An unmanned aerial vehicle cluster resilience evaluation method fusing space influence range and cooperative coverage intensity

Abstract

The present application aims to provide a UAV cluster resilience evaluation method fusing spatial influence range and cooperative coverage strength, through analyzing task characteristics, combining spatial coverage and cooperative strength dual perspectives to build a resilience evaluation model, realizing comprehensive evaluation of the overall system resilience, and providing effective support for guiding UAV cluster reliability design and operation. The steps are as follows: constructing a UAV cluster resilience evaluation model facing spatial influence range; constructing a UAV cluster resilience evaluation model facing cooperative coverage strength; based on the dual perspective resilience evaluation results and combined with the core task requirements, the overall UAV cluster resilience is calculated.

Description

Technical Field

[0001] This invention provides a method for evaluating the resilience of unmanned aerial vehicle (UAV) swarms by integrating spatial influence range and collaborative coverage intensity, belonging to the field of reliability and systems engineering technology. Background Technology

[0002] With the deep integration of technologies such as unmanned systems, sensor networks, and artificial intelligence, drone swarms, as a new form of unmanned operation, have demonstrated significant application value in complex mission scenarios such as wide-area reconnaissance and regional containment. However, in complex adversarial environments or under extreme weather conditions, swarms inevitably encounter sudden disturbances, leading to node failures. Resilience, as the core capability of a system to quickly reconstruct its functions and continuously complete its intended tasks after being disturbed, is crucial for measuring the overall operational capability of a swarm. Therefore, research on drone swarm resilience evaluation methods is of great significance for guiding its reliability design and operation and maintenance.

[0003] Existing methods for evaluating the resilience of UAV swarms are mainly categorized into three types: those based on complex networks, those based on performance changes, and those based on operational logic. Methods based on complex networks focus on analyzing network connectivity and topology evolution; methods based on performance changes focus on the curve fluctuations of single indicators such as coverage; and methods based on operational logic assess the maintenance status of key functions based on the mission chain. These methods have promoted the development of UAV swarm resilience evaluation technology from different perspectives, but they still have significant limitations when facing the demands of complex missions. Specifically, the core of UAV swarm mission execution lies in payload activities within a specific spatiotemporal range. The actual spatial impact range and the intensity of the combined effect of multi-UAV collaborative coverage change significantly with mission progress and local disturbances. However, existing methods are mostly limited to static topology or single indicators, failing to effectively integrate system resilience with the evolution of the actual spatial impact range of the UAV swarm during missions and the dynamic characteristics of the intensity of multi-node collaborative action. This makes it difficult for existing evaluation results to accurately reproduce the system's true performance in resisting disturbances and to fully reflect the resilience level throughout the entire mission cycle. Therefore, this invention proposes a UAV swarm resilience evaluation method that integrates spatial impact range and collaborative coverage intensity to overcome the limitations of existing technologies. Summary of the Invention

[0004] This invention aims to provide a method for evaluating the resilience of UAV swarms by integrating spatial impact range and collaborative coverage intensity. By analyzing mission characteristics and combining spatial coverage and collaborative intensity from two perspectives, a resilience evaluation model is constructed to achieve accurate quantification and comprehensive evaluation of the overall system resilience.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for evaluating the resilience of UAV swarms that integrates spatial impact range and collaborative coverage intensity mainly includes the following steps:

[0007] S100: Constructing a drone swarm resilience evaluation model oriented towards spatial impact range;

[0008] S200: Constructing a drone swarm resilience evaluation model oriented towards collaborative coverage intensity;

[0009] S300: Comprehensive calculation of the overall resilience of drone swarms.

[0010] In step S100, a drone swarm resilience evaluation model oriented towards spatial impact range is constructed. This is used to describe the coverage capability of a drone swarm over a mission area, characterizing the spatial influence range of the drone swarm. The calculation process is as follows:

[0011]

[0012] In the formula, I represents the number of drones in the drone swarm, and M represents the mission area. This is a function for measuring the area of ​​a plane. Represents any Time of the first The coverage area of ​​the drone's payload. This is the ratio of the current coverage area of ​​the drone swarm to the area of ​​the mission region, with a value ranging from [0,1]. When the drone swarm experiences disturbances that cause some nodes to fail, The magnitude of the decrease directly reflects the degree of damage to its spatial influence range.

[0013] The resilience assessment process for the spatial impact range of drone swarms is as follows:

[0014]

[0015] In the formula, t d t represents the moment when the drone swarm is disturbed. r A time to restore stability, It indicates the spatial extent of the disturbance at the moment before it occurred. It is used to characterize the resilience of UAV swarms in the spatial influence range, with a value range of [0,1]. The larger the value, the stronger the anti-interference and recovery ability of the UAV swarm in the spatial influence range.

[0016] In step S200, a drone swarm resilience evaluation model oriented towards collaborative coverage intensity is constructed. The purpose is to describe the collaborative coverage intensity of key target areas. The calculation process is as follows:

[0017]

[0018]

[0019]

[0020] In the formula, For drones At any time t, for the region service intensity, , For total strength density, This represents the actual coverage intensity of the drone swarm. This represents the coordinated coverage strength of the drone swarm at time t, calculated by comparing the actual coverage strength with the average coverage strength required for the mission. The ratio is quantized, and the value range is [0,1]. It can dynamically capture the ability of available nodes to maintain cooperative coverage strength by reconstructing after a drone swarm is disturbed.

[0021] The resilience evaluation process for collaborative cover strength is as follows:

[0022]

[0023] In the formula, This indicates the strength of the coordinated coverage at the moment before the disturbance. It is used to characterize the resilience of UAV swarms in terms of collaborative coverage strength, and the value ranges from [0,1]. The larger the value, the stronger the anti-interference and recovery ability of the UAV swarm in terms of collaborative coverage strength.

[0024] In step S300, the overall resilience of the drone swarm is calculated. The calculation process is as follows:

[0025]

[0026]

[0027] In the formula, R is used to characterize the resilience of the drone swarm, with a value range of [0,1]; α and β are respectively... and The weighting coefficients can be dynamically adjusted according to the core requirements of the task to flexibly reflect the contribution of different dimensions to the overall resilience of the drone swarm.

[0028] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a method for evaluating the resilience of UAV swarms by integrating spatial influence range and collaborative coverage intensity, overcoming the technical limitations of traditional methods that fail to effectively integrate the spatiotemporal dynamic characteristics of the spatial influence range and collaborative coverage intensity of UAV swarms performing tasks. Furthermore, by utilizing weight allocation to flexibly adapt to the core requirements of different tasks, the evaluation results can more intuitively and accurately reflect the resilience and reconfiguration capabilities of UAV swarms throughout the entire mission cycle. Attached Figure Description

[0029] Figure 1 This is a flowchart of a method for evaluating the resilience of unmanned aerial vehicle (UAV) swarms that integrates spatial influence range and collaborative coverage intensity, according to the present invention.

[0030] Figure 2 This invention provides a model describing the spatial influence range and collaborative coverage intensity of unmanned aerial vehicle (UAV) swarms. Detailed Implementation

[0031] The following will refer to the appendix. Figure 1 With appendix Figure 2 Specific embodiments of the invention are described in detail below. While specific embodiments of the invention have been discussed, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, the embodiments are provided to enable a more thorough understanding of the invention and to fully convey the information of the invention to those skilled in the art.

[0032] This invention provides a method for evaluating the resilience of unmanned aerial vehicle (UAV) swarms by integrating spatial influence range and collaborative coverage intensity. The flowchart is as follows: Figure 1 As shown, it includes:

[0033] S100: Construct a drone swarm resilience evaluation model oriented towards spatial impact range. This is used to describe the coverage capability of a drone swarm over a mission area, characterizing the spatial influence range of the drone swarm. The calculation process is as follows:

[0034]

[0035] In the formula, I represents the number of drones in the drone swarm, and M represents the mission area. This is a function for measuring the area of ​​a plane. Represents any Time of the first The coverage area of ​​the drone's payload. This is the ratio of the current coverage area of ​​the drone swarm to the area of ​​the mission region, with a value ranging from [0,1]. When the drone swarm experiences disturbances that cause some nodes to fail, The magnitude of the decrease directly reflects the degree of damage to its spatial influence range.

[0036] The resilience assessment process for the spatial impact range is as follows:

[0037]

[0038] In the formula, t d t represents the moment when the drone swarm is disturbed. r A time to restore stability, It indicates the spatial extent of the disturbance at the moment before it occurred. It is used to characterize the resilience of UAV swarms in the spatial influence range, with a value range of [0,1]. The larger the value, the stronger the anti-interference and recovery ability of the UAV swarm in the spatial influence range.

[0039] Example 1: such as Figure 2 As shown, a drone swarm consists of 8 drones and performs an area reconnaissance mission. The mission area M has a total area of ​​200x200, and the effective coverage radius of a single drone's payload is 55. The swarm is initially deployed in a uniformly distributed formation. Based on this, a drone swarm resilience evaluation model oriented towards spatial impact range is constructed, and the model is applied from mission commencement to t... d At 100 minutes, the cluster encountered a sudden disturbance, resulting in the failure and destruction of one drone. The remaining available drone nodes detected the fault and autonomously reconfigured their structure, ultimately recovering at time t. r The system converged to a stable operating state at time 190 min. The spatial coverage capability at the time preceding the disturbance was extracted. The value is 0.996, after the perturbation reconstruction is completed. The calculated result is 0.963.

[0040] S200: Constructing a drone swarm resilience evaluation model oriented towards collaborative coverage intensity. The purpose is to describe the collaborative coverage intensity of key target areas. The calculation process is as follows:

[0041]

[0042]

[0043]

[0044] In the formula, For drones At any time t, for the region service intensity, , For total strength density, This represents the actual coverage intensity of the drone swarm. This represents the coordinated coverage strength of the drone swarm at time t, calculated by comparing the actual coverage strength with the average coverage strength required for the mission. The ratio is quantized, and the value range is [0,1]. It can dynamically capture the ability of available nodes to maintain cooperative coverage strength by reconstructing after a drone swarm is disturbed.

[0045] The resilience evaluation process for collaborative cover strength is as follows:

[0046]

[0047] In the formula, This indicates the strength of the coordinated coverage at the moment before the disturbance. It is used to characterize the resilience of UAV swarms in terms of collaborative coverage strength, and the value ranges from [0,1]. The larger the value, the stronger the anti-interference and recovery ability of the UAV swarm in terms of collaborative coverage strength.

[0048] Continuing from the previous example: Construct a drone swarm resilience evaluation model oriented towards collaborative coverage intensity, and define the overall mission phases. , in t d To t r During the disturbance reconstruction phase, the cooperative coverage strength of the drone swarm at the moment before the disturbance occurred is extracted. The value was 0.246. After the system stabilized, t... r The cooperative coverage strength value at time t is 0.201. The calculated result is 0.835.

[0049] S300: Comprehensive calculation of the overall resilience of the drone swarm, the calculation process is as follows:

[0050]

[0051]

[0052] In the formula, R is used to characterize the resilience of the drone swarm, with a value range of [0,1]; α and β are respectively... and The weighting coefficients can be dynamically adjusted according to the core requirements of the task to flexibly reflect the contribution of different dimensions to the overall resilience of the drone swarm.

[0053] Continuing from the previous example: Based on the core requirements and preferences of the current regional reconnaissance mission regarding spatial coverage and intensity coverage, the weighting coefficient α for the spatial influence range dimension is set to 0.6, and the weighting coefficient β for the collaborative coverage intensity dimension is set to 0.4. Calculations show that the overall resilience R of this UAV swarm in this scenario is 0.910.

[0054] This implementation method fully presents the execution flow of the UAV swarm resilience evaluation method based on spatial influence range and collaborative coverage intensity. By setting specific task scenario parameters, calculating dual-view resilience indicators and assigning weights, the system resilience process is quantified and comprehensively evaluated.

[0055] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.

Claims

1. A method for evaluating the resilience of unmanned aerial vehicle (UAV) swarms by integrating spatial impact range and collaborative coverage intensity, characterized in that, include: S100: Construct a drone swarm resilience evaluation model oriented towards spatial impact range. This is used to describe the coverage capability of a drone swarm over a mission area, characterizing the spatial influence range of the drone swarm. The calculation process is as follows: In the formula, I represents the number of drones in the drone swarm, and M represents the mission area. This is a function for measuring the area of ​​a plane. Represents any Time of the first The coverage area of ​​the drone's payload. This is the ratio of the current coverage area of ​​the drone swarm to the area of ​​the mission region, with a value ranging from [0,1]. When the drone swarm experiences disturbances that cause some nodes to fail, The magnitude of the decrease directly reflects the degree of damage to its spatial influence range. The resilience assessment process for the spatial impact range is as follows: In the formula, t d t represents the moment when the drone swarm is disturbed. r A time to restore stability, It indicates the spatial extent of the disturbance at the moment before it occurred. It is used to characterize the resilience of UAV swarms in the spatial influence range, with a value range of [0,1]. The larger the value, the stronger the anti-interference and recovery ability of the UAV swarm in the spatial influence range. S200: Constructing a drone swarm resilience evaluation model oriented towards collaborative coverage intensity. The purpose is to describe the collaborative coverage intensity of key target areas. The calculation process is as follows: In the formula, For drones At any time t, for the region service intensity, , For total strength density, This represents the actual coverage intensity of the drone swarm. This represents the coordinated coverage strength of the drone swarm at time t, calculated by comparing the actual coverage strength with the average coverage strength required for the mission. The ratio is quantized, and the value range is [0,1]. It can dynamically capture the ability of available nodes to maintain cooperative coverage strength by reconstructing after a drone swarm is disturbed. The resilience evaluation process for collaborative cover strength is as follows: In the formula, This indicates the strength of the coordinated coverage at the moment before the disturbance. It is used to characterize the resilience of UAV swarms in terms of collaborative coverage strength, and the value ranges from [0,1]. The larger the value, the stronger the anti-interference and recovery ability of the UAV swarm in terms of collaborative coverage strength. S300: Comprehensive calculation of the overall resilience of the drone swarm, the calculation process is as follows: In the formula, R is used to characterize the resilience of the drone swarm, with a value range of [0,1]; α and β are respectively... and The weighting coefficients can be dynamically adjusted according to the core requirements of the task to flexibly reflect the contribution of different dimensions to the overall resilience of the drone swarm.

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