Unmanned aerial vehicle network multipath selection and network coding redundancy transmission method

Abstract

The invention discloses an unmanned aerial vehicle network multipath selection and network coding redundancy transmission method. The method comprises the following steps that each unmanned aerial vehicle carries out node state perception and link prediction; the source node initiates a multi-path routing discovery process based on the extended AOMDV routing protocol, performs path quality measurement, and selects a plurality of paths with the highest path quality as active transmission paths; the source node divides an original data block to be sent into a plurality of original data packets and encodes the original data packets respectively; performing optimal distribution based on a water injection algorithm; data forwarding and decoding receiving are carried out according to an optimal distribution result; and continuously detecting that the link communication actively triggers the maintenance process before the link is actually broken. According to the method, high-reliability, low-delay and energy-balanced data transmission is realized in a highly dynamic unmanned aerial vehicle network through a rigorous evaluation and optimization system, and the overall survival time of the network is effectively prolonged; and predictive route maintenance and fast route switching are carried out through the extended routing table.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle ad hoc network, and particularly relates to a method for multi-path selection and network coding redundant transmission of unmanned aerial vehicle network. BACKGROUND

[0002] Unmanned aerial vehicles (UAV) have been widely used in various scenarios such as environmental monitoring, emergency rescue, material delivery and battlefield reconnaissance due to their strong maneuverability, flexible deployment and low cost. In complex or infrastructure-lacking task environments, the capabilities of a single unmanned aerial vehicle are often greatly limited, so multiple unmanned aerial vehicles need to be coordinated to form a UAV cluster to improve the execution efficiency of tasks and the robustness of the system. To further realize information interaction and collaborative decision-making within the UAV cluster, a flying ad-hoc network (FANET) can be constructed. This network does not need to rely on fixed communication infrastructure, but dynamically networks through wireless links between nodes and uses multi-hop relay mechanisms to complete data transmission tasks.

[0003] In a flying ad-hoc network, due to the high-speed movement of unmanned aerial vehicle nodes and the dynamic changes in network topology, an efficient routing protocol must be constructed to maintain the effectiveness and stability of end-to-end communication. Routing protocols are responsible for discovering and maintaining feasible transmission paths and are the core mechanism for ensuring reliable and low-delay data transmission. Single-path routing protocols usually only establish a main path for data forwarding, which is difficult to adapt to frequent link interruptions and topology changes in a flying ad-hoc network. When the only path in the network fails due to node movement or channel fading, the routing discovery process needs to be re-initiated, resulting in an extended communication interruption time. At the same time, continuous use of a single path can easily cause the rapid depletion of energy of local nodes, thereby causing uneven network energy consumption and reducing network lifetime.

[0004] To address the limitations of single-path routing protocols, multi-path routing protocols have become an important research direction for improving the performance of unmanned aerial vehicle networks. Multi-path routing can establish and maintain multiple redundant paths from the source node to the destination node, quickly switch to backup paths when the main path fails, effectively enhance the fault tolerance and robustness of communication, and also achieve load balancing and improve network throughput by distributing traffic on multiple paths.

[0005] AOMDV (Ad-hoc On-demand Multipath Distance Vector) is a typical on-demand multipath routing protocol, an extension of the single-path routing protocol AODV. Based on the principle of minimizing hop count, this protocol utilizes Route Request (RREQ) and Route Reply (RREP) control messages to discover and maintain multiple disjoint (link-disjoint or node-disjoint) paths. However, in the context of UAV ad hoc networks, AOMDV has significant shortcomings: its path selection mechanism uses only hop count as a metric, ignoring key network state parameters such as node remaining energy, link lifetime, and node queue load, making it difficult to comprehensively reflect the overall quality of the path; AOMDV's data forwarding strategy typically employs static allocation or simple failover mechanisms, lacking the ability to perceive and respond to real-time path performance, making it difficult to achieve efficient and adaptive traffic scheduling in highly dynamic UAV networks. To address these challenges, Chinese patent CN106686659A proposes an energy-aware, node-disjoint multipath routing algorithm based on AOMDV. This algorithm, while ensuring path disjointness, introduces an energy-aware mechanism to dynamically select the energy-optimal path, achieving load balancing and extending network lifetime. Chinese patent CN106685819A proposes an AOMDV protocol energy-saving method based on node energy partitioning. This method actively constructs paths with more uniform energy levels through energy classification and threshold partitioning, achieving a balance in energy consumption across the entire network and thus extending network lifetime. Chinese patent CN118748648A proposes an energy load balancing method for Ad hoc networks in AOMDV multipath scenarios. This method, through a round-robin mechanism and dual-standard path selection, achieves a balance between load balancing and energy protection in multipath scenarios, optimizing overall network performance. Chinese patent CN114339661... A proposes an AOMDV-WOA multipath routing mechanism for aircraft ad hoc networks based on whale optimization. This mechanism combines the whale optimization algorithm with a multi-dimensional evaluation model that integrates node state, link quality, network load, and mobility prediction, thereby achieving smarter, more stable, and more balanced multipath routing in complex aircraft network environments.

[0006] Network coding is a communication technique that performs algebraic operations (such as XOR, linear combination, etc.) on multiple data streams at intermediate nodes before forwarding them, breaking through the limitations of the traditional network routing "store-and-forward" mechanism. The core idea of ​​network coding is to allow nodes in the network to encode data packets during forwarding, and the receiving end to decode and recover the original information. This technique can improve network throughput and enhance transmission robustness, and is particularly suitable for wireless communication networks with limited bandwidth and frequently changing topologies.For example, Hao Song et al. [H. Song, L. Liu, SM Pudlewski and ES Bentley, "Random Network Coding Enabled Routing Protocol in Unmanned Aerial Vehicle Networks," in IEEE Transactions on Wireless Communications, vol. 19, no. 12, pp. 8382-8395, Dec. 2020, doi:10.1109 / TWC.2020.3022399.] proposed two Random Network Coding (RNC) routing protocols that do not require path exploration to address the challenges of dynamic topology and resource constraints in UAV networks. The enhanced protocol significantly reduces end-to-end transmission delay and delay violation probability by having intermediate nodes forward only newly generated encoded packets without decoding them, and its performance is significantly better than traditional routing methods, especially in high-density networks. Bo Song et al. [B. Song et al., "DRL-AdCAR: Adaptive Coding-Aware Routing With Maximum Coding Opportunities and High-Quality via Deep [Reinforcement Learning in FANET," in IEEE Transactions on Vehicular Technology, vol. 74, no. 1, pp. 1280-1295, Jan.2025, doi: 10.1109 / TVT.2024.3461161.] This paper proposes an adaptive code-aware routing algorithm (DRL-AdCAR) based on deep reinforcement learning. This algorithm transforms the routing problem into a Markov decision model. It improves the deep deterministic policy gradient (DDPG) algorithm by combining gated recurrent units (GRU) and long short-term memory networks (LSTM) (forming GL-DDPG), and considers coding opportunity, coding gain and link quality in the reward function at the same time, avoiding the defect of traditional algorithms that only pursue coding opportunity and affect other performance aspects.

[0007] In ad hoc UAV networks, combining network coding with multipath routing can effectively mitigate data loss caused by link instability. By transmitting encoded data packets across multiple paths, even if some paths fail, the receiving node can still recover the original data by receiving a certain number of linearly independent encoded data packets. However, existing solutions typically employ round-robin or random strategies to distribute encoded data packets across multiple paths, failing to dynamically optimize based on the real-time transmission capabilities of each path (such as bandwidth, latency, and stability). Therefore, they cannot fully leverage the throughput gain and latency advantages of network coding.

[0008] In summary, existing technologies struggle to achieve high reliability, high throughput, and high resource utilization in dynamic UAV networks. Therefore, there is an urgent need to design an end-to-end routing solution that deeply integrates intelligent path assessment and network coding redundancy to achieve highly reliable, low-latency, and energy-efficient data transmission. Summary of the Invention

[0009] The purpose of this invention is to solve the problems in the prior art.

[0010] The technical solution adopted by this invention to solve its technical problem is: to provide a method for multipath selection and network coding redundancy transmission in UAV networks, comprising the following steps:

[0011] Each drone performs node status perception and link prediction;

[0012] The source node initiates a multi-path route discovery process based on the extended AOMDV routing protocol and performs path quality measurement, selecting the highest quality paths as active transmission paths.

[0013] The source node divides the raw data block to be sent into several raw data packets and encodes them separately;

[0014] Optimized allocation based on water injection algorithm;

[0015] Data forwarding and decoding reception are performed based on the optimized allocation results;

[0016] Continuous monitoring of link communication proactively triggers maintenance procedures before the link actually breaks.

[0017] Preferably, the node status perception and link prediction performed by each UAV includes the following steps:

[0018] Each UAV node periodically collects its own status information, including: remaining energy, three-dimensional spatial coordinates, three-dimensional velocity vector and transmission queue occupancy rate, and exchanges information with neighboring nodes by broadcasting enhanced HELLO messages;

[0019] Based on the Gauss-Markov movement model, according to the neighboring nodes in Relative position vector at time Relative velocity vector and communication radius Solve for any future time. The quadratic equation:

[0020] ;

[0021] make , , ,get:

[0022] ;

[0023] Calculate the discriminant of the equation. ;like This indicates that the equation has a positive real root, and its smallest positive real root is taken as the predicted Link Time of Survival (LET). This indicates that the equation has no real roots, and LET is infinite, predicting that the link will exist for a long time.

[0024] Preferably, the source node initiates a multi-path route discovery process based on the extended AOMDV routing protocol and performs path quality measurement, selecting the highest quality paths as active transmission paths, including the following steps:

[0025] The source node initiates a multi-path route discovery process based on the extended AOMDV routing protocol. The extended RREQ message contains four cumulative fields used to collect bottleneck performance metrics for the path: minimum remaining energy, worst-case link lifetime, maximum queue occupancy, and hop count. These fields are updated by intermediate nodes during the flooding process. The destination node replies with an RREP message for each loop-free path. Based on the bottleneck performance metrics in the RREP message, the source node identifies each candidate path... The comprehensive path quality metric (PQMF) value is calculated and expressed as follows:

[0026] ;

[0027] in, The normalized value representing the minimum remaining energy of a path is defined as:

[0028] ;

[0029] in, Representing a path The minimum remaining energy of the middle node. This represents the maximum energy of a node; The normalized value representing the worst-case link lifetime of a path is defined as:

[0030] ;

[0031] in, This represents the worst-case link lifetime in the path. Indicates the preset maximum link lifetime; The normalized value representing the maximum queue occupancy rate of a path is defined as:

[0032] ;

[0033] in, This indicates the maximum queue occupancy rate in the path; The normalized value representing the path hop count is defined as:

[0034] ;

[0035] in, This indicates the number of hops in the path. This represents the smoothing factor. , , and Represents configurable weight coefficients, satisfying And the values ​​of each weight coefficient are non-negative;

[0036] Source node based on Sort the values ​​from highest to lowest, and select the top ones. One path is designated as the active transmission path, while the remaining paths are stored as backup paths in the backup path set.

[0037] Preferably, the source node divides the raw data block to be sent into several raw data packets and encodes them separately, including the following steps:

[0038] The source node divides the raw data block to be sent into raw data packets And in a finite field Above, randomly generate one dimensional coding coefficient matrix ,in ;

[0039] Calculated A set of encoded data packets:

[0040] ;

[0041] in, Indicates the first One encoded data packet, Indicates the first One raw data packet, Representation matrix Random elements in;

[0042] Each encoded packet header encapsulates its corresponding Global Encoding Vector (GEV), represented as a row coefficient vector. ;

[0043] When forwarding, the intermediate node performs a random linear combination of the received multiple encoded packets over a finite field to generate a new encoded packet, and updates the global encoding vector according to the same linear relationship; this operation does not require decoding.

[0044] Preferably, the optimized allocation based on the water injection algorithm includes the following steps:

[0045] For path The effective throughput factor ETF is calculated as follows:

[0046] ;

[0047] in, Represents the raw throughput factor (RTF), representing the path. Transmission capacity under ideal, interference-free conditions can be estimated by the physical transmission rate at the link layer or by a smoothed estimate using historical average throughput. Representing a path The PQMF value, the higher the value, the stronger the path. The better the overall long-term quality; This represents the path weight index, which amplifies the advantages of high PQMF paths while severely penalizing low PQMF paths. This represents the maximum current send queue load rate of all nodes. When the value is 0, it means that the node is unloaded, and when it is 1, it means that the node's load queue is full.

[0048] The optimal coded data packet allocation ratio for each path is adopted using the water-filling algorithm to minimize the maximum path transmission time. Solve the following convex optimization problem with the objective in mind:

[0049]

[0050] in, This represents the path propagation delay, which is determined by the physical distance and the electromagnetic wave speed of the propagation medium. Indicates the total amount of data in the encoded data packet to be transmitted; Indicates the allocation ratio of encoded data packets; This represents the set of active paths. The number of encoded packets allocated to each path is calculated based on the optimal allocation ratio, and encoded packets with high linear independence are prioritized for scheduling.

[0051] Preferably, the data forwarding and decoding reception based on the optimized allocation result includes the following steps:

[0052] Encoded data packets are forwarded along the corresponding allocated path, and intermediate nodes can perform encoding operations after receiving the encoded data packets;

[0053] The destination node receives encoded data packets from all active paths, obtaining a reception matrix. Each row of the reception matrix consists of the global encoding vector (GEV) of the received encoded data packets. When the rank of the reception matrix reaches... At that time, it indicates that it has been received. Given a linearly independent encoded data packet, the destination node immediately performs Gaussian elimination to solve for the... The original data packets were used to recover the original data blocks.

[0054] Preferably, the continuous monitoring of link communication proactively triggers a maintenance process before the link actually breaks, including the following steps:

[0055] Continuously monitor the end-to-end LET on each active path. When the end-to-end LET of a path is detected to be lower than the preset warning threshold, an alert will be issued. In such cases, before the link actually breaks, a maintenance process is proactively triggered, including traffic migration, immediately removing the path from the active path set, and allocating a certain percentage of it. Set to zero, stop scheduling new encoded packets to it; Path replacement, select the path with the highest PQMF value from the backup path set to activate it and add it to the active path set; Re-optimize allocation, based on the new active path set, re-execute the water-filling algorithm-based optimization allocation, calculate the new global optimal allocation ratio, and continue the data transmission process.

[0056] The present invention also provides an electronic device, comprising:

[0057] One or more processors;

[0058] Storage device for storing one or more programs.

[0059] When the one or more programs are executed by the one or more processors, the one or more processors perform any of the methods described above.

[0060] The present invention also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements any of the methods described above.

[0061] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements any of the methods described above.

[0062] The present invention has the following beneficial effects:

[0063] (1) Extended network lifecycle: The path quality measurement function introduces two indicators: node remaining energy and queue occupancy rate, which can encourage routing decisions to choose paths with sufficient energy and balanced load, thereby effectively avoiding premature failure or congestion of some nodes.

[0064] (2) Network performance optimization: The core concept of effective throughput factor is introduced. Taking into account the overall quality of the path and the instantaneous load, and based on the dynamic allocation strategy of the water-filling algorithm, the transmitted data is intelligently scheduled to the high-quality path with "high quality and light load", which can minimize the transmission delay and maximize the network throughput.

[0065] (3) Improved transmission reliability: The network coding provides mathematical redundancy, the intermediate node recoding enhances information diffusion, and the proactive maintenance based on LET prediction avoids transmission interruption. The synergy of these three mechanisms greatly enhances the transmission reliability of the network.

[0066] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, but the present invention is not limited to the embodiments. Attached Figure Description

[0067] Figure 1 This is a diagram illustrating the method steps of an embodiment of the present invention;

[0068] Figure 2 This is a schematic diagram of the data flow according to an embodiment of the present invention;

[0069] Figure 3 This is a diagram of the improved AOMDV routing protocol RREQ message format according to an embodiment of the present invention;

[0070] Figure 4 This is a diagram of the improved AOMDV routing protocol RREP message format according to an embodiment of the present invention;

[0071] Figure 5 This is a schematic diagram of the path quality metric function calculation model according to an embodiment of the present invention;

[0072] Figure 6 This is a diagram of the multi-path routing table structure according to an embodiment of the present invention;

[0073] Figure 7 This is a diagram illustrating a multi-path routing communication scenario in an unmanned aerial vehicle (UAV) self-organizing network according to an embodiment of the present invention.

[0074] Figure 8This is a flowchart illustrating the dynamic encoding packet allocation process based on the water-filling algorithm in an embodiment of the present invention.

[0075] Figure 9 This is a schematic diagram of the water injection algorithm according to an embodiment of the present invention;

[0076] Figure 10 This is a flowchart illustrating the predictive routing maintenance and seamless handover in an embodiment of the present invention;

[0077] Figure 11 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0079] See Figure 1 As shown, an embodiment of the present invention provides a method for multipath selection and network coding redundancy transmission in a UAV network, comprising the following steps:

[0080] S101, each UAV performs node status perception and link prediction;

[0081] S102, the source node initiates a multi-path route discovery process based on the extended AOMDV routing protocol and performs path quality measurement, selecting the several paths with the highest path quality as active transmission paths.

[0082] S103, the source node divides the raw data block to be sent into several raw data packets and encodes them separately;

[0083] S104, optimized allocation based on water injection algorithm;

[0084] S105, performs data forwarding and decoding reception based on the optimized allocation results;

[0085] S106, continuously monitors link communication and proactively triggers maintenance procedures before the link actually breaks.

[0086] like Figure 2 As shown, the system of this invention is deployed as software modules within the network protocol stack of each UAV node, forming a closed-loop control system of "perception-evaluation-decision-execution". The system includes the following four functional modules:

[0087] State awareness and neighbor management module: used to periodically collect node status, broadcast HELLO messages, maintain the neighbor table, and calculate LET based on Gauss-Markov model.

[0088] Multipath routing management module: used to extend the AOMDV protocol to discover multiple paths, calculate PQMF values, maintain routing tables, and perform proactive path switching based on predictions.

[0089] Network coding processing module: used to perform random linear network coding on the raw data at the source node, perform opportunistic recoding at the intermediate node, and complete decoding at the destination node.

[0090] Intelligent scheduling and allocation module: used to dynamically adjust ETF (Effective Throughput Factor) based on the real-time status of the path, run the water-filling algorithm to solve the optimal allocation ratio, and schedule the encoded packets to multiple paths.

[0091] Specifically, step S101 is executed by the state awareness and neighbor management module, as follows:

[0092] 1. Node status information collection: Each drone node is set with a fixed collection cycle. Collect the following information: node remaining energy (Unit: J), read from the power management system of each UAV; node three-dimensional position vector (Unit: m), read from the GPS navigation unit of each UAV; node three-dimensional velocity vector (Unit: m / s), read from the inertial navigation system of each UAV; transmission queue occupancy rate. It reads from the MAC layer queue manager of each drone operating system.

[0093] 2. Neighbor Information Exchange and Neighbor Table Maintenance: An extended HELLO message is defined for each UAV node. The payload includes the following fields: node ID, timestamp, remaining node energy, node 3D position vector, node 3D velocity vector, and transmission queue occupancy rate. In addition, each node also maintains a dynamic neighbor table, which records the following: neighbor node IDs, and the latest status information of neighbor nodes (…). , , , ) and the calculated LET.

[0094] 3. Calculation of Link Lifetime (LET): Assuming a drone node... and exist The three-dimensional spatial position vectors at time t are as follows: and The velocity vectors are as follows: and Their motion follows a Gaussian-Markov model, so their relative position vectors can be expressed as:

[0095]

[0096] The relative velocity vector can be expressed as:

[0097]

[0098] To obtain a more accurate prediction of link lifetime, a reasonable constant velocity approximation is required. At the start of the prediction... Given relative positions and relative velocity Assuming a short-term forecast period Within the space, the relative velocity remains constant and equal to its current value. Then at any future time The distance between the two nodes is:

[0099]

[0100] Let the effective communication range of the two nodes be . Therefore, when the link is about to be interrupted, the following condition is met:

[0101]

[0102] To simplify the solution, squaring and expanding both sides of the above equation yields:

[0103]

[0104] make , , We can obtain the following equation:

[0105]

[0106] Calculate the discriminant of the equation. .like This indicates that the equation has no real roots, in which case LET will be infinite, and the predicted link will exist for a long time. If This indicates that the equation has two real roots. and ( ):like ,but ;like ,but ;like ,but The calculated LET values ​​are stored in a dynamic neighbor table to provide a basis for subsequent path evaluation and maintenance.

[0107] Specifically, step S102 is executed by the multi-path routing management module, as follows:

[0108] 1. Extend the route discovery message of the AOMDV protocol: such as Figure 3 As shown, three cumulative fields are added after the RREQ message format of the standard AOMDV protocol:

[0109] (1) Minimum Remaining Energy (MRE): Minimum remaining energy;

[0110] (2) Worst Link Expiration Time (WLET): Worst-case link survival time;

[0111] (3) Maximum Queue Occupancy Rate (MQOR): Maximum queue occupancy rate.

[0112] When the source node initiates an RREQ, it initializes the WLET field to a maximum value INF, ensuring that when the RREQ reaches the first neighbor node, the calculated LET value of the first actual link can replace INF; the other two fields are initialized to the source node's own current state value, i.e., MRE = MQOR = .

[0113] like Figure 4 As shown, the same three cumulative fields are added after the RREP message format of the standard AOMDV protocol to send the final bottleneck performance index of the path back to the source node.

[0114] 2. Intermediate Node Update Processing: When an intermediate node... Upon receiving a non-duplicate RREQ message, update the fields according to the following rules before forwarding:

[0115] (1) MRE = ,in This indicates the energy received. Represents a node Its own remaining energy;

[0116] (2) WLET = ,in This indicates the received LET value. This indicates the LET between this node and its previous hop node;

[0117] (3) MQOR= ,in This indicates the received node queue occupancy rate. Represents a node Its own queue occupancy rate;

[0118] (4) Hop Count = ,in Represents a node The number of hops to the previous hop node.

[0119] 3. PQMF (Path Quality Metric Function) Calculation: After receiving the RREP message, the source node extracts each candidate path. The four bottleneck performance indicators: , , , ,in This represents the minimum remaining energy in the path, corresponding to the field Minimum Remaining Energy. This indicates the worst-case link expiration time in the path, corresponding to the field Worst Link Expiration Time. This indicates the maximum queue occupancy rate in the path, corresponding to the field Maximum Queue Occupancy Rate; This indicates the number of hops on the path, corresponding to the Hop Count field. For example... Figure 5 As shown, by normalizing these four bottleneck performance indicators and calculating the comprehensive path quality metric function value, we can obtain:

[0120]

[0121] in, The normalized value representing the minimum remaining energy of a path is defined as:

[0122]

[0123] in, Representing a path The minimum remaining energy of the middle node. This represents the maximum energy of a node; The normalized value representing the worst-case link lifetime of a path is defined as:

[0124]

[0125] in, This represents the worst-case link lifetime in the path. Indicates the preset maximum link lifetime; The normalized value representing the maximum queue occupancy rate of a path is defined as:

[0126]

[0127] in, This indicates the maximum queue occupancy rate along the path; the higher the node load, the lower the score for this item. The normalized value representing the path hop count is defined as:

[0128]

[0129] in, This indicates the number of hops in the path. This represents the smoothing factor (usually set to 1 to prevent the denominator from being 0 when the number of jumps is 0, making the fraction meaningless). , , and Represents configurable weight coefficients, satisfying Furthermore, the values ​​of each weight coefficient are non-negative and can be preset according to different task types. For example, when performing persistent monitoring tasks, the following settings can be configured: When performing high-speed data backhaul tasks, the following settings can be configured: .

[0130] 4. Path selection and maintenance: The source node calculates the path length of all known paths. Values ​​and sort them. Then select... The highest value Path (usually set) As the active path, it is stored in the routing table. For example... Figure 6 As shown, each path entry records the following information: destination node IP address, next hop, path ID, Value, list of path nodes, estimated path propagation delay And so on. The rest. Path ( Then, it is stored as a backup path in the backup path set. Among them, communication scenarios such as Figure 7 As shown.

[0131] Specifically, step S103 is executed by the network coding processing module at the source node, as follows:

[0132] 1. Parameter settings:

[0133] (1) Number of original data packets: (For example ).

[0134] (2) Redundancy coefficient: (Usually taken as:) ).

[0135] (3) Number of encoded data packets: (For example ).

[0136] (4) Finite field: (usually selected) ).

[0137] 2. Source node encoding process:

[0138] (1) Data segmentation: Divide the data to be sent into blocks (such as a file or video frame, with a total data volume of 10 ... bytes) divided into Equal-length raw data packets Each package is [length] byte.

[0139] (2) Generate coefficient matrix: Randomly generate one coding coefficient matrix Each element in the matrix exist The sample is randomly selected from the top.

[0140] (3) Linear encoding: calculated Encoded data packets:

[0141]

[0142] in, Indicates the first One encoded data packet, Indicates the first One raw data packet.

[0143] (4) Encapsulation of encoded data packets: for each encoded data packet Add another encoded header containing the following fields:

[0144] Data block ID, packet ID, Global Encoding Vector (GEV) )as well as The value is then used to deliver the encapsulated, encoded data packet to the intelligent scheduling module.

[0145] 3. Intermediate Node Re-encoding: Before forwarding the encoded packet, the intermediate node does not need to decode the received encoded packet, but instead re-encodes it with a certain probability. (like Perform re-encoding on the encoded data packets:

[0146] (1) The intermediate node randomly selects two encoded data packets to be forwarded from the sending buffer. and Their GEVs are respectively and .

[0147] (2) Randomly generate two coefficients and ( (and is a non-zero coefficient).

[0148] (3) Calculate the new encoded data packet: .

[0149] (4) Calculate the new GEV: .

[0150] (5) Forward the new encoded data packet.

[0151] Specifically, step S104 is executed by the intelligent scheduling and allocation module, such as... Figure 8 As shown, the specific content is as follows:

[0152] 1. Collect real-time status of paths: For each active path Retrieve the following information from the routing table: Path quality metric function Estimated path propagation delay Current maximum queue occupancy rate And the Raw Throughput Factor (RTF) .

[0153] 2. Dynamic capacity correction: Calculate the effective throughput factor for each path after correction using the following formula:

[0154]

[0155] in, The path weight index is usually taken as... .

[0156] 3. The water-filling algorithm is used to find the optimal ratio: minimizing the maximum path transmission time. To solve the following convex optimization problem with the objective function:

[0157]

[0158] in, , which represents the total amount of data in the encoded data packet to be transmitted; The size of a single encoded data packet at the source node; Represents the set of active paths. For example... Figure 9 As shown, through iterative calculation using the water-filling algorithm, the final allocation ratio of encoded data packets can be determined as follows:

[0159]

[0160] 4. Packet scheduling: Based on the calculated... Determine the number of encoded packets allocated to each path. ,make sure The system prioritizes packets with high GEV linear independence to accelerate decoding at the receiver. The selected encoded data packets are then sent to the network interface queue of the corresponding path for transmission.

[0161] Specifically, step S105 is executed by the network coding processing module at the destination node, as follows:

[0162] 1. Data forwarding: Intermediate nodes forward encoded data packets according to the selected route, and during the forwarding process, they do so with a certain probability. Perform a re-encoding operation.

[0163] 2. Destination Node Decoding: The destination node maintains a decoding context for each encoded data packet ID, containing the following relevant information:

[0164] (1) A dynamically growing receiver matrix , used to store the GEV of the received encoded data packets;

[0165] (2) A dynamically growing data vector , used to store the corresponding encoded data packet payload;

[0166] (3) A counter, used to record the number of linearly independent packets received (i.e., rank ).

[0167] Each time the destination node receives a new encoded data packet belonging to this data block, it adds its GEV as a line. Data added Then, the rank of the matrix is ​​updated using the rank calculation algorithm. When At that time, Gaussian elimination is performed to decode and recover the original data packet. Finally, the data is processed sequentially. The original data packets are reassembled to restore the original data blocks.

[0168] Specifically, step S106 is completed collaboratively by the multi-path routing management module and the intelligent scheduling and allocation module, such as... Figure 10 As shown, the specific content is as follows:

[0169] 1. Continuous monitoring: The source node periodically (e.g., every 500ms) monitors each active path. Calculate its end-to-end link lifetime. :

[0170]

[0171] in, Representing a path The LET (Link Term) for each hop can be obtained by periodically querying the local neighbor table. A warning threshold can be set. (For example: 1000ms). When an active path is detected that meets the following conditions... If so, the route is determined to be an endangered route. This triggers a seamless switching process.

[0172] 2. Seamless switching process:

[0173] (1) Traffic migration: Immediately notify the intelligent scheduler to relocate endangered paths. The allocation ratio is forcibly set to 0, and it is removed from the active path set. The source node suspends sending new encoded data packets to that path.

[0174] (2) Path replacement: from the set of alternative paths In the middle, select The highest path Activate it and add it to the active path set. among.

[0175] (3) Encoding state preservation: Due to the characteristics of network encoding, the destination node only cares about the total number of linearly independent packets received during decoding. Exiting does not affect encoded packets already delivered via other paths. To ensure continuous decoding, the system records packets already delivered via other paths. If an encoded packet is sent but no acknowledgment is received, the source node generates a new encoded packet that is linearly equivalent to it, and the data is retransmitted through the new active path set.

[0176] (4) Re-optimization: based on the new active path set The intelligent scheduling and allocation module then re-executes step 4 to calculate a new optimal allocation ratio.

[0177] (5) Resume transmission: Subsequent encoded data packets will be scheduled and transmitted according to the new allocation ratio.

[0178] The steps S101-S106 above do not necessarily represent the order of the steps, but are represented by step symbols. The order of the steps can be adjusted.

[0179] Figure 11This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. The electronic device in this embodiment includes: a processor 1101 and a memory 1102; wherein the memory 1102 is used to store computer execution instructions; the processor 1101 is used to execute the computer execution instructions stored in the memory to implement the various steps performed by the electronic device in the above embodiment. For details, please refer to the relevant descriptions in the foregoing method embodiments.

[0180] Alternatively, the memory 1102 can be either standalone or integrated with the processor 1101.

[0181] When the memory 1102 is set up independently, the electronic device also includes a bus 1103 for connecting the memory 1102 and the processor 1101.

[0182] This invention also provides a computer storage medium storing computer execution instructions, which, when executed by a processor, implement the method described above.

[0183] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0184] In the embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0185] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0186] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0187] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0188] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0189] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0190] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0191] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0192] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. Both the processor and the storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic device or host device.

[0193] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for multipath selection and network coding redundancy transmission in unmanned aerial vehicle (UAV) networks, characterized in that, Includes the following steps: Each drone performs node status perception and link prediction; The source node initiates a multi-path route discovery process based on the extended AOMDV routing protocol and performs path quality measurement, selecting the highest quality paths as active transmission paths. The source node divides the raw data block to be sent into several raw data packets and encodes them separately; Optimized allocation based on water injection algorithm; Data forwarding and decoding reception are performed based on the optimized allocation results; Continuous monitoring of link communication proactively triggers maintenance procedures before the link actually breaks.

2. The method for multi-path selection and network coding redundancy transmission in UAV networks according to claim 1, characterized in that, Each UAV performs node status perception and link prediction, including the following steps: Each UAV node periodically collects its own status information, including: remaining energy, three-dimensional spatial coordinates, three-dimensional velocity vector and transmission queue occupancy rate, and exchanges information with neighboring nodes by broadcasting enhanced HELLO messages; Based on the Gauss-Markov movement model, according to the neighboring nodes in Relative position vector at time Relative velocity vector and communication radius Solve for any future time. The quadratic equation: ； make , , ,get: ； Calculate the discriminant of the equation. ;like This indicates that the equation has a positive real root, and its smallest positive real root is taken as the predicted Link Time of Survival (LET). This indicates that the equation has no real roots, and LET is infinite, predicting that the link will exist for a long time.

3. The method for multi-path selection and network coding redundancy transmission in UAV networks according to claim 1, characterized in that, The source node initiates a multi-path route discovery process based on the extended AOMDV routing protocol and performs path quality measurement, selecting the highest quality paths as active transmission paths, including the following steps: The source node initiates a multi-path route discovery process based on the extended AOMDV routing protocol. The extended RREQ message contains four cumulative fields used to collect bottleneck performance metrics for the path: minimum remaining energy, worst-case link lifetime, maximum queue occupancy, and hop count. These fields are updated by intermediate nodes during the flooding process. The destination node replies with an RREP message for each loop-free path. Based on the bottleneck performance metrics in the RREP message, the source node identifies each candidate path... The comprehensive path quality metric (PQMF) value is calculated and expressed as follows: ； in, The normalized value representing the minimum remaining energy of a path is defined as: ； in, Representing a path The minimum remaining energy of the middle node. This represents the maximum energy of a node; The normalized value representing the worst-case link lifetime of a path is defined as: ； in, This represents the worst-case link lifetime in the path. Indicates the preset maximum link lifetime; The normalized value representing the maximum queue occupancy rate of a path is defined as: ； in, This indicates the maximum queue occupancy rate in the path; The normalized value representing the path hop count is defined as: ； in, This indicates the number of hops in the path. Indicates the smoothing factor; , , and Represents configurable weight coefficients, satisfying And the values ​​of each weight coefficient are non-negative; Source node based on Sort the values ​​from highest to lowest, and select the top ones. One path is designated as the active transmission path, while the remaining paths are stored as backup paths in the backup path set.

4. The method for multi-path selection and network coding redundancy transmission in UAV networks according to claim 1, characterized in that, The source node divides the raw data block to be sent into several raw data packets and encodes them separately, including the following steps: The source node divides the raw data block to be sent into raw data packets And in a finite field Above, randomly generate one dimensional coding coefficient matrix ,in ; Calculated A set of encoded data packets: ； in, Indicates the first One encoded data packet, Indicates the first One raw data packet, Representation matrix Random elements in; Each encoded packet header encapsulates its corresponding Global Encoding Vector (GEV), represented as a row coefficient vector. ; When forwarding, the intermediate node performs a random linear combination of the received multiple encoded packets over a finite field to generate a new encoded packet, and updates the global encoding vector according to the same linear relationship; this operation does not require decoding.

5. The method for multi-path selection and network coding redundancy transmission in UAV networks according to claim 1, characterized in that, The optimized allocation based on the water injection algorithm includes the following steps: For path The effective throughput factor ETF is calculated as follows: ； in, Represents the raw throughput factor (RTF), representing the path. Transmission capacity under ideal, interference-free conditions can be estimated by the physical transmission rate at the link layer or by a smoothed estimate using historical average throughput. Representing a path The PQMF value represents the path length; the higher the value, the stronger the path length. The better the overall long-term quality; This represents the path weight index, which amplifies the advantages of high PQMF paths while severely penalizing low PQMF paths. This represents the maximum current send queue load rate of all nodes. When the value is 0, it means that the node is unloaded, and when it is 1, it means that the node's load queue is full. The optimal coded data packet allocation ratio for each path is adopted using the water-filling algorithm to minimize the maximum path transmission time. Solve the following convex optimization problem with the objective in mind: ； in, This represents the path propagation delay, which is determined by the physical distance and the electromagnetic wave speed of the propagation medium. Indicates the total amount of data in the encoded data packet to be transmitted; Indicates the allocation ratio of encoded data packets; This represents the active path set; the number of encoded packets allocated to each path is calculated based on the optimal allocation ratio, and encoded packets with high linear independence are prioritized for scheduling.

6. The method for multi-path selection and network coding redundancy transmission in UAV networks according to claim 1, characterized in that, The process of forwarding and decoding data based on the optimized allocation results includes the following steps: Encoded data packets are forwarded along the corresponding allocated path, and intermediate nodes can perform encoding operations after receiving the encoded data packets; The destination node receives encoded data packets from all active paths, obtaining a reception matrix. Each row of the reception matrix consists of the global encoding vector (GEV) of the received encoded data packets. When the rank of the reception matrix reaches... When, it means that it has been received. Given a linearly independent encoded data packet, the destination node immediately performs Gaussian elimination to solve for the... The original data packets were used to recover the original data blocks.

7. The method for multi-path selection and network coding redundancy transmission in UAV networks according to claim 1, characterized in that, The continuous monitoring of link communication proactively triggers a maintenance process before the link actually breaks, including the following steps: Continuously monitor the end-to-end LET on each active path. When the end-to-end LET of a path is detected to be lower than the preset warning threshold, an alert will be issued. In such cases, before the link actually breaks, a maintenance process is proactively triggered, including traffic migration, immediately removing the path from the active path set, and allocating a certain percentage of it. Set to zero, stop scheduling new encoded packets to it; Path replacement, select the path with the highest PQMF value from the backup path set to activate it and add it to the active path set; Re-optimize allocation, based on the new active path set, re-execute the water-filling algorithm-based optimization allocation, calculate the new global optimal allocation ratio, and continue the data transmission process.

8. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-7.

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