Relay network topology generation method to support drone inspection in remote mountainous areas

Abstract

A method for generating a relay network topology to support drone inspections in remote mountainous areas, including obtaining node information; setting constraints on communication nodes; completing algorithm modeling according to optimization goals; designing a coverage topology generation algorithm; generating a visualized network coverage topology; and testing the network performance of the topology. The present invention solves the technical problem of building a relay network that can meet communication requirements and optimize costs to support drone power inspection operations in remote mountainous areas where wired networks are difficult to reach and networked devices are numerous and chaotic. The present invention has the following advantages: with the overall goal of generating an optimal cost wireless network coverage topology that meets specific communication needs, the deployment of drone relay networks is realized, which greatly reduces the labor cost of building coverage topologies, and can be widely used in wireless topology coverage map generation scenarios in various unmanned environments; cost optimization and guaranteed connectivity and fluency of wireless networks.

Description

Technical Field

[0001] The present invention relates to the field of wireless network topology generation and deployment algorithms, and in particular to a method for generating and deploying relay network coverage topology supporting unmanned aerial vehicle power inspections in remote mountainous areas. Background Art

[0002] With the development of Internet technology and the popularization of wireless networks, human life and work have been greatly facilitated. However, in unmanned environments such as remote mountainous areas, ordinary wired networks are difficult to reach these areas due to their complex and changeable characteristics and sparsely populated areas. Mobile wireless networks, as a wireless network composed of movable communication nodes, can cope with complex and changeable outdoor environments, provide network coverage for unmanned areas in remote mountainous areas, and help realize the automation and intelligence of unmanned environmental monitoring, mountain forest fire prevention, and power line inspection. For the task of using drones for power inspection, since the physical device nodes in this case need to achieve a certain specific role, the corresponding device nodes often have a fixed position, but the number of these device nodes is large and the positions are disorderly, and the cost of providing wireless nodes covering the network is high. Therefore, designing a relay network that can support drone power inspection in remote mountainous areas and meet certain communication and cost requirements is of great significance to promoting the development of wireless network deployment in unmanned mountainous environments.

[0003] The existing technology mainly has the following problems: First, the existing relay wireless network topology coverage algorithm mainly focuses on the generation of wireless base station coverage topology algorithm. The main optimization goal of the wireless base station network coverage topology is to maximize the coverage area of ​​the network in a certain area, while the main optimization goal of the wireless relay network coverage topology in remote mountainous areas is to minimize the number of nodes that the network covers certain communication nodes or equipment points, so as to achieve a balance between coverage effect and cost; Second, the existing wireless network coverage topology algorithm usually randomly selects node coverage areas within the coverage range to gradually optimize and generate the final network topology. There are few modeling algorithms based on longitude and latitude information. Physical devices in unmanned environments are usually fixed at a specific location for some purpose. Therefore, network topology optimization can be modeled based on longitude and latitude location information; Third, the optimization goals of existing wireless network topology algorithms are relatively complex, resulting in more constraints. Among them, the more classic optimization goals include network throughput and link utilization, etc., while it is more important to ensure the network fluency of fixed devices in wireless nodes in unmanned mountainous environments, and only the maximum network throughput needs to be guaranteed.

[0004] The problem that the existing technology urgently needs to solve is how to quickly and effectively build a relay network that can meet communication requirements and optimize costs in remote mountainous areas where wired networks are difficult to reach and networked devices are numerous and chaotic, so as to support drone power inspection operations. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a relay network topology generation method that supports drone power inspections in remote mountainous areas, which greatly reduces the labor cost of building a coverage topology. The method can be widely used in wireless topology coverage map generation scenarios in various unmanned environments, optimizes costs and ensures the connectivity and smoothness of wireless networks.

[0006] The technical concept of the present invention is to build a system that uses node information to generate network topology and can visualize the network topology and the overall network performance. It has complete functions from information acquisition to automatic generation to performance verification. When obtaining node information in any specified area, this system can be used to obtain a complete solution and result presentation.

[0007] The technical solution of the present invention comprises the following steps:

[0008] (1) Obtain node information;

[0009] (2) Setting constraints for communication nodes;

[0010] (3) Complete algorithm modeling according to the optimization goal;

[0011] (4) Design an algorithm for generating coverage topology;

[0012] (5) Generate a visual network coverage topology;

[0013] (6) Test the network performance of the topology.

[0014] The step (1) of the present invention for obtaining node information comprises: 1) obtaining all node information to be covered, including node number, longitude and latitude and altitude of the node location, and node name; 2) setting the communication node coverage radius: setting the communication node coverage radius according to the actual communication distance of the node or the communication distance that meets certain bandwidth and delay performance conditions, and calculating all nodes within the communication range of each node; 3) analyzing the visibility between nodes: generating the visibility between nodes according to the longitude and latitude and altitude information of the node and the actual terrain; 4) constructing a node connectivity graph: using the communication range and the visibility as the criteria for determining whether the nodes are neighbor nodes, traversing the neighbor nodes within the coverage range of the initial node, respectively calculating the number of neighbor nodes of all nodes within the specific range, and selecting the node with the largest number of neighbors as the cluster head node. All nodes in the target area constitute a graph structure, and each node is regarded as a vertex of the graph. If two nodes can be directly connected, there is a corresponding edge between the corresponding vertices in the graph. Whether there is a corresponding edge depends on the communication distance and visibility of the nodes mentioned above. If the two nodes are within the communication range of each other and can see each other, the two nodes can be directly connected as neighbor nodes, so there is a corresponding edge in the graph. According to the above method, the overall node connectivity graph and the neighbor nodes of each node are obtained.

[0015] The constraints described in step (2) of the present invention are: the generated network topology needs to ensure full coverage of all trajectories and the connectivity of network communications, wherein full network coverage means that all nodes should be directly adjacent to at least one cluster head node, that is, all nodes are directly covered by the cluster head nodes, and network connectivity means that any node among all cluster head nodes should be able to be directly connected to another cluster head node, that is, all cluster head nodes can form a connected subgraph; communication performance, such as bandwidth and latency, must also be met.

[0016] In step (3) of the present invention, the algorithm modeling is completed according to the optimization target, the optimal cost is converted into the minimum number of cluster head nodes in the network topology; the full network coverage in the constraint condition is converted into all ordinary nodes should be directly connected to at least a cluster head node, that is, each node is covered by at least one cluster head node; the connectivity of the network topology in the constraint condition is converted into each cluster head node should be directly adjacent to at least another cluster head node, that is, each cluster head node has at least one neighbor node as a cluster head node, and the formula is expressed as follows:

[0017]

[0018]

[0019] Where V represents the set of all nodes, CD represents the set of cluster head nodes, N(m) and N(k) represent the sets of neighbor nodes of nodes m and k, and x iIndicates whether the i-th node is a cluster head node. If x i = 1, then node i is the cluster head node that constitutes the network topology. Otherwise, if x i = 0, node i is a common node, so the optimization target is x of all nodes i The sum of the values ​​is the smallest, that is, the number of cluster head nodes is the least. This problem can be transformed into the problem of finding the minimum target connected dominating set in the graph. It is a classic NP-Hard problem and it is impossible to find the optimal solution;

[0020] It also includes the initial node selection: the node with the largest degree is selected as the initial node among all nodes, that is, the node has the most neighbor nodes. The selection of the initial node is based on the degree of the node, so it can be expressed as:

[0021]

[0022] Among them, V represents all nodes, and v represents any node;

[0023] The method also includes constructing a coverage topology: determining an initial communication node, using the node connectivity graph obtained in step (1) as a basis for searching neighbor nodes, thereby searching for all neighbor nodes covered by the initial node and adding them to a coverage node set Covered, obtaining the number of neighbors of each node in the set Covered in turn, and selecting a node with the largest number of neighbors as a cluster head node, adding it to the cluster head node set CDS, and adding all neighbor nodes covered by the node to the coverage node set Covered; selecting cluster head nodes from the coverage node set one by one according to the above process, adding them to the cluster head node set CDS and adding their neighbor nodes to the coverage node set Covered, until all nodes are covered, that is, all nodes are added to the coverage node set Covered, and finally generating a network coverage topology composed of the cluster head node set CDS; in the entire process of constructing the network topology, the selection basis of the cluster head node can be converted into a node weight, and the node with the largest weight will be selected to be added to the cluster head node set, and the calculation of the node weight is expressed as:

[0024]

[0025] Among them, V is all nodes, CDS is the cluster head node set, and N1(CDS) is the one-hop neighbor node of CDS.

[0026] The step (4) of the present invention designs a coverage topology generation algorithm as follows: 1) Initialize cluster head nodes: generate a node connectivity graph based on given node information, find the node with the most visible connection edges in the node connectivity graph as the initial cluster head node, and initialize the cluster head node set;

[0027] 2) Initialize the set of covered nodes: obtain the neighbor node set of the initial cluster head node and use it to initialize the set of covered nodes CDS;

[0028] 3) Select cluster head nodes: According to the line-of-sight connection between the cluster head node set and the non-cluster head node set, select the node with the largest number of nodes in the difference set between the neighbor node set and the covered node from the point set with line-of-sight connection edges with the cluster head node set, that is, the point with the largest number of nodes not covered by the topology among the neighbor nodes, and add it to the cluster head node set;

[0029] 4) Expand coverage: According to the new cluster head node set, update the neighbor nodes of the new cluster head node to the coverage node set to expand the network topology coverage;

[0030] 5) According to step 3), the cluster head node set is continuously updated until the coverage node set includes all nodes;

[0031] In the final CDS cluster head set, if there are corresponding edges between two cluster head nodes in the node connectivity graph, a corresponding communication link is established between the two cluster head nodes, thereby generating the final network coverage topology, which consists of all cluster head nodes and the communication links between cluster head nodes.

[0032] The step (5) of the present invention generates a visualized network coverage topology by drawing a network coverage topology map of the nodes in the CDS according to the communication distance set as the center with the nodes as the radius through the visualization dependency library. In order to facilitate the viewing of the coverage of the topology of the field node communication points, the remaining ordinary nodes are drawn as points on the visualization map according to their location information.

[0033] The step (6) of the present invention tests the network performance of the topology, and performs simulation performance tests on the generated network topology, including throughput, transmission delay, jitter delay, packet loss rate, and network capacity, etc. Specifically, 1) node information processing: converting the longitude and latitude of all cluster head nodes obtained by the topology generation method into Cartesian product coordinate position points;

[0034] 2) Node position setting: Set the position of the simulation node according to the node coordinate position point information;

[0035] 3) Node parameter setting: Set the performance parameters of the simulation node according to the actual performance parameters of the physical node in the real scene;

[0036] 4) Node function setting: according to different requirements, different data packet sending and receiving functions are set on different simulation nodes;

[0037] 5) Simulation experiment settings: Start the data packet sending and receiving functions of all nodes at the same time, set the experiment duration to 60s, and measure the communication performance of the generated network coverage topology by using the mutual communication between nodes;

[0038] 6) Performance indicator measurement: Use the flow monitoring module (flowMonitor) provided by NS-3 to count the performance indicators of packet loss, jitter delay, delay and throughput.

[0039] The present invention selects the node with the largest node degree as the initial node, and then constructs the network coverage topology by selecting the cluster head node as the main communication node. The cluster head node is searched in the above manner until all nodes that need to be covered are within the coverage range of the cluster head node set, and the network coverage topology is formed by all the cluster head nodes.

[0040] The present invention visualizes all common nodes and cluster head nodes on a web page.

[0041] In an unmanned remote mountain environment, the deployed device nodes generally have a specific fixed position, but their number and distribution position are uncertain. If the number of device nodes is small, a good coverage effect and network performance can be achieved by manually building a relay network. Once the number of nodes increases to a certain extent, the cost and complexity of manually building a network will increase significantly. At this time, it is obviously not a suitable way to build a relay network manually. At the same time, the construction of a relay network is a repetitive process of continuously screening nodes. This process conforms to the working characteristics of a computer, so it is a good choice to design a network topology generation and deployment algorithm. When screening network topology nodes, the screening requirement is usually to achieve the optimal cost on the basis of meeting the communication requirements. Therefore, the present invention first mathematically models the problem and finds that it is a classic NP-Hard problem. Then, a cost-optimal relay network coverage topology generation fitting algorithm is designed. By traversing and selecting nodes that meet the communication requirements one by one to form a network coverage topology, the relay network topology finally generated can cover all nodes, forming a fully connected communication wireless network, ensuring reliable data transmission, and making the network deployment cost close to the optimal, that is, the number of network nodes in the coverage topology is close to the minimum. In addition, in order to simply verify the availability of the network coverage topology generated by the present invention, the generated network topology and the device nodes in the unmanned environment in the mountainous area are visualized on a web page through a visualization tool to view the final coverage effect.

[0042] The present invention has the following advantages: with the overall goal of generating the optimal cost wireless network coverage topology that meets specific communication needs, it realizes the drone relay network deployment algorithm, greatly reduces the labor cost of building the coverage topology, and can be widely used in wireless topology coverage map generation scenarios in various unmanned environments; it optimizes costs and ensures the connectivity and fluency of wireless networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Indicates the whole process of invention;

[0044] Figure 2 Represents the visualization of the overlay topology;

[0045] Figure 3 Indicates the network topology for capacity testing;

[0046] Figure 4 Represents the overall framework of the system. DETAILED DESCRIPTION

[0047] The present invention will be described in detail below with reference to the accompanying drawings.

[0048] Figure 1 The whole process of the invention is represented as follows: obtaining information of all device nodes; setting constraints of communication nodes; completing algorithm modeling according to optimization objectives; designing an automatic generation algorithm for coverage topology; generating a visualized self-organizing network coverage topology; and testing the network performance of the topology.

[0049] Table 1 shows the pseudo code of the relay network coverage topology generation fitting algorithm: input node connectivity graph (V, E), where V represents each power-saving device and E represents the connectivity relationship between nodes; initialize the cluster head node set and the coverage node set; each time select a node that connects the largest number of uncovered nodes from the neighbor node set of the cluster head node set and put it into the cluster head node set; update the cluster head node set and the coverage node set; repeat the above process until all nodes are covered, and output the final cluster head node set CDS.

[0050] Table 1

[0051]

[0052] Figure 2 Represents a visualized relay network coverage topology: the topology generation algorithm of the present invention is used to test the area shown in the figure. The light-colored dots in the figure represent the positions of various device nodes, the lines between the dots represent the connectivity between the nodes, and the larger circles centered on some of the dots represent the physical areas covered within the communication radius of the cluster head nodes of the network. The figure shows the network topology generated when the communication radius of the nodes is 3km. Only 9 cluster head nodes are needed to cover all device nodes.

[0053] Figure 3 Indicates the topology of the network performance test. The nodes are divided into servers and clients, and the number of clients is increased in the order of 1-4 to test the performance of the overall network topology.

[0054] Figure 4 The overall framework of the system in the present invention is represented as follows: obtaining node information, including longitude and latitude, altitude, communication performance, etc.; node visibility analysis, analyzing the visibility between nodes according to the node information and the digital terrain model, and generating a node connectivity graph; coverage topology generation, searching for a suitable set of cluster head nodes by traversal until all nodes are covered, and the final network coverage topology is formed by the final set of cluster head nodes; result visualization, visualizing the results of the network graph coverage topology through a visualization dependency library; simulation testing, measuring network performance indicators such as throughput and latency. Example

[0055] (1) Obtain node data: Read all field equipment node location information that needs to be covered in the specified location file.

[0056] Mainly includes node number, node name, longitude, latitude and altitude;

[0057] Node data processing: All communication nodes are divided into two categories, one is the node with strong communication performance that can surround 360°, and the other is the node that provides point-to-point communication. In point-to-point communication, due to the complex terrain of the uninhabited environment in the mountainous area, it is easy to produce occlusions and there are many communication interference factors. Therefore, it is necessary to conduct line-of-sight analysis between nodes. Based on the digital terrain model (DEM), ray tracing is performed from the observation point to the target point, the elevation difference is calculated, and whether it intersects with obstacles is detected, so as to determine whether there is a continuous visible path between the two points, so as to analyze the visibility and occlusion relationship between the two points. In addition to the line-of-sight situation, the communication distance of the communication node has a great influence on the final generated network coverage topology. It is necessary to comprehensively consider and set the communication distance and coverage range of the node in combination with the node's own hardware characteristics, the actual required communication distance, and the achievable communication performance;

[0058] Construct a node connectivity graph: All nodes in the target area constitute a graph structure, with each node as a graph vertex. If two nodes can be directly connected, there is a corresponding edge between the corresponding vertices in the graph. Whether there is a corresponding edge depends on the communication distance and visibility of the nodes mentioned above. If two nodes are within each other's communication range and visible to each other, the two nodes can be directly connected as neighbor nodes, so there is a corresponding edge in the graph. According to the above method, the overall node connectivity graph and the neighbor nodes of each node are obtained.

[0059] (2) Setting constraints: The generated network topology needs to ensure full coverage of all trajectories and the connectivity of network communication. Full network coverage means that all nodes should be directly adjacent to at least one cluster head node, that is, all nodes are directly covered by cluster head nodes, and network connectivity means that any node among all cluster head nodes should be directly connected to another cluster head node, that is, all cluster head nodes can form a connected subgraph. In addition, certain communication performance must be met, such as bandwidth and latency.

[0060] (3) Problem modeling: Our optimization goal is to achieve the optimal cost of network topology construction on the basis of satisfying the constraints. The main cost of the network coverage topology depends mainly on the number of physical nodes used, so the optimal cost can be converted into the minimum number of cluster head nodes in the network topology. At the same time, the full network coverage in the constraint condition is converted to all ordinary nodes should be directly connected to at least one cluster head node, that is, each node is covered by at least one cluster head node; the connectivity of the network topology in the constraint condition is converted to each cluster head node should be directly adjacent to at least another cluster head node, that is, each cluster head node has at least one neighbor node as a cluster head node. Therefore, the formula can be expressed as:

[0061]

[0062]

[0063] Where V represents the set of all nodes, CD represents the set of cluster head nodes, N(m) and N(k) represent the sets of neighbor nodes of nodes m and k, and x i Indicates whether the i-th node is a cluster head node. If x i = 1, then node i is the cluster head node that constitutes the network topology. Otherwise, if x i = 0, node i is a common node, so the optimization target is x of all nodes i The sum of the values ​​is the smallest, that is, the number of cluster head nodes is the least. This problem can be transformed into the problem of finding the minimum target connected dominating set in the graph. It is a classic NP-Hard problem and there is no optimal solution.

[0064] (4) Initial node selection: Building a network coverage topology is a traversal process. The initial node affects each subsequent calculation and selection. Therefore, the selection of the initial node plays an important role. We select the node with the largest degree among all nodes as the initial node. That is, the node has the most neighbor nodes. Therefore, in the subsequent traversal and calculation, there are the most choices and possibilities, which is closer to the optimal result. The selection of the initial node is based on the degree of the node, so it can be expressed as:

[0065]

[0066] Among them, V represents all nodes and v represents any node.

[0067] (5) Constructing the coverage topology: First, determine the initial communication node, and use the node connectivity graph obtained in (3) as the basis for searching neighbor nodes. In this way, find all neighbor nodes covered by the initial node and add them to the coverage node set Covered. Obtain the number of neighbors of each node in the set Covered in turn, and select the node with the largest number of neighbors as the cluster head node, add it to the cluster head node set CDS, and at the same time add all neighbor nodes covered by the node to the coverage node set Covered. According to the above process, select cluster head nodes from the coverage node set one by one, add them to the cluster head node set CDS, and add their neighbor nodes to the coverage node set Covered until all nodes are covered, that is, all nodes are added to the coverage node set Covered, and finally generate a network coverage topology composed of the cluster head node set CDS. In the entire process of constructing the network topology, the selection basis of the cluster head node can be converted into node weight. The node with the largest weight will be selected to join the cluster head node set. The calculation of node weight is expressed as:

[0068]

[0069] Among them, V is all nodes, CDS is the cluster head node set, and N1(CDS) is the one-hop neighbor node of CDS.

[0070] (6) Coverage topology visualization: Through the visualization dependency library, the nodes in the CDS are plotted with the communication distance set as the radius. At the same time, in order to facilitate the viewing of the coverage of the field node communication points, the remaining ordinary nodes are plotted as points on the visualization map according to their location information.

[0071] (7) Simulation performance test: In order to determine the availability and network performance of the generated network coverage topology, we constructed the topology and performed performance testing in the NS-3 network simulation simulator. First, in order to test the upper limit of communication between nodes in NS-3, we tested single-hop communication tests under different node distances and different packet sending rates. Our specific test performance indicators include throughput, transmission delay, jitter delay, packet loss rate, and the number of data packets received. The details are shown in Table 1 (simulation results when the node distance is 1m) and Table 2 (simulation results when the node distance is 3km):

[0072] Table 1

[0073]

[0074] Table 2

[0075]

[0076] The performance indicators tested in the two tables are consistent with the parameter variables. Among them, the node spacing in Table 1 is about 1m, and the node spacing in Table 2 is about 3km, the actual node distance of the generated topology. We mainly measure the upper limit of node communication performance by increasing the node packet rate in the two cases. It can be seen that the upper limit of the node communication rate in both cases is about 20Mbps. When it is greater than 20Mbps, the network fails due to the high network packet loss rate. By comparing different packet rates at the same node distance and different node distances at the same packet rate, it can be found that when the packet rate is small, the node distance has no obvious effect on the node communication performance. With the increase of the packet rate, the network performance of both has decreased, but the network performance of both is in an available state within the packet rate of 20Mbps. Finally, we tested the overall network topology. The test topology is as follows: Figure 4 As shown in the figure, the nodes are divided into servers and clients, and the number of clients is increased in the order of 1-4. Considering the number of nodes and the communication requirements in the real network, we set the transmission rate of all clients to 1Mbps. The results are shown in Table 3 (Simulation results of the overall network topology):

[0077] Table 3

[0078]

[0079] It can be seen that the throughput does not change significantly with the increase in the number of transmission nodes. Although the transmission delay and packet loss rate increase significantly when the fourth client is added and the packet loss rate also increases, the overall network is in an available state.

[0080] The changes in structure, method, and function combination made by researchers and users in the field to which the present invention belongs according to the embodiments of the present invention are all within the protection scope of the present invention, and can be modified and expanded as needed without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be based on the definition of the claims.

Claims

1. A method for generating a relay network topology to support drone inspections in remote mountainous areas, characterized in that The following steps are involved: (1) Obtain node information; (2) Setting constraints for communication nodes; (3) Complete algorithm modeling according to the optimization goal; (4) Design an algorithm for generating coverage topology; (5) Generate a visual network coverage topology; (6) Test the network performance of the topology; The step (1) of obtaining node information includes: 1) obtaining all node information that needs to be covered, including node number, longitude and latitude and altitude of the node location, and node name; 2) Setting the coverage radius of the communication node: Setting the coverage radius of the communication node according to the actual communication distance of the node or the communication distance that meets the performance conditions of bandwidth and delay, and calculating all the nodes within the communication range of each node; 3) Analysis of inter-node visibility: Generate inter-node visibility based on the latitude, longitude, and altitude information of the nodes and the actual terrain; 4) Constructing a node connectivity graph: Using the communication range and visibility as the criteria for determining whether nodes are neighbor nodes, all nodes in the target area form a graph structure, with each node as a vertex. If two nodes can be directly connected, there is a corresponding edge between the corresponding vertices in the graph. Whether there is a corresponding edge depends on the communication distance and visibility of the nodes mentioned above. If two nodes are within each other's communication range and can see each other, the two nodes can be directly connected as neighbor nodes, and there is a corresponding edge in the graph. The overall node connectivity graph and the neighbor nodes of each node are obtained according to the above method. The constraints described in step (2) are: the generated network topology needs to ensure full coverage of all trajectories and connectivity of network communications, where full network coverage means that all nodes should be directly adjacent to at least one cluster head node, that is, all nodes are directly covered by the cluster head nodes, and network connectivity means that any node among all cluster head nodes should be directly connected to another cluster head node, that is, all cluster head nodes can form a connected subgraph; and communication performance of bandwidth and latency must also be met; In step (3), the algorithm modeling is completed according to the optimization target as follows: the optimal cost is converted into the minimum number of cluster head nodes in the network topology; the full network coverage in the constraint condition is converted into all ordinary nodes should be directly connected to at least one cluster head node, that is, each node is covered by at least one cluster head node; the connectivity of the network topology in the constraint condition is converted into each cluster head node should be directly adjacent to at least another cluster head node, that is, each cluster head node has at least one neighbor node as a cluster head node, and the formula is expressed as follows: Where V represents the set of all nodes, CD represents the set of cluster head nodes, N(m) and N(k) represent the sets of neighbor nodes of nodes m and k, and x i Indicates whether the i-th node is a cluster head node. If x i = 1, then node i is the cluster head node that constitutes the network topology. Otherwise, if x i = 0, node i is a common node, so the optimization target is x of all nodes i The sum of the values ​​is the smallest, that is, the number of cluster head nodes is the least; It also includes the initial node selection: the node with the largest degree is selected as the initial node among all nodes, that is, the node has the most neighbor nodes. The selection of the initial node is based on the degree of the node, so it can be expressed as: Among them, V represents all nodes, and v represents any node; The method also includes constructing a coverage topology: determining an initial communication node, using the node connectivity graph obtained in step (1) as a basis for searching neighbor nodes, thereby searching for all neighbor nodes covered by the initial node and adding them to a coverage node set Covered, obtaining the number of neighbors of each node in the set Covered in turn, and selecting a node with the largest number of neighbors as a cluster head node, adding it to the cluster head node set CDS, and adding all neighbor nodes covered by the node to the coverage node set Covered; selecting cluster head nodes from the coverage node set one by one according to the above process, adding them to the cluster head node set CDS and adding their neighbor nodes to the coverage node set Covered, until all nodes are covered, that is, all nodes are added to the coverage node set Covered, and finally generating a network coverage topology composed of the cluster head node set CDS; in the entire process of constructing the network topology, the selection basis of the cluster head node can be converted into a node weight, and the node with the largest weight will be selected to be added to the cluster head node set, and the calculation of the node weight is expressed as: Among them, V is all nodes, CDS is the set of cluster head nodes, and N1(CDS) is the one-hop neighbor node of CDS.

2. According to claim 1, the method for generating a relay network topology for supporting drone inspections in remote mountainous areas is characterized in that The step (4) designs a coverage topology generation algorithm as follows: 1) Initialize cluster head nodes: generate a node connectivity graph based on given node information, find the node with the most visible connection edges in the node connectivity graph as the initial cluster head node, and initialize the cluster head node set; 2) Initialize the set of covered nodes: obtain the neighbor node set of the initial cluster head node and use it to initialize the set of covered nodes CDS; 3) Select cluster head nodes: According to the line-of-sight connection between the cluster head node set and the non-cluster head node set, select the node with the largest number of nodes in the difference set between the neighbor node set and the covered node from the point set with line-of-sight connection edges with the cluster head node set, that is, the point with the largest number of nodes not covered by the topology among the neighbor nodes, and add it to the cluster head node set; 4) Expand coverage: According to the new cluster head node set, update the neighbor nodes of the new cluster head node to the coverage node set to expand the network topology coverage; 5) Continue to update the cluster head node set according to step (3) until the coverage node set includes all nodes; In the final CDS cluster head set, if there are corresponding edges between two cluster head nodes in the node connectivity graph, a corresponding communication link is established between the two cluster head nodes, thereby generating the final network coverage topology, which consists of all cluster head nodes and the communication links between cluster head nodes.

3. According to claim 1, the method for generating a relay network topology for supporting drone inspections in remote mountainous areas is characterized in that The step (5) generates a visualized network coverage topology by drawing a network coverage topology map of the nodes in the CDS according to the communication distance set at the center of the nodes through the visualization dependency library. In order to facilitate the viewing of the coverage of the topology of the field node communication points, the remaining ordinary nodes are drawn as points on the visualization map according to their location information.

4. According to claim 1, the method for generating a relay network topology for supporting drone inspections in remote mountainous areas is characterized in that The network performance of the topology tested in step (6) is as follows: 1) Node information processing: converting the longitude and latitude of all cluster head nodes obtained by the topology generation method into Cartesian product coordinate position points; 2) Node position setting: Set the position of the simulation node according to the node coordinate position point information; 3) Node parameter setting: Set the performance parameters of the simulation node according to the actual performance parameters of the physical node in the real scene; 4) Node function setting: according to different requirements, different data packet sending and receiving functions are set on different simulation nodes; 5) Simulation experiment settings: Start the data packet sending and receiving functions of all nodes at the same time, set the experiment duration to 60s, and measure the communication performance of the generated network coverage topology by using the mutual communication between nodes; 6) Performance indicator measurement: Use the traffic monitoring module provided by NS-3 to measure the performance indicators of packet loss, jitter delay, latency and throughput.

Images