme, and complete the collaborative operation of wind turbine blade processing.

2. The method for scheduling multi-robot tasks for wind turbine blades based on a multi-layer Stackelberg game model according to claim 1, characterized in that, The S1 determines the game participants, constructs the Stackelberg game framework, and divides the game into leaders and followers based on process dominance, including: S11. Based on the type of post-processing procedure for wind turbine blades, define the game participants and divide them into a set of edge-cutting robots, a set of grinding robots, and a set of roller coating robots. S12. Define the strategy space for the edge cutting robot, the strategy space for the grinding robot, and the strategy space for the roll coating robot. S13. Divide the game roles according to the dominance of the process, with the edge-cutting robot as the first-level leader, the grinding robot as the second-level leader and follower of the edge-cutting robot, and the roller coating robot as a follower of both the edge-cutting robot and the grinding robot.

3. The method for scheduling multi-robot tasks for wind turbine blades based on a multi-layer Stackelberg game model according to claim 1, characterized in that, The S2 mechanism, which designates the edge-cutting robot as a primary leader and uses an auction mechanism to determine the initial task scheduling strategy for the edge-cutting robot, includes: S21. Using the edge-cutting robot as the primary leader, construct the edge-cutting robot evaluation function to determine the initial scheduling strategy; whereby the evaluation function of the i-th edge-cutting robot for the m-th work area is expressed by the following formula (1): （1） in, This is the distance from the cutting robot to the next target point; The total time for the edge-cutting robot to perform the target point operation; The total energy consumption for the edge-cutting robot to perform target point operations; Indicates the first weighting coefficient; This represents the second weighting coefficient; This represents the third weighting coefficient, and ; S22. Set the set of cutting operation areas for each auction item and the corresponding quantitative indicators associated with each auction item, including: the distance from the cutting robot to the next target point, the total time for the cutting robot to perform the target point operation, and the total energy consumption of the cutting robot to perform the target point operation. S23. Use the evaluation function value of the edge-cutting robot as the bid price to participate in the bidding. Allocate the work area according to the lowest bid rule. Even if the evaluation function of the i-th edge-cutting robot for the m-th work area is the smallest, determine the initial task scheduling strategy of the edge-cutting robot.

4. The method for scheduling multi-robot tasks for wind turbine blades based on a multi-layer Stackelberg game model according to claim 1, characterized in that, S3 takes the polishing robot as a secondary leader and the edge-cutting robot as a follower, and determines the optimal task scheduling strategy for the polishing robot through an auction mechanism within the same level; based on the optimal task scheduling strategy of the polishing robot, the initial task scheduling strategy of the edge-cutting robot is modified, and the optimal task scheduling strategy of the edge-cutting robot is output. The optimal task scheduling strategy of the edge-cutting robot and the optimal task scheduling strategy of the grinding robot are combined to form the first layer of the Stackelberg game, including: S31. The polishing robot is designated as a secondary leader. After the task scheduling strategy of the edge cutting robot is announced, the robot adopts a same-level auction mechanism to determine its own strategy, and sets the set of polishing operation areas for the auction targets and the completion sequence constraints of the corresponding edge cutting areas associated with each target. S32. Calculate the price submitted by the polishing robot; whereby the price quoted by the j-th polishing robot for the n-th work area is expressed by the following formula (2): （2） in, This represents the price quoted by the j-th polishing robot for the n-th work area; To estimate the distance; To estimate the time when the next work session will begin; The total energy required is projected. S33. Allocate work areas based on the lowest bid as the core rule to determine the optimal task scheduling strategy for the polishing robot; S34. Based on the optimal task scheduling strategy of the grinding robot, the initial task scheduling strategy of the edge cutting robot is modified to meet the following requirements. That is, given the known grinding robot's strategy based on every possible strategy of the edge-cutting robot. Give the optimal response Under the premise that the edge-cutting robot can maximize its own benefits The strategy to maximize The optimal task scheduling strategy for the edge-cutting robot is obtained. S35. The optimal task scheduling strategy of the edge-cutting robot and the optimal task scheduling strategy of the grinding robot form the first-level Stackelberg game equilibrium.

5. The multi-robot task scheduling method for wind turbine blades based on a multi-layer Stackelberg game model according to claim 1, characterized in that, S4 treats the roller coating robot as a follower, and simultaneously as a follower of both the edge-cutting robot and the grinding robot. Based on the shared strategy of the edge-cutting robot and the grinding robot, it determines the optimal task scheduling strategy for the roller coating robot. The optimal task scheduling strategy for the grinding robot is then combined with the optimal task scheduling strategy for the roller coating robot to form a second-layer Stackelberg game, including: S41. Taking the roller coating robot as a follower, based on the first-level Stackelberg game equilibrium, and with the goal of maximizing its own profit, determine the optimal task scheduling strategy for the roller coating robot; wherein, the process of determining the optimal task scheduling strategy for the roller coating robot is represented by the following formula (3): （3） in, This represents the optimal task scheduling strategy for the roller coating robot; This represents the optimal task scheduling strategy for the edge-cutting robot; This represents the optimal task scheduling strategy for the polishing robot; This refers to a cutting robot; S42. Combine the optimal task scheduling strategy of the grinding robot and the optimal task scheduling strategy of the roller coating robot to form a second-level Stackelberg game.

6. The method for scheduling multi-robot tasks for wind turbine blades based on a multi-layer Stackelberg game model according to claim 1, characterized in that, The S5 constructs the profit functions for the edge-cutting robot, the grinding robot, and the roller coating robot, respectively. Based on work efficiency, energy consumption, load balancing, and conflict penalty terms, a multi-layer Stackelberg game model is established, including: S51. Construct the profit function of the edge-cutting robot, which is expressed by the following formula (4): （4） in, This represents the revenue value of the edge-cutting robot. For roller coating robots; To polish the robot; For edge-cutting robots; To improve the working efficiency of the edge-cutting robot itself; This represents the total energy consumption of the edge-cutting robot. ∈[0,1] represents the load balancing degree of the edge-cutting robot; This is a conflict penalty term for the edge-cutting robot, applied when a path / process conflict occurs. =1, when there is no conflict =0; This indicates the importance that the edge-cutting robot places on operational efficiency; This indicates the penalty weight for total energy consumption; Weights indicating load balancing effectiveness; The penalty weight for path or process conflicts, and + + =1; S52. Construct the profit function of the polishing robot, expressed by the following formula (5): （5） in, This represents the revenue generated by the polishing robot; To improve the robot's own operational efficiency, To optimize the robot's total energy consumption, ∈[0,1] represents the load balancing degree of the grinding robot. To refine the robot's conflict penalty system, when a path / process conflict occurs... =1, when there is no conflict =0; This indicates the importance that the polishing robot places on its own operational efficiency; The penalty weight represents the total energy consumption. Weights indicating load balancing effectiveness; Indicates the penalty weight for path or process conflicts; S53. Construct the profit function of the roller coating robot, which is expressed by the following formula (6): （6） in, This represents the revenue value of the roller coating robot; To improve the operating efficiency of the roller coating robot itself. The total energy consumption of the roller coating robot. ∈[0,1] represents the load balance of the roller coating robot; For roller coating robots, a conflict penalty is applied when a path / process conflict occurs. =1, when there is no conflict =0; This indicates the importance that the roller coating robot places on its own operational efficiency; This indicates the penalty weight for total energy consumption; Weights indicating load balancing effectiveness; Indicates the penalty weight for path or process conflicts; S54. Construct a multi-level Stackelberg game model based on the profit functions of the edge-cutting robot, the grinding robot, and the roller coating robot.

7. The multi-robot task scheduling method for wind turbine blades based on a multi-layer Stackelberg game model according to claim 1, characterized in that, The S6 algorithm, based on the first and second layer Stackelberg games, solves a multi-layer Stackelberg game model to obtain a global Stackelberg equilibrium strategy. The edge-cutting robot, grinding robot, and roller-coating robot execute the global Stackelberg equilibrium strategy, outputting a scheduling scheme, including: S61. Based on the first-level Stackelberg game and the second-level Stackelberg game, the multi-level Stackelberg game model is solved by backward induction. According to the payoff function of the edge-cutting robot, the optimal response of the roller coating robot to any combination of edge-cutting strategy and grinding strategy is solved. S62. Substitute the optimal response of the roller coating robot to any combination of cutting and polishing strategies into the profit function of the polishing robot, and solve for the optimal response of the polishing robot to any roller coating robot. S63. Substitute the optimal response of the roller coating robot to any combination of cutting and polishing strategies and the optimal response of the polishing robot to any roller coating robot into the profit function of the cutting robot to solve for the optimal response of the cutting robot. S64. Based on the optimal response of the grinding robot to any roller coating robot, the optimal response of the grinding robot to any roller coating robot, and the optimal response of the edge cutting robot, a global Stackelberg equilibrium strategy is constructed. S65. Each robot executes the global Stackelberg load balancing strategy and outputs the scheduling scheme for each robot; wherein, the scheduling scheme includes: the work area allocation result of each robot, the work sequence table, the path planning scheme, energy consumption and load balancing indicators.

8. A multi-robot task scheduling device for wind turbine blades based on a multi-level Stackelberg game model, wherein the multi-robot task scheduling device for wind turbine blades is used to implement the multi-robot task scheduling method for wind turbine blades based on a multi-level Stackelberg game model as described in any one of claims 1-7, characterized in that, The device includes: The game element construction module is used to build the Stackelberg game framework and classify the leaders and followers of the game according to the dominance of the process; among them, the game participants include: edge cutting robot, grinding robot and roller coating robot; The first-level scheduling strategy generation module is used to determine the initial task scheduling strategy of the edge-cutting robot as the first-level leader using an auction mechanism, including: work sub-region, work sequence and resource allocation; The second-level scheduling strategy optimization module is used to determine the optimal task scheduling strategy for the grinding robot as the second-level leader and the edge-cutting robot as the follower within the same level through an auction mechanism. Based on the optimal task scheduling strategy of the grinding robot, the initial task scheduling strategy of the edge-cutting robot is modified, and the optimal task scheduling strategy of the edge-cutting robot is output. The optimal task scheduling strategies of the edge-cutting robot and the optimal task scheduling strategies of the grinding robot are combined to form the first-level Stackelberg game. The follower strategy matching module is used to treat the roller coating robot as a follower, and simultaneously as a follower of the edge-cutting robot and the grinding robot. It determines the optimal task scheduling strategy for the roller coating robot based on the common strategy of the edge-cutting robot and the grinding robot. The optimal task scheduling strategy of the grinding robot and the optimal task scheduling strategy of the roller coating robot are combined to form a second-level Stackelberg game. The game model construction module is used to construct the profit functions of the edge-cutting robot, the grinding robot, and the roller coating robot, respectively. Based on the operation efficiency, energy consumption, load balance degree, and conflict penalty term, a multi-level Stackelberg game model is established. The equilibrium solution and execution module is used to solve the multi-layer Stackelberg game model based on the first-layer Stackelberg game and the second-layer Stackelberg game to obtain the global Stackelberg equilibrium strategy. The edge-cutting robot, grinding robot and roller coating robot execute the global Stackelberg equilibrium strategy, output the scheduling scheme, and complete the collaborative operation of wind turbine blade processing.

9. A multi-robot task scheduling device for wind turbine blades based on a multi-level Stackelberg game model, characterized in that, The wind turbine blade multi-robot task scheduling device based on a multi-layer Stackelberg game model includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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