A Demand-Based Predictive Drone Network Routing Method

Abstract

This invention relates to the field of routing technology, and more particularly to an on-demand predictive routing method for unmanned aerial vehicle (UAV) networks. The invention determines the state information of neighboring UAVs based on an improved Kalman filter model, predicts the latest state information of neighboring UAVs based on this state information, determines the communication status between the UAV and its neighbors and the message coverage rate of the neighboring UAVs for flooding commands based on the latest state information, sets replay delays and replay probabilities for the neighboring UAVs, determines the distance correlation factor and traffic load factor of the UAVs in response to the communication status between the UAV and its neighbors, calculates the routing cost value of candidate links, determines the optimal path to forward data, and for the failure of the optimal path, finds alternative links to replace the failed optimal path or performs path repair. This invention combines node mobility prediction and a neighbor coverage flooding mechanism to achieve fast addressing and dynamic optimization of routes.

Description

Technical Field

[0001] This invention relates to the field of routing technology, and in particular to a method for predictive on-demand unmanned aerial vehicle (UAV) network routing. Background Technology

[0002] Unmanned aerial vehicle (UAV) ad hoc network communication is a network structure formed by UAV nodes through self-organization, used to support efficient data transmission in highly dynamic scenarios. Research in this field includes issues such as route optimization, link quality improvement, and dynamic topology adaptation to ensure stable communication of UAV swarms in complex environments. Routing technology is one of the core technologies in network communication, used to establish optimal paths between network nodes to achieve efficient data transmission. In UAV ad hoc networks, routing technology needs to address challenges such as the high mobility of UAV nodes, link interruptions, and multi-hop communication.

[0003] For example, Chinese Patent Publication No. CN114390631A discloses a multipath routing protocol method for mobility prediction in UAV ad hoc networks. This method includes: establishing a UAV mobile node system model based on the UAV's movement path; establishing a primary and backup route using a multipath routing algorithm based on the constructed UAV mobile node system model, wherein the primary and backup route includes a primary path and a backup path; obtaining information about the node to be predicted, inputting the node information into the node mobility prediction model to obtain the node's mobility prediction result, and constructing a route list based on the prediction result; and performing route switching based on the route list. The multipath routing protocol scheme for mobility prediction in UAV ad hoc networks proposed in this invention not only considers the high dynamism of UAV nodes, the remaining energy of UAV nodes, signal strength, and link failure time, but also considers the problem of route reconstruction caused by frequent route breaks, thus reducing the time required for route reconstruction.

[0004] For example, Chinese Patent Publication No. CN118075836A discloses an adaptive routing method for NDN UAV ad hoc networks based on topology prediction. This method includes: leveraging the table-driven nature of the NLSR routing protocol, allowing each UAV node to control itself independently; indirectly feeding back the state change frequency of neighboring UAV nodes and the dynamic update frequency of intra-network name prefixes through the LSDB update frequency to reflect the link status of wireless links; predicting the link status update frequency, including the neighbor node status update frequency and LSDB change frequency, using the Holt two-parameter smoothing method; and adaptively adjusting the link probe Hello message interval and LSDB state synchronization interval based on the prediction results to reduce LSDB state update latency and improve routing convergence speed. This invention can be applied to large-scale UAV swarm communication and is suitable for the ad hoc network communication needs of various complex scenarios such as disaster relief and reconnaissance.

[0005] However, the following problems still exist in the existing technology.

[0006] In practice, existing UAV network routing methods are ill-suited to the characteristics of UAVs' high-speed movement and frequent topology changes, resulting in a significant decrease in the reliability and efficiency of data transmission. Furthermore, the high control overhead and link interruption issues brought about by flooding mechanisms limit the stability and real-time performance of communication. Secondly, UAVs can only communicate with their neighboring UAVs, making it difficult for UAVs to obtain information about their neighboring UAVs in networks with highly dynamic topology changes. Summary of the Invention

[0007] To address this, the present invention provides an on-demand predictive UAV network routing method to solve the problem that existing UAV network routing methods are unable to adapt to the characteristics of UAVs' high-speed movement and frequent topology changes, resulting in a significant decrease in the reliability and efficiency of data transmission. In addition, the high control overhead and link interruption problems brought about by the flooding mechanism limit the stability and real-time performance of communication. Furthermore, UAVs can only communicate with their neighboring UAVs, making it difficult for UAVs to obtain information about their neighboring UAVs in a network with highly dynamic topology changes.

[0008] To achieve the above objectives, the present invention provides an on-demand predictive unmanned aerial vehicle (UAV) network routing method, comprising:

[0009] The state information of neighboring UAVs is determined based on an improved Kalman filter model, and the latest state information of neighboring UAVs is predicted based on the state information.

[0010] Based on the latest status information, determine the communication status between the UAV and neighboring UAVs and the message coverage rate of the neighboring UAVs for the flooding command, and set the replay delay and replay probability for the neighboring UAVs.

[0011] In response to the communication status between the drone and neighboring drones, determine the distance correlation factor and traffic load factor of the drone, calculate the routing cost value of candidate links, and determine the optimal path to forward data;

[0012] For the failure of the optimal path, find a backup link to replace the failed optimal path;

[0013] Alternatively, perform path repair.

[0014] Furthermore, the process of determining the state information of neighboring UAVs based on the improved Kalman filter model includes,

[0015] Determine the spatial coordinates and spatial velocity of the neighboring drone;

[0016] Determine the time interval for receiving adjacent control packets;

[0017] The position-velocity relationship is determined based on the spatial coordinates, spatial velocity, and reception time interval.

[0018] Determine the state vector of the neighboring UAV based on the position-velocity relationship;

[0019] The state vector is determined to be the state information of the neighboring drone;

[0020] The spatial coordinates and spatial velocity are obtained through an interactive control package.

[0021] Furthermore, the process of predicting the latest state information of neighboring drones based on the state information includes,

[0022] Determine the measurement equations and the state prediction equations;

[0023] Based on the measurement equation, the state prediction equation, and the state information, the latest state information of the neighboring drone is predicted.

[0024] Furthermore, the step of determining the communication status between the UAV and neighboring UAVs based on the latest status information, wherein,

[0025] If the distance between a drone and its neighboring drones is less than the minimum threshold distance for transmission between drones, then the drones are considered to be neighboring drone nodes and can communicate directly.

[0026] If the distance between a drone and its neighboring drones is greater than or equal to the minimum threshold distance for transmission between drones and their neighboring drones, then the drones are considered not to be neighboring drone nodes and cannot communicate directly.

[0027] The distance between the drone and its neighboring drones is obtained through an interactive control package.

[0028] Furthermore, the process of determining the message coverage of the flooding command by neighboring drones based on the latest status information includes,

[0029] The set of neighboring drones that did not receive the flooding command is identified as the uncovered neighbor set.

[0030] Determine the neighbor set of the neighboring drone;

[0031] The message coverage rate is determined based on the uncovered neighbor set and its own neighbor set.

[0032] Furthermore, the process of setting the replay delay and replay probability for the neighboring drone includes,

[0033] Determine the overlap between drone neighbors;

[0034] The replay delay is determined based on the neighbor overlap.

[0035] The replay probability is determined based on the message coverage and the number of drones.

[0036] Furthermore, the process of determining the distance-related factor and traffic load factor of the UAV includes,

[0037] Determine the three-dimensional spatial distance between the drone and neighboring drones, and the corresponding path hop count;

[0038] Distance-related factors are determined based on the three-dimensional spatial distance and the number of jumps;

[0039] The traffic load factor is determined based on the monitoring time interval, the number of data packets received by neighboring drones within the monitoring time interval, and the average number of data packets in the buffer queue.

[0040] The monitoring time interval is determined by the adjacent control packet, and the spatial distance, path hop count, number of received data packets, and number of data packets are obtained by the interactive control packet.

[0041] Furthermore, the process of calculating the routing cost value of candidate links and determining the optimal path to forward data includes,

[0042] The routing cost value is determined based on the influence of the distance-related factor and the traffic load factor.

[0043] The link corresponding to the lowest routing cost is identified as the optimal path.

[0044] Furthermore, the process of finding an alternative link to replace the failed optimal path includes,

[0045] Identify all possible paths to establish connections with neighboring drones;

[0046] Paths with fewer than the hop count threshold and latency and / or packet loss rate within a preset range are designated as backup links.

[0047] Furthermore, the path repair process includes,

[0048] Query the drone's local routing table;

[0049] Determine whether it is possible to connect with neighboring drones based on the query results;

[0050] If a connection cannot be established, the search continues until a usable path is successfully found.

[0051] Compared with existing technologies, this invention determines the state information of neighboring drones based on an improved Kalman filter model, predicts the latest state information of neighboring drones based on this state information, determines the communication status between the drone and neighboring drones and the message coverage rate of the neighboring drones for flooding commands based on the latest state information, sets replay delay and replay probability for the neighboring drones, determines the distance correlation factor and traffic load factor of the drones in response to the communication status between the drones and neighboring drones, calculates the routing cost value of candidate links, determines the optimal path to forward data, and for the failure of the optimal path, finds backup links to replace the failed optimal path, or performs path repair. This invention combines node mobility prediction and neighbor coverage flooding mechanism to achieve fast routing addressing and dynamic optimization.

[0052] In particular, this invention predicts the latest status information of neighboring drones during the route discovery phase, quickly updates the routing table based on the latest status information, promptly reflects the joining or leaving of neighboring nodes, and uses the prediction results to set different replay delays and replay probabilities for neighboring drones. In practice, traditional routing methods are difficult to effectively predict link interruptions or rapid topology changes, resulting in high data packet loss rates, frequent route switching, and low communication stability. Moreover, in existing protocols, flooding route discovery methods generate a large number of redundant data packets, consuming network bandwidth, increasing the energy consumption and processing burden of drone nodes, and failing to meet diverse quality requirements. Based on this, this invention considers predicting the latest status information of neighboring drones based on spatial data, and improves positioning accuracy through error correction and node status update mechanisms. Based on the prediction results, the message coverage of flooding commands is determined to accurately determine the replay delay and replay probability, improving the communication stability of drones, effectively reducing redundant data transmission, and alleviating network congestion.

[0053] In particular, this invention dynamically selects the optimal next-hop node based on the service flow density and cluster topology information fed back by the control packet during the routing and data transmission phases, and updates its own routing table in real time. It combines multiple indicators to evaluate routing costs, keeping the path dynamically optimal. In practice, in UAV ad hoc networks, the data streams of different tasks (such as video and sensor data) have different requirements for indicators such as latency, throughput, and reliability. Traditional protocols are difficult to provide optimized routing selection based on dynamic network conditions. Moreover, UAV network tasks have strong real-time requirements. If the selected transmission path is incorrect, it will lead to poor real-time network performance, causing tasks to expire or even fail. Based on this, this invention determines the routing cost value by determining the distance-related factor indicator and the traffic load factor indicator to dynamically select the optimal path, realizing dynamic selection and optimization of routing.

[0054] In particular, this invention replaces the traditional First-In-First-Out (FIFO) model with priority processing of control packets during the route maintenance and repair phase. It monitors expired data packets that exceed the remaining link time limit in real time for cleanup and adjusts the cleanup frequency according to link quality, thereby improving the data packet transmission efficiency in dynamic network environments. Especially, it promptly determines backup links or repairs paths in time when the optimal path fails to ensure normal data transmission. This solves the core problems of traditional routing methods such as link interruption, high latency, and low reliability, and improves the communication stability and efficiency of UAVs. Attached Figure Description

[0055] Figure 1 A schematic diagram illustrating the steps of an on-demand predictive drone network routing method according to an embodiment of the invention;

[0056] Figure 2 This is a framework for an on-demand predictive drone network routing method according to an embodiment of the invention;

[0057] Figure 3 This is a logic block diagram illustrating how a drone determines the communication status between itself and a neighboring drone, according to an embodiment of the invention. Detailed Implementation

[0058] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0059] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0060] Please see Figure 1 and Figure 2 , Figure 1 This is a schematic diagram illustrating the steps of the on-demand predictive drone network routing method according to an embodiment of the invention. Figure 2 This invention provides a framework for an on-demand predictive drone network routing method. One such method includes:

[0061] The state information of neighboring UAVs is determined based on an improved Kalman filter model, and the latest state information of neighboring UAVs is predicted based on the state information.

[0062] Based on the latest status information, determine the communication status between the UAV and neighboring UAVs and the message coverage rate of the neighboring UAVs for the flooding command, and set the replay delay and replay probability for the neighboring UAVs.

[0063] In response to the communication status between the drone and neighboring drones, determine the distance correlation factor and traffic load factor of the drone, calculate the routing cost value of candidate links, and determine the optimal path to forward data;

[0064] For the failure of the optimal path, find a backup link to replace the failed optimal path;

[0065] Alternatively, perform path repair.

[0066] Specifically, the process of determining the state information of neighboring UAVs based on the improved Kalman filter model includes,

[0067] Determine the spatial coordinates and spatial velocity of the neighboring drone;

[0068] Determine the time interval for receiving adjacent control packets;

[0069] The position-velocity relationship is determined based on the spatial coordinates, spatial velocity, and reception time interval.

[0070] Determine the state vector of the neighboring UAV based on the position-velocity relationship;

[0071] The state vector is determined to be the state information of the neighboring drone;

[0072] The spatial coordinates and spatial velocity are obtained through an interactive control package.

[0073] Specifically, the position-velocity relationship is expressed by formulas (1)-(3).

[0074] x i (t+dt)=x i (t)+v xi (t)dt (1)

[0075] y i (t+dt)=y i (t)+v yi (t)dt (2)

[0076] z i (t+dt)=z i (t)+v zi (t)dt (3)

[0077] In formulas (1)-(3), x i (·) represents the coordinate of the i-th UAV in the x-axis direction at time ·, y i (·) represents the coordinate of the i-th UAV in the y-axis direction at time ·, z i (·) represents the coordinate of the i-th UAV in the z-axis direction at time ·, v xi(t) represents the velocity of the i-th UAV in the x-axis direction at time t, v yi (t) represents the velocity of the i-th UAV in the y-axis direction at time t, v zi (t) represents the velocity of the i-th UAV in the z-axis direction at time t.

[0078] Using dt as the unit time interval, the continuous-time equations of formulas (1)-(3) are transformed into position-velocity equations at discrete moments, which are expressed by formulas (4)-(6).

[0079] x i [k+1]=x i [k]+v xi [k]dt (4)

[0080] y i [k+1]=y i [k]+v yi [k]dt (5)

[0081] z i [k+1]=z i [k]+v zi [k]dt (6)

[0082] In formulas (4)-(6), k represents the universal discrete time point.

[0083] Specifically, the state vector is represented by formula (7).

[0084] s i (t)=(x i (t),v xi (t),y i (t),v yi (t),z i (t),v zi (t) (7)

[0085] In formula (7), s i (t) represents the state vector.

[0086] Specifically, the process of predicting the latest state information of neighboring drones based on the aforementioned state information includes,

[0087] Determine the measurement equations and the state prediction equations;

[0088] Based on the measurement equation, the state prediction equation, and the state information, the latest state information of the neighboring drone is predicted.

[0089] Specifically, the measurement equation is expressed by formula (8).

[0090] m i[k]=B i s i [k]+u i [k] (8)

[0091] In formula (8), m i [k] represents the observation vector of the i-th UAV at time k, B i Represents the state measurement matrix, s i [k] represents the state vector of the i-th UAV at time k, u i [k] represents the system measurement noise of the i-th UAV at time k.

[0092] The state prediction equation is expressed by formula (9).

[0093]

[0094] In formula (9), Let A represent the state vector of the i-th drone at time k+1. i Let w represent the state transition matrix of the i-th UAV at time k. i [k] represents the system state noise of the i-th UAV at time k.

[0095] Specifically, A i and B i It can be derived from the relationship between position and velocity, and expressed by formulas (10) and (11).

[0096]

[0097] B 3×6 =[I 3×3 O 3×3 ] T (11)

[0098] In formulas (10) and (11), I 3×3 Represents a 3x3 identity matrix, O 3×3 This represents a 3x3 matrix consisting entirely of zeros.

[0099] In implementation, when the j-th UAV needs to predict the state information of the i-th UAV at time k, the state information s of the i-th UAV is extracted from the data packets they previously interacted with at time k. i [k-1], and perform preliminary prediction, expressed by formula (12),

[0100]

[0101] Meanwhile, the predicted state covariance matrix of the i-th UAV at time k is estimated by formula (13).

[0102]

[0103] In formula (13), Let P represent the predicted state covariance matrix of the i-th UAV at time k. i [k-1] represents the state covariance matrix of the i-th UAV at time k-1, Q i Let represent the covariance matrix of the system state noise of the i-th UAV.

[0104] Specifically, Kalman filtering improves positioning accuracy through error correction and node state update mechanisms. First, the optimal Kalman gain is calculated, expressed by formula (14).

[0105]

[0106] In formula (14), K i [k] represents the optimal Kalman gain, R i Let represent the covariance matrix of the measurement noise of the i-th UAV system.

[0107] Understandably, the calculated Kalman gain can be used to correct the measurement residuals and improve the accuracy of the predicted state. Specifically, the predicted state vector can be updated based on the Kalman gain, as expressed by formula (15).

[0108]

[0109] Meanwhile, the predicted state covariance matrix of the i-th UAV at time k prepares for predicting the UAV state at the next time moment, and is expressed by formula (16).

[0110]

[0111] In formula (16), I represents the identity matrix.

[0112] Based on the above process, and the updated state The j-th drone can successfully predict the state information of the i-th drone at time t.

[0113] Please see Figure 3 , Figure 3 This is a logic block diagram illustrating how to determine the communication status between a drone and its neighboring drones, according to an embodiment of the invention. Specifically, the communication status between the drone and its neighboring drones is determined based on the latest status information, wherein...

[0114] If the distance between a drone and its neighboring drones is less than the minimum threshold distance for transmission between drones, then the drones are considered to be neighboring drone nodes and can communicate directly.

[0115] If the distance between a drone and its neighboring drones is greater than or equal to the minimum threshold distance for transmission between drones and their neighboring drones, then the drones are considered not to be neighboring drone nodes and cannot communicate directly.

[0116] The distance between the drone and its neighboring drones is obtained through an interactive control package.

[0117] Specifically, the minimum threshold distance represents the theoretically maximum communication distance that a UAV can reach in an open three-dimensional airspace. The minimum threshold distance is selected within a certain range [500m, 1000m].

[0118] Specifically, the process of determining the message coverage of the flooding command by neighboring drones based on the latest status information includes,

[0119] The set of neighboring drones that did not receive the flooding command is identified as the uncovered neighbor set.

[0120] Determine the neighbor set of the neighboring drone;

[0121] The message coverage rate is determined based on the uncovered neighbor set and its own neighbor set.

[0122] Specifically, the uncovered neighbor set is represented by formula (17).

[0123] U(i)=N(i)-[N(i)∩N(s)]-{s} (17)

[0124] In formula (17), U(i) represents the set of uncovered neighbors, N(i) represents the set of neighbor nodes of drone i, N(s) represents the set of neighbor nodes of drone s, and [N(i)∩N(s)]+{s} represents the set of drone nodes that have received this flooding instruction.

[0125] It is understandable that the set of drone i's own neighbors is the set of drone i's neighbor nodes.

[0126] Message coverage is represented by formula (18).

[0127]

[0128] In formula (18), C f (i) represents message coverage.

[0129] Specifically, the process of setting replay delay and replay probability for the neighboring drone includes,

[0130] Determine the overlap between drone neighbors;

[0131] The replay delay is determined based on the neighbor overlap.

[0132] The replay probability is determined based on the message coverage and the number of drones.

[0133] Specifically, the replay delay is expressed by formula (19).

[0134]

[0135] In formula (19), τ d (i) represents the replay delay, τ max Represents a fixed constant delay, |·| represents the number of elements in the set, and the value of |·| represents the neighbor overlap.

[0136] Understandably, the higher the overlap between neighbors, the shorter the replay delay.

[0137] Specifically, the replay probability is represented by formula (20).

[0138]

[0139] In formula (20), P re (i) represents the replay probability, and N represents the number of drones in the network.

[0140] Specifically, this invention predicts the latest status information of neighboring drones during the route discovery phase, quickly updates the routing table based on the latest status information, promptly reflects the joining or leaving of neighboring nodes, and uses the prediction results to set different replay delays and replay probabilities for neighboring drones. In practice, traditional routing methods are difficult to effectively predict link interruptions or rapid topology changes, resulting in high data packet loss rates, frequent route switching, and low communication stability. Furthermore, in existing protocols, flooding route discovery methods generate a large number of redundant data packets, consuming network bandwidth, increasing the energy consumption and processing burden of drone nodes, and failing to meet diverse quality requirements. Based on this, this invention considers predicting the latest status information of neighboring drones based on spatial data, and improves positioning accuracy through error correction and node status update mechanisms. Based on the prediction results, the message coverage of flooding commands is determined to accurately determine the replay delay and replay probability, improving the communication stability of drones, effectively reducing redundant data transmission, and alleviating network congestion.

[0141] Specifically, the process of determining the distance-related factors and traffic load factors of drones includes,

[0142] Determine the three-dimensional spatial distance between the drone and neighboring drones, and the corresponding path hop count;

[0143] Distance-related factors are determined based on the three-dimensional spatial distance and the number of jumps;

[0144] The traffic load factor is determined based on the monitoring time interval, the number of data packets received by neighboring drones within the monitoring time interval, and the average number of data packets in the buffer queue.

[0145] The monitoring time interval is determined by the adjacent control packet, and the spatial distance, path hop count, number of received data packets, and number of data packets are obtained by the interactive control packet.

[0146] Specifically, the distance correlation factor is represented by formula (21).

[0147] f d (c→d)=HC(c→d)×Dis(c→d) (21)

[0148] In formula (21), f d (c→d) represents the distance correlation factor of the link c→d from UAV c to UAV d, HC(c→d) represents the number of hops in the link c→d, and Dis(c→d) represents the three-dimensional spatial distance between UAV c and UAV d.

[0149] Specifically, the flow load factor is expressed by formula (22).

[0150]

[0151] In formula (22), f l (c→d) represents the traffic load factor of the link c→d from drone c to drone d, BO θ (i) indicates that drone i is in τ M The number of data packets received internally, τ M Indicates the monitoring time interval. This indicates the average number of data packets in the buffer queue.

[0152] Specifically, the remaining link lifetime of link c→d and the expected transmission delay of data packets must meet the basic threshold, which is expressed by formulas (23) and (24).

[0153] RLT(c→d)>τ δ (twenty three)

[0154] ETD(c→d)<τ h (twenty four)

[0155] In formulas (23) and (24), RLT(c→d) represents the remaining link lifetime of link c→d, and τ δ τ represents the specified maximum single-hop transmission time, ETD(c→d) represents the expected transmission delay of data packets along link c→d, and τ represents the expected transmission delay of data packets along link c→d. h Indicates the maximum transmission time limit for data packets.

[0156] Specifically, the process of calculating the routing cost of candidate links and determining the optimal path to forward data includes:

[0157] The routing cost value is determined based on the influence of the distance-related factor and the traffic load factor.

[0158] The link corresponding to the lowest routing cost is identified as the optimal path.

[0159] Specifically, the routing cost is determined according to formula (25).

[0160] COST(c→d)=w1f d (c→d)+w2f l (c→d) (25)

[0161] In formula (25), COST(c→d) represents the routing cost from node c to node d, w1 represents the distance-related factor weight coefficient, and w2 represents the traffic load factor weight coefficient.

[0162] Among them, the sum of the weight coefficients of the distance correlation factor and the traffic load factor is 1, the weight coefficient of the distance correlation factor is 0.5, and the weight coefficient of the traffic load factor is 0.5.

[0163] Specifically, the optimal path is determined for data forwarding, thereby activating the selected source-destination link layer communication.

[0164] Specifically, this invention dynamically selects the optimal next-hop node based on the service flow density and cluster topology information fed back by the control packet during the routing and data transmission phases, and updates its own routing table in real time. It also combines multiple indicators to evaluate routing costs, ensuring the path remains dynamically optimal. In practice, in UAV ad hoc networks, the data flows of different tasks (such as video and sensor data) have varying requirements for latency, throughput, and reliability. Traditional protocols struggle to provide optimized routing based on dynamic network conditions. Furthermore, UAV network tasks have strong real-time requirements; errors in the selected transmission path can lead to poor real-time network performance, causing tasks to expire or even fail. Therefore, this invention determines the routing cost value using distance-related factors and traffic load factors to dynamically select the optimal path, achieving dynamic routing selection and optimization.

[0165] Specifically, the process of finding alternative links to replace the failed optimal path includes,

[0166] Identify all possible paths to establish connections with neighboring drones;

[0167] Paths with fewer than the hop count threshold and latency and / or packet loss rate within a preset range are designated as backup links.

[0168] Specifically, a backup link refers to a non-optimal path that is slightly inferior in performance to the current primary route (such as higher latency or lower bandwidth) but can still achieve connectivity under existing network conditions. It can be understood that if there are multiple paths that meet the conditions, the one with the lowest load is selected as the preferred backup link.

[0169] Specifically, the hop count threshold is calculated in advance by obtaining the hop count of several historical paths in advance and determining that twice the average hop count of the historical paths is the hop count threshold.

[0170] Specifically, the preset range is calculated in advance, and a number of historical packet loss rates are obtained in advance, and the preset range is determined to be greater than 0.5 times the average of the historical packet loss rates.

[0171] Specifically, the path repair process includes,

[0172] Query the drone's local routing table;

[0173] Determine whether it is possible to connect with neighboring drones based on the query results;

[0174] If a connection cannot be established, the search continues until a usable path is successfully found.

[0175] Specifically, during the process of re-establishing network connections, the UAV will select the node with the lowest load that meets the connection requirements from the candidate paths as the new relay node, based on the node load information obtained during the path repair process. Subsequently, the latest path information and network topology will be synchronized to all participating nodes through global broadcast to ensure the synchronization and consistency of data forwarding.

[0176] Specifically, this invention replaces the traditional First-In-First-Out (FIFO) model with priority processing of control packets during the route maintenance and repair phase. It monitors expired data packets that exceed the remaining link time limit in real time for cleanup and adjusts the cleanup frequency according to link quality, thereby improving the data packet transmission efficiency in dynamic network environments. In particular, it promptly determines backup links or repairs paths in a timely manner when the optimal path fails to ensure normal data transmission. This solves the core problems of traditional routing methods such as link interruption, high latency, and low reliability, and improves the communication stability and efficiency of UAVs.

[0177] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0178] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predictive unmanned aerial vehicle (UAV) network routing on demand, characterized in that, include: The state information of neighboring UAVs is determined based on an improved Kalman filter model, and the latest state information of neighboring UAVs is predicted based on the state information. Based on the latest status information, determine the communication status between the UAV and neighboring UAVs and the message coverage rate of the neighboring UAVs for the flooding command, and set the replay delay and replay probability for the neighboring UAVs. In response to the communication status between the drone and neighboring drones, determine the distance correlation factor and traffic load factor of the drone, calculate the routing cost value of candidate links, and determine the optimal path to forward data; For the failure of the optimal path, find a backup link to replace the failed optimal path; Alternatively, perform path repair; The process of determining the message coverage of the flooding command by neighboring drones based on the latest status information includes: The set of neighboring drones that did not receive the flooding command is identified as the uncovered neighbor set. Determine the neighbor set of the neighboring drone; The message coverage rate is determined based on the uncovered neighbor set and its own neighbor set. The process of setting the replay delay and replay probability for the neighboring drone includes: Determine the overlap between drone neighbors; The replay delay is determined based on the neighbor overlap. The replay probability is determined based on the message coverage and the number of drones.

2. The on-demand predictive UAV network routing method according to claim 1, characterized in that, The process of determining the state information of neighboring UAVs based on the improved Kalman filter model includes: Determine the spatial coordinates and spatial velocity of the neighboring drone; Determine the time interval for receiving adjacent control packets; The position-velocity relationship is determined based on the spatial coordinates, spatial velocity, and reception time interval. Determine the state vector of the neighboring UAV based on the position-velocity relationship; The state vector is determined to be the state information of the neighboring drone; The spatial coordinates and spatial velocity are obtained through an interactive control package.

3. The on-demand predictive UAV network routing method according to claim 1, characterized in that, The process of predicting the latest state information of neighboring drones based on the state information includes: Determine the measurement equations and the state prediction equations; Based on the measurement equation, the state prediction equation, and the state information, the latest state information of the neighboring drone is predicted.

4. The on-demand predictive UAV network routing method according to claim 1, characterized in that, The communication status between the UAV and its neighboring UAVs is determined based on the latest status information, wherein, If the distance between a drone and its neighboring drones is less than the minimum threshold distance for transmission between drones, then the drones are considered to be neighboring drone nodes and can communicate directly. If the distance between a drone and its neighboring drones is greater than or equal to the minimum threshold distance for transmission between drones and their neighboring drones, then the drones are considered not to be neighboring drone nodes and cannot communicate directly. The distance between the drone and its neighboring drones is obtained through an interactive control package.

5. The on-demand predictive UAV network routing method according to claim 1, characterized in that, The process of determining the distance correlation factor and traffic load factor of the UAV includes, Determine the three-dimensional spatial distance between the drone and neighboring drones, and the corresponding path hop count; Distance-related factors are determined based on the three-dimensional spatial distance and the number of jumps; The traffic load factor is determined based on the monitoring time interval, the number of data packets received by neighboring drones within the monitoring time interval, and the average number of data packets in the buffer queue. The monitoring time interval is determined by the adjacent control packet, and the spatial distance, path hop count, number of received data packets, and number of data packets are obtained by the interactive control packet.

6. The on-demand predictive UAV network routing method according to claim 1, characterized in that, The process of calculating the routing cost of candidate links and determining the optimal path to forward data includes: The routing cost value is determined based on the influence of the distance-related factor and the traffic load factor. The link corresponding to the lowest routing cost is identified as the optimal path.

7. The on-demand predictive UAV network routing method according to claim 1, characterized in that, The process of finding an alternative link to replace the failed optimal path includes, Identify all possible paths to establish connections with neighboring drones; Paths with fewer than the hop count threshold and latency and / or packet loss rate within a preset range are designated as backup links.

8. The on-demand predictive UAV network routing method according to claim 1, characterized in that, The path repair process includes... Query the drone's local routing table; Determine whether it is possible to connect with neighboring drones based on the query results; If a connection cannot be established, the search continues until a usable path is successfully found.

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