Low-altitude unmanned aerial vehicle cluster communication method based on self-organizing network

Abstract

The invention discloses a low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network, and the method comprises the steps: obtaining neighbor information through broadcasting the ID, the current state and the flight intention of an unmanned aerial vehicle node, carrying out the prospective evaluation of the future topology stability and centrality of each node, intelligently electing a cluster head, dynamically forming and maintaining a cluster, and then, carrying out the cluster communication through the self-organizing network. And generating a layered intra-cluster routing table and an inter-cluster forwarding rule by using a cluster structure, and finally realizing efficient layered forwarding of the data packet. In this way, the routing storm problem in large-scale unmanned aerial vehicle cluster communication is solved, and the expansibility and robustness of the network are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle communication, and more particularly, to a low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network. BACKGROUND

[0002] With the rapid development of unmanned aerial vehicle technology and its wide application in military and civilian fields, multi-unmanned aerial vehicle cooperative operation and cluster task execution have become an important trend in the future aviation field. In order to fully exert the cluster advantage and ensure efficient cooperation and information sharing among unmanned aerial vehicle nodes, building a stable, reliable, low-delay and real-time communication network is the core prerequisite. However, the high dynamics of low-altitude environment, frequent changes of topology structure and limited computing and energy resources of unmanned aerial vehicle platform all pose severe challenges to the communication scheme, and an self-organizing communication method that can adapt to these characteristics is urgently needed to support the cooperative operation of unmanned aerial vehicle cluster.

[0003] However, in the prior art, the traditional self-organizing network communication scheme applied to low-altitude unmanned aerial vehicle cluster, especially when facing large-scale unmanned aerial vehicle cluster, exposes significant technical bottlenecks. For example, self-organizing network routing protocols based on flat topology structure, such as on-demand distance vector routing protocol (AODV) and optimized link state routing protocol (OLSR), can effectively operate in scenarios with fewer nodes or relatively flat topology changes, but when the scale of unmanned aerial vehicle cluster is rapidly expanded and the number of nodes increases from dozens to hundreds or even thousands, its performance will be greatly reduced. The high-speed relative mobility between unmanned aerial vehicles in large-scale cluster results in rapid changes in network topology, forcing these traditional protocols to frequently perform routing discovery and topology update operations. This repeated broadcasting and maintenance of link state and routing information will generate huge control signaling overhead, forming a so-called routing storm, which not only consumes a large amount of network bandwidth resources, severely squeezing the space for effective data transmission, leading to a sharp decline in data throughput, but also may cause network paralysis due to network traffic congestion and excessive processing load, seriously hindering the engineering and scaling development of unmanned aerial vehicle cluster from small-scale experiments to large-scale practical applications.

[0004] Therefore, an optimized low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network is expected. SUMMARY

[0005] To solve the above technical problems, the application is proposed. Embodiments of the application provide a low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network. The method broadcasts an unmanned aerial vehicle node's own ID, current state and flight intention, acquires neighbor information, evaluates future topological stability and centrality of each node in a forward-looking manner, intelligently elects a cluster head, dynamically forms and maintains a cluster, then generates a hierarchical intra-cluster routing table and inter-cluster forwarding rule using the cluster structure, and finally realizes efficient hierarchical forwarding of data packets. In this way, the routing storm problem in large-scale unmanned aerial vehicle cluster communication is solved, and the scalability and robustness of the network are significantly improved.

[0006] According to one aspect of the application, a low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network is provided, which comprises: broadcasting node state and intention based on node ID, current state and flight intention to obtain a neighbor information table of each node; evaluating and electing cluster head eligibility of each node based on the current state, flight intention and neighbor information table of each node to obtain a node role list; extracting cluster head nodes from the node role list to obtain cluster head IDs, and dynamically forming and maintaining clusters for each cluster head ID to obtain a cluster member list; generating mixed routing information based on the cluster member list to obtain an intra-cluster routing table and inter-cluster forwarding rule; performing hierarchical forwarding of data packets based on the intra-cluster routing table and inter-cluster forwarding rule.

[0007] Compared with the prior art, the low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network provided by the application broadcasts an unmanned aerial vehicle node's own ID, current state and flight intention, acquires neighbor information, evaluates future topological stability and centrality of each node in a forward-looking manner, intelligently elects a cluster head, dynamically forms and maintains a cluster, then generates a hierarchical intra-cluster routing table and inter-cluster forwarding rule using the cluster structure, and finally realizes efficient hierarchical forwarding of data packets. In this way, the routing storm problem in large-scale unmanned aerial vehicle cluster communication is solved, and the scalability and robustness of the network are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0008] The above and other objects, features and advantages of the application will become more apparent from the following detailed description of embodiments of the application, taken in conjunction with the accompanying drawings. The accompanying drawings are provided to provide a further understanding of embodiments of the application, and constitute a part of the specification, together with the application embodiments, serve to explain the application, and do not constitute a limitation on the application. In the drawings, the same reference numerals generally represent the same components or steps.

[0009] Figure 1Flow chart of the low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network according to an embodiment of the present application; Figure 2 Data flow schematic diagram of the low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network according to an embodiment of the present application. DETAILED DESCRIPTION

[0010] Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of embodiments of the present application, and thus are not to be used to limit the whole embodiments of the present application, and it should be appreciated that the present application covers all the technical solutions falling within the scope of the present application.

[0011] As shown in the present application and claims, unless the context clearly indicates otherwise, the words "one", "an", "a", and / or "the" do not mean to specify a single number, but also include a plural number. Generally, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0012] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0013] Flow charts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in sequence. Instead, various steps can be processed in reverse order or simultaneously, as needed. Meanwhile, other operations can also be added to these processes, or one or more steps of the operations can be removed from these processes.

[0014] Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of embodiments of the present application, and thus are not to be used to limit the whole embodiments of the present application, and it should be appreciated that the present application covers all the technical solutions falling within the scope of the present application.

[0015] In the technical solution of the present application, a low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network is proposed. Figure 1 Flow chart of the low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network according to an embodiment of the present application. Figure 2 System architecture diagram of the low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network according to an embodiment of the present application. As Figure 1 and Figure 2As shown, the self-organizing network based low-altitude unmanned aerial vehicle cluster communication method according to the embodiments of the present application comprises the following steps: S1, performing node state and intention broadcast based on node ID, current state and flight intention to obtain a neighbor information table of each node; S2, performing cluster head qualification evaluation and election on each node based on the current state, flight intention and neighbor information table of each node to obtain a node role list; S3, extracting cluster head nodes from the node role list to obtain cluster head IDs, and performing dynamic cluster formation and maintenance on each cluster head ID to obtain a cluster member list; S4, performing hybrid routing information generation based on the cluster member list to obtain an intra-cluster routing table and inter-cluster forwarding rule; and S5, performing layered forwarding on data packets based on the intra-cluster routing table and inter-cluster forwarding rule.

[0016] In particular, the S1, performing node state and intention broadcast based on node ID, current state and flight intention to obtain a neighbor information table of each node. It should be understood that in such a high-dynamic network environment of low-altitude unmanned aerial vehicle cluster, the position, speed and network connectivity of nodes (i.e. unmanned aerial vehicles) will change frequently. In order to enable each unmanned aerial vehicle node to autonomously perceive the surrounding network topology, understand the state of its directly reachable neighbors, and predict the future stability of possible communication links, it is necessary to periodically broadcast the state and intention of itself. Among them, the broadcast of node ID is crucial for identifying each independent entity in the network, which gives each unmanned aerial vehicle a unique identity, ensuring the accuracy of information attribution; the broadcast of current state (such as real-time position, speed, remaining energy, etc.) enables each node to master the immediate physical characteristics and resource status of its neighbors. This is crucial for assessing the real-time connectivity and link quality of the network, and is the basis for making immediate routing decisions and resource allocation; the broadcast of flight intention is important in terms of predicting and stability considerations of future network topology. Unlike traditional communication protocols that only rely on current state, the introduction of flight intention enables each unmanned aerial vehicle to predict the movement trend and planned path of its neighbors in the future period of time. For example, if a neighbor node broadcasts its intention to change course or altitude soon, the receiving node can predict potential link interruption or formation of new links accordingly, and take proactive measures such as finding alternative routes in advance or adjusting its own communication strategy. This intention-based prediction capability significantly improves the persistence of low-altitude unmanned aerial vehicle cluster communication links and the stability of network topology, effectively avoiding communication interruption caused by sudden topology changes, and has a profound impact on reducing routing storm, improving network efficiency and robustness. By broadcasting these comprehensive information, each node can construct a detailed and accurate neighbor information table, providing indispensable input for subsequent more complex intelligent decisions.

[0017] In implementation, first, the node ID, current state and flight intention of each node are encapsulated into a state and intention packet. In this process, each UAV node collects its own node ID, current real-time state (such as accurate position coordinates, flight velocity vector, and remaining energy value) and preset or dynamically updated flight intention (such as expected route, task target, etc.). These information are then effectively encapsulated into a unified state and intention packet to ensure the integrity and standardization of data transmission; Then, each node broadcasts its state and intention packet to its one-hop neighbors at a fixed frequency; wherein the fixed frequency is once every 2 seconds. This means that every 2 seconds, each UAV node will actively send a data packet containing its latest state and flight intention to all direct neighbors within its communication range. This periodic broadcast mechanism ensures the timeliness of information update in the network, especially suitable for the high dynamic operation environment of low-altitude UAV cluster; Further, each node receives and parses the state and intention packets from all its one-hop neighbors, and stores the parsed neighbor information in the neighbor information table. That is, when each UAV node receives the state and intention packets broadcasted by all its one-hop neighbors, it will immediately receive and parse them. Through the parsing operation, the node can accurately extract the node ID, current state and flight intention of the neighbor from the received data packet. Subsequently, these parsed neighbor information will be sequentially stored in the neighbor information table maintained by the node itself. The neighbor information table is essentially a dynamically updated local database that stores the real-time key data of all direct neighbors, providing a comprehensive local network view for the node to perform subsequent cluster head qualification evaluation, routing decision and other complex operations.

[0018] In particular, the S2 performs cluster head eligibility evaluation and election on each node based on the current state, flight intention and neighbor information table of each node to obtain a node role list. It should be understood that in a highly dynamic low-altitude UAV network environment, traditional static or simple reactive cluster head election mechanism is difficult to adapt to the rapid change of topology, which may lead to frequent replacement of cluster head, unstable communication link and excessive control signaling overhead. In order to cope with these challenges and ensure the stability, scalability and communication efficiency of the UAV cluster communication network, the cluster head election mechanism is introduced in the technical solution of the present application, which can more accurately evaluate which node is most suitable for the core role of cluster head by comprehensively considering the current state, flight intention and network topology cognition (neighbor information table) of the node. The cluster head node plays a key role in data forwarding and management in the cluster, and its stability and centrality are directly related to the communication performance of the whole cluster. Therefore, strict cluster head eligibility evaluation and election are performed to select those nodes that are expected to maintain stable connection, have more neighbors and have sufficient resources in the future as cluster heads, so as to build a more robust and efficient cluster communication architecture.

[0019] In specific implementation, first, based on the current state, flight intention and neighbor information table of each node, the future state of the node and the network topology stability are predicted to obtain a predicted stability score and a predicted centrality score. It should be understood that in the dynamic environment of low-altitude UAV cluster, the topology of the network will change continuously with the movement of the UAV. Traditional cluster head selection method often relies on current state information, which leads to the fact that the cluster head may quickly move away from the original cluster or the center position of the cluster head may deviate significantly, frequently triggering cluster head reselection and cluster structure reconstruction, thereby generating a large amount of control signaling overhead, causing so-called routing storm, and seriously affecting the communication efficiency and stability. In order to overcome this challenge, in the technical solution of the present application, through predictive evaluation, the system can identify those nodes that are expected to maintain stable communication with neighbors and are located in the center position of the network in the future, so as to preferentially select them as cluster heads. This forward-looking decision greatly reduces the replacement frequency of cluster heads and the impact of topology changes, enhances the stability and robustness of the network, and is the key to improving the communication performance of large-scale UAV cluster.

[0020] In this process, firstly, the prediction of the future state of the node is carried out to obtain the key position information, which directly affects the calculation of the subsequent stability and centrality scores. Specifically, firstly, based on the current position and speed of each node, the position of the node at the end of the evaluation window is predicted to obtain a list of predicted positions of the node; specifically, each node will use its known current accurate position and real-time speed (vector information), and combine its own flight intention (for example, the established track, task point or expected maneuvering behavior), and use the prediction model to calculate the predicted position of the node at the end of the preset evaluation window. At the same time, the node will also derive the predicted positions of all neighbors in the same evaluation window according to the current positions, speeds and flight intentions of all neighbors received in the neighbor information table. Through this process, each node can obtain a complete list containing the predicted positions of itself and all neighbors at a certain time point in the future; Then, the average value of the total duration of the distance between each node and each neighbor node within the evaluation window being less than the communication distance is calculated as the predicted stability score of each node. Specifically, the predicted stability score of each node is calculated by the following formula: , Wherein, is the neighbor node of the node in the evaluation window, is the neighbor node set of the node ; by analyzing the relative motion of the node and each neighbor in the prediction period, the time of the communication link between the two being continuously effective is accumulated and the average value is obtained. The higher the score, the better the stability of the node and its neighbors to maintain communication connection in the future, and the more suitable it is as a cluster head.

[0021] Further, based on the predicted future state, the predicted centrality score of each node is determined. Specifically, firstly, based on the list of predicted positions of the node, the geometric center of the predicted positions of all neighbor nodes of each node at the end of the evaluation window is calculated; that is, after obtaining the list of predicted positions of the node, for each node, the system will calculate the geometric center of the predicted positions of all neighbor nodes of the node at the end of the evaluation window. This geometric center can be understood as the future center of gravity of the neighbor group of the node; Further, based on the Euclidean distance between the node predicted position of each node and the geometric center of the predicted positions of all neighbor nodes of each node at the end of the evaluation window, the predicted centrality score of each node is determined. Specifically, the predicted centrality score of each node is calculated by the following formula: , , in, For nodes The predicted location of the node, For nodes The geometric center of the predicted positions of all neighboring nodes at the end of the evaluation window; this score measures the centrality of the node in the future network topology. A higher score means that the node is more likely to be located at the center of its neighbors in the future. A high centrality score means that the node is in a more core position in the future network topology, which is conducive to reducing the number of communication hops and improving data forwarding efficiency.

[0022] Then, a multi-dimensional indicator fusion is performed on the predicted stability score and the predicted centrality score to obtain the final adaptation score. It should be understood that the complexity of the drone cluster environment determines that it is difficult to fully evaluate the suitability of a node as a cluster head with only a single or a few indicators. For example, only considering stability may ignore whether the node is at the center of the network, and only considering centrality may ignore its energy status. In order to ensure that the selected cluster head not only maintains a stable connection in the future and can efficiently forward data, but also has sufficient resources to support its long-term operation, in the technical solution of the present application, a multi-dimensional indicator fusion is performed on the predicted stability score and the predicted centrality score to more accurately identify the most suitable cluster head node, avoid frequent replacement of cluster heads or resource exhaustion due to one-sided considerations, thereby effectively reducing communication overhead and improving the overall stability and reliability of the network.

[0023] In specific implementation, the current connectivity of each node is first determined based on its neighbor information table. This connectivity is calculated based on the neighbor information table each node has obtained, and directly reflects the number of neighbors with which the node can directly communicate at the current moment. A node with a high connectivity usually means it has stronger network radiation capabilities and more routing options, and is an important indicator of the node's activity in the current network. Next, the remaining energy value of each node is read as an energy factor. Since drones are strictly limited by battery life when performing missions, the cluster head plays a key role, and its energy reserves are crucial to its service life. Therefore, each node obtains its own remaining energy in real time and incorporates it into the cluster head evaluation system as an energy factor. This avoids electing a node with nearly exhausted energy as the cluster head, thereby extending the cluster lifecycle and maintaining the network's continued operation capability. Further, the current connectivity, the energy factor, the predicted stability score and the predicted centrality score of each node are calculated to obtain a final fitness score of each node. This is a comprehensive calculation process, which weights and sums the current connectivity determined above, the energy factor read, and the predicted stability score and the predicted centrality score obtained in the previous step, by setting appropriate weight coefficients for different indicators, the system can focus on different characteristics according to the actual application requirements, so as to obtain the comprehensive cluster head suitability score of each node. Finally, this weighted sum is the final fitness score of the node, and the higher the score, the more suitable the node is to be selected as the cluster head.

[0024] Subsequently, a local neighborhood optimal decision and role declaration are made based on the final fitness score to obtain the node role list. It should be understood that the self-organization of the unmanned aerial vehicle cluster requires nodes to make decisions autonomously rather than relying on a centralized control entity. However, if all nodes blindly declare themselves as cluster heads or there is no clear mechanism to determine who should become a cluster head, the network will be in chaos, the cluster structure cannot be effectively established, and thus the communication efficiency is low and resources are wasted. Therefore, in the technical solution of the present application, by allowing each node to determine whether to act as a cluster head based on only its local neighbor information and its own comprehensive evaluation (i.e., the final fitness score), the huge overhead of global information exchange can be avoided, and the decision efficiency and network scalability can be significantly improved. This distributed decision-making mode enables the network to dynamically adapt to topology changes and quickly form a stable cluster structure, thereby providing a solid foundation for subsequent hierarchical routing and data forwarding, and ensuring the quality of communication services in a large-scale and highly dynamic environment.

[0025] In specific implementation, in response to the final fitness score of the node being greater than the final fitness scores of all neighbor nodes of the node , the role of the node is determined as a cluster head. In this process, first, after the multi-dimensional index fusion and the calculation of the respective final fitness scores are completed, each unmanned aerial vehicle node transmits its own final fitness score information to all one-hop neighbors through a broadcast mechanism. At the same time, each node also continuously listens to and receives the final fitness score information from its neighbors and stores it in the local neighbor information table; then, once the node has the latest final fitness scores of itself and all one-hop neighbors, it will make a comparison and judgment locally. The node will compare its own final fitness score with the final fitness scores of all neighbor nodes in the neighbor set. If the node finds that its own final fitness score is strictly greater than the final fitness scores of all neighbor nodes, i.e., it meets the condition “in response to the final fitness score of the node It determines itself as the most suitable node to be the cluster head in the current local neighborhood. This comparison is completely distributed, and each node makes decisions independently within its local field of view; eventually, when the node When it is determined to become the cluster head according to the above decision logic, it will broadcast a cluster head declaration to its neighbors. If the neighboring nodes that receive this declaration message have not decided to become the cluster head before and are within the communication range of the newly declared cluster head, they will recognize the declared node as their own cluster head and automatically determine their own role as an ordinary member of the cluster; on the contrary, if the node If a drone's final fitness score is not greater than that of all its neighbors, it is typically assigned a role of ordinary member or continues to wait to join a cluster. After all nodes execute this distributed decision-making and declaration process in parallel, the entire cluster forms a hierarchical structure consisting of multiple clusters, with each node ultimately determining its role (cluster leader or ordinary member). The set of roles confirmed by all nodes constitutes the node role list, which details each drone's identity within the cluster.

[0026] Specifically, S3 extracts cluster head nodes from the node role list to obtain cluster head IDs, and dynamically forms and maintains clusters for each cluster head ID to obtain a cluster member list. It should be understood that although cluster head qualification evaluation and election produce a list identifying each node role, this list alone is not sufficient to form a viable hierarchical communication network. To truly achieve effective cluster communication, the elected cluster head nodes must be visualized as actual cluster managers, and ordinary member nodes must be able to attach to appropriate cluster heads, thereby forming logical functional entities, namely clusters. In the highly dynamic environment of low-altitude drone swarms, the constant movement of drones causes frequent changes in the network topology, and cluster membership and structure are not static. Therefore, cluster formation must be dynamic and require ongoing maintenance to adapt to these changes. Extracting cluster head IDs is the basis for identifying these management entities, and dynamic cluster formation and maintenance ensure that each node can find and maintain a connection to its cluster regardless of drone movement. This ensures stable hierarchical routing and data forwarding, minimizing communication interruptions and control overhead caused by topology changes.

[0027] In practice, the cluster head ID is first extracted. Each node in the node role list clearly defines its specific identity within the cluster. At this point, the system identifies and extracts the unique identifiers of all nodes identified as cluster heads from the global or local (depending on the node's perspective) node role list. These cluster head IDs serve as the starting point for subsequent cluster formation, indicating which drones will assume cluster management responsibilities. Next, the formation of dynamic clusters. Once the cluster head ID is extracted, the corresponding cluster head node will initiate the cluster formation process. The specific steps are as follows: first, cluster head broadcast invitation: each elected cluster head node will periodically broadcast cluster invitation messages to all neighbor nodes within its communication range. These messages usually contain the cluster head's own ID, its current best fitness (or key performance indicators), and other information that may help ordinary nodes make joining decisions; then, ordinary nodes select cluster heads: ordinary member nodes that are not elected as cluster heads will receive invitation messages from multiple neighbor cluster heads. Instead of joining randomly, ordinary nodes evaluate the overall attractiveness of these potential cluster heads according to a set of pre-set or optimized strategies. The basis for evaluation may include received signal strength (RSSI), predicted link stability, distance from potential cluster heads (tending to choose closer cluster heads to reduce communication delay), and even considering the residual energy of cluster heads. This evaluation ensures that each ordinary node can choose to join the most optimal cluster for it. For example, a certain ordinary node may choose to join the cluster head with the highest predicted stability score; further, join request and confirmation: after determining the cluster head it wants to join, the ordinary node will send a join request message to the cluster head. After receiving and confirming the request, the cluster head node will add its ID to the cluster member list it maintains, and send a "join confirmation" message to the ordinary node. At this point, a dynamic cluster is initially formed, and the cluster head has a list of its members, while the ordinary members also explicitly belong to the cluster.

[0028] Finally, dynamic cluster maintenance. Due to the high mobility of the low-altitude UAV swarm, the cluster structure is not static and needs to be continuously maintained to adapt to the topology changes, including: member state monitoring: the cluster head node will continuously monitor the active state and communication link quality of its cluster members. This can be achieved by periodically receiving heartbeat packets or status update messages from members. If a member does not respond within a certain time, the cluster head may remove it from the cluster member list; Member voluntary departure / join: ordinary member nodes will also continuously monitor the link quality and predicted stability with the current cluster head. If it finds that the communication quality with the current cluster head has dropped significantly, or it detects a better potential cluster head (for example, by receiving a beacon from a new cluster head), the member node can voluntarily leave the current cluster and re-initiate a join request to find a new more suitable cluster; Cluster head state monitoring and reselection: the state of the cluster head (such as energy, predicted fitness) will also be concerned by its members and other cluster heads in the neighborhood. If a cluster head is about to run out of energy, or its predicted fitness has dropped significantly, it may trigger a cluster head reselection process in the local area to ensure that the cluster master can continue to provide services. This may result in the dissolution of a cluster and the redistribution of members, or the quick replacement of a new cluster head; Cluster merging and splitting: when two or more clusters gradually approach each other in geographical location, their cluster heads can negotiate to merge the clusters to reduce the number of clusters in the network and simplify the routing complexity. Conversely, when the members of a large cluster are too widely dispersed, or the cluster head is too heavily loaded, the cluster may also split into multiple smaller clusters to optimize local communication efficiency. These merging and splitting processes are also based on the current state, flight intention and shared neighbor information table of each node to make intelligent decisions.

[0029] Through the above dynamic formation and maintenance mechanism, the final generated cluster member list can reflect the real-time structure of each cluster in the swarm, ensuring the efficient self-adaptation of the network and the continuous availability of the link.

[0030] In particular, S4 generates hybrid routing information based on the cluster member list to obtain an intra-cluster routing table and inter-cluster forwarding rules. It should be understood that as drone swarms expand in size and flight environments become more dynamic, the traditional flat routing protocol, which maintains all routing information between each node, will lead to overly large routing tables and frequent routing updates, which in turn can cause routing storms and severe control signaling overhead. The technical solution of this application introduces a hierarchical routing strategy, namely hybrid routing. Hybrid routing can decompose the complex routing problem into two levels: intra-cluster and inter-cluster. Through this hierarchical structure, each node only needs to maintain detailed routing information within its cluster, while the cluster head is responsible for inter-cluster routing. This significantly reduces the amount of routing state information required to be maintained by a single node, thereby reducing the complexity of the routing protocol, control signaling overhead, and the frequency of routing table updates. Therefore, generating hybrid routing information based on the established cluster member list is intended to provide a scalable, efficient, and stable routing mechanism that ensures accurate and rapid transmission of data packets within and outside the cluster, thereby significantly improving the communication efficiency and robustness of large-scale drone swarms.

[0031] In specific implementation, the first step is to generate the intra-cluster routing table. After the dynamic cluster is formed and maintained, each cluster head will use the cluster member list it maintains to build the routing table within its cluster. The cluster head perceives the connectivity between any members in the cluster by collecting neighbor information of all its cluster members, or by running a lightweight routing protocol within the cluster (for example, a distance vector algorithm or link state algorithm optimized in a small range). The intra-cluster routing table records how to forward data packets from the cluster head to any of its cluster members, and how cluster members communicate through the cluster head or directly (if they are within a one-hop communication range). The cluster head updates these intra-cluster routing tables in real time to cope with the movement of members within the cluster. In this way, when a source node needs to send data to a target node in the same cluster, it first sends the data packet to its cluster head, and the cluster head routes the data packet to the target node based on the intra-cluster routing table; Secondly, inter-cluster forwarding rules are generated. This is mainly done by the cluster heads in coordination. A cluster head not only manages the communication within its cluster, but also acts as a gateway to other clusters, and is responsible for communication with other clusters. For this purpose, each cluster head node exchanges its cluster information (e.g., cluster head ID, network address prefix of the cluster it is responsible for, or a digest of the list of cluster members it can reach, and its link quality information with other cluster heads, etc.) with other cluster head nodes. Based on this information, a high-level virtual network topology is formed among cluster heads. On this virtual topology, a routing protocol adapted to the characteristics of Ad-Hoc networks (e.g., a link layer protocol optimized for routing among cluster heads or a simple forwarding strategy based on geographical location) is run by the cluster heads to generate inter-cluster forwarding rules. These rules indicate how to forward a data packet from the current cluster head to the target cluster head. For example, when the destination of a data packet is in another cluster, the cluster head of the source cluster will select the best path to relay the data packet to the destination cluster head according to the inter-cluster forwarding rules, possibly through the forwarding of multiple intermediate cluster heads. The continuous updating of these rules ensures that even in the case of relative movement between cluster heads, data can find the best path to reach the target cluster.

[0032] Through the generation of routing information at these two levels, the UAV cluster can achieve efficient hybrid routing: intra-cluster communication is based on detailed intra-cluster routing tables, while inter-cluster communication is relayed by cluster heads according to inter-cluster forwarding rules, thereby providing a structured and flexible communication framework.

[0033] In particular, the S5 performs hierarchical forwarding of data packets based on the intra-cluster routing table and the inter-cluster forwarding rules. That is, in the technical solution of the present application, by dividing the routing information into intra-cluster and inter-cluster two levels and performing hierarchical forwarding accordingly, the amount of routing knowledge required by each node can be significantly reduced, avoiding the proliferation of global routing information. This divide-and-conquer strategy not only improves the efficiency of routing decisions and reduces control signaling overhead, but also enhances the robustness of the network. When the local topology changes, only local (intra-cluster) routing information needs to be updated, without affecting the routing update of the entire network. Therefore, hierarchical forwarding aims to maximize the use of the established cluster structure and routing information to ensure that data packets can reach the destination with the most optimized path, the smallest delay and the highest reliability, thereby effectively supporting the cooperative tasks of the UAV cluster and improving the overall combat or operational effectiveness.

[0034] In specific implementation, the execution process of this step is to flexibly use the intra-cluster routing table and the inter-cluster forwarding rules for path selection and data transmission according to the cluster positions of the source node and the target node, mainly including the following two cases: The first case is intra-cluster forwarding: when the source node and the target node of a data packet belong to the same cluster, the forwarding process is mainly completed within the cluster. The process specifically includes: data packet generation and initial judgment: the source node generates the data packet to be sent and first judges whether the target node is directly connected to itself. If directly connected, the data packet can be directly sent; forwarding to the cluster head: if the target node is not directly connected to the source node, the source node will first send the data packet to the cluster head node to which it belongs. The cluster head node is the coordinator and forwarding center of intra-cluster communication; cluster head forwarding according to intra-cluster routing table: after receiving the data packet, the cluster head node will query the intra-cluster routing table maintained by itself. The routing table records in detail the reachable paths between each member node in the cluster. The cluster head will find the best next hop path to the target node from the intra-cluster routing table according to the target address of the data packet, and forward the data packet to the next appropriate intermediate node or directly to the target node (if the target node is a directly connected member of the cluster head); transmission along the intra-cluster path: the data packet will be transmitted along the path determined by the intra-cluster routing table within the cluster until it finally reaches the target node; The second case is inter-cluster forwarding: when the source node and the target node of a data packet belong to different clusters, the forwarding process needs to cross multiple clusters and is completed by the cluster heads in cooperation. The process specifically includes: data packet forwarding to the source cluster head: after the source node generates the data packet, since the target node is not within its own cluster, the source node will send the data packet to the cluster head node to which it belongs; source cluster head routing according to inter-cluster forwarding rules: after the source cluster head node receives the data packet and identifies that the target node is located in another cluster, it will query the inter-cluster forwarding rules maintained by itself. These rules guide the cluster head on how to forward the data packet between clusters to reach the cluster head of the target cluster. The source cluster head selects an optimal next hop cluster head (which may be a cluster head directly communicating with the target cluster head, or an intermediate cluster head) according to the rules and sends the data packet out; transmission along the inter-cluster path: the data packet will be relayed and forwarded between multiple cluster heads. Each intermediate cluster head, upon receiving the data packet, will also query its own inter-cluster forwarding rules to determine which next cluster head to transmit the data packet to, until the data packet finally reaches the cluster head of the target cluster. This process builds a logical routing path at the cluster head level; target cluster head performs intra-cluster forwarding: once the data packet reaches the cluster head of the target cluster, the target cluster head will identify that the final target node of the data packet is a member within its cluster. At this time, the target cluster head will switch to the intra-cluster forwarding mode, query its own intra-cluster routing table, determine the best path to the target node, and forward the data packet within the cluster until it reaches the target node.

[0035] Through this hierarchical forwarding mechanism, whether the data packet is transmitted within the cluster or across clusters, efficient decision-making and transmission can be performed at different routing levels, significantly improving the routing efficiency and data transmission performance of the network in large-scale scenarios.

[0036] In summary, the low-altitude unmanned aerial vehicle cluster communication method based on a self-organizing network according to the embodiments of the present application is illustrated, which broadcasts the unmanned aerial vehicle node's own ID, current state and flight intention, acquires neighbor information, evaluates the future topology stability and centrality of each node in advance, intelligently elects a cluster head, dynamically forms and maintains a cluster, then generates a hierarchical cluster routing table and inter-cluster forwarding rules using the cluster structure, and finally realizes efficient hierarchical forwarding of data packets. In this way, the routing storm problem in large-scale unmanned aerial vehicle cluster communication is solved, and the network scalability and robustness are significantly improved.

[0037] Embodiments of the present disclosure have been described above, the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles, practical applications, or improvements to the technology in the market of the embodiments, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A low-altitude UAV cluster communication method based on self-organizing network, characterized in that: include: Based on the node ID, current state and flight intention, the node state and intention are broadcast to obtain the neighbor information table of each node; Based on the current status, flight intention and neighbor information table of each node, cluster head qualification evaluation and election are performed on each node to obtain a node role list; Extract the cluster head node from the node role list to obtain the cluster head ID, and perform dynamic cluster formation and maintenance on each cluster head ID to obtain the cluster member list; Generate hybrid routing information based on the cluster member list to obtain the intra-cluster routing table and inter-cluster forwarding rules; Based on the intra-cluster routing table and inter-cluster forwarding rules, data packets are forwarded hierarchically.

2. The low-altitude UAV cluster communication method based on self-organizing network according to claim 1 is characterized in that: Based on the node ID, current state and flight intent, the node state and intent are broadcast to obtain the neighbor information table of each node, including: Encapsulate the node ID, current state and flight intention of each node into a state and intention data packet; Each node broadcasts its status and intention packets to its one-hop neighbors at a fixed frequency; Each node receives and parses the status and intent data packets from all of its one-hop neighbors, and stores the parsed neighbor information in the neighbor information table.

3. The low-altitude UAV cluster communication method based on self-organizing network according to claim 2 is characterized in that: The fixed frequency is once every 2 seconds.

4. The low-altitude UAV cluster communication method based on self-organizing network according to claim 1 is characterized in that: Based on the current status, flight intention and neighbor information table of each node, cluster head qualification evaluation and election are performed on each node to obtain a node role list, including: Based on the current state, flight intention and neighbor information table of each node, the future state of the node and the network topology stability are predicted to obtain the predicted stability score and predicted centrality score; The predicted stability score and predicted centrality score are fused with multi-dimensional indicators to obtain the final adaptation score; Based on the final fitness score, local neighborhood optimal decision and role declaration are performed to obtain the node role list.

5. The low-altitude UAV cluster communication method based on self-organizing network according to claim 4 is characterized in that: Based on the current state, flight intention, and neighbor information table of each node, the future state of the node and the network topology stability are predicted to obtain the predicted stability score and predicted centrality score, including: Based on the current position and velocity of each node, predict its position at the end of the evaluation window to obtain a list of predicted node positions; The average value of the total time duration during which the distance between each node and each neighboring node is continuously less than the communication distance within the evaluation window is calculated as the predicted stability score of each node.

6. The low-altitude UAV cluster communication method based on self-organizing network according to claim 5 is characterized in that: Calculating the average value of the total duration during which the distance between each node and each neighboring node is continuously less than the communication distance within the evaluation window as the predicted stability score of each node, including: calculating the predicted stability score of each node using the following formula, wherein the formula is: in, Neighbor nodes With node The total time duration during which the distance is continuously less than the communication distance within the evaluation window, Is a node The set of neighbor nodes.

7. The low-altitude UAV cluster communication method based on self-organizing network according to claim 5 is characterized in that: Based on the current state, flight intention, and neighbor information table of each node, the future state of the node and the network topology stability are predicted to obtain the predicted stability score and predicted centrality score, including: Based on the list of node predicted positions, calculate the geometric center of the predicted positions of all neighboring nodes of each node at the end of the evaluation window; The predicted centrality score of each node is determined based on the Euclidean distance between the predicted position of each node and the geometric center of the predicted positions of all neighboring nodes of each node at the end of the evaluation window.

8. The low-altitude UAV cluster communication method based on self-organizing network according to claim 7 is characterized in that: Determine the predicted centrality score of each node based on the Euclidean distance between the predicted node position of each node and the geometric center of the predicted positions of all neighboring nodes of each node at the end of the evaluation window, including: calculating the predicted centrality score of each node using the following formula, wherein the formula is: in, For nodes The predicted location of the node, For nodes The geometric center of the predicted positions of all neighbor nodes at the end of the evaluation window.

9. The low-altitude UAV cluster communication method based on self-organizing network according to claim 4 is characterized in that: The predicted stability score and predicted centrality score are fused with multi-dimensional indicators to obtain the final adaptation score, including: Based on the neighbor information table of each node, determine the current connectivity of each node; Read the remaining energy value of each node as the energy factor; The weighted sum of the current connectivity, energy factor, predicted stability score, and predicted centrality score of each node is calculated to obtain the final fitness score of each node.

10. The low-altitude UAV cluster communication method based on self-organizing network according to claim 4 is characterized in that: Based on the final adaptation score, the local neighborhood optimal decision and role declaration are performed to obtain the node role list, including: responding to the node The final fitness score of the node is greater than the final fitness score of all its neighbor nodes. The role is determined as cluster head.

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