Unmanned aerial vehicle cluster routing protocol optimization method in mixed interference scene

Abstract

The invention discloses an unmanned aerial vehicle cluster routing protocol optimization method in a mixed interference scene, which aims at unmanned aerial vehicle clusters in different scenes, and comprises the following steps: firstly, identifying and classifying peripheral interference sources through a frequency spectrum detection module; secondly, combining interference information and a local knowledge base to dynamically adjust basic parameters of a protocol; and finally, comprehensively considering the communication performance and the network performance of the unmanned aerial vehicle nodes, confirming multi-point relay nodes in the network topology, and integrating and updating Hello messages so as to improve the routing stability. The routing stability and the network performance of the unmanned aerial vehicle cluster network can be effectively improved under the condition that an interference source exists in a wireless communication environment.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) self-organizing networks, and in particular to a method for optimizing a UAV cluster routing protocol in a mixed interference scenario. Background Art

[0002] The presence of interference in wireless communication environments can have various impacts on communication systems, causing degradation of wireless signal quality, reduced communication range, communication interruptions, and data loss. Spectrum sensing technology can be used to address these interference environments. By using sensing devices, these devices monitor and analyze radio spectrum usage. These devices collect spectrum data, including frequency band occupancy, signal strength, and spectrum utilization. By processing and analyzing this data, spectrum sensing systems can gain a comprehensive understanding of the spectrum environment, enabling applications such as electronic surveillance, radio interference detection, and radio spectrum regulation.

[0003] A drone ad hoc network (DAN) is a wireless mobile communication network composed of a number of drones acting as network nodes through self-organization and self-management. These nodes can act as source nodes, destination nodes, or intermediate nodes, and can forward data independently. Data transmission is fundamental to the efficient execution of tasks in a drone network, and routing protocols are primarily responsible for determining the transmission paths of data packets within the drone network, ensuring that information can be efficiently transmitted from the source node to the destination node.

[0004] However, spectrum resources currently available for drone use are scarce, and the available communication channel bandwidth in ad hoc networks is limited. Furthermore, differences in node configuration, routing selection, network congestion levels, and link instability caused by potential interference within wireless communication environments can lead to packet loss, impacting subsequent data transmission. Therefore, it is necessary to optimize routing protocols to make cluster routing more efficient and stable. Designing adaptive routing protocol optimization methods to improve drone performance in environments with limited bandwidth and interference sources is one of the challenges currently facing drone clusters. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing the routing protocol of drone clusters in mixed interference scenarios, which can effectively improve the routing stability and network performance of drone cluster networks when the communication channel bandwidth is limited and there are interference sources.

[0006] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0007] A method for optimizing a UAV cluster routing protocol in a mixed interference scenario, the method comprising the following steps:

[0008] S1, install an OLSR setting knowledge base in the drone node. The OLSR setting knowledge base contains the optimal parameter combinations of the OLSR routing protocol under different typical interference scenarios, and initializes the OLSR routing protocol parameters of each node in the drone cluster;

[0009] S2, the UAV node detects the surrounding spectrum environment when performing the mission, writes the integrated interference source information into the Hello message of the standard OLSR broadcast, and broadcasts it to the entire network at the broadcast time;

[0010] In step S3, each UAV node analyzes the Hello messages broadcast by neighboring nodes and extracts its own one-hop neighbor nodes and two-hop neighbor nodes. It determines the interference type based on the interference source information contained in the Hello message, searches for the optimal OLSR routing protocol parameter combination adapted to the corresponding interference scenario in the OLSR setting knowledge base, and updates the OLSR parameter settings adapted to the current interference environment.

[0011] S4: Determine the willingness value of each drone node's one-hop neighbor node to become a multi-point relay node, set the one-hop neighbor node with the willingness value "willalways" as a multi-point relay node and add it to the multi-point relay node set corresponding to the drone node; determine whether the multi-point relay node set of each drone node can cover all the two-hop neighbor nodes corresponding to the drone node; if so, jump to step S8; otherwise, go to step S5;

[0012] S5, analyze the nodes in the one-hop neighbor nodes that are not set as relay nodes, calculate the relative position change between the drone node and the one-hop neighbor node, and calculate the available communication time between the drone node and the one-hop neighbor node based on the communication range to obtain the communication performance of the one-hop neighbor node; calculate the network performance of the one-hop neighbor node based on the packet error rate of the physical layer and the number of packet losses caused by the lack of routing at the data link layer; comprehensively consider the communication performance and network performance of the one-hop neighbor node to obtain the comprehensive performance of the one-hop neighbor node, and sort the one-hop neighbor nodes to obtain an ordered set N;

[0013] S6: For the ordered set N, select the one-hop neighbor node with the highest comprehensive performance score, set the one-hop neighbor node as a multipoint relay node, remove it from the ordered set N and add it to the multipoint relay node set;

[0014] S7, determine whether the current multi-point relay node set can cover all two-hop neighbor nodes corresponding to the drone node. If so, proceed to step S8; otherwise, proceed to step S6;

[0015] S8, optimizing and judging the multi-point relay node set of each drone node, judging whether there is a node in the current multi-point relay node set whose willingness value is less than the set threshold and after removing the node, the multi-point relay node set can still cover all two-hop neighbor nodes; if so, remove the node that meets the conditions from the multi-point relay node set to obtain the final multi-point relay node set; if not, use the current multi-point relay node set as the final multi-point relay node set;

[0016] S9, maintain and update the neighbor status and link status information of each drone node according to the final multi-point relay node set, and go to step S2.

[0017] Furthermore, in step S1, the OLSR routing protocol parameters include a periodic interval for uploading Hello packets, a topology change protection time interval, a maximum time for maintaining a neighbor node connection state, and a topology structure maintenance time.

[0018] Furthermore, in step S1, a heuristic algorithm is used to simulate and determine the optimal parameter combination of the OLSR routing protocol under different typical interference scenarios.

[0019] Furthermore, the process of determining the interference type according to the interference source information includes:

[0020] The integrated interference source information is analyzed. If blocking interference and sweeping frequency interference are perceived at the same time, the interference type is judged as blocking interference; when only multiple blocking interference sources are perceived at the same time, the interference type is judged as blocking interference; when only multiple sweeping frequency interference sources are perceived at the same time, the interference type is judged as sweeping frequency interference; when no interference source is perceived, the interference type is judged as no interference.

[0021] Furthermore, the interference source information includes interference mode, interference range and interference frequency band.

[0022] Furthermore, in step S5, the communication performance calculation formula of the neighboring node is as follows:

[0023]

[0024] Among them, S CP,j is the normalized communication performance of one-hop neighbor node j; △t j is the communication available time of one-hop neighbor node j; is the maximum value of the communication available time among the one-hop neighbor nodes of drone node i, is the minimum value of the communication available time among the one-hop neighbor nodes of drone node i.

[0025] Furthermore, in step S5, the network performance calculation formula of the neighboring node is as follows:

[0026]

[0027] Where S NP,j is the normalized network performance of one-hop neighbor node j; P j is the packet error rate of one-hop neighbor node j, min i P i is the minimum value of the packet error rate among the one-hop neighbor nodes of drone node i, max i P i is the maximum packet error rate among the one-hop neighbor nodes of drone node i; A j is the number of packet losses caused by the lack of routing for one-hop neighbor node j, min i A i is the minimum number of packet losses caused by the lack of routing in the one-hop neighbor node of drone node i, max i A i is the maximum number of packet losses caused by the lack of routing in the one-hop neighboring node of drone node i; I is the interference mode factor, when the interference type is blocking interference, I = 1, when the interference type is no interference, I = 0, when the interference type is sweeping interference, I = η, η is the proportion of the interference signal covering the communication frequency band.

[0028] Furthermore, in step S5, the comprehensive performance calculation formula of the one-hop neighbor node is as follows:

[0029] S C,j =7×(α×S CP,j +(1-α)×S NP,j )

[0030] Where S C,j is the comprehensive performance of one-hop neighbor node j, S CP,j is the network performance of one-hop neighbor node j, S NP,j is the communication performance of one-hop neighbor node j, α is the weight factor, 0<α<1.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] First, the method for optimizing the UAV cluster routing protocol under mixed interference scenarios of the present invention takes into account the impact of different interference scenarios on UAV node communications and the changes in physical layer and data link layer parameters caused by spectrum interference in the presence of complex spectrum interference in the network, better evaluates the comprehensive performance of neighbor nodes, and optimizes the protocol's neighbor discovery and multi-point relay (MPR) node selection to better adapt to the interference environment; thereby solving the technical problem that the standard OLSR routing protocol in traditional technical solutions cannot be adaptively adjusted according to the network status and has poor communication performance in scenarios involving spectrum interference.

[0033] Second, the drone cluster routing protocol optimization method under mixed interference scenarios of the present invention takes into account the impact of different interference scenarios on drone node communications, accelerates the network topology update speed, and thus improves the stability and reliability of the network.

[0034] Third, the UAV cluster routing protocol optimization method under mixed interference scenarios of the present invention comprehensively considers the impact of interference on the UAV communication performance, the update of network topology and the mobility of nodes, adjusts the neighbor discovery mechanism, selects more stable MPR nodes, optimizes routing performance, and improves the data packet delivery rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is the overall flow chart of the UAV cluster routing protocol optimization method under mixed interference scenarios of the present invention;

[0036] Figure 2 This is a flow chart of the MPR node selection part of the present invention;

[0037] Figure 3 This is a diagram of the actual deployment. DETAILED DESCRIPTION

[0038] The embodiments of the present invention are described in further detail below with reference to the accompanying drawings.

[0039] The present invention discloses a method for optimizing a UAV cluster routing protocol in a mixed interference scenario, the method comprising the following steps:

[0040] A method for optimizing a UAV cluster routing protocol in a mixed interference scenario, the method comprising the following steps:

[0041] S1, install an OLSR setting knowledge base in the drone node. The OLSR setting knowledge base contains the optimal parameter combinations of the OLSR routing protocol under different typical interference scenarios, and initializes the OLSR routing protocol parameters of each node in the drone cluster;

[0042] S2, the UAV node detects the surrounding spectrum environment when performing the mission, writes the integrated interference source information into the Hello message of the standard OLSR broadcast, and broadcasts it to the entire network at the broadcast time;

[0043] In step S3, each UAV node analyzes the Hello messages broadcast by neighboring nodes and extracts its own one-hop neighbor nodes and two-hop neighbor nodes. It determines the interference type based on the interference source information contained in the Hello message, searches for the optimal OLSR routing protocol parameter combination adapted to the corresponding interference scenario in the OLSR setting knowledge base, and updates the OLSR parameter settings adapted to the current interference environment.

[0044] S4: Determine the willingness value of each drone node's one-hop neighbor node to become a multi-point relay node, set the one-hop neighbor node with the willingness value "willalways" as a multi-point relay node and add it to the multi-point relay node set corresponding to the drone node; determine whether the multi-point relay node set of each drone node can cover all the two-hop neighbor nodes corresponding to the drone node; if so, jump to step S8; otherwise, go to step S5;

[0045] S5, analyze the nodes in the one-hop neighbor nodes that are not set as relay nodes, calculate the relative position change between the drone node and the one-hop neighbor node, and calculate the available communication time between the drone node and the one-hop neighbor node based on the communication range to obtain the communication performance of the one-hop neighbor node; calculate the network performance of the one-hop neighbor node based on the packet error rate of the physical layer and the number of packet losses caused by the lack of routing at the data link layer; comprehensively consider the communication performance and network performance of the one-hop neighbor node to obtain the comprehensive performance of the one-hop neighbor node, and sort the one-hop neighbor nodes to obtain an ordered set N;

[0046] S6: For the ordered set N, select the one-hop neighbor node with the highest comprehensive performance score, set the one-hop neighbor node as a multipoint relay node, remove it from the ordered set N and add it to the multipoint relay node set;

[0047] S7, determine whether the current multi-point relay node set can cover all two-hop neighbor nodes corresponding to the drone node. If so, proceed to step S8; otherwise, proceed to step S6;

[0048] S8, optimizing and judging the multi-point relay node set of each drone node, judging whether there is a node in the current multi-point relay node set whose willingness value is less than the set threshold and after removing the node, the multi-point relay node set can still cover all two-hop neighbor nodes; if so, remove the node that meets the conditions from the multi-point relay node set to obtain the final multi-point relay node set; if not, use the current multi-point relay node set as the final multi-point relay node set;

[0049] S9, maintain and update the neighbor status and link status information of each drone node according to the final multi-point relay node set, and go to step S2.

[0050] See also Figure 1 and Figure 2 The UAV cluster routing protocol optimization method of the present invention specifically includes the following steps:

[0051] Step 1: First, an OLSR setting knowledge base is installed in the UAV node. The OLSR setting knowledge base contains the optimal parameter combinations of the OLSR routing protocol under different typical interference scenarios, and the OLSR routing protocol parameters of the node are initialized. In this embodiment, the OLSR routing protocol parameters include the following four parameters: the periodic interval time (HelloInterval) between the transmission of two Hello data packets on the interface; the topology change protection time interval (TCInterval) configured in the network device; the maximum time to maintain the connection state when establishing and maintaining neighbor relationships between network devices (Neighbor Holdtime); and the topology structure holding time (Topology Hold Time). Preferably, in this step, a heuristic algorithm simulation can be used to determine the optimal parameter combination of the OLSR routing protocol under different typical interference scenarios.

[0052] Step 2: While performing its mission, the UAV node detects the surrounding spectrum environment and determines whether there are any interference sources. If there are any, it integrates the interference source information and writes the integrated information into the Hello message broadcast by the standard OLSR. After the specified Hello message broadcast time, it is broadcast to the entire network. In this embodiment, the interference source information includes the interference mode, interference range, and interference frequency band.

[0053] Step 3: After receiving the Hello message broadcast by the neighboring node, the drone node analyzes it and first extracts the node's one-hop and two-hop neighbor nodes, generating the one-hop neighbor set N1 and the two-hop neighbor set N2, respectively. If the newly received Hello message contains interference source information, the interference type is determined based on this information. The optimal OLSR routing protocol parameter combination adapted to the current interference environment is searched within the OLSR settings knowledge base, and the OLSR routing protocol parameters of the drone node are updated.

[0054] Considering that the comprehensive performance of neighboring nodes needs to be calculated based on the interference type, in this embodiment, the interference source type is determined as follows:

[0055] Set the interference pattern factor I. The value of I represents the interference pattern sensed by the spectrum sensing module:

[0056] (1) When blocking interference and sweeping interference are detected simultaneously, it is determined to be blocking interference and I is set to 1;

[0057] (2) When multiple blocking interference sources are detected simultaneously, it is determined to be blocking interference and I is set to 1;

[0058] (3) When multiple sweep frequency interference sources are detected simultaneously, they are judged as sweep frequency interference, and the η of different sweep frequency interferences are compared, and I is set to the maximum value of different η;

[0059] (4) When no interference source is detected, it is determined that there is no interference and I is set to 0.

[0060] Step 4: The drone node determines the willingness value of its one-hop neighbor node to become an MPR set node, sets the one-hop neighbor node with the willingness value willalways as an MPR node, and adds it to the MPR set.

[0061] Step 5: Determine whether all two-hop neighbor nodes are covered by one-hop neighbor nodes in the MPR set. If so, jump to step 8; otherwise, jump to step 6.

[0062] Step 6: Filter out the remaining one-hop neighbor nodes in the one-hop neighbor node set N1 that are not selected into the MPR set; calculate the relative position change between the drone node and the one-hop neighbor node based on the position change of the one-hop neighbor node, and calculate the available communication time between the drone node and the one-hop neighbor node based on the communication range to obtain the communication performance of the one-hop neighbor node; calculate the network performance of the one-hop neighbor node based on the packet error rate of the physical layer and the number of packet losses caused by the lack of routing at the data link layer; finally, comprehensively consider the communication performance and network performance to obtain the comprehensive performance of the one-hop neighbor node and sort them to obtain the ordered set N.

[0063] In this embodiment, the communication performance of a one-hop neighbor node is calculated as follows:

[0064] The communication available time corresponding to the one-hop neighbor node is calculated based on the relative speed between the drone node and the one-hop neighbor node. The calculation formula for the communication available time is as follows:

[0065]

[0066] in:

[0067]

[0068] Where △t is the communication available time between UAV nodes i and j, x i (t) is the x coordinate of drone node i at time t, x j (t) is the x-coordinate of drone node j at time t, v x,rel (t) is the relative motion speed of drone node j relative to drone node i on the x-axis, yi (t) is the y coordinate of drone node i at time t, y j (t) is the y coordinate of drone node j at time t, v y,rel (t) is the relative motion speed of UAV node j relative to UAV node i on the y-axis, d(t) is the distance between UAV node i and UAV node j at time t, and R is the communication range between UAVs.

[0069] According to the communication available time, the communication performance of one-hop neighbor node j is calculated using the following formula:

[0070]

[0071] Among them, S CP,j is the normalized communication performance of one-hop neighbor node j; △t j is the communication available time of one-hop neighbor node j, is the maximum value of the communication available time among the one-hop neighbor nodes of drone node i, is the minimum value of the communication available time among the one-hop neighbor nodes of drone node i.

[0072] The calculation process of the network performance of a one-hop neighbor node is:

[0073] The first network performance of a one-hop neighbor node is calculated based on the packet error rate of the physical layer statistics of the one-hop neighbor node. The specific calculation formula is as follows:

[0074]

[0075] Where, P j is the packet error rate of drone node j, min i P i is the minimum value of the packet error rate among the one-hop neighbor nodes of drone node i, max i P i is the maximum value of the packet error rate among the one-hop neighbor nodes of drone node i.

[0076] The second network performance of the one-hop neighbor node is calculated based on the number of packet losses caused by the lack of routing statistics at the data link layer of the one-hop neighbor node. The specific calculation formula is as follows:

[0077]

[0078] Where A j is the number of packet losses caused by the lack of routing for drone node j, min i A i is the minimum number of packet losses caused by the lack of routing in the one-hop neighbor node of drone node i, max i A iis the maximum number of packet losses caused by the lack of routing in the one-hop neighbor node of drone node i

[0079] Based on the calculated first network performance and second network performance, the comprehensive network performance of the one-hop neighbor node is calculated. The specific calculation formula is as follows:

[0080]

[0081] Where S NP,j is the comprehensive network performance of one-hop neighbor node j.

[0082] On this basis, the calculation formula for the comprehensive performance of a one-hop neighbor node is:

[0083] S C,j =7×(α×S CP,j +(1-α)×S NP,j )

[0084] Where S C,j is the comprehensive performance of one-hop neighbor node j, S CP,j is the network performance of one-hop neighbor node j, S NP,j is the communication performance of one-hop neighbor node j, and α is the weight factor.

[0085] Step 7: For the ordered set N, select the one-hop neighbor node with the highest comprehensive performance score, set it as the MPR node and add it to the MPR set, and remove it from the ordered set N at the same time, and judge whether all the two-hop neighbor nodes of the drone node are covered by the one-hop neighbor nodes in the MPR set. If not, repeat this step and continue to select the one with the highest comprehensive performance score among the remaining one-hop neighbor nodes to add to the MPR set until all the two-hop neighbor nodes of the drone node are covered by the one-hop neighbor nodes in the MPR set.

[0086] Step 8: The UAV node optimizes and judges its own MPR set to determine whether there is a node in the current MPR set whose willingness to become an MPR node is less than the set threshold and all two-hop neighbor nodes are still fully covered after the node is removed. If so, the node that meets the conditions is removed to obtain the final MPR set. If not, the current MPR set is selected as the final MPR set.

[0087] Step 9: The drone node maintains and updates the neighbor status and link status information based on the final MPR set, and updates the Hello information. After the specified Hello message broadcast time, it broadcasts it to the entire network.

[0088] Examples

[0089] In the EXata5.4 simulation platform, a 1500m*1500m UAV self-organizing network area was constructed, and 30 UAV source nodes, 2 sweep frequency interference source nodes, and 1 blocking interference source node were set. The sweep frequency interference band was set to 2-6 GHZ, and the sweep frequency block was set to 1 GHZ. The blocking interference band was set to 2-6 GHZ. Figure 3 A schematic diagram of the actual deployment is provided. When calculating the MPR set for drone node E, its first-hop neighbor nodes are {B, D, F, I}, and its second-hop neighbor nodes are {A, C, G, H, J, K}. Compared to the traditional OSLR routing protocol, the optimized routing protocol achieves significant improvements in throughput, delivery rate, and other performance.

[0090] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0091] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for optimizing UAV cluster routing protocols in mixed interference scenarios, characterized by: The method comprises the following steps: S1, install an OLSR setting knowledge base in the drone node. The OLSR setting knowledge base contains the optimal parameter combinations of the OLSR routing protocol under different typical interference scenarios, and initializes the OLSR routing protocol parameters of each node in the drone cluster; S2, the UAV node detects the surrounding spectrum environment when performing the mission, writes the integrated interference source information into the Hello message of the standard OLSR broadcast, and broadcasts it to the entire network at the broadcast time; In step S3, each UAV node analyzes the Hello messages broadcast by neighboring nodes and extracts its own one-hop neighbor nodes and two-hop neighbor nodes. It determines the interference type based on the interference source information contained in the Hello message, searches for the optimal OLSR routing protocol parameter combination adapted to the corresponding interference scenario in the OLSR setting knowledge base, and updates the OLSR parameter settings adapted to the current interference environment. S4: Determine the willingness value of each drone node's one-hop neighbor node to become a multi-point relay node, set the one-hop neighbor node with the willingness value "willalways" as a multi-point relay node and add it to the multi-point relay node set corresponding to the drone node; determine whether the multi-point relay node set of each drone node can cover all the two-hop neighbor nodes corresponding to the drone node; if so, jump to step S8; otherwise, go to step S5; S5, analyze the nodes in the one-hop neighbor nodes that are not set as relay nodes, calculate the relative position change between the drone node and the one-hop neighbor node, and calculate the available communication time between the drone node and the one-hop neighbor node based on the communication range to obtain the communication performance of the one-hop neighbor node; calculate the network performance of the one-hop neighbor node based on the packet error rate of the physical layer and the number of packet losses caused by the lack of routing at the data link layer; comprehensively consider the communication performance and network performance of the one-hop neighbor node to obtain the comprehensive performance of the one-hop neighbor node, and sort the one-hop neighbor nodes to obtain an ordered set N; S6: For the ordered set N, select the one-hop neighbor node with the highest comprehensive performance score, set the one-hop neighbor node as a multipoint relay node, remove it from the ordered set N and add it to the multipoint relay node set; S7, determine whether the current multi-point relay node set can cover all two-hop neighbor nodes corresponding to the drone node. If so, proceed to step S8; otherwise, proceed to step S6; S8, optimizing and judging the multi-point relay node set of each drone node, judging whether there is a node in the current multi-point relay node set whose willingness value is less than the set threshold and after removing the node, the multi-point relay node set can still cover all two-hop neighbor nodes; if so, remove the node that meets the conditions from the multi-point relay node set to obtain the final multi-point relay node set; if not, use the current multi-point relay node set as the final multi-point relay node set; S9, maintain and update the neighbor status and link status information of each drone node according to the final multi-point relay node set, and go to step S2.

2. The method for optimizing the routing protocol of UAV clusters in mixed interference scenarios according to claim 1 is characterized in that: In step S1, the OLSR routing protocol parameters include the periodic interval for uploading Hello packets, the topology change protection time interval, the maximum maintenance time of the neighbor node connection state, and the topology structure maintenance time.

3. The method for optimizing the routing protocol of UAV clusters in mixed interference scenarios according to claim 1 is characterized in that: In step S1, a heuristic algorithm is used to simulate and determine the optimal parameter combination of the OLSR routing protocol under different typical interference scenarios.

4. The method for optimizing the routing protocol of a UAV cluster in a mixed interference scenario according to claim 1 is characterized in that: The process of determining the interference type based on the interference source information includes: The integrated interference source information is analyzed. If blocking interference and sweeping frequency interference are perceived at the same time, the interference type is judged as blocking interference; when only multiple blocking interference sources are perceived at the same time, the interference type is judged as blocking interference; when only multiple sweeping frequency interference sources are perceived at the same time, the interference type is judged as sweeping frequency interference; when no interference source is perceived, the interference type is judged as no interference.

5. The method for optimizing the routing protocol of a UAV cluster in a mixed interference scenario according to claim 4 is characterized in that: The interference source information includes interference mode, interference range and interference frequency band.

6. The method for optimizing the UAV cluster routing protocol in a mixed interference scenario according to claim 1 is characterized in that: In step S5, the communication performance calculation formula of the neighboring node is as follows: Among them, S CP,j is the normalized communication performance of one-hop neighbor node j; △t j is the communication available time of one-hop neighbor node j; is the maximum value of the communication available time among the one-hop neighbor nodes of drone node i, is the minimum value of the communication available time among the one-hop neighbor nodes of drone node i.

7. The method for optimizing the routing protocol of a UAV cluster in a mixed interference scenario according to claim 1 is characterized in that: In step S5, the network performance calculation formula of the neighboring node is as follows: Where S NP,j is the normalized network performance of one-hop neighbor node j; P j is the packet error rate of one-hop neighbor node j, min i P i is the minimum value of the packet error rate among the one-hop neighbor nodes of drone node i, max i P i is the maximum packet error rate among the one-hop neighbor nodes of drone node i; A j is the number of packet losses caused by the lack of routing for one-hop neighbor node j, min i A i is the minimum number of packet losses caused by the lack of routing in the one-hop neighbor node of drone node i, max i A i is the maximum number of packet losses caused by the lack of routing in the one-hop neighboring node of drone node i; I is the interference mode factor. When the interference type is blocking interference, I=1; when the interference type is no interference, I=0; when the interference type is sweeping interference, I=η, where η is the proportion of the interference signal covering the communication frequency band.

8. The method for optimizing the UAV cluster routing protocol in a mixed interference scenario according to claim 1 is characterized in that: In step S5, the comprehensive performance calculation formula of the one-hop neighbor node is as follows: S C,j =7×(α×S CP,j +(1-a)×S NP,j ) Where S C,j is the comprehensive performance of one-hop neighbor node j, S CP,j is the network performance of one-hop neighbor node j, S NP,j is the communication performance of one-hop neighbor node j, α is the weight factor, 0<α<1.

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