A data transmission method of a dynamic unmanned aerial vehicle cluster network and the cluster network

Abstract

The application discloses a kind of dynamic unmanned aerial vehicle cluster network data transmission method and cluster network, it is related to unmanned aerial vehicle networking technical field.The application provides data mixed transmission mode for the characteristics of highly dynamic unmanned aerial vehicle cluster network, which fully considers node mobility and link interference, by intelligently combining predictive transmission protocol and multi-layer tree protocol to work complementarily, and when predictive transmission protocol, with transmission utility maximum as index to carry out node data forwarding, when multi-layer tree protocol, with predictive transmission time minimum as index to carry out node data forwarding, effectively reduces transmission delay, improves the robustness of data transmission, and makes the average information age significantly 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:

[0006] 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.

[0007] Data transmission methods for dynamic unmanned aerial vehicle (UAV) swarm networks include:

[0008] 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;

[0009] 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;

[0010] 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;

[0011] 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.

[0012] 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.

[0013] 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.

[0014] This type of dynamic unmanned aerial vehicle (UAV) swarm network implements the methods or processes according to embodiments of this disclosure.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 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.

[0017] 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

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the following description only represent some of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0019] Figure 1 The flow chart of the data transmission method of the dynamic unmanned aerial vehicle cluster network provided for Embodiment 1 of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely in the following description with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments only represent some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0021] It should be noted that when a component is referred to as being “mounted on” another component, it can be directly on the other component or there can be a middle component. When a component is referred to as being “disposed on” another component, it can be directly disposed on the other component or there can be a middle component. When a component is referred to as being “fixed on” another component, it can be directly fixed on the other component or there can be a middle component.

[0022] 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 the present application belongs. The terminology used in the description of the present application herein only for the purpose of describing specific embodiments and is not intended to limit the present application. The term “or / and” used herein includes any and all combinations of one or more related listed items.

[0023] First of all, it should be noted that the present application is directed to a dynamic unmanned aerial vehicle cluster network, which is actually composed of a plurality of unmanned aerial vehicles serving as nodes, i.e., one unmanned aerial vehicle as one node, and a plurality of unmanned aerial vehicles forming a cluster network. Each node maintains its corresponding state information, including: its own position, its own speed, neighbor node information, and target node (i.e., the final destination of the node for data transmission) information.

[0024] 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.

[0025] Example 1

[0026] 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.

[0027] 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.

[0028] In general, the data transmission method in dynamic UAV swarm networks can be summarized as follows:

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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:

[0033] S1, on the one hand, records a certain current node. uCorresponding current time t 1, and in a single time slice Δ t From all the neighbor nodes of u Select several candidate nodes that meet the mobility constraints and link interference constraints at the same time.

[0034] ①, the mobility constraint is to select part of the neighbor nodes with small relative displacement in Δ t The mathematical formula of the mobility constraint can be written as:

[0035] ;

[0036] In the formula, Indicates u , neighbor_u i The mobility constraint between and ; u Indicates a certain current node; neighbor_u i Indicates u The first neighbor node of i ; i ∈[1, I ]; I Indicates u The total number of all neighbor nodes; R C Indicates the maximum communication distance of the UAV (usually determined according to the actual model of the UAV); , Indicate the x-axis and y-axis coordinates of u At t1; t , x Indicate the x-axis and y-axis coordinates of y At t1; , Indicate the x-axis and y-axis coordinates of neighbor_u i At t1; t , x Indicate the x-axis and y-axis coordinates of y At t1; , Indicate the x-axis and y-axis coordinates of neighbor_u i , u At t1; t

[0037] ②, the link interference constraint is to select part of the neighbor nodes with small interference to the transmission signal. u

[0038] Therefore, the mathematical formula of the link interference constraint can be written as:

[0039] ;

[0040] 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 (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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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 _ uThen the virtual tree construction and updating is to be carried out based on Source u Target u

[0045] Specifically, the method for carrying out the virtual tree construction comprises:

[0046] S101, taking Source u Target u as two foci of an ellipse and defining a target ellipse region.

[0047] The target ellipse region is to limit the range of the candidate virtual tree construction, so as to avoid the redundant energy consumption caused by the conventional virtual tree without region limitation.

[0048] S102, predicting the expected future position of Target u at Future u and setting the node closest to t Future at u as the reference root node corresponding to u at t . Root u_t Wherein,

[0049] is the position of Future u at t 1+Δ t , which is calculated according to the position of Target u at t 1 Target and the velocity vector of u Target . u That is, the calculation formula of

[0050] Future is: u

[0051]

[0052]

[0053] In the formula, x and y represent the x-axis and y-axis coordinates of . Future u x y ,​​​​​​​​​​​​​​​​​​​​​​​ denotes Target u at t 1+Δ t the x axis, y axis coordinates; denotes Target u at t 1 the x axis, y axis coordinates; denotes Target u at t 1 the velocity vector.

[0054] Since the position of each node is known and shared, the node closest to t 1 at Future u is determined as Root u_t 1.

[0055] Of course, referring to the above description, there are cases where different source nodes correspond to the same target node, so there are:

[0056] If t 1 there are K other current nodes other_u 1~ other_u K , and satisfy other_u k the corresponding reference root node Root_other_u k _t 1 and Root u_t 1 the distance between them is less than the distance threshold Δ D (generally take empirical value), it is determined that other_u 1~ other_u K and u the corresponding unified root node New_Root u_t 1, and update New_Root u_t 1 to Root u_t 1; k ∈[1, K ]; otherwise, keep Root u_t 1 unchanged.

[0057] where, New_Root u_t ​​​​​​​​​​​​​​There are various ways to determine the target ellipse. For example, the target ellipse can be determined from the following formula: other_u other_u K , u Any one of the corresponding reference root nodes can be taken as the reference root node of the target ellipse. New_Root u_t 1; or a minimum circle containing the corresponding reference root nodes can be constructed, and the center point of the minimum circle can be taken as the reference root node of the target ellipse. other_u other_u K , u The center of the minimum circle containing the corresponding reference root nodes can be taken as the reference root node of the target ellipse. New_Root u_t

[0058] It should be noted that, since the value of Δ t is not large, Future u will still fall within the target ellipse region, and the determined Root u_t 1 will also fall within the target ellipse region.

[0059] S103, within the target ellipse region, a tree structure is established from the starting point of Root u_t 1 to form a virtual tree. u

[0060] In this way, the target ellipse region is taken as a region limit, and the redundancy of the virtual tree construction is effectively reduced.

[0061] It should be noted that, in the process of establishing the tree structure, the expected transmission times (ETX for short) of the nodes on the path to their corresponding reference root nodes are calculated.

[0062] If a certain node is x , then the calculation formula of the ETX of x to its corresponding reference root node is as follows:

[0063] ;

[0064] In the formula, ε x represents the ETX of x to its corresponding reference root node; root represents the corresponding reference root node of x ; ε y represents the ETX of the node y to its corresponding reference root node; neigbor_x represents the neighbor nodes of x ; prr ( x , y ) represents the distance between x to​​​​​​​​​y The data packet acceptance rate.

[0065] As described above, the virtual tree construction process is accompanied by periodic virtual tree updates—which include three aspects:

[0066] ① Based on the positional relationship between the nodes and the target elliptical region, perform selective pruning on the virtual tree.

[0067] 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.

[0068] ② Update the reference root node based on the changes in the node's position and integrate it into the virtual tree.

[0069] 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.

[0070] 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.

[0071] 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 topology of the drone swarm network.

[0072] 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.

[0073] Otherwise (i.e. u the preset condition is not satisfied), u selects a candidate node with the maximum transmission utility as a forwarding node to forward data, and updates the forwarding node to u , and returns to S1.

[0074] That is: u when the preset condition is satisfied, the MTP is continued to ensure reliable data transmission; u when the preset condition is not satisfied, the PTP is continued to perform fast data transmission.

[0075] In this way, when the network topology is relatively stable, the PTP can provide faster transmission efficiency than the MTP. The virtual tree of the MTP provides reliable links to ensure that data transmission can be reliably performed even in the case that the PTP cannot work effectively.

[0076] Referring to the above, both requirements in the preset condition need to be satisfied:

[0077] I. Determine whether u is on the virtual tree at t 1 by the following method:

[0078] Check whether u is non-empty at t 1; wherein,

[0079] the expression of is:

[0080] ;

[0081] wherein, denotes the expected number of transmission times from the u layer to t _ j of the virtual tree at Root 1; u ∈[1, j ]; J denotes the total number of layers of the virtual tree at J 1. t If

[0082] is non-empty, then is on the virtual tree at u 1; otherwise, t is not on the virtual tree at u 1. t

[0083] II. Determine whether a candidate node of u is on the virtual tree at​t 1+Δ t Methods on the virtual tree at that time include:

[0084] Detection candidate_u m exist t 1+Δ t Multi-layer ETX set Is it non-empty?

[0085] 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:

[0086] ;

[0087] 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.

[0088] 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.

[0089] 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 timeTarget _ u Simply send the data.

[0090] The formula for calculating the estimated transmission time is as follows:

[0091] ;

[0092] 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.

[0093] 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.

[0094] When I and II are not simultaneously satisfied, it means that... t 1+Δt The virtual tree at time t still does not achieve the goal of u candidate nodes and Target u establish a stable link, then continue PTP, and take the maximum transmission utility as an index to forward data of nodes.

[0095] The formula for calculating the transmission utility is:

[0096] ;

[0097] In the formula, represents the estimated transmission delay from t to u at time t candidate_u m to Target u forward data at time t

[0098] t represents the calculation time - it should be noted that if u is not on the virtual tree at time t t 1, then t takes t 1; if u is on the virtual tree at time t t 1, but all candidate nodes of u are not on the virtual tree at time t t 1+Δ t , then t takes t 1+Δ t .

[0099] represents the estimated transmission delay from t to a at time t b ; a , b represent two object nodes.

[0100] Specifically, the formula for calculating the transmission utility is:

[0101] ;

[0102] In the formula, Z ( a , b ) represents the mesh set passed through by the transmission path from a to b ; z represents a single mesh in Z ( a , b ); represents​​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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Embodiment 2

[0107] The embodiment 2 discloses a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the data transmission method of the dynamic UAV cluster network disclosed in the embodiment 1 when executing the computer program.

[0108] The computer device can be a mobile terminal or a fixed terminal. The mobile terminal can be, for example, a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Portable Application Description), a PMP (Portable Media Player), a vehicle terminal (such as a vehicle navigation terminal), and the like. The fixed terminal can be, for example, a digital TV, a desktop computer, and the like.

[0109] The embodiment 2 further discloses a readable storage medium, which stores computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the data transmission method of the dynamic UAV cluster network disclosed in the embodiment 1 are executed.

[0110] The readable storage medium can include, but is not limited to, an electrical connection with one or more conductive wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0111] The embodiment 2 further discloses a computer program product, which comprises a computer program. When the computer program is executed by a processor, the steps of the data transmission method of the dynamic UAV cluster network disclosed in the embodiment 1 are implemented.

[0112] It should be noted that the computer programs described above can be written in one or more programming languages or combinations thereof. Among the programming languages that can be used are object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer programs described above can execute entirely on the user's computer, partly on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, 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), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0113] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the patent protection scope of the present application should be subject to 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; methods for constructing the virtual tree include: S101, will 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; Future _ u for 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; S103, within the target elliptical region, from Root _ u_t 1 as the starting point, towards u Establish a tree structure to form a virtual tree; 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. In the data transmission method of the dynamic unmanned aerial vehicle (UAV) swarm network according to claim 1, 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.

4. The data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network according to claim 1, 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 position of the nodes, the reference root node is updated and integrated into the virtual tree; The beacon information of the nodes is updated 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.

5. The data transmission method for a dynamic unmanned aerial vehicle (UAV) swarm network according to claim 1, 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.

6. 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 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.

7. 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.

8. 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.

9. 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 1-8.

Images