Unmanned aerial vehicle cluster head dynamic pushing method and system facing air-space integrated network

Abstract

The invention relates to an air-space integrated network-oriented unmanned aerial vehicle cluster head dynamic pushing method and system, and the method comprises the steps: obtaining the link stability, resource margin and topology centrality of all unmanned aerial vehicle nodes in an unmanned aerial vehicle group through a satellite-borne scoring engine, and enabling the satellite-borne scoring engine to be disposed on a stationary orbit satellite; based on the link stability, the resource margin and the topology centrality, performing cluster first title scoring on all unmanned aerial vehicle nodes through a multi-dimensional dynamic scoring model to obtain a cluster first title score; and selecting the cluster head node of the unmanned aerial vehicle group based on the cluster head title score. The method can solve the problems that the existing unmanned aerial vehicle cluster head election cannot adapt to a high-speed moving scene, the election communication overhead is large, and the survivability is poor.

Description

Technical Field

[0001] This application relates to the field of wireless communication network technology, and in particular to a method and system for dynamic election of UAV cluster leaders for integrated air-space networks. Background Technology

[0002] Drone cluster leader election refers to selecting a specific drone from the nodes of a drone swarm (or drone group) as the cluster leader, responsible for core tasks such as node management, data forwarding, and resource scheduling within the cluster. This aims to improve network communication efficiency, reduce energy consumption, and ensure stable cluster operation. However, existing drone cluster leader election methods suffer from problems such as inability to adapt to high-speed mobile scenarios, high election communication overhead, and poor resilience. Summary of the Invention

[0003] This application provides a method and system for dynamic election of UAV cluster leaders for integrated air-space networks, which can solve the problems of existing UAV cluster leader elections being unable to adapt to high-speed mobile scenarios and having high election communication overhead.

[0004] Firstly, this application provides a method for dynamic leader election of unmanned aerial vehicle (UAV) clusters in an integrated air-space network, including: The link stability, resource margin, and topology centrality of all UAV nodes in the UAV swarm are obtained through a satellite-borne scoring engine, wherein the satellite-borne scoring engine is located on a geostationary satellite. Based on the link stability, resource margin, and topology centrality, a multi-dimensional dynamic scoring model is used to score the cluster head competence of all UAV nodes, resulting in a cluster head competence score. The cluster leader node of the drone swarm is selected based on the cluster leader's competence score.

[0005] In some embodiments, the multi-dimensional dynamic scoring model is S = α·link stability + β·resource margin + γ·topology centrality, where α is the link stability weight, β is the resource margin weight, and γ is the topology centrality weight.

[0006] In some embodiments, the link stability weight α = continuous connection time / total connection duration, the resource margin weight β = 0.6 * (remaining battery percentage) + 0.4 * (1 – CPU utilization), and the topology centrality γ = 1 / average number of hops to other nodes.

[0007] In some embodiments, the step of selecting the cluster leader node of the drone swarm based on the cluster leader competence score further includes: Detect the real-time cluster head resource adequacy of the cluster head node; If the real-time cluster head resource margin of the cluster head node is less than the lower limit of the resource margin, the cluster head of the UAV swarm will be switched through the onboard scoring engine.

[0008] In some embodiments, if the real-time cluster head resource margin of the cluster head node is less than the lower limit of the resource margin, then performing cluster head switching on the UAV swarm through the onboard scoring engine includes: Obtain the real-time cluster head competency score of all drone nodes in the drone swarm; If the real-time cluster head resource margin of the cluster head node is less than the lower limit of the resource margin, a cluster head switching instruction is sent to the UAV swarm through the onboard scoring engine. The cluster head switching instruction includes the real-time cluster head competence score of all UAV nodes. The new cluster leader node of the drone swarm is selected based on the real-time cluster leader competence scores of all drone nodes.

[0009] In some embodiments, the step of selecting the cluster leader node of the drone swarm based on the cluster leader competence score further includes: Detect the real-time cluster head link stability of the cluster head node; If the real-time cluster head link stability of the cluster head node is less than the lower limit of link stability, then the UAV swarm will be switched using the onboard scoring engine.

[0010] In some embodiments, if the real-time cluster head link stability of the cluster head node is less than the lower limit of link stability, then performing cluster head switching on the UAV swarm through the onboard scoring engine includes: Obtain the real-time cluster head competency score of all drone nodes in the drone swarm; If the real-time cluster head link stability of the cluster head node is less than the lower limit of link stability, a cluster head switching instruction is sent to the UAV swarm through the onboard scoring engine. The cluster head switching instruction includes the real-time cluster head competence score of all UAV nodes. The new cluster leader node of the drone swarm is selected based on the real-time cluster leader competence scores of all drone nodes.

[0011] In some embodiments, the step of selecting the cluster leader node of the drone swarm based on the cluster leader competence score further includes: The heartbeat signal of the cluster head node is detected by the member nodes in the drone swarm. If the heartbeat signal of the cluster head node is lost for more than a preset time threshold, a cluster head loss alarm is sent to the onboard scoring engine. The onboard scoring engine performs an emergency cluster head switch for the drone swarm.

[0012] In some embodiments, the emergency cluster head switching of the drone swarm via the onboard scoring engine includes: Check whether the onboard scoring engine has pre-stored a spare cluster head; If the onboard scoring engine has a pre-stored backup cluster head, the backup cluster head is activated by the onboard scoring engine so that the backup cluster head can take over the drone swarm. If the onboard scoring engine does not have a pre-stored backup cluster head, then the real-time cluster head competence score of all drone nodes in the drone swarm is calculated. A new cluster leader node is selected for the drone swarm based on the real-time cluster leader competence scores of all drone nodes, and the new cluster leader node takes over the drone swarm.

[0013] Secondly, embodiments of this application provide a dynamic leader election system for UAV clusters in an integrated air-space network, applied to the dynamic leader election method for UAV clusters in an integrated air-space network as described in any one of the first aspects, including: An onboard scoring engine, located on the geostationary satellite, is used to obtain the link stability, resource margin, and topology centrality of all UAV nodes in the UAV swarm; based on the link stability, resource margin, and topology centrality, a multi-dimensional dynamic scoring model is used to score the cluster head competence of all UAV nodes, resulting in a cluster head competence score; and a cluster head switching command is sent to the UAV swarm. A smart beamforming antenna is installed on the UAV node to form a directional beam to establish communication links between the UAV node and the geostationary satellite, and between the UAV nodes themselves. A multi-channel satellite communication terminal, located on the UAV node, is used to acquire and report the link stability, resource margin, and topology centrality of all UAV nodes through the communication link; and to connect to other UAV nodes and perform silent switching control and topology awareness through the communication link.

[0014] The technical solutions provided in this application have the following advantages compared with the prior art: The UAV cluster leader dynamic election method and system provided in this application for integrated air-space networks obtain the link stability, resource margin, and topology centrality of all UAV nodes in the UAV swarm through a satellite-borne scoring engine, wherein the satellite-borne scoring engine is located on a geostationary satellite. Based on the link stability, resource margin, and topology centrality, a multi-dimensional dynamic scoring model is used to score the cluster leader competence of all UAV nodes, resulting in a cluster leader competence score. The cluster leader node of the UAV swarm is selected based on the cluster leader competence score, which can accurately capture the node motion state and network topology changes, ensuring that the elected cluster leader is always in a critical position in the network, avoiding cluster leader overload or link breakage problems caused by sudden wind speed changes, tactical maneuvers, etc. By integrating link stability, resource margin, and topology centrality through the multi-dimensional dynamic scoring model, a comprehensive evaluation of the node cluster leader competence is achieved, which can prioritize the selection of nodes with sufficient resources, stable links, and located in the core position of the topology as cluster leaders, avoiding network congestion caused by insufficient cluster leader resources, and reducing the additional overhead caused by frequent cluster leader switching. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0018] Figure 1 A flowchart of a method for dynamic election of UAV cluster leaders for an integrated air-space network provided in an embodiment of this application; Figure 2 This is a schematic diagram of dynamic election of cluster heads in a dynamic network for unmanned aerial vehicles (UAVs) according to an embodiment of this application. Detailed Implementation

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

[0020] The following disclosure provides numerous different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of the invention. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0021] The UAV Dynamic Ad Hoc Network uses UAVs as communication nodes. It does not rely on fixed infrastructure such as base stations and routers. It uses wireless communication technologies (such as 5G, millimeter wave, and WiFi-6) to achieve dynamic discovery, connection and networking between nodes, forming a distributed and collaborative network system. It has shown irreplaceable application value in key areas such as military communication, emergency rescue, Internet of Things data collection and intelligent transportation collaboration, and has become one of the core development directions of the next generation of communication technology systems.

[0022] UAV cluster leader election (or nomination) is a core component of UAV ad hoc network cluster management. The cluster leader is equivalent to the core of a small cluster after clustering, undertaking key tasks such as cluster management, data aggregation and forwarding, and task allocation. Its election result directly affects network energy consumption, stability, and task execution efficiency. However, existing cluster leader election schemes still have significant technical defects in complex dynamic scenarios, making it difficult to meet the application requirements of high mobility and high reliability of UAV clusters. Specific problems are as follows: 1) Insufficient adaptability of static decision-making mechanisms: Traditional cluster leader election schemes (such as the LEACH protocol) are mostly based on static parameters such as the initial position and fixed ID of nodes, without considering the dynamic changes in network topology caused by the high-speed movement of UAVs. When the movement state of cluster nodes changes abruptly (such as wind speed disturbances, tactical maneuvers, etc.), it is easy to cause the cluster leader to be too far away from the members in the cluster, the communication link to break, or the cluster leader to be overloaded. 1) Imbalance, ultimately leading to data transmission interruption; 2) Periodic re-election leads to resource waste: Most traditional solutions use a network-wide periodic re-election mechanism to update the cluster head. Although this mode can balance node energy consumption to a certain extent, frequent election interactions will generate a lot of additional communication overhead. According to actual test data, it can occupy more than 30% of the network bandwidth resources, which not only squeezes the business data transmission channel, but also aggravates node energy consumption and shortens the cluster's endurance; 3) Weak resilience and fault tolerance: Existing solutions lack an efficient cluster head failure emergency response mechanism. When the cluster head node fails due to enemy attack, equipment failure or link interruption, the cluster members need to re-initiate the network-wide election process, which usually causes the cluster structure reorganization delay to exceed 5 seconds. This long delay can easily cause network splitting and data transmission interruption. In scenarios with strict real-time requirements, such as military confrontation, it may directly lead to mission failure.

[0023] Firstly, such as Figure 1 , 2 As shown, to address the aforementioned technical problems, this application provides a method for dynamic leader election of UAV clusters in an integrated air-space network, characterized by comprising: S101: Obtain the link stability, resource margin, and topology centrality of all UAV nodes in the UAV swarm through a satellite-borne scoring engine, wherein the satellite-borne scoring engine is located on a geostationary satellite. S102: Based on the link stability, resource margin, and topology centrality, a multi-dimensional dynamic scoring model is used to score the cluster head competence of all UAV nodes, resulting in a cluster head competence score. S103: Select the cluster leader node of the UAV swarm based on the cluster leader competence score.

[0024] It should be noted that the geostationary orbit satellites (GEO satellites, geostationary orbit satellites) deployed by the onboard scoring engine have wide-area coverage capabilities, and can simultaneously acquire global status information of all UAV nodes in a large airspace. This solves the limitation that nodes can only acquire local information in traditional distributed elections, making the cluster head election decision more in line with the heterogeneity and wide-area characteristics of the integrated air-space network, and improving the overall collaborative efficiency of the cluster.

[0025] It should be noted that the onboard scoring engine obtains the link stability (reflecting the real-time connectivity status of communication links between nodes) and topology centrality (reflecting the importance of a node's position in a dynamic network) of UAV nodes in real time, replacing the traditional static election parameters based on initial position and fixed ID. Combined with the characteristics of high-speed maneuvering of UAVs and wide coverage of orbital satellites in the integrated air-space network, it can accurately capture the movement status of nodes and changes in network topology, ensuring that the elected cluster head is always in a critical position in the network, avoiding cluster head overload or link breakage caused by sudden wind speed changes, tactical maneuvers and other scenarios, and significantly reducing the data packet loss rate.

[0026] It should be noted that by integrating link stability, resource margin (reflecting the remaining energy, computing / communication bandwidth, and other resource status of nodes), and topology centrality through the multi-dimensional dynamic scoring model, a comprehensive assessment of the cluster head competence of nodes can be achieved. Compared with the traditional single-dimensional election scheme, it can prioritize the selection of nodes with sufficient resources, stable links, and located in the core position of the topology as cluster heads, avoiding network congestion caused by insufficient cluster head resources, and reducing the additional overhead caused by frequent cluster head switching. Furthermore, since the cluster head election is based on real-time dynamic scoring rather than a fixed period, re-election is only triggered when the node status changes significantly (such as resource exhaustion, link breakage, or topology reconstruction), eliminating the need for the network-wide periodic re-election process in traditional schemes. This significantly reduces the bandwidth resources occupied by election signaling, keeps communication overhead within a reasonable range, effectively releases network bandwidth for business data transmission, reduces node energy consumption, and extends the endurance of the UAV swarm.

[0027] In some embodiments, the multi-dimensional dynamic scoring model is S = α·link stability + β·resource margin + γ·topology centrality, where α is the link stability weight, β is the resource margin weight, and γ is the topology centrality weight.

[0028] It should be noted that by using a linear weighting formula to transform the three-dimensional indicators into a unified cluster head competence score (S), the competence of cluster heads of different nodes has a clear quantitative basis for comparison, ensuring that the cluster head election process is objective and traceable. In this multi-dimensional dynamic scoring model, the weights of α, β, and γ can be flexibly adjusted according to the specific application scenarios and task requirements of the integrated air-space network, realizing an "on-demand" election strategy. For example, in areas with strong satellite signals and gentle topology changes, the weight of link stability can be appropriately reduced and the weight of resource margin can be increased; in areas with high-speed UAV maneuvers and frequent topology changes, the weights of link stability and topology centrality can be increased to ensure that the model adapts to the dynamic changes of the air-space environment.

[0029] In some embodiments, the link stability weight α = continuous connection time / total connection duration, the resource margin weight β = 0.6 * (remaining battery percentage) + 0.4 * (1 – CPU utilization), and the topology centrality γ = 1 / average number of hops to other nodes.

[0030] It should be noted that the link stability weight α is calculated using the formula "continuous connection time / total connection duration". This directly transforms the historical stability performance of the communication link between nodes into a quantifiable weight, which has an intuitive and traceable physical meaning. Furthermore, this calculation logic makes the α value positively correlated with the actual stability of the link: that is, the longer the continuous connection time, the higher the α value, and the more likely the cluster head will choose a node with a stable communication link during election. This fundamentally avoids the problem of data packet loss caused by sudden link interruptions in traditional static elections. Compared with a fixed α value, this dynamic calculation method can reflect changes in link status in real time (such as link fluctuations caused by satellite coverage switching and electromagnetic interference), enabling the scoring model to dynamically adapt to changes in link quality and ensuring the continuity of communication between the cluster head and its members.

[0031] It should be noted that the resource margin weight β is calculated using the weighted formula of "0.6 × remaining battery percentage + 0.4 × (1 – CPU utilization)". This formula takes into account both node energy resources and computing resources. That is, the remaining battery percentage determines the cluster head's endurance, and (1 – CPU utilization) reflects the redundancy of the node's computing resources. The combination of the two can comprehensively evaluate the service sustainability of the node as a cluster head (avoiding cluster head failure due to energy depletion or computing overload). Among them, the remaining battery percentage is given a higher weight (0.6), which is in line with the characteristic of UAV nodes that "energy constraints are the core bottleneck". At the same time, the CPU utilization index is included, which makes up for the shortcomings of traditional resource assessment that only considers energy. This ensures that the cluster head has sufficient endurance and can efficiently undertake computing-intensive tasks such as data aggregation and task scheduling within the cluster, reducing network congestion caused by cluster head overload.

[0032] It should be noted that the topology centrality γ is calculated using the formula "1 / average hops to other nodes," which quantifies the hub status of a node in the network topology: the lower the average hops, the higher the γ value, indicating that the transmission path from the node to other nodes is shorter and the forwarding layers are fewer. This logic causes the cluster head to prioritize nodes located in the core of the topology, which can significantly reduce hop loss and latency during data forwarding and improve the overall data transmission efficiency of the network. Moreover, the calculation method is simple and objective, dynamically generating the γ value based on the actual network topology structure, adapting to the characteristics of frequent changes in the air-space integrated network topology, ensuring that the cluster head is always in the optimal position for data forwarding, and avoiding the forwarding path redundancy problem caused by traditional fixed topology evaluation.

[0033] In some embodiments, the step of selecting the cluster leader node of the drone swarm based on the cluster leader competence score further includes: Detect the real-time cluster head resource adequacy of the cluster head node; If the real-time cluster head resource margin of the cluster head node is less than the lower limit of the resource margin, the cluster head of the UAV swarm will be switched through the onboard scoring engine.

[0034] It should be noted that by real-time monitoring of the cluster head node's resource margin (integrating remaining battery power and CPU utilization), the cluster head's resource consumption trend can be accurately captured, avoiding the problem of "passive failure after the cluster head's resources are exhausted" in traditional solutions. When the real-time resource margin of the cluster head node is lower than a preset lower limit (which can be 20%), cluster head switching is actively triggered to ensure that the handover between the old and new cluster heads is completed before the cluster head can no longer undertake management and forwarding tasks, ensuring that core services such as data transmission and member management within the cluster are not interrupted.

[0035] In some embodiments, if the real-time cluster head resource margin of the cluster head node is less than the lower limit of the resource margin, then performing cluster head switching on the UAV swarm through the onboard scoring engine includes: Obtain the real-time cluster head competency score of all drone nodes in the drone swarm; If the real-time cluster head resource margin of the cluster head node is less than the lower limit of the resource margin, a cluster head switching instruction is sent to the UAV swarm through the onboard scoring engine. The cluster head switching instruction includes the real-time cluster head competence score of all UAV nodes. The new cluster leader node of the drone swarm is selected based on the real-time cluster leader competence scores of all drone nodes.

[0036] It should be noted that the onboard scoring engine centrally obtains the real-time cluster head competence scores of all UAV nodes, eliminating the need for distributed negotiation among nodes within the cluster or sequential hop transmission of scoring data. This avoids the decision lag caused by "local information asymmetry" in traditional distributed switching. When the cluster head resource margin (i.e., the real-time cluster head resource margin of the cluster head node) is lower than the lower limit, the onboard scoring engine directly sends a cluster head switching command (including the real-time cluster head competence scores of all nodes) to all nodes in the network. No additional interaction confirmation information between nodes is required, which greatly simplifies the switching process. Compared with the multi-step process of "intra-cluster initiation - negotiation of candidates - voting confirmation" in the traditional scheme (reorganization delay > 5 seconds), this centralized command issuance mode can compress the total switching time to the millisecond level, avoiding service interruption caused by cluster head vacancies.

[0037] It should be noted that the cluster head switching command directly carries the real-time scores of all nodes. The drone nodes do not need additional calculations or interactions to obtain the decision basis. They only need to confirm the new cluster head according to the score ranking, which greatly reduces the computing power consumption and communication overhead of the nodes. It can be adapted to the hardware constraints of "limited resources" of drone nodes, ensuring that the nodes can still undertake business data transmission tasks normally during the switching process, and avoiding the service quality degradation caused by the switching.

[0038] In some embodiments, the step of selecting the cluster leader node of the drone swarm based on the cluster leader competence score further includes: Detect the real-time cluster head link stability of the cluster head node; If the real-time cluster head link stability of the cluster head node is less than the lower limit of link stability, then the UAV swarm will be switched using the onboard scoring engine.

[0039] It should be noted that by continuously monitoring the real-time link stability of the cluster head node (based on the quantitative indicator of "continuous connection time / total connection duration"), the trend of link quality degradation can be accurately captured (such as the distance increase caused by the high-speed maneuvering of drones, link fluctuations caused by electromagnetic interference, signal interruptions caused by satellite coverage switching, etc.). When the link stability is lower than the lower limit (i.e., the lower limit of link stability, which can be 0.7), the cluster head switching is actively triggered, avoiding the passive situation of "passive reorganization only after the link is completely interrupted" in the traditional solution, and ensuring the communication continuity between cluster members and the cluster head from the source.

[0040] It should be noted that, due to its wide-area coverage and real-time data acquisition capabilities, the onboard scoring engine can simultaneously obtain the link stability and cluster head competence scores of all network nodes. This eliminates the need for distributed negotiation or localized information exchange among nodes within a cluster. Compared to the reassembly delay (typically >5 seconds) caused by "local information asymmetry" in traditional distributed handover, the onboard platform can achieve millisecond-level link status awareness and handover decisions, significantly shortening recovery time after link failures. When link stability triggers a handover, the onboard scoring engine directly sends cluster head handover instructions (including real-time cluster head competence scores for all nodes) to all network nodes, avoiding handover conflicts caused by information asynchrony between nodes. The centralized instruction transmission mode simplifies the handover process, ensuring that all nodes respond synchronously and quickly confirm the new cluster head, achieving a "seamless" link handover and reducing communication interruption time.

[0041] In some embodiments, if the real-time cluster head link stability of the cluster head node is less than the lower limit of link stability, then performing cluster head switching on the UAV swarm through the onboard scoring engine includes: Obtain the real-time cluster head competency score of all drone nodes in the drone swarm; If the real-time cluster head link stability of the cluster head node is less than the lower limit of link stability, a cluster head switching instruction is sent to the UAV swarm through the onboard scoring engine. The cluster head switching instruction includes the real-time cluster head competence score of all UAV nodes. The new cluster leader node of the drone swarm is selected based on the real-time cluster leader competence scores of all drone nodes.

[0042] It should be noted that the switching process is proactively triggered by "link stability falling below the lower limit," which is a "preventive maintenance" rather than a post-fault remedy. After the election of the new cluster head, the original cluster head can quickly complete the handover of data and task status (such as the cluster member list, unforwarded data, task scheduling information, etc.) with the new cluster head. Compared with the passive reorganization after the traditional cluster head link is completely interrupted, this method achieves seamless connection of communication services, effectively avoiding problems such as data loss and task interruption. It is especially suitable for scenarios with stringent requirements for real-time performance and continuity, such as military communications and emergency rescue. Moreover, the space-based deployment mode of the spaceborne scoring engine is less susceptible to electromagnetic interference, terrain obstruction, and other factors compared to ground communication links, ensuring the stable issuance of switching commands and reliable transmission of scoring data. In complex electromagnetic environments such as military confrontations, this feature can ensure that the cluster head switching process is uninterrupted, further enhancing the network's resilience and survivability.

[0043] In some embodiments, the step of selecting the cluster leader node of the drone swarm based on the cluster leader competence score further includes: The heartbeat signal of the cluster head node is detected by the member nodes in the drone swarm. If the heartbeat signal of the cluster head node is lost for more than a preset time threshold, a cluster head loss alarm is sent to the onboard scoring engine. The onboard scoring engine performs an emergency cluster head switch for the drone swarm.

[0044] It should be noted that the preset time threshold can be 300ms, and the cluster head loss alarm can be [0x55][failed cluster head ID][self ID][location]. The cluster head heartbeat signal is detected in real time by the member nodes within the cluster. By taking advantage of the short-range communication between nodes, the status of the cluster head can be perceived at the millisecond level, ensuring that the emergency process is triggered as soon as the cluster head fails. The alarm frame (i.e., the cluster head loss alarm) contains only 4 core fields (frame header [0x55] + failed cluster head ID + self ID + location). The data length is short (usually <20 bytes), which avoids long frame transmission occupying a lot of bandwidth. At the same time, the short frame design reduces the transmission power consumption of member nodes.

[0045] It should be noted that this mechanism is designed for scenarios where "the cluster head suddenly fails even though the link stability / resource margin has not triggered a switchover" (such as the cluster head being destroyed or a sudden circuit failure in military confrontation). It complements the proactive switchover triggered by resource margin and link stability mentioned above, achieving dual protection of "proactive prevention + emergency remediation". It comprehensively covers various operating conditions of cluster head failure and significantly improves network resilience. Moreover, the space-based deployment mode of the spaceborne scoring engine is not affected by ground terrain, electromagnetic interference, or local node failures. Even if some member nodes fail to report alarms, as long as one node successfully reports, a global switchover can be triggered. In harsh scenarios such as military confrontation and disaster relief, this feature ensures that the switchover process is not interrupted after the cluster head fails, ensuring the continuous availability of core network functions.

[0046] In some embodiments, the emergency cluster head switching of the drone swarm via the onboard scoring engine includes: Check whether the onboard scoring engine has pre-stored a spare cluster head; If the onboard scoring engine has a pre-stored backup cluster head, the backup cluster head is activated by the onboard scoring engine so that the backup cluster head can take over the drone swarm. If the onboard scoring engine does not have a pre-stored backup cluster head, then the real-time cluster head competence score of all drone nodes in the drone swarm is calculated. A new cluster leader node is selected for the drone swarm based on the real-time cluster leader competence scores of all drone nodes, and the new cluster leader node takes over the drone swarm.

[0047] It should be noted that the pre-storage process for backup cluster heads can be completed during periods of network idleness (such as when the cluster is flying smoothly and the task load is low). The onboard scoring engine calculates and caches nodes with high cluster head competence as backup cluster heads. When an emergency switchover is triggered, the pre-storage results are directly called, avoiding the high load pressure of the onboard platform centrally calculating the scores of all nodes during sudden failures, and ensuring the operational stability of the onboard engine when handling multiple cluster switchover requests simultaneously. When a backup cluster head is activated, the onboard scoring engine only needs to issue an "activation command + cluster member list", without transmitting real-time score data of all nodes, reducing the amount of communication data. Even if a real-time score election is triggered, it is quickly completed by relying on the onboard centralized computing capabilities, avoiding the additional communication overhead of distributed negotiation between nodes, and adapting to the energy and bandwidth resource constraints of UAV nodes. During cluster head switching, other UAV member nodes remain silent.

[0048] Secondly, embodiments of this application provide a dynamic leader election system for UAV clusters in an integrated air-space network, applied to the dynamic leader election method for UAV clusters in an integrated air-space network as described in any one of the first aspects, including: An onboard scoring engine, located on the geostationary satellite, is used to obtain the link stability, resource margin, and topology centrality of all UAV nodes in the UAV swarm; based on the link stability, resource margin, and topology centrality, a multi-dimensional dynamic scoring model is used to score the cluster head competence of all UAV nodes, resulting in a cluster head competence score; and a cluster head switching command is sent to the UAV swarm. A smart beamforming antenna is installed on the UAV node to form a directional beam to establish communication links between the UAV node and the geostationary satellite, and between the UAV nodes themselves. A multi-channel satellite communication terminal, located on the UAV node, is used to acquire and report the link stability, resource margin, and topology centrality of all UAV nodes through the communication link; and to connect to other UAV nodes and perform silent switching control and topology awareness through the communication link.

[0049] The device / system embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0050] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0051] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also mean including the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a specific order described or illustrated unless the order of execution is explicitly indicated. It should also be understood that additional or alternative steps may be used. The above description is merely a specific embodiment of the invention to enable those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for dynamic cluster head election of UAVs in space-air-ground integrated network, characterized in that, The application comprises: obtaining link stability, resource margin and topological centrality of all unmanned aerial vehicle nodes in the unmanned aerial vehicle group through a satellite-borne scoring engine, wherein the satellite-borne scoring engine is arranged on a stationary orbit satellite; performing cluster head competence scoring on all unmanned aerial vehicle nodes based on the link stability, the resource margin and the topological centrality through a multi-dimensional dynamic scoring model to obtain cluster head competence scores; selecting a cluster head node of the unmanned aerial vehicle group based on the high and low of the cluster head competence scores.

2. The method of claim 1, wherein, The multi-dimensional dynamic scoring model is S = α · link stability + β · resource margin + γ · topological centrality, wherein α is a link stability weight, β is a resource margin weight, and γ is a topological centrality weight.

3. The method of claim 2, wherein, The link stability weight α = continuous connection time / connection total time length, the resource margin weight β = 0.6*(remaining battery capacity percentage) + 0.4*(1-CPU usage rate), and the topological centrality γ = 1 / average hop number to other nodes.

4. The method of claim 1, wherein, The method for selecting the cluster head node of the unmanned aerial vehicle group based on the high and low of the cluster head competence scores further comprises: detecting real-time cluster head resource margin of the cluster head node; if the real-time cluster head resource margin of the cluster head node is less than a resource margin lower limit value, performing cluster head switching on the unmanned aerial vehicle group through the satellite-borne scoring engine.

5. The method of claim 4, wherein, The method for performing cluster head switching on the unmanned aerial vehicle group through the satellite-borne scoring engine if the real-time cluster head resource margin of the cluster head node is less than the resource margin lower limit value comprises: obtaining real-time cluster head competence scores of all unmanned aerial vehicle nodes in the unmanned aerial vehicle group; if the real-time cluster head resource margin of the cluster head node is less than the resource margin lower limit value, sending a cluster head switching instruction to the unmanned aerial vehicle group through the satellite-borne scoring engine, wherein the cluster head switching instruction comprises the real-time cluster head competence scores of all unmanned aerial vehicle nodes; selecting a new cluster head node of the unmanned aerial vehicle group based on the high and low of the real-time cluster head competence scores of all unmanned aerial vehicle nodes.

6. The method of claim 1, wherein, The method for selecting the cluster head node of the unmanned aerial vehicle group based on the high and low of the cluster head competence scores further comprises: detecting real-time cluster head link stability of the cluster head node; if the real-time cluster head link stability of the cluster head node is less than a link stability lower limit value, performing cluster head switching on the unmanned aerial vehicle group through the satellite-borne scoring engine.

7. The method of claim 6, wherein, The method for performing cluster head switching on the unmanned aerial vehicle group through the satellite-borne scoring engine if the real-time cluster head link stability of the cluster head node is less than the link stability lower limit value comprises: obtaining real-time cluster head competence scores of all unmanned aerial vehicle nodes in the unmanned aerial vehicle group; if the real-time cluster head link stability of the cluster head node is less than the link stability lower limit value, sending a cluster head switching instruction to the unmanned aerial vehicle group through the satellite-borne scoring engine, wherein the cluster head switching instruction comprises the real-time cluster head competence scores of all unmanned aerial vehicle nodes; selecting a new cluster head node of the unmanned aerial vehicle group based on the high and low of the real-time cluster head competence scores of all unmanned aerial vehicle nodes.

8. The method of claim 1, wherein, The method for selecting the cluster head node of the unmanned aerial vehicle group based on the high and low of the cluster head competence scores further comprises: detecting a heartbeat signal of the cluster head node through a member node in the unmanned aerial vehicle group; If the heartbeat signal of the cluster head node is lost for more than a preset time threshold, a cluster head loss alarm is sent to the satellite-based scoring engine; An emergency cluster head switching of the UAV group is performed by the satellite-based scoring engine.

9. The method of claim 8, wherein, The emergency cluster head switching of the UAV group by the satellite-based scoring engine comprises: It is detected whether the satellite-based scoring engine pre-stores a backup cluster head; If the satellite-based scoring engine pre-stores a backup cluster head, the backup cluster head is activated by the satellite-based scoring engine so that the backup cluster head takes over the UAV group; If the satellite-based scoring engine does not pre-store a backup cluster head, real-time cluster head competency scores of all UAV nodes in the UAV group are calculated; Based on the high and low of the real-time cluster head competency scores of all UAV nodes, a new cluster head node of the UAV group is selected, and the new cluster head node takes over the UAV group.

10. A UAV cluster head dynamic election system for space-air integrated network, characterized in that, The method for dynamically promoting a cluster head of a UAV for an aerospace integrated network as claimed in any one of claims 1-9 comprises: A satellite-based scoring engine is arranged on the stationary orbit satellite, and is used to acquire link stability, resource margin and topological centrality of all UAV nodes in the UAV group; based on the link stability, the resource margin and the topological centrality, a multi-dimensional dynamic scoring model is used to score the cluster competency of all UAV nodes to obtain cluster competency scores; a cluster head switching instruction is sent to the UAV group; An intelligent beam forming antenna is arranged on the UAV node, and is used to form a directional beam to establish a communication link between the UAV node and the stationary orbit satellite and between the UAV nodes; A multi-channel satellite communication terminal is arranged on the UAV node, and is used to acquire and report the link stability, the resource margin and the topological centrality of all UAV nodes through the communication link; the multi-channel satellite communication terminal is connected with other UAV nodes and a silent switching control and a topology perception through the communication link.

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