Unmanned aerial vehicle network routing method for on-demand prediction

Abstract

The invention relates to the technical field of routing, in particular to an on-demand prediction unmanned aerial vehicle network routing method, which determines state information of a neighbor unmanned aerial vehicle based on an improved Kalman filtering model, and predicts latest state information of the neighbor unmanned aerial vehicle based on the state information. Determining a communication state between the unmanned aerial vehicle and a neighbor unmanned aerial vehicle and a message coverage rate of the neighbor unmanned aerial vehicle to a flooding instruction based on the latest state information, setting a replay delay and a replay probability for the neighbor unmanned aerial vehicle, and responding to the communication state between the unmanned aerial vehicle and the neighbor unmanned aerial vehicle. Determining a distance correlation factor and a traffic load factor of the unmanned aerial vehicle, calculating a routing cost value of a candidate link, determining an optimal path to forward data, and searching a standby link for a failure state of the optimal path to replace the failed optimal path, or performing path repair. According to the method, node mobility prediction and a flooding mechanism of neighbor coverage are combined, and rapid addressing and dynamic optimization of routing are realized.

Description

Technical Field

[0001] The present invention relates to the field of routing technology, and in particular to a drone network routing method with on-demand prediction. Background Art

[0002] Drone ad-hoc network communication is a network structure formed by drone nodes through self-organization, which is used to support efficient data transmission in high-dynamic scenarios. The research in this field includes issues such as routing optimization, link quality improvement, and dynamic topology adaptability to ensure stable communication of drone swarms in complex environments. Routing technology is one of the core technologies in network communication, which is used to establish an optimal path between network nodes to achieve efficient data transmission. In drone ad-hoc networks, routing technology needs to address challenges such as the high mobility of drone nodes, link interruption, and multi-hop communication.

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

[0004] For example, Chinese Patent Publication No.: CN118075836A discloses an NDN drone ad-hoc network adaptive routing method based on topology prediction. The method includes: based on the table-driven characteristic of the NLSR routing protocol, each drone node is independently controlled, and the state change frequency of neighbor drone nodes and the dynamic update frequency of in-network name prefixes are indirectly fed back through the update frequency of the LSDB, and the link state of the wireless link is fed back; predicting the link state update frequency, including the neighbor node state update frequency and the LSDB change frequency, by the Holt double-parameter smoothing method, and adaptively adjusting the time interval of the link detection Hello message and the time interval of LSDB state synchronization according to the prediction result to reduce the LSDB state update delay and improve the routing convergence speed. The invention can be applied to the field of large-scale drone cluster communication and is suitable for the self-organizing network communication requirements of special tasks in various disaster relief, detection, and other complex scenarios.

[0005] However, the following problems still exist in the prior art.

[0006] In actual situations, existing UAV network routing methods are difficult to adapt to the characteristics of high-speed movement and frequent topological changes of UAVs, resulting in a significant decline in the reliability and efficiency of data transmission. In addition, the high control overhead and link interruption problems brought by the flooding mechanism limit the stability and real-time performance of communication. Secondly, UAVs can only communicate with their neighboring UAVs, and in a network with high-dynamic topological changes, it is difficult for UAVs to obtain information about their neighboring UAVs. Summary of the Invention

[0007] To this end, the present invention provides a demand prediction-based UAV network routing method to solve the problems that existing UAV network routing methods are difficult to adapt to the characteristics of high-speed movement and frequent topological changes of UAVs, resulting in a significant decline in the reliability and efficiency of data transmission. In addition, the high control overhead and link interruption problems brought by the flooding mechanism limit the stability and real-time performance of communication. Secondly, UAVs can only communicate with their neighboring UAVs, and in a network with high-dynamic topological changes, it is difficult for UAVs to obtain information about their neighboring UAVs.

[0008] To achieve the above object, the present invention provides a demand prediction-based UAV network routing method, which includes:

[0009] Determine the state information of neighboring UAVs based on an improved Kalman filter model, and predict the latest state information of neighboring UAVs based on the state information;

[0010] Determine the communication state between the UAV and neighboring UAVs and the message coverage rate of neighboring UAVs for flooding instructions based on the latest state information, and set a replay delay and a replay probability for the neighboring UAVs;

[0011] In response to the communication state between the UAV and neighboring UAVs, determine the distance-related factor and traffic load factor of the UAV, calculate the routing cost value of the candidate link, and determine the optimal path to forward data;

[0012] For the failure state of the optimal path, search for an alternative link to replace the failed optimal path;

[0013] Or, perform path repair.

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

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

[0016] Determine the reception time interval of adjacent control packets;

[0017] Determine the position-velocity relationship based on the spatial coordinates, spatial velocity, and reception time interval;

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

[0019] Determine the state vector as the state information of neighboring UAVs;

[0020] Wherein, the spatial coordinates and spatial velocity are obtained through an interactive control packet.

[0021] Further, the process of predicting the latest state information of neighboring UAVs based on the state information includes,

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

[0023] Predict the latest state information of neighboring UAVs based on the measurement equation, the state prediction equation and the state information.

[0024] Further, the process of determining the communication state between the UAV and neighboring UAVs based on the latest state information, wherein,

[0025] If the distance between the UAV and the neighboring UAV is less than the minimum threshold distance for transmission between the UAV and the neighboring UAV, it is considered that the UAV and the neighboring UAV are neighboring UAV nodes to each other and can communicate directly;

[0026] If the distance between the UAV and the neighboring UAV is greater than or equal to the minimum threshold distance for transmission between the UAV and the neighboring UAV, it is considered that the UAV and the neighboring UAV are not neighboring UAV nodes and cannot communicate directly;

[0027] Wherein, the distance between the UAV and the neighboring UAV is obtained through an interactive control packet.

[0028] Further, the process of determining the message coverage rate of neighboring UAVs for the flooding instruction based on the latest state information includes,

[0029] Determine the set of neighboring UAVs that have not received the flooding instruction as the uncovered neighbor set;

[0030] Determine the self-neighbor set of neighboring UAVs;

[0031] Determine the message coverage rate based on the uncovered neighbor set and the self-neighbor set.

[0032] Further, the process of setting the replay delay and replay probability for the neighboring UAVs includes,

[0033] Determine the UAV neighbor overlap degree;

[0034] Determine the replay delay based on the neighbor overlap degree;

[0035] Determine the replay probability based on the message coverage rate and the number of drones.

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

[0037] Determine the three - dimensional spatial distance between the drone and its neighbor drones and the corresponding number of path hops;

[0038] Determine the distance - related factor based on the three - dimensional spatial distance and the number of hops;

[0039] Determine the traffic load factor based on the monitoring time interval, the number of data packets received by the neighbor drones within the monitoring time interval, and the average number of data packets in the cache queue;

[0040] Among them, the monitoring time interval is determined by adjacent control packets, and the spatial distance, the number of path hops, the number of received data packets, and the number of data packets are obtained by interactive control packets.

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

[0042] Determine the routing cost value based on the influence of the distance - related factor and the traffic load factor;

[0043] Determine the link corresponding to the lowest routing cost value as the optimal path.

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

[0045] Determine all possible paths to establish connections with neighbor drones;

[0046] Determine the paths with the number of path hops less than the hop threshold and the delay and / or packet loss rate within the preset range as alternative links.

[0047] Furthermore, the process of performing path repair includes,

[0048] Query the local routing table of the drone;

[0049] Determine whether it can communicate with neighbor drones based on the query result;

[0050] If communication cannot be established, continue searching until a usable path is successfully found.

[0051] Compared with the prior art, the present 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 the state information, determines the communication state between the drone and neighboring drones and the message coverage rate of neighboring drones for flood instructions based on the latest state information, sets a replay delay and a replay probability for the neighboring drones, determines a distance-related factor and a traffic load factor of the drone in response to the communication state between the drone and neighboring drones, calculates the routing cost value of candidate links, determines an optimal path to forward data, and in the case of a failure state of the optimal path, finds an alternative link to replace the failed optimal path or performs path repair. The present invention combines a flooding mechanism for node mobility prediction and neighbor coverage to achieve fast addressing and dynamic optimization of routing.

[0052] In particular, in the route discovery phase, the present invention predicts the latest state information of neighboring drones, quickly updates the routing table based on the latest state information, timely reflects the addition or departure of neighboring nodes, and uses the prediction results to set different replay delays and replay probabilities for neighboring drones. In actual situations, traditional routing methods are difficult to effectively predict link interruptions or rapid topological changes, resulting in a high data packet loss rate, frequent routing switches, low communication stability. Moreover, in existing protocols, the flooding-based route discovery method generates a large number of redundant data packets, consumes network bandwidth, increases the energy consumption and processing burden of drone nodes, and cannot meet diverse quality requirements. Based on this, the present invention considers predicting the latest state information of neighboring drones according to spatial data and improving the positioning accuracy through an error correction and node state update mechanism, determines the message coverage rate of flood instructions based on the prediction results to accurately determine the replay delay and replay probability, improves the communication stability of drones, effectively reduces redundant data transmission, and alleviates network congestion.

[0053] In particular, in the route selection and data transmission phase, the present invention dynamically selects the best next-hop node according to the traffic flow density and cluster topology information fed back by control packets, and updates its own routing table in real time, combines multiple metrics to evaluate the routing cost, and keeps the path dynamically optimal. In actual situations, in a drone ad hoc network, data streams of different tasks (such as video and sensor data) have different requirements for metrics such as delay, throughput, and reliability. Traditional protocols are difficult to provide optimized route selection according to dynamic network conditions, and the tasks of the drone network have strong real-time requirements. If there are errors in the selected transmission path, it will lead to poor real-time performance of the network, causing tasks to expire or even fail. Based on this, the present invention determines a distance-related factor index and a traffic load factor index to determine the routing cost value, so as to dynamically select the optimal path, realizing dynamic selection and optimization of routing.

[0054] In particular, in the routing maintenance and repair phases, the present invention replaces the traditional first-in, first-out (FIFO) model by preferentially processing control packets, monitors expired data packets that exceed the remaining link time limit in real time for cleaning, and adjusts the cleaning frequency according to the link quality to improve the data packet transmission efficiency in a dynamic network environment. In particular, when the optimal path fails, a backup link is determined in a timely manner, or path repair is performed in a timely manner to ensure normal data transmission, solving the core problems of traditional routing methods in aspects such as link interruption, high latency, and low reliability, and improving the communication stability and efficiency of unmanned aerial vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of the steps of the on-demand prediction unmanned aerial vehicle network routing method according to an embodiment of the invention;

[0056] Figure 2 Framework of the on-demand prediction unmanned aerial vehicle network routing method according to an embodiment of the invention;

[0057] Figure 3 Logic block diagram for determining the communication status between an unmanned aerial vehicle and its neighboring unmanned aerial vehicles according to an embodiment of the invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] In order to make the objectives and advantages of the present invention more clear and understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0060] Please refer to Figure 1 and Figure 2 , Figure 1 which is a schematic diagram of the steps of the on-demand prediction unmanned aerial vehicle network routing method according to an embodiment of the invention, Figure 2 and is a framework of the on-demand prediction unmanned aerial vehicle network routing method according to an embodiment of the invention. An on-demand prediction unmanned aerial vehicle network routing method of the present invention includes:

[0061] Determine the state information of neighboring unmanned aerial vehicles based on an improved Kalman filter model, and predict the latest state information of neighboring unmanned aerial vehicles based on the state information;

[0062] Determine the communication status between the unmanned aerial vehicle and neighboring unmanned aerial vehicles and the message coverage rate of neighboring unmanned aerial vehicles for flooding instructions based on the latest state information, and set a replay delay and a replay probability for the neighboring unmanned aerial vehicles;

[0063] Determine the distance-related factor and traffic load factor of the UAV in response to the communication status between the UAV and neighboring UAVs, calculate the routing cost value of the candidate link, and determine the optimal path to forward data;

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

[0065] Or, 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 of the neighboring UAV and the spatial velocity of the neighboring UAV;

[0068] Determine the reception time interval of adjacent control packets;

[0069] Determine the position-velocity relationship 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] Determine that the state vector is the state information of the neighboring UAV;

[0072] Among them, the spatial coordinates and spatial velocity are obtained through the interactive control packet.

[0073] Specifically, the position-velocity relationship is represented 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 the · moment, y i (·) represents the coordinate of the i-th UAV in the y-axis direction at the · moment, z i (·) represents the coordinate of the i-th UAV in the z-axis direction at the · moment, v xivix(t) represents the velocity of the i-th UAV in the x-axis direction at time t. yi viy(t) represents the velocity of the i-th UAV in the y-axis direction at time t. zi viz(t) represents the velocity of the i-th UAV in the z-axis direction at time t.

[0078] Taking dt as the unit time interval, the continuous-time equations of formulas (1)-(3) are converted into position-velocity equations at discrete times, represented by formulas (4)-(6).

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

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

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

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

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

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

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

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

[0087] determining the measurement equation and the state prediction equation;

[0088] predicting the latest state information of neighboring UAVs based on the measurement equation, the state prediction equation, and the state information.

[0089] Specifically, the measurement equation is represented 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, and B i represents the state measurement matrix, s i [k] represents the state vector of the i-th UAV at time k, and u i [k] represents the system measurement noise of the i-th UAV at time k.

[0092] The state prediction equation is represented by formula (9),

[0093]

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

[0095] Specifically, A i and B i can be derived based on the relationship between position and velocity and are represented by formula (10) and formula (11),

[0096]

[0097] B 3×6 = [I 3×3 O 3×3 T (11)

[0098] In formula (10) and formula (11), I 3×3 represents a 3x3 identity matrix, and O 3×3 represents a 3x3 matrix of all 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 i [k - 1] of the i-th UAV will be extracted from the data packets they interacted with at the previous time, and a preliminary prediction will be performed, represented by formula (12),

[0100]

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

[0102]

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

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

[0105]

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

[0107] It can be understood that the calculated Kalman gain can be used to correct the measurement residual and improve the accuracy of the predicted state. Among them, the predicted state vector can be updated based on the Kalman gain, as expressed by formula (15),

[0108]

[0109] At the same time, the predicted state covariance matrix of the i-th drone at time k prepares for predicting the state of the drone at the next moment, as expressed by formula (16),

[0110]

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

[0112] According to the above process, based on the updated state the j-th drone can successfully predict the state information of the i-th drone at time

[0113] Please refer to Figure 3 , Figure 3 which is the logic block diagram for determining the communication state between the drone and its neighboring drones in the embodiment of the invention. Specifically, based on the latest state information, the communication state between the drone and its neighboring drones is determined, where

[0114] if the distance between the drone and its neighboring drone is less than the minimum threshold distance for transmission between the drone and its neighboring drone, it is considered that the drone and its neighboring drone are neighbor drone nodes to each other and can communicate directly;

[0115] If the distance between a drone and a neighbor drone is greater than or equal to the minimum threshold distance for transmission between the drone and the neighbor drone, it is considered that the drone and the neighbor drone are not neighbor drone nodes and cannot communicate directly;

[0116] Among them, the distance between the drone and the neighbor drone is obtained by interacting with control packets.

[0117] Specifically, the minimum threshold distance represents the theoretically maximum achievable farthest communication distance of the drone in an open three-dimensional airspace, and the minimum threshold distance is selected within the determined interval [500m, 1000m].

[0118] Specifically, the process of determining the message coverage rate of neighbor drones for the flooding instruction based on the latest status information includes,

[0119] Determine the set of neighbor drones that have not received the flooding instruction as the uncovered neighbor set;

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

[0121] Determine the message coverage rate based on the uncovered neighbor set and the 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 uncovered neighbor set, 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 can be understood that the own neighbor set of drone i is the set of neighbor nodes of drone i.

[0126] The message coverage rate is represented by formula (18),

[0127]

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

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

[0130] Determine the drone neighbor overlap degree;

[0131] Determine the replay delay based on the neighbor overlap degree;

[0132] Determine the retransmission probability based on the message coverage rate and the number of drones.

[0133] Specifically, the retransmission delay is represented by formula (19),

[0134]

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

[0136] It can be understood that the higher the neighbor overlap degree, the shorter the retransmission delay.

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

[0138]

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

[0140] Specifically, in the route discovery phase of the present invention, the latest status information of neighbor drones is predicted, the routing table is quickly updated based on the latest status information, the addition or departure of neighbor nodes is promptly reflected, and different retransmission delays and retransmission probabilities are set for neighbor drones using the prediction results. In actual situations, traditional routing methods are difficult to effectively predict link interruptions or rapid topological changes, resulting in a high data packet loss rate, frequent routing switches, low communication stability. Moreover, in existing protocols, the flooding-based route discovery method generates a large number of redundant data packets, consumes network bandwidth, increases the energy consumption and processing burden of drone nodes, and cannot meet diverse quality requirements. Based on this, the present invention considers predicting the latest status information of neighbor drones according to spatial data, improving the positioning accuracy through an error correction and node status update mechanism, determining the message coverage rate of the flooding instruction based on the prediction results to accurately determine the retransmission delay and retransmission 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 factor and traffic load factor of the drone includes,

[0142] Determine the three-dimensional spatial distance between the drone and neighbor drones and the corresponding number of path hops;

[0143] Determine the distance-related factor based on the three-dimensional spatial distance and the number of hops;

[0144] Determine the traffic load factor based on the monitoring time interval, the number of data packets received by neighboring UAVs within the monitoring time interval, and the average number of data packets in the cache queue;

[0145] Among them, the monitoring time interval is determined by adjacent control packets, and the spatial distance, the number of path hops, the number of received data packets, and the number of data packets are obtained by interactive control packets.

[0146] Specifically, the distance-related 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-related factor of the link c→d from UAV c to UAV d, HC(c→d) represents the number of hops of the link c→d, and Dis(c→d) represents the three-dimensional spatial distance from UAV c to UAV d.

[0149] Specifically, the traffic load factor is represented by formula (22),

[0150]

[0151] In formula (22), f l (c→d) represents the traffic load factor of the link c→d from UAV c to UAV d, BO θ (i) represents the number of data packets received by UAV i within τ M , τ M represents the monitoring time interval, represents the average number of data packets in the cache queue.

[0152] Specifically, the remaining link lifetime and the expected transmission delay of data packets of the link c→d need to meet the basic thresholds, which are represented by formula (23) and formula (24),

[0153] RLT(c→d) > τ δ (23)

[0154] ETD(c→d) < τ h (24)

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

[0156] Specifically, the process of calculating the routing cost value of candidate links and determining the optimal path for forwarding data includes

[0157] determining the routing cost value based on the influence of the distance-related factor and the traffic load factor;

[0158] determining the link corresponding to the lowest routing cost value as the optimal path.

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

[0160] COST(c→d) = w 1 f d (c→d) + w 2 f l (c→d) (25)

[0161] In formula (25), COST(c→d) represents the routing cost value from node c to node d, and w 1 represents the weight coefficient of the distance-related factor, and w 2 represents the weight coefficient of the traffic load factor.

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

[0163] Specifically, determine the optimal path for data forwarding, and then activate the selected source-destination link layer communication.

[0164] Specifically, in the routing selection and data transmission stage of the present invention, the best next-hop node is dynamically selected according to the traffic flow density and cluster topology information fed back by the control packet, and its own routing table is updated in real time. Combining multiple indicators to evaluate the routing cost makes the path dynamically optimal. In actual situations, in the UAV ad hoc network, 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 according to dynamic network conditions, and the tasks of the UAV network have strong real-time requirements. If there is an error in the selected transmission path, it will lead to poor real-time performance of the network, causing the task to expire or even fail. Based on this, the present invention determines the distance-related factor index and the traffic load factor index to determine the routing cost value, so as to dynamically select the optimal path, realizing the dynamic selection and optimization of routing.

[0165] Specifically, the process of finding a backup link to replace the failed optimal path includes

[0166] determining all possible paths to establish connections with neighbor UAVs;

[0167] Determine that the path with a hop count less than the hop count threshold and a delay and / or packet loss rate within a preset range is the backup link.

[0168] Specifically, a backup link refers to a non-optimal path that is slightly inferior in performance to the current main route (such as higher delay or lower bandwidth), but can still achieve connectivity under the 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. Obtain several historical path hop counts in advance, and determine that twice the average value of the historical path hop counts is the hop count threshold.

[0170] Specifically, the preset range is calculated in advance. Obtain several historical packet loss rates in advance, and determine that the preset range is greater than 0.5 times the average value of the historical packet loss rates.

[0171] Specifically, the process of path repair includes,

[0172] Query the local routing table of the drone;

[0173] Based on the query result, determine whether it is possible to communicate with neighboring drones;

[0174] If communication cannot be established, continue to search until a usable path is successfully found.

[0175] Specifically, during the process of re-establishing the network connection, the source drone will select the node with the lowest load and meeting the connection requirements from the candidate paths as the new relay node according to the node load information obtained during the path repair process. Subsequently, the latest path information and network topology structure are synchronized to all participating nodes through global broadcasting to ensure the synchronization and consistency of data forwarding.

[0176] Specifically, in the routing maintenance and repair phases, the present invention replaces the traditional first-in-first-out (FIFO) model by preferentially processing control packets, monitors expired data packets that exceed the remaining link time limit in real time for cleaning, and adjusts the cleaning frequency according to the link quality to improve the data packet transmission efficiency in a dynamic network environment. In particular, when the optimal path fails, a backup link is determined in a timely manner, or path repair is performed in a timely manner to ensure normal data transmission, solving the core problems of traditional routing methods in aspects such as link interruption, high latency, and low reliability, and improving the communication stability and efficiency of the drone.

[0177] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, those skilled in the art can easily understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle 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 fall within the protection scope of the present invention.

[0178] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting on-demand UAV network routing, characterized in that: include: Determine the state information of the neighboring UAV based on the improved Kalman filter model, and predict the latest state information of the neighboring UAV based on the state information; Determine the communication status between the drone and the neighboring drones and the message coverage rate of the neighboring drones for the flooding instruction based on the latest status information, and set a replay delay and a replay probability for the neighboring drones; In response to the communication status of the UAV with the neighboring UAVs, determining the distance-related factor and the traffic load factor of the UAV, calculating the routing cost value of the candidate link, and determining the optimal path to forward the data; When the optimal path fails, find a backup link to replace the failed optimal path. Or, perform path repair.

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

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

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

5. The on-demand prediction UAV network routing method according to claim 1, characterized in that: The process of determining the message coverage rate of the flooding instruction by the neighboring drone based on the latest status information includes: Determine the set of neighboring drones that have not received the flooding command as the uncovered neighbor set; Determine the neighbor drone’s own neighbor set; The message coverage rate is determined based on the uncovered neighbor set and the own neighbor set.

6. The on-demand prediction UAV network routing method according to claim 1, characterized in that: The process of setting the replay delay and replay probability for the neighboring drone includes: Determine the degree of overlap of drone neighbors; determining a replay delay based on the neighbor overlap; The rebroadcast probability is determined based on the message coverage and the number of drones.

7. The on-demand prediction UAV network routing method according to claim 1, characterized in that: The process of determining the distance-related factor and the traffic load factor of the drone includes: Determine the three-dimensional space distance between the drone and its neighboring drones and the corresponding number of path hops; Determining a distance correlation factor based on the three-dimensional space distance and the number of hops; determining a traffic load factor based on a monitoring time interval, a number of packets received by a neighboring UAV during the monitoring time interval, and an average number of packets in a buffer queue; The monitoring time interval is determined by adjacent control packets, and the spatial distance, the number of path hops, the number of received data packets and the number of data packets are obtained by interactive control packets.

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

9. The on-demand prediction UAV network routing method according to claim 1, characterized in that: The process of searching for a backup link to replace the failed optimal path includes: Identify all possible paths to establish connections with neighboring drones; A path whose hop count is less than a hop count threshold and whose delay and / or packet loss rate is within a preset range is determined as a backup link.

10. The on-demand prediction UAV network routing method according to claim 1, characterized in that: The process of performing path repair includes: Query the drone’s local routing table; Determine whether it is possible to connect with neighboring drones based on the query results; If connectivity is not possible, continue searching until a usable path is successfully found.

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