Cluster layering dynamic coverage control method facing network attack

Abstract

The invention discloses a network attack-oriented cluster hierarchical dynamic coverage control method. The method comprises the following steps: constructing a system mathematical model based on an unmanned aerial vehicle cluster system network; the whole process is divided into a robust construction stage and a dynamic coverage stage, in the robust construction stage, a group of unmanned aerial vehicles in initial random distribution construct a communication topological graph meeting robust conditions, and in the dynamic coverage stage, a robust cluster formed in the previous stage is controlled to move, so that the robustness of the unmanned aerial vehicles is improved. The robust attribute of the cluster formation is maintained while dynamic coverage is considered, and the targets of dynamic coverage, collision avoidance, obstacle avoidance and robust maintenance are achieved at the same time in the task process. The method can effectively ensure that the cluster can still effectively eliminate the interference of malicious nodes in a network environment in which malicious network attacks possibly exist, so that the state values of normal nodes are quickly consistent, the dynamic region coverage task is efficiently completed, and the research and practical application of the existing method in the cluster cooperative control field are expanded.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) swarm system coordination and control technology, specifically to a swarm layered dynamic coverage control method for network attacks. Background Technology

[0002] Unmanned Aerial Vehicle Swarms (UAVS) leverage multi-agent coordinated control technology to achieve autonomous perception, decision-making, and action, playing a crucial role in disaster search and rescue, and area detection. Coverage control, as one of the core research problems, aims to enable the swarm to efficiently detect target areas through reasonable strategies. However, most existing coverage strategies assume an ideal communication environment free from malicious interference. In practical applications, UAVS typically operate in open network environments, making them highly vulnerable to network attacks such as denial-of-service attacks, spoofed data injection, or Byzantine attacks. The consensus algorithm for UAVS is the fundamental control algorithm for its cooperative control and plays a vital role; however, malicious nodes can exploit distributed update mechanisms to conceal their identities and disrupt the swarm system's consensus control algorithm. To defend against these network attacks, an effective existing solution is to first ensure that the UAVS system's communication topology meets specific robustness conditions, and then design a corresponding resilient consensus algorithm within a robust communication network.

[0003] Existing dynamic coverage methods often focus on coverage efficiency or path optimization, requiring UAVs to continuously discover uncovered areas and adjust their paths in real time during movement, neglecting the challenge of maintaining robust communication topology during dynamic movement; while existing secure formation strategies clarify topology conditions, they lack specific control schemes to effectively combine them with dynamic coverage tasks. Therefore, how to achieve efficient dynamic coverage and robust maintenance of communication topology in environments with network attacks is an urgent problem to be solved. Specifically, UAV swarm dynamic coverage control faces two key problems in practical applications facing network attacks: (1) After the communication links between nodes are attacked, malicious nodes affect the state updates of normal nodes by sending false state information, causing the system convergence target to deviate from the normal value or even fail to converge, making it difficult to ensure the safe and stable operation of the system; (2) During dynamic coverage, the continuous movement of individuals and obstacle avoidance will cause frequent changes in network topology, making it difficult to maintain robustness, resulting in traditional methods being unable to ensure the security and effectiveness of coverage tasks under network attack scenarios. Summary of the Invention

[0004] The purpose of this invention is to provide a cluster-based hierarchical dynamic coverage control method for network attacks, so as to solve the problems mentioned in the background art.

[0005] The present invention provides the following technical solution: a cluster-based hierarchical dynamic coverage control method for network attacks, comprising the following operational steps: Step S1: Construct a mathematical model of the unmanned aerial vehicle (UAV) swarm system network.

[0006] Preferably, the specific implementation process includes: Modeling drone swarms that include leader nodes and follower nodes; Model the unknown task area that needs to be dynamically covered.

[0007] Step S2: Divide the entire process into a robust construction phase and a dynamic coverage phase. In the robust construction phase, construct a communication topology that meets the robust conditions. Preferably, during the robust construction phase, each follower node calculates its speed based on its own perception information, builds a robust cluster near the leader's starting position, and defines a speed control strategy for the followers during the robust construction phase.

[0008] Preferably, the speed control strategy includes: using the approach-to-leader speed to represent the speed at which each node moves toward the leader; when a node is too far from the leader, the approach-to-leader speed will drive it to move toward the leader to prevent isolated nodes; when a node is too close to its neighboring node, the collision avoidance speed will cause the two nodes to repel each other in opposite directions; and when the distance between adjacent nodes is less than or equal to the defined danger distance, the collision avoidance speed will increase as the distance between nodes decreases.

[0009] Preferably, obstacle avoidance speed is used to prevent collisions between nodes and obstacles. The closer the node is to the obstacle, the greater the repulsion speed. The obstacle avoidance speed is the superposition of radial obstacle avoidance speed and tangential obstacle avoidance speed, and the tangential obstacle avoidance speed always allows the follower to bypass the obstacle in a clockwise direction. When the distance between the node and the obstacle is less than the obstacle avoidance distance, it indicates that a collision has occurred. When the distance between the node and the obstacle is greater than the obstacle avoidance distance but less than the safe distance, it indicates that the node and the obstacle will collide.

[0010] Preferably, when the number of robust neighbors of all follower nodes in the system reaches a predetermined threshold, their associated communication topology graph is robust; the number of neighbors of nodes is adjusted using robust speed to realize the construction of a robust cluster; when the number of robust neighbors of all nodes reaches the robust requirement, the robust topology construction is completed and enters the dynamic coverage stage.

[0011] Step S3: During the dynamic coverage phase, control the robust cluster to move, thereby achieving dynamic coverage under network attack scenarios.

[0012] Preferably, for the dynamic coverage phase: the leader analyzes the global coverage situation in real time and plans paths based on the value of the grid. Followers move towards the leader and perform coverage tasks, ensuring the formation remains robust while avoiding obstacles and collisions to resist network attacks. Specific implementation includes: Step S31: At each moment, the leader calculates the comprehensive value of the candidate grids based on the set of candidate target grids and selects the grid with the highest value as the target grid; Step S32: Leaders improve The algorithm plans the optimal path to the target grid and moves accordingly; Step S33: The follower cluster moves according to the leader's position and the speed control strategy, covering under the leader's guidance and always maintaining the robust topology of the cluster; Step S34: The leader uses the disconnection detection mechanism to determine whether there are followers within its communication radius.

[0013] Compared with the prior art, the beneficial effects achieved by the present invention are: (1) This invention divides the entire process into a robust construction phase and a dynamic coverage phase. In the robust construction phase, a group of initially randomly distributed UAVs constructs a communication topology that meets the robust conditions. In the dynamic coverage phase, the robust cluster formed in the previous phase is controlled to move. While taking into account dynamic coverage, the robust properties of the cluster formation are maintained. This can effectively eliminate interference from malicious nodes and enable normal nodes to quickly reach a consensus on their state values. It has strong network intrusion tolerance capabilities. (2) This invention adopts an improved version The algorithm's dynamic coverage method achieves a reduction in coverage time and improves upon previous methods. Compared to traditional algorithms The algorithm is more efficient, and its performance is significantly improved when the distance between the starting point and the ending point is large. Attached Figure Description

[0014] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the cluster hierarchical dynamic coverage control method provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of the obstacle avoidance speed during the movement of a follower, provided in an embodiment of the present invention. Figure 3 This is an improvement on the integrated jump point search mechanism provided in the embodiments of the present invention. Algorithm flowchart; Figure 4 This is a schematic diagram of the jump point search mechanism provided in an embodiment of the present invention; Figure 5 This is a trajectory diagram of each node after using the dynamic coverage strategy provided in an embodiment of the present invention; Figure 6 This is a graph showing the state value update behavior of each node in a cluster without considering robust strategies, provided by an embodiment of the present invention. Figure 7 This is a graph showing the state value update behavior of each node in a cluster considering robust strategies, provided by an embodiment of the present invention. Figure 8 This is the traditional embodiment provided by the present invention. Algorithms and Improvements Comparison of coverage time under different algorithms. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1 In this embodiment, combined with Figure 1-4 As shown, the present invention provides a technical solution: a cluster-based hierarchical dynamic coverage control method for network attacks, comprising the following operational steps: Step S1: Construct a mathematical model of the system.

[0017] In this embodiment, the specific implementation process includes: Modeling a drone swarm: A drone swarm consists of two types of nodes: a set of leader nodes. and follower node set This invention designates a drone as a trusted leader node. Deployed at high altitudes to gain a global view, the leader drone is equipped with panoramic cameras and a computing platform to analyze the coverage status of the mission area. The leader drone itself does not directly perform coverage tasks and is not affected by cyberattacks. The remaining drones are follower nodes. Deployed at low altitudes to perform coverage missions, the drones are equipped with infrared sensors for ground coverage monitoring and image data acquisition. Initially, the drones are randomly distributed throughout the area. All followers have the same communication radius. and coverage radius , No. One follower is located at point Its coverage area For With center and radius The circle, the leader Communication range For With center and radius The circle. For ease of modeling, each drone is considered as a point mass moving at a uniform velocity. Follows the following equations of motion: The control input of the single integrator model physically corresponds to velocity; the subsequent implementation will use a velocity control strategy. As control input .

[0018] Modeling the unknown task area that needs dynamic coverage: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Discretized into a set of equally spaced grids The grid side length is denoted as Assign dynamic weights to each grid Represents a grid At any moment The degree of uncovering. Initial moment. Represents a grid Completely uncovered; for meshes containing obstacles, set their cover weight to... Indicates unreachable. If covering the grid. The four corner points If all elements of a grid are within the coverage area of ​​a particular drone, the grid is considered completely covered at that moment, and its weight is reset to 0. Complete coverage occurs only when the entire grid area is within the drone's coverage area. If only the center of the grid is within coverage but the corners are not, it is still considered uncovered and its weight is maintained. .

[0019] Step S2: In the robust construction phase, each follower node calculates its speed based on its own perception information, builds a robust cluster near the leader's starting position, and defines the speed control strategy for followers in this phase.

[0020] In this embodiment, to simultaneously achieve the goals of dynamic coverage, collision avoidance, obstacle avoidance, and robust maintenance during the task, the entire process is divided into a robust construction phase and a dynamic coverage phase. Specifically, as follows... Figure 1 As shown, in the robust construction phase, the main goal of the system is to enable a group of initially randomly distributed UAVs to construct a communication topology that satisfies robustness conditions. In the dynamic coverage phase, the main goal of the system is to control the movement of the robust cluster formed in the previous phase, taking into account dynamic coverage while maintaining the robust properties of the cluster formation.

[0021] For example, during the robust construction phase, the leader node remains at the starting position, and each follower node calculates its speed based on its own perceived information. A robust cluster is built near the leader's starting position, while avoiding collisions between nodes and with obstacles. The follower's speed control strategy for this phase is defined as follows: in, Indicates the speed at which the leader approaches. Indicates the collision avoidance speed. Indicates the obstacle avoidance speed. This indicates the robustness construction speed. It occurs when the number of robust neighbors for all nodes meets the robustness requirement, i.e. At this point, the robust topology construction is complete, and the dynamic coverage phase begins.

[0022] Furthermore, the component velocities in the follower speed control strategy are designed as follows: Approaching the leader speed This represents the speed at which each node moves towards the leader; when a node is too far from the leader, This will drive it to move towards the leader, preventing isolated nodes, and increasing the leader's speed. The calculation formula is: in, This represents the weight of the velocity component that approaches the leader.

[0023] When node Collision avoidance speed when too close to its neighboring nodes The two nodes repel each other in opposite directions, avoiding collisions due to their velocities. The calculation is as follows: in, This indicates the weight of the collision avoidance velocity component. Represents a node Position, function The design is as follows: in, The danger distance is defined as the distance between adjacent nodes being less than or equal to the danger distance. At that time, collision avoidance speed The speed increases as the distance between nodes decreases, and conversely, the collision avoidance speed is 0 when the distance between nodes increases.

[0024] In this embodiment, obstacle avoidance speed Its function is to prevent nodes from colliding with obstacles. The closer a node is to an obstacle, the greater the repulsion velocity. Assuming there are a total of There are 1 obstacle grid, and the position of each grid is 1. If the distance between the node and the obstacle is less than the obstacle avoidance distance This indicates that a collision has occurred. For example... Figure 2 As shown, to ensure nodes successfully avoid obstacles, the obstacle avoidance speed is designed as the superposition of the radial and tangential obstacle avoidance speeds. Furthermore, the tangential obstacle avoidance speed is stipulated to always allow the follower to bypass the obstacle in a clockwise direction, thus avoiding local minima. The radial obstacle avoidance unit vector is defined as... The tangential obstacle avoidance unit vector is Radial obstacle avoidance speed The design is as follows: in, This indicates the weight of the obstacle avoidance speed component. This indicates the safe distance; if the distance between the node and the obstacle is greater than the obstacle avoidance distance. And less than the safe distance When the obstacle avoidance speed is 0, it indicates that a collision may occur between the node and the obstacle, resulting in obstacle avoidance; otherwise, the obstacle avoidance speed is 0, and it will not affect the movement of the node. Correspondingly, the tangential obstacle avoidance speed... The design is as follows: Ultimately, the node obstacle avoidance speed for: in, To adjust the weights of radial and tangential obstacle avoidance velocities, when the number of robust neighbors of all follower nodes in the system reaches a predetermined threshold, i.e. At that time, its associated communication topology is robust. Therefore, robust speed The goal is to build a robust cluster by adjusting the number of neighbors for each node. Robust speed. The specific design is as follows: in, The weights of the robust velocity components are represented when the node... When the number of neighbors is less than a threshold, the set express The subset with the largest number of neighbors among the neighbors, the guiding node. Move to the node with the most neighbors; otherwise, move the set. express The function checks all of its neighbors, ensuring that the number of its neighbors does not decrease further. Its function is to adjust the movement speed of nodes under different conditions. Specifically: Step S3: In the dynamic coverage phase, the leader analyzes the global coverage in real time and plans paths based on the value of the grid. Followers move toward the leader and perform coverage tasks, while ensuring the formation remains robust to resist network attacks while avoiding obstacles and collisions.

[0025] In this embodiment, the specific implementation process includes: Step S31: At each time step, the leader calculates the comprehensive value of the candidate target grids based on the candidate target grid set and selects the grid with the highest value as the target grid. The overall value of each grid It consists of three types of indicators: path cost indicators Coverage Value Indicators Turnaround cost indicators .

[0026] For example, when selecting a target grid, leaders evaluate its value based on the following criteria: Set path cost metrics This allows leaders to prioritize grids that are close and have low movement costs, reducing the total distance traveled between grids. For each non-barrier grid... An improved method using a jump-point search mechanism. Algorithm calculation from leader position To grid Shortest path length: in, This represents the number of grid cells in the optimal path. and express On the optimal path planned by the algorithm, the first The and the first The coordinates of the center of each grid. Jump point search mechanism as follows: Figure 4 As shown, it extends in a straight line along the current direction of movement, directly skipping a large number of redundant gray intermediate grids on the path. It only stops and expands when it encounters a black obstacle that forces it to change direction or generates a critical location (i.e., a jump point) that forces a neighbor, thereby reducing the number of nodes to be expanded and significantly reducing the amount of computation.

[0027] Set coverage value metrics This allows leaders to prioritize moving to areas with more uncovered grid cells within their neighborhood, accelerating coverage progress and reducing duplicate coverage. Non-barrier grid The field specifically includes The number of uncovered grid cells in the grid cell and its eight surrounding grid cells is used as the coverage value index for that grid cell. in, express Dynamic grid coverage weights at any given time. This indicates that it has not yet been covered.

[0028] Set turning cost indicators This reduces sharp turns during leader movement, ensuring smooth formation movement for followers and preventing difficulties in following due to sudden turns. For each non-obstacle grid... Calculate the initial direction unit vector of the optimal path. : in, This indicates the initial grid position for the optimal path.

[0029] The steering cost metric for this grid is defined as the current velocity direction and... The angle between : in, This indicates the leader's current movement speed.

[0030] Finally, the value of each grid cell is obtained by normalizing and weighting the three indicators mentioned above. : in, , , These represent the weights of each indicator. , , These are the normalized values ​​of each indicator, with larger values ​​being better.

[0031] Step S32: Leaders improve The algorithm plans the optimal path to the target grid and moves accordingly: in, Indicates the leader's movement speed. It represents the distance a leader can move within a time step. This indicates the current optimal path direction.

[0032] Step S33: The follower cluster moves according to the leader's position and the speed control strategy in step S31, covering under the leader's guidance and always maintaining the robust topology of the cluster.

[0033] Step S34: To prevent followers from losing contact with the leader during movement due to avoiding obstacles or other reasons, the leader uses a disconnection detection mechanism to determine whether there are followers within its communication radius, i.e.: in, Represents the set of leader nodes. "Subject to" is an abbreviation for "satisfying constraints". This represents the communication radius. If the radius is not met, the leader's speed is set to 0, and the followers move according to the last received leader position until the connection is re-established.

[0034] Example 2 Combination Figures 5-8 As shown, in this embodiment, to verify the effectiveness of the control method designed in this invention, this embodiment considers a drone swarm system consisting of 8 nodes, including 1 leader node and 7 follower nodes. All nodes are located in a square coverage area. Inside, set parameters , , , , , The motion trajectory diagram of each node after using the dynamic coverage strategy is as follows: Figure 5 As shown.

[0035] To verify the necessity of robust neighbor number constraints in the proposed method, this embodiment conducts experiments on the aforementioned UAV swarm system in the same simulation environment. One node is set as a malicious node, sending false state values ​​to its neighbors. Subsequently, a coverage method without robust constraints and the proposed method are used to guide the node to dynamically cover the area. During the coverage process, state value convergence tests are performed on the swarm, and a secure consensus algorithm is used to update the state values. In the swarm system without robust strategies, the state values ​​of normal nodes are affected by the malicious node, causing the state values ​​of normal nodes to fail to converge even under the secure consensus algorithm. Figure 6 As shown in the figure. Using this method for dynamic coverage, the convergence status of each node in the system after running the secure consensus algorithm is as follows. Figure 7 As shown, it can effectively eliminate interference from malicious nodes and enable normal nodes to quickly reach a consensus on their state values, thus verifying that the cluster network running the dynamic coverage strategy in this paper has network intrusion tolerance capabilities.

[0036] The dynamic coverage algorithm proposed in this invention is analyzed, and it is based on traditional methods. The algorithm's dynamic coverage method and the improved method of this invention The algorithm's dynamic coverage method compares coverage times for clusters with different numbers of nodes, such as... Figure 8 As shown, the proposed method reduces coverage time compared to traditional methods, indicating that the improvement... Compared to traditional algorithms The algorithm is more efficient. Analysis shows that traditional... The time complexity of the algorithm is And the improved The algorithm's time complexity is The algorithm's performance is significantly improved, especially when the distance between the starting point and the ending point is large.

[0037] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0038] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cluster-based hierarchical dynamic coverage control method for network attacks, characterized in that: The following steps are included: Step S1: Construct a mathematical model of the UAV swarm system network; Step S2: Divide the entire process into a robust construction phase and a dynamic coverage phase. In the robust construction phase, construct a communication topology that meets the robust conditions. Step S3: During the dynamic coverage phase, control the robust cluster to move, thereby achieving dynamic coverage under network attack scenarios.

2. The cluster-based hierarchical dynamic coverage control method for network attacks according to claim 1, characterized in that: Step S1 includes: Modeling drone swarms that include leader nodes and follower nodes; Model the unknown task area that needs to be dynamically covered.

3. The cluster-based hierarchical dynamic coverage control method for network attacks according to claim 2, characterized in that: Modeling drone swarms includes: One drone is designated as a trusted leader node to obtain a global view, equipped with a panoramic camera and computing platform to analyze the coverage status of the task area, while the other drones are follower nodes to perform coverage tasks, equipped with infrared sensors to monitor ground coverage and collect image data. Initially, drones are randomly distributed across the area, with all followers having the same communication and coverage radius. One follower is located at point Its coverage area is based on points With center and radius The circle, the leader's communication range With center and radius The circle is defined, and each drone is considered as a point mass moving at a uniform velocity, with the drone following the corresponding equation of motion.

4. The cluster-based hierarchical dynamic coverage control method for network attacks according to claim 3, characterized in that: Modeling unknown task regions that require dynamic coverage includes: Discretize the coverage area into a set of equally spaced grids; Each grid is assigned a dynamic weight, which represents the degree of uncovering of the grid at a given time. Initially, the mesh is completely uncovered. For meshes containing obstacles, their coverage weight is set to unreachable. If all four corner points of a covered grid are within the coverage area of ​​a certain drone, then the grid is considered to be completely covered at the current moment, and its weight is reset to 0; Complete coverage is only achieved when the entire grid area is within the coverage range of a drone. If only the center of the grid is within the coverage range but the corners are not covered, it is still considered uncovered.

5. A cluster-based hierarchical dynamic coverage control method for network attacks according to claim 4, characterized in that: Step S2 also includes: during the robust construction phase, each follower node calculates its speed based on its own perception information, builds a robust cluster near the leader's starting position, and defines a speed control strategy for followers during the robust construction phase.

6. A cluster-based hierarchical dynamic coverage control method for network attacks according to claim 5, characterized in that: The speed control strategy includes: The approach-to-leader velocity represents the speed at which each node moves toward the leader. When a node is too far from the leader, the approach-to-leader velocity will drive it to move toward the leader, preventing isolated nodes from appearing. When a node gets too close to its neighbor, the collision avoidance velocity causes the two nodes to repel each other. When the distance between adjacent nodes is less than or equal to the defined danger distance, the collision avoidance velocity increases as the distance between nodes decreases.

7. A cluster-based hierarchical dynamic coverage control method for network attacks according to claim 6, characterized in that: The speed control strategy also includes: The obstacle avoidance speed is used to prevent nodes from colliding with obstacles. The closer the node is to the obstacle, the greater the repulsion speed. The obstacle avoidance speed is the sum of the radial obstacle avoidance speed and the tangential obstacle avoidance speed, and the tangential obstacle avoidance speed always allows the follower to bypass the obstacle in a clockwise direction; A collision occurs when the distance between a node and an obstacle is less than the obstacle avoidance distance; a collision occurs when the distance between a node and an obstacle is greater than the obstacle avoidance distance but less than the safe distance.

8. A cluster-based hierarchical dynamic coverage control method for network attacks according to claim 7, characterized in that: The robust construction phase also includes: When the number of robust neighbors of all follower nodes in the system reaches a predetermined threshold, their associated communication topology graph is robust. Robust cluster construction is achieved by adjusting the number of neighbors of nodes using robust speed. When the number of robust neighbors of all nodes reaches the robustness requirement, the robust topology construction is completed and the dynamic coverage phase begins.

9. A cluster-based hierarchical dynamic coverage control method for network attacks according to claim 8, characterized in that: The implementation of the dynamic overriding phase includes: Step S31: At each moment, the leader calculates the comprehensive value of the candidate grids based on the set of candidate target grids and selects the grid with the highest value as the target grid; Step S32: Leaders improve The algorithm plans the optimal path to the target grid and moves accordingly; Step S33: The follower cluster moves according to the leader's position and the speed control strategy, covering under the leader's guidance and always maintaining the robust topology of the cluster; Step S34: The leader uses a disconnection detection mechanism to determine whether there are followers within its communication radius.

10. A cluster-based hierarchical dynamic coverage control method for network attacks according to claim 9, characterized in that: The comprehensive value of each grid in step S31 consists of three types of indicators: path cost indicator, coverage value indicator, and turning cost indicator.

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