Data transmission method of dynamic unmanned aerial vehicle cluster network and cluster network

Abstract

The invention discloses a data transmission method of a dynamic unmanned aerial vehicle cluster network and the cluster network, and relates to the technical field of unmanned aerial vehicle networking. According to the invention, a data hybrid transmission mode is provided aiming at the characteristic that the unmanned aerial vehicle cluster network is highly dynamic, node mobility and link interference are fully considered, and complementary work is carried out by intelligently combining a prediction transmission protocol and a multi-layer tree protocol; the data forwarding of the nodes is carried out by taking the maximum transmission utility as an index when the transmission protocol is predicted, and the data forwarding of the nodes is carried out by taking the minimum predicted transmission time as an index when the multi-layer tree protocol is carried out, so that the transmission delay is effectively reduced, the robustness of data transmission is improved, and the average information age is obviously reduced.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) networking technology, and more specifically, to: 1. a data transmission method for minimizing the average information age (AoI) in a dynamic unmanned aerial vehicle (UAV) swarm network environment; 2. a dynamic UAV swarm network employing the data transmission method. Background Technology

[0002] In dynamic UAV swarm networks, average AoI is a key metric for ensuring timely and effective responses, measuring the freshness of information. However, most existing AoI optimization methods neglect the impact of link interference and node mobility on dynamic UAV swarm networks—factors that make the topology of dynamic UAV swarm networks highly dynamic, with unstable and intermittent links, leading to inefficient routing and higher transmission latency. Furthermore, concurrent transmission frequently occurs in dynamic UAV swarm networks, easily causing severe link interference and packet corruption, which also affects the average AoI.

[0003] Therefore, existing technologies cannot meet the real-time communication requirements of dynamic drone swarm networks for minimizing average AoI. Summary of the Invention

[0004] Therefore, it is necessary to address the problem that existing methods cannot meet the real-time communication requirements of dynamic UAV swarm networks for minimizing average AoI, and to provide a data transmission method and swarm network for dynamic UAV swarm networks.

[0005] This invention is achieved using the following technical solution: In a first aspect, the present invention discloses a data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network, which is used to perform multi-path concurrent transmission and minimize the average information age in a dynamic UAV swarm network composed of several UAVs acting as nodes.

[0006] Data transmission methods for dynamic unmanned aerial vehicle (UAV) swarm networks include: S1 records a current node. u The corresponding current time t 1. and in a single time slice Δ t From the inside u Select several candidate nodes from all neighboring nodes that simultaneously satisfy mobility constraints and link interference constraints; exist t 1 hour based on u corresponding source node Source _ u Target Node Target _ u A virtual tree is constructed between them, and Δ tThe virtual tree is updated periodically. S2, if u In t On the virtual tree at time 1, and u Some or all of the candidate nodes are in t 1+Δ t On the virtual tree at that time, u To its position t 1+Δ t Data is forwarded to the candidate node with the shortest expected transmission time on the virtual tree, and then along... t 1+Δ t The link of the virtual tree at that time Target _ u Send data; otherwise, u The candidate node with the highest transmission efficiency is selected as the forwarding node for data forwarding, and the forwarding node is updated after successful data forwarding. u And return to S1.

[0007] The data transmission method of this dynamic unmanned aerial vehicle (UAV) swarm network implements the method or process according to the embodiments of this disclosure.

[0008] Secondly, the present invention discloses a dynamic unmanned aerial vehicle (UAV) swarm network, which employs the data transmission method of the dynamic UAV swarm network as disclosed in the first aspect.

[0009] This type of dynamic unmanned aerial vehicle (UAV) swarm network implements the methods or processes according to embodiments of this disclosure.

[0010] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention provides a hybrid data transmission method for the highly dynamic characteristics of UAV swarm networks. It fully considers node mobility and link interference, and works in a complementary manner by intelligently combining predictive transmission protocols and multi-layer tree protocols. In predictive transmission protocols, data forwarding of nodes is based on maximizing transmission utility, while in multi-layer tree protocols, data forwarding of nodes is based on minimizing predicted transmission time. This effectively reduces transmission latency, improves the robustness of data transmission, and significantly reduces the average information age.

[0011] 2. In the construction and periodic updating of the virtual tree, this invention not only applies the regional restriction mechanism and the path merging mechanism to effectively eliminate redundant tree structures and reduce energy consumption, but also applies the beacon update mechanism to adapt to the constantly changing network topology. Attached Figure Description

[0012] 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, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network provided in Embodiment 1 of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] It should be noted that when a component is said to be "installed on" another component, it can be directly on the other component or it may be in a component that is centered on it. When a component is said to be "set on" another component, it can be directly set on the other component or it may also be in a component that is centered on it. When a component is said to be "fixed to" another component, it can be directly fixed to the other component or it may also be in a component that is centered on it.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0017] First, it should be noted that this invention targets dynamic drone swarm networks, which are essentially composed of several drones acting as nodes—that is, one drone acts as one node, and multiple drones form a swarm network. Each node maintains its corresponding state information—including: its own position, its own speed, neighbor node information, and target node information (i.e., the final destination for data transmission by this node).

[0018] In a dynamic drone swarm network, multiple concurrent transmissions occur. Each path has one source node (the initial starting point for data transmission) and one target node (the final destination for data transmission). At any given moment, there may be multiple different nodes simultaneously transmitting data—these can be called multiple transmitting nodes. The target nodes of these transmitting nodes can be completely different, partially the same, or completely the same.

[0019] Example 1 As described above, this embodiment 1 provides a dynamic unmanned aerial vehicle (UAV) swarm network, which consists of several UAVs acting as nodes and performs multi-path concurrent transmission.

[0020] The dynamic UAV swarm network adopts a data transmission method for dynamic UAV swarm networks, which is provided later, to minimize the average AoI while achieving multi-path concurrent transmission.

[0021] In general, the data transmission method in dynamic UAV swarm networks can be summarized as follows: For a given current node u In other words, we examine whether it meets the preset conditions: u In t On the virtual tree at time 1, and u Some or all of the candidate nodes are in t 1+Δ t On the virtual tree at that time; among them, t 1 is u The corresponding current time; Δ t It is a single time slice, and its value should not be too large or too small, generally 1 second.

[0022] If the preset conditions are met, data transmission will be performed according to the Predictive Transmission Protocol (PTP); otherwise, data transmission will be performed according to the Multi-Level Tree Protocol (MTP).

[0023] It should be noted that at the initial moment t 0, u That is, the source node Source _ u As time went on, u It will be updated to the next node on the transmission path.

[0024] The following section provides a more detailed introduction to the data transmission methods in dynamic UAV swarm networks. See [link / reference]. Figure 1 Data transmission methods for dynamic unmanned aerial vehicle (UAV) swarm networks include: S1, on the one hand, records a certain current node. u The corresponding current time t 1. and in a single time slice Δt From the inside u Select several candidate nodes from all neighboring nodes that simultaneously satisfy mobility constraints and link interference constraints.

[0025] ① Mobility constraints are used to select the position in Δ t For neighboring nodes with relatively small displacements within a given area, the mathematical formula for mobility constraints can be written as: ; In the formula, express u , neighbor_u i Mobility constraints between them; u This represents a specific current node; neighbor_u i express u The i One neighboring node; i ∈[1, I ]; I express u The total number of all neighboring nodes; R C This indicates the maximum communication distance of the drone (generally determined based on the actual drone model). , They represent u exist t 1 o'clock x axis, y Axis coordinates; , express neighbor_u i exist t 1 o'clock x axis, y Axis coordinates; , They represent neighbor_u i , u exist t The velocity vector at time 1.

[0026] ② Link interference constraints are for selecting the pair u The neighboring nodes with less interference in the transmission signal.

[0027] Therefore, the mathematical formula for link interference constraints can be written as: ; In the formula, SINR u,neighbor_ui express u arrive neighbor_u i The signal-to-interference-to-noise ratio; SINRσ express SINR Threshold (usually an empirical value); p u express u The transmission power; η Indicates the reference loss factor; ξ Indicates environmental noise; κ Indicates the path loss index; T u Indicates and u The set of other current nodes that are transmitting at the same time; w express T u A single current node in; p w express w The transmission power.

[0028] Therefore, by selecting neighboring nodes that simultaneously satisfy both ① and ②, we obtain... u Candidate nodes - using candidate_u m express u The m One candidate node; m ∈[1, M ]; M express u The total number of all candidate nodes.

[0029] It should be noted that regardless of whether PTP or MTP is selected, the above filtering... u The process of identifying candidate nodes is a necessary step.

[0030] On the other hand, t 1 hour based on u corresponding source node Source _ u Target Node Target _ u A virtual tree is constructed between them, and Δ t The virtual tree is updated periodically.

[0031] Virtual tree construction and updating are essential operations in MTP. As described above, u There is one corresponding source node. Source _ u 1 target node Target _ u Therefore, the construction and updating of the virtual tree must be based on Source _ u , Target _ u conduct.

[0032] Specifically, methods for constructing virtual trees include: S101, Source _ u , Target _ u As the two foci of the ellipse, the target elliptical region is delineated.

[0033] The target elliptical region is used to limit the scope of the candidate virtual tree construction, thereby avoiding redundant energy consumption caused by the lack of regional restrictions in conventional virtual trees.

[0034] S102, predicted Target _ u Expected future position Future _ u and set t 1 o'clock is closest Future _ u The node as u exist t The reference root node corresponding to time 1 Root _ u_t 1.

[0035] in, Future _ u that is t 1+Δ t hour Target _ u The location, based on t 1 o'clock Target _ u Location and Target _ u The velocity vector is calculated.

[0036] In other words, Future _ u The calculation formula is: ; ; In the formula, , express Future _ u of x axis, y Axis coordinates; , express Target _ u exist t 1+Δ t time x axis, y Axis coordinates; , express Target _u exist t 1 o'clock x axis, y Axis coordinates; express Target _ u exist t The velocity vector of 1.

[0037] Since the location of each node is known and shared, then... t 1 o'clock is closest Future _ u The node as Root _ u_t 1.

[0038] Of course, referring to the above records, there are cases where different source nodes correspond to the same target node, then we have: like t 1 exists K Other current nodes other_u 1~ other_u K And satisfy other_u k Corresponding reference root node Root_other_u k _t 1 and Root _ u_t The distance between 1 is less than the distance threshold Δ D (Generally, empirical values ​​are used) to determine other_u 1~ other_u K and u Corresponding unified root node New_Root _ u_t 1. and will New_Root _ u_t 1 updated to Root _ u_t 1; k ∈[1, K Otherwise, keep Root _ u_t 1. Unchanged.

[0039] in, New_Root _ u_t There are several ways to determine 1. For example, it can be determined from... other_u 1~ other_u K , u Take any one of the corresponding reference root nodes as New_Root _ u_t 1; It is also possible to construct a system containing other_u 1~ other_u K ,u The smallest circle corresponding to the root node, and its center point is taken as... New_Root _ u_t 1.

[0040] It should be noted that, due to Δ t Not much value. Future _ u It will still fall within the target elliptical region, and the determined Root _ u_t 1 will also fall within the target elliptical region.

[0041] S103, within the target elliptical region, from Root _ u_t 1 as the starting point, towards u Build a tree structure to form a virtual tree.

[0042] Using the target elliptical region as the region constraint effectively reduces redundant construction of the virtual tree.

[0043] It should be noted that during the process of building the tree structure, the expected number of transmissions (ETX) from each node along the path to its corresponding reference root node is calculated.

[0044] If we let a certain node be x ,So x The formula for calculating the ETX to its corresponding reference root node is: ; In the formula, ε x express x To its corresponding reference root node's ETX; root express x The corresponding reference root node; ε y Represents a node y To its corresponding reference root node's ETX; neigbor_x express x The neighboring nodes; prr ( x , y )express x arrive y The data packet acceptance rate.

[0045] As described above, the virtual tree construction process is accompanied by periodic virtual tree updates—which include three aspects: ① Based on the positional relationship between the nodes and the target elliptical region, perform selective pruning on the virtual tree.

[0046] Specifically, taking a certain update cycle as an example: if a node is within the target ellipse region at the beginning of the update cycle, it means that the node needs to participate in the virtual tree construction. In this case, the ETX from the node to its corresponding reference root node is recalculated, and the selection of the parent node is adjusted according to the ETX value to update the virtual tree. Otherwise, it means that the node does not need to participate in the virtual tree construction. In this case, the ETX update of the node is stopped, and the node is temporarily removed from the virtual tree—that is, it is equivalent to pruning the node from the virtual tree.

[0047] ② Update the reference root node based on the changes in the node's position and integrate it into the virtual tree.

[0048] Specifically, taking a certain update cycle as an example: since the position of a node has changed compared to the previous update cycle, the reference root node is re-determined based on the position of the node in this update cycle; if the reference root node changes, a new subtree will be constructed and integrated into the virtual tree.

[0049] ③ Update the beacon information of the nodes to ensure that the virtual tree structure matches the network topology of the UAV swarm; the beacon information includes location information and ETX information.

[0050] Specifically, taking a certain update cycle as an example: nodes exchange beacon information through their neighboring nodes to ensure information synchronization and sharing, thereby ensuring that the virtual tree structure matches the network topology of the drone swarm.

[0051] S2, if u In t On the virtual tree at time 1, and u Some or all of the candidate nodes are in t 1+Δ t On the virtual tree at that time (i.e. u If the preset conditions are met, then u To its position t 1+Δ t Data is forwarded to the candidate node with the shortest expected transmission time on the virtual tree, and then along... t 1+Δ t The link of the virtual tree at that time Target _ u Send data.

[0052] Otherwise (i.e.) u (The preset conditions are not met) u The candidate node with the highest transmission efficiency is selected as the forwarding node for data forwarding, and the forwarding node is updated after successful data forwarding. u And return to S1.

[0053] In other words: uWhen the preset conditions are met, MTP will continue to ensure reliable data transmission; u If the preset conditions are not met, PTP will continue to facilitate rapid data transmission.

[0054] Thus, when the network topology is relatively stable, PTP can provide faster transmission efficiency than MTP. Meanwhile, MTP's virtual tree provides a reliable link, ensuring reliable data transmission even when PTP is not functioning effectively.

[0055] Referring to the above, both requirements in the preset conditions must be met: Ⅰ. Judgment u Is it in t The methods on the virtual tree at time 1 include: examine u exist t Multi-layer ETX set at time 1 Is it non-empty? in, The expression is: ; In the formula, express u exist t The first virtual tree j Layer to Root _ u The expected number of transmissions; j ∈[1, J ]; J express t 1. The total number of levels in the virtual tree.

[0056] like If not empty, then u In t On the virtual tree at time 1; otherwise, u Not in t On the virtual tree at time 1.

[0057] Ⅱ. Judgment u Is a candidate node in t 1+Δ t Methods on the virtual tree at that time include: Detection candidate_u m exist t 1+Δ t Multi-layer ETX set Is it non-empty? in, candidate_u m express u The m One candidate node; m∈[1, M ]; M express u The total number of all candidate nodes; The expression is: ; In the formula, express candidate_u m exist t 1+Δ t The first virtual tree g Layer to Root _ u The expected number of transmissions; g ∈[1, G ]; G express t 1+Δ t The total number of levels in the virtual tree.

[0058] like If not empty, then candidate_u m In t 1+Δ t On the virtual tree at that time; otherwise candidate_u m Not in t 1+Δ t On the virtual tree at that time.

[0059] Therefore, when both I and II are satisfied, it means that t 1+Δ t The virtual tree at that time has been implemented u candidate nodes and Target _ u Establish a stable link, i.e., continue MTP and use the minimum predicted transmission time as the metric for node data forwarding, and then directly follow the path. t 1+Δ t The link of the virtual tree at that time Target _ u Simply send the data.

[0060] The formula for calculating the estimated transmission time is as follows: ; In the formula, express C_u from t 1+Δ t The first virtual tree g Layer to Target _ u The estimated transmission time; C_u express u In t 1+Δt A candidate node on the virtual tree at that time; Indicates in t 1+Δ t hour C_u The expected number of transmissions to its reference root node; express t 1+Δ t The first virtual tree g Layers and C_u The path contains a set of links with overlapping topologies; , They represent Includes u x , u y The link; u x , u y express t 1+Δ t The first virtual tree g Layers and C_u There are two nodes with topological overlap; This indicates the concurrent transmission delay caused by topological overlap.

[0061] It should be noted that, based on the formula for calculating the estimated transmission time above, if Target _ u belong u The candidate nodes, then Target _ u Too u The candidate node with the highest transmission efficiency—forwarding data to it has essentially completed the final transmission of data.

[0062] When I and II are not simultaneously satisfied, it means that... t 1+Δ t The virtual tree at that time has not yet been implemented. u candidate nodes and Target _ u Once a stable link is established, PTP will continue, and data forwarding at nodes will be based on maximizing transmission efficiency.

[0063] The formula for calculating transmission utility is as follows: ; In the formula, Indicates in t hour u pass candidate_u m Towards Target _ u Transmission efficiency when forwarding data; t Indicates the calculation time—it should be noted that: if u Not in t On the virtual tree at time 1, then t Pick t 1; if u In t On the virtual tree at 1 o'clock, but u All candidate nodes are not in t 1+Δ t On the virtual tree at that time, t Pick t 1+Δ t .

[0064] Indicates in t From a arrive b The estimated transmission delay; a , b This represents two object nodes.

[0065] Specifically, The calculation formula is: ; In the formula, Z ( a , b ) indicates from a arrive b The set of grids that the transmission path passes through; z express Z ( a , b A single grid within a single grid; express z Estimated transmission delay within; Indicates from a Transmit to b The transmission vector of the data packet; Indicates the set of transmission vectors; express The elements in, for and Concurrent send vectors; express z Inside a arrive b The transmission distance; This represents the average transmission distance per hop; express t hour z Data packet acceptance rate within; This represents the cumulative transmission time error within z at time t; express , The concurrent region; express The estimated time.

[0066] Based on the above formula for calculating transmission effectiveness, it can be seen that the difference method is used for calculation—that is, calculating separately... u arrive Target _ u Estimated transmission delay candidate_u m arrive Target _ u The estimated transmission delay is then calculated, and the difference between the two is obtained. This calculation is based on the fact that directly calculating... u arrive candidate_u m Estimating the transmission delay too early might cause the next selected node to transmit data in the opposite direction, while using the difference method ensures that the data always travels in the correct direction. Target _ u Sending in the direction.

[0067] Of course, PTP does not complete the final data transmission even after successful data forwarding. Therefore, the forwarding node needs to be updated after successful data forwarding. u Then return to S1, thus starting a new round of candidate node selection and determining whether to continue with PTP or MTP, thus completing the final data transmission.

[0068] In summary, this embodiment 1 provides a hybrid data transmission method (DHTP) that organically combines PTP and MTP, minimizing the average AoI in dynamic UAV swarm networks, and also improving energy efficiency and system robustness, providing an innovative solution for future real-time communication applications of UAV swarms.

[0069] Example 2 This embodiment 2 discloses a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the data transmission method for a dynamic unmanned aerial vehicle swarm network disclosed in embodiment 1.

[0070] The computer equipment can be either a mobile terminal or a fixed terminal. Examples of the former include mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (such as in-vehicle navigation terminals); examples of the latter include digital TVs and desktop computers.

[0071] This embodiment 2 also discloses a readable storage medium that stores computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the data transmission method for the dynamic UAV swarm network disclosed in embodiment 1 are performed.

[0072] The readable storage medium may include, but is not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

[0073] This embodiment 2 also discloses a computer program product, including a computer program. When executed by a processor, the computer program implements the steps of the data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network disclosed in embodiment 1.

[0074] It should be noted that the computer program used to execute the above can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar languages. The computer program can be executed entirely on the user's computer, partially on the user's computer, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer through any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN).

[0075] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network, used for multi-path concurrent transmission and minimizing the average information age in a dynamic UAV swarm network composed of several UAVs acting as nodes; characterized in that, It includes: S1 records a current node. u The corresponding current time t 1. and in a single time slice Δ t From the inside u Select several candidate nodes from all neighboring nodes that simultaneously satisfy mobility constraints and link interference constraints; exist t 1 hour based on u corresponding source node Source _ u Target Node Target _ u A virtual tree is constructed between them, and Δ t The virtual tree is updated periodically. S2, if u In t On the virtual tree at time 1, and u Some or all of the candidate nodes are in t 1+Δ t On the virtual tree at that time, u To its position t 1+Δ t Data is forwarded to the candidate node with the shortest expected transmission time on the virtual tree, and then along... t 1+Δ t The link of the virtual tree at that time Target _ u Send data; otherwise, u The candidate node with the highest transmission efficiency is selected as the forwarding node for data forwarding, and the forwarding node is updated after successful data forwarding. u And return to S1.

2. The data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network according to claim 1, characterized in that, In S1, the mathematical formula for mobility constraints is: ； In the formula, express u , neighbor_u i Mobility constraints between them; u This represents a specific current node; neighbor_u i express u The i One neighboring node; i ∈[1, I ]; I express u The total number of all neighboring nodes; R C Indicates the maximum communication distance of the drone; , They represent u exist t 1 o'clock x axis, y Axis coordinates; , express neighbor_u i exist t 1 o'clock x axis, y Axis coordinates; , They represent neighbor_u i , u exist t The velocity vector at time 1; The mathematical formula for link interference constraints is: ； In the formula, SINR u,neighbor_ui express u arrive neighbor_u i The signal-to-interference-to-noise ratio; SINR σ express SINR Threshold; p u express u The transmission power; η Indicates the reference loss factor; ξ Indicates environmental noise; κ Indicates the path loss index; T u Indicates and u The set of other current nodes that are transmitting at the same time; w express T u A single current node in; p w express w The transmission power.

3. The data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network according to claim 1, characterized in that, In S1, the methods for constructing a virtual tree include: S101, Source _ u , Target _ u As the two foci of the ellipse, the target elliptical region is delineated; S102, predicted Target _ u Expected future position Future _ u and set t 1 o'clock is closest Future _ u The node as u exist t The reference root node corresponding to time 1 Root _ u_t 1; S103, within the target elliptical region, from Root _ u_t 1 as the starting point, towards u Build a tree structure to form a virtual tree.

4. In the data transmission method of the dynamic unmanned aerial vehicle (UAV) swarm network according to claim 3, in S102, if t 1 exists K Other current nodes other_u 1~ other_u K And satisfy other_u k Corresponding reference root node Root_other_u k _t 1 and Root _ u_t The distance between 1 is less than the distance threshold Δ D Then determine other_u 1~ other_u K and u Corresponding unified root node New_Root _ u_t 1. and will New_Root _ u_t 1 updated to Root _ u_t 1; k ∈[1, K Otherwise, keep Root _ u_t 1 remains unchanged; in, New_Root _ u_t The method for determining 1 is as follows: from other_u 1~ other_u K , u Take any one of the corresponding reference root nodes as New_Root _ u_ t 1; Or, construct a system containing other_u 1~ other_u K , u The smallest circle corresponding to the root node, and its center point is taken as... New_Root _ u_t 1.

5. The data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network according to claim 3, characterized in that, In S1, the virtual tree is updated in the following ways: Based on the positional relationship between the nodes and the target elliptical region, the virtual tree is selectively pruned; Based on the changes in the node's position, the reference root node is updated and integrated into the virtual tree; The nodes are updated with beacon information to ensure that the virtual tree structure matches the topology of the UAV swarm network; the beacon information includes location information and expected number of transmissions.

6. The data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network according to claim 3, characterized in that, In S2, determine u Is it in t The methods on the virtual tree at time 1 include: examine u exist t Multi-layer ETX set at time 1 Is it non-empty; where, The expression is: ； In the formula, express u exist t The first virtual tree j Layer to Root _ u The expected number of transmissions; j ∈[1, J ]; J express t The total number of levels in the virtual tree at time 1; like If not empty, then u In t On the virtual tree at time 1; otherwise, u Not in t On the virtual tree at time 1; judge u Is a candidate node in t 1+Δ t Methods on the virtual tree at that time include: Detection candidate_u m exist t 1+Δ t Multi-layer ETX set Is it non-empty? in, candidate_u m express u The m One candidate node; m ∈[1, M ]; M express u The total number of all candidate nodes; The expression is: ； In the formula, express candidate_u m exist t 1+Δ t The first virtual tree g Layer to Root _ u The expected number of transmissions; g ∈[1, G ]; G express t 1+Δ t The total number of levels in the virtual tree at that time; like If not empty, then candidate_u m In t 1+Δ t On the virtual tree at that time; otherwise candidate_u m Not in t 1+Δ t On the virtual tree at that time.

7. The data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network according to claim 3, characterized in that, In S2, the formula for calculating the estimated transmission time is: ； In the formula, express C_u from t 1+Δ t The first virtual tree g Layer to Target _ u The estimated transmission time; C_ u express u In t 1+Δ t A candidate node on the virtual tree at that time; Indicates in t 1+Δ t hour C_u The expected number of transmissions to its reference root node; express t 1+Δ t The first virtual tree g Layers and C_u The path contains a set of links with overlapping topologies; , They represent Includes u x , u y The link; u x , u y express t 1+Δ t The first virtual tree g Layers and C_u There are two nodes with topological overlap; This indicates the concurrent transmission delay caused by topological overlap.

8. The data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network according to claim 1, characterized in that, In S2, if Target _ u belong u The candidate nodes, then Target _ u Too u The candidate node with the highest transmission efficiency.

9. The data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network according to claim 1, characterized in that, In S2, the formula for calculating transmission utility is: ； In the formula, Indicates in t hour u pass candidate_u m Towards Target _ u Transmission efficiency when forwarding data; t Indicates the calculation time; if u Not in t The virtual tree at time 1, then t for t 1; if u In t On the virtual tree at time 1, and u All candidate nodes are not in t 1+Δ t On the virtual tree at that time, t Pick t 1+Δ t ; Indicates in t From a arrive b The estimated transmission delay; a , b This represents two object nodes; in, The calculation formula is: ； In the formula, Z ( a , b ) indicates from a arrive b The set of grids that the transmission path passes through; z express Z ( a , b A single grid within a single grid; express z Estimated transmission delay within; Indicates from a Transmit to b The transmission vector of the data packet; Indicates the set of transmission vectors; express The elements in, for and Concurrent send vectors; express z Inside a arrive b The transmission distance; This represents the average transmission distance per hop; express t hour z Data packet acceptance rate within; This represents the cumulative transmission time error within z at time t; express , The concurrent region; express The estimated time.

10. A dynamic unmanned aerial vehicle (UAV) swarm network, characterized in that, It employs the data transmission method for dynamic unmanned aerial vehicle (UAV) swarm networks as described in any one of claims 1-9.

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