UAV Charging Scheduling Method, Product and Device Based on Wireless Sensor Network

Abstract

The present invention discloses a method, product and device for charging scheduling of unmanned aerial vehicles based on a wireless sensor network, belonging to the technical field of wireless charging. The method includes: S1, constructing a two-dimensional plane wireless rechargeable sensor network model; S2, based on the wireless rechargeable sensor network model, using a hierarchical clustering algorithm to solve the clustering and calculate the center of each cluster, obtaining the number and deployment positions of wireless charging boards; S3, based on the number and deployment positions of wireless charging boards, using the A-star algorithm to calculate the paths of the unmanned aerial vehicle from each sensor node to any other sensor node, thereby obtaining the static routing table of the unmanned aerial vehicle; S4, according to the charging requests of the sensor nodes and in combination with the static routing table of the unmanned aerial vehicle, obtaining the multi-hop charging scheduling path of the unmanned aerial vehicle. The present invention can reduce the flight distance and time of the unmanned aerial vehicle through reasonable charging path planning, maximize the charging efficiency, and extend the operation time of the sensor network.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless charging, and particularly relates to a method, product and device for charging scheduling of unmanned aerial vehicles based on a wireless sensor network. Background Art

[0002] A wireless sensor network (WSN) is a collection of a large number of small and inexpensive battery-powered nodes that can monitor and collect data in a sensing area. As a key technology of the Internet of Things (IoT), WSN is widely used in many fields such as military, aerospace science and industry, intelligent transportation, precision agriculture, environmental monitoring, medical and health, security protection, industrial automation monitoring, smart grid, building structure status monitoring, and home applications.

[0003] From the perspective of network composition, most of the sensor nodes in WSN rely on batteries with limited energy. The particularity, danger, and inaccessibility of the node deployment environment, as well as the large costs of manpower, material resources, and financial resources caused by node replacement, make it much more difficult to extend the network lifetime by replacing batteries. In recent years, related technologies for wireless sensor networks have received extensive attention, including power saving, target tracking, network data routing, network coverage, network link strength, mobile data collection, wireless charging mechanism, etc. Among these related technologies, how to solve the energy problem of sensors plays a crucial role in the application of wireless sensor networks. Currently, existing technologies use methods such as balancing the power consumption load, building a power-saving data collection method, and using mobile collectors to slow down the speed of energy consumption. However, since the total power supply of the overall WSN is still continuously consumed, these methods do not substantially extend the lifetime of the WSN.

[0004] With the application of wireless charging technology to WSN, wireless sensor networks equipped with rechargeable devices will become a development platform for many applications. Such networks are called wireless rechargeable sensor networks (WRSN). By using Wireless Power Transfer (WPT) technology to charge sensor nodes, the lifespan of sensor nodes can be extended. In traditional WRSN, a Wireless Charging Vehicle (WCV) is generally used to charge sensor nodes. The WCV moves in the network according to its designed charging scheduling path and stops at the sensor nodes with charging requests to provide charging services. In addition, drone technology has been widely used in people's lives in recent years. To break through the limitation of the drone battery capacity, many wireless charging technologies for drones have been developed. Considering installing a charger on the drone to provide instant charging services for WRSN, and at the same time using a wireless charging board to provide a new wireless charging technology for the drone. Therefore, how to fully and collaboratively utilize the wireless charging technologies of drones and wireless charging boards to provide a low-cost and efficient charging system for WRSN has important theoretical guiding significance and high practical value for the development of the future Internet of Things.

[0005] With the wide application of drone technology in human life, and considering the small size, low cost, flexible flight route and a maximum flight speed of up to 289 km / h of drones, some studies have begun to consider using drones as mobile carriers to charge sensor nodes. However, due to their small size, drones have limited battery capacity and are greatly restricted in flight distance, unable to directly fly long distances to perform charging tasks over relatively long distances. At the same time, although the cost of drones is relatively low, the power energy they carry is limited and they cannot perform multiple consecutive charging tasks simultaneously. Therefore, they cannot independently complete all the charging requirements of the entire network.

[0006] The development of wireless charging board technology provides a new technology to solve the endurance problem of wireless charging drones. By simply installing a wireless charging device on the fuselage, the drone can land at any time to replenish its own energy. In view of the advantages and disadvantages of wireless charging drones, a new wireless rechargeable sensor network model with a single wireless charging drone and a group of wireless charging boards is designed. This model uses the wireless charging drone to provide charging services for sensor nodes, and the wireless charging board provides active charging for the wireless charging drone, overcoming the shortcoming that the wireless charging drone cannot fly long distances, thereby extending the lifespan of WRSN.

[0007] Therefore, how to design a drone charging scheduling method assisted by a charging board for a wireless rechargeable sensor network, how to reasonably deploy the minimum number of wireless charging boards, design a charging scheduling scheme for the drone according to the charging requests that occur in real time in the network, reduce the unnecessary flight of the drone, complete the work of each charging scheduling in the shortest time, and extend the running time and overall lifespan of the sensor network are problems that those skilled in the art urgently need to solve. Summary of the Invention

[0008] The present invention aims to solve at least one of the technical problems in the above related technologies to some extent.

[0009] To this end, the object of the present invention is to provide a drone charging scheduling method, product and device based on a wireless sensor network, which can minimize the charging path through reasonable charging path planning, reduce the total flight distance and flight time of the drone, maximize the charging efficiency of each round of charging tasks, extend the running time and overall lifespan of the sensor network, and reduce the maintenance cost.

[0010] In order to solve the above technical problems, the present invention is implemented as follows:

[0011] An embodiment of the present invention provides a drone charging scheduling method based on a wireless sensor network, and the method includes:

[0012] S1. Construct a two-dimensional plane wireless rechargeable sensor network model;

[0013] S2. Based on the wireless rechargeable sensor network model, use the hierarchical clustering algorithm to solve the clustering and calculate the center of each cluster to obtain the number and deployment positions of the wireless charging boards;

[0014] S3. Based on the number and deployment positions of the wireless charging boards, use the A* algorithm to calculate the paths of the drone from each sensor node to any other sensor node, so as to obtain the static routing table of the drone;

[0015] S4. According to the charging requests of the sensor nodes and in combination with the static routing table of the drone, obtain the multi-hop charging scheduling path of the drone.

[0016] In addition, according to the drone charging scheduling method based on a wireless sensor network of the present invention, it may also have the following additional technical features:

[0017] In some of the embodiments, the content of constructing the wireless rechargeable sensor network model in step S1 includes: defining the positions of the base station and the sensor nodes, and determining the maximum flight distance of the drone.

[0018] In some of the embodiments, the content of step S2 includes:

[0019] S21. Encode the base station and sensor nodes in the wireless rechargeable sensor network model;

[0020] S22. Calculate the Euler distance between each pair of sensor nodes to obtain a distance matrix;

[0021] S23. Based on the distance matrix, use the hierarchical clustering algorithm to calculate the clustering of sensor nodes and obtain the number of clusters;

[0022] S24. Based on the calculated clustering, calculate the center of each cluster and use it as the deployment location of the corresponding charging board.

[0023] In some of the embodiments, the content of step S23 includes:

[0024] S231. Initialize the clustering, with each sensor node as a separate cluster;

[0025] S232. Find the two clusters with the closest Euler distance in the distance matrix and merge them to form a new cluster;

[0026] S233. Recalculate the Euler distance between the new cluster and each of the other clusters according to the farthest neighbor method and update the distance matrix;

[0027] S234. Repeat S232 and S233 until the Euler distance between every two clusters in the distance matrix is greater than D dmax / 2, D dmax which is the maximum flight distance of the drone.

[0028] In some of the embodiments, the content of step S24 includes:

[0029] S241. Calculate the center of each cluster in turn using the mean method;

[0030] S242. Determine whether the distance between any two cluster centers meets the requirements. If so, draw an edge between these two cluster centers to construct a first graph composed of a sensor node set and an edge set;

[0031] S243. Determine whether the first graph is connected. If so, proceed to the next step; otherwise, add a center point and the corresponding edge to make it a connected graph.

[0032] In some of the embodiments, the content of step S3 includes:

[0033] S31. Set two arrays open and close ; where the array openUsed to store data related to the charging boards that the drone may pass through, array close Used to store data related to the charging boards that have already been passed through; set the current starting sensor node as the i th node, and the ending sensor node as the j th node; and make i = 0, j = 1;

[0034] S32. Store the current starting sensor node into the array open ;

[0035] S33. Traverse the array open , and find the node with the smallest F value as the node to be processed currently; F The definition of the F value is: R + H ; R is the distance from the starting point to the current node, H is the Manhattan distance from the current node to the ending point;

[0036] S34. Add the node to be processed currently into the array close ;

[0037] S35. Traverse the charging board nodes that the node to be processed currently can reach, judge whether they have been passed through, and ensure that the relevant data of the charging board nodes are stored in the array close or the array open ; judge whether the path of the currently traversed charging board node is a better path; if so, go to the next step, otherwise, continue to traverse;

[0038] S36. Judge whether the currently traversed node can reach the ending node; if so, add this path to the static routing table, otherwise, return to S35;

[0039] S37. Judge whether j is less than n , n being the total number of sensor nodes; if so, make j = j + 1, clear the array open and the array close , and jump to S32; otherwise, go to the next step;

[0040] S38. Judge whether i is less than n - 1, if so, make i = i + 1, clear the array open and the array close, and jump to S32; otherwise, end the A* algorithm.

[0041] In some of these embodiments, in step S35, the way to determine the nodes that the currently processed node can reach is:

[0042] Determine whether the currently processed node is a sensor node or a charging board node;

[0043] If it is a sensor node, then the Euler distance from this node to a certain charging board node ≤ D dmax / 2, then determine that this charging board node is a reachable node;

[0044] If it is a charging board node, then the Euler distance from this node to a certain charging board node ≤ D dmax , then determine that this charging board node is a reachable node;

[0045] D dmax is the maximum flight distance of the drone.

[0046] In some of these embodiments, in step S4, according to the charging requests of the sensor nodes in each round, the sensor nodes with charging requests are sorted for charging according to the principle of nearest distance first;

[0047] Find the charging board nodes to pass through between two sensor nodes in the static routing table of the drone and insert them between the two sensor nodes, so as to obtain the multi-hop charging scheduling path of the drone.

[0048] The embodiment of the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned drone charging scheduling method based on a wireless sensor network are implemented.

[0049] The embodiment of the present invention also provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the above-mentioned drone charging scheduling method based on a wireless sensor network.

[0050] Compared with the prior art, the present invention has at least the following beneficial effects:

[0051] In the embodiments of the present invention, the provided UAV charging scheduling method based on a wireless sensor network calculates through an accurate charging board deployment method to minimize the number of deployed charging boards, optimize the deployment positions of the charging boards, optimize the multi-hop flight path of the wireless charging UAV, maximize the total charging efficiency of the UAV, minimize the total time to complete a charging task, thereby extending the running time and overall lifespan of the sensor network and reducing the maintenance cost.

[0052] In the embodiments of the present invention, the provided UAV charging scheduling method based on a wireless sensor network comprehensively considers the advantages and disadvantages that the UAV has a fast flight speed but a limited flight distance. It needs the assistance of charging boards to reach all sensor nodes in the sensing area to complete the charging task. Through the construction of a planar wireless rechargeable sensor network model, the present invention ensures the rationality of network deployment and the consistency with the actual application scenario. In subsequent steps, it not only focuses on the optimal deployment of charging boards but also considers the multi-hop flight of the UAV with the assistance of charging boards, optimizes the multi-hop flight route, minimizes the charging task completion time, and improves the operation efficiency of the overall system.

[0053] In the embodiments of the present invention, the provided UAV charging scheduling method based on a wireless sensor network introduces a hierarchical clustering charging board deployment method. Since the K-means clustering algorithm requires the pre-specification of the number of clusters, while the hierarchical clustering algorithm can dynamically adjust the number of clusters according to the distribution of sensor nodes and is more suitable for dynamic environments. Although the DBSCAN clustering algorithm can automatically determine the number of clusters, it is sensitive to parameters and cannot generate a hierarchical cluster structure, while the hierarchical clustering algorithm can generate a hierarchical cluster structure and is more suitable for multi-level charging board deployment requirements. Therefore, this method combines the hierarchical clustering algorithm for the deployment of charging boards. This method fully considers the disadvantage of the limited flight distance of the UAV. Through a bottom-up approach, it groups sensor nodes according to their geographical locations to form multiple clusters and calculates the center point of each cluster as the deployment position of the charging board. The deployment position of the charging board is based on the center point of the cluster, which can maximize the coverage of sensor nodes, reduce the flight distance of the UAV. This deployment method reasonably deploys charging boards, minimizes the number of deployed charging boards, maximizes the utilization rate of charging boards, and reduces the waste of charging board resources.

[0054] In the embodiments of the present invention, the provided UAV charging scheduling method based on a wireless sensor network uses the A* algorithm to calculate and plan the charging scheduling path of the UAV. The A* algorithm is a heuristic search algorithm. Compared with the Dijkstra algorithm, which has the disadvantage of low computational efficiency in complex environments, and the genetic algorithm, which can find the global optimal solution but has a high computational complexity and is not suitable for real-time scheduling, the A* algorithm can find an approximate optimal solution in a short time through heuristic search, significantly improving the computational efficiency and being more suitable for real-time scheduling. By comprehensively considering the path length and the heuristic estimate value, the A* algorithm finds the optimal path from the base station to each sensor node with a charging request. After the charging board is deployed, the static routing table generated by the A* algorithm can provide a multi-hop charging scheduling path for the UAV, ensuring that the UAV can efficiently complete multiple charging tasks. The A* algorithm can quickly find the optimal solution for the design of UAV progress charging scheduling from a large number of feasible flight scheduling routes, ensuring that the UAV completes the task with the shortest path and the least energy consumption. The A* algorithm can adapt to complex environments, meet the dynamic scheduling requirements under the on-demand charging architecture of the wireless rechargeable sensor network, adjust the charging scheduling in real time, and improve the flexibility and robustness of the completion of charging tasks.

[0055] In the embodiments of the present invention, the provided UAV charging scheduling method based on a wireless sensor network realizes the global optimization from the deployment of the charging board to the path planning of the UAV by combining the hierarchical clustering algorithm and the A* algorithm. The hierarchical clustering algorithm is used to calculate the number of deployed charging boards and their position coordinates, and the A* algorithm is used to optimize the charging scheduling path of the UAV. This combination is not only reasonable in principle but also shows significant advantages in practical applications, especially in dynamic and complex environments. Compared with other algorithms, this method can significantly improve the charging efficiency, reduce the deployment cost, and extend the endurance of the UAV, providing reliable technical support for the efficient operation of the wireless rechargeable sensor network.

[0056] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flowchart of the UAV charging scheduling method assisted by a charging board disclosed in an embodiment of the present invention;

[0058] Figure 2 It is a schematic diagram of a two-dimensional plane wireless rechargeable sensor network model disclosed in an embodiment of the present invention;

[0059] Figure 3 It is a flowchart of obtaining the deployment of the charging board using the hierarchical clustering algorithm disclosed in an embodiment of the present invention;

[0060] Figure 4 Flowchart of calculating the clustering of sensor nodes using the hierarchical clustering algorithm disclosed in an embodiment of the present invention;

[0061] Figure 5 Flowchart of calculating each cluster center as the deployment position of the charging board disclosed in an embodiment of the present invention;

[0062] Figure 6 Based on the charging board deployment diagram disclosed in an embodiment of the present invention G p using the A* algorithm to plan the multi-hop charging flight path of the unmanned aerial vehicle. Specific implementation manners

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0064] Next, the embodiments of the present invention will be described in detail through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0065] In some embodiments of the present invention, a method for unmanned aerial vehicle charging scheduling based on a wireless sensor network is provided. Static charging boards are reasonably deployed according to the limited flight distance of the unmanned aerial vehicle, and a related flight network is designed according to the advantages and disadvantages of the unmanned aerial vehicle to perform the design of the wireless multi-hop charging scheduling path of the unmanned aerial vehicle, thereby obtaining the charging scheduling of the unmanned aerial vehicle.

[0066] Please refer to Figure 1 As shown, in some embodiments of the present invention, the steps of the method for unmanned aerial vehicle charging scheduling include:

[0067] Step 1: Construct a two-dimensional plane wireless rechargeable sensor network model; the wireless rechargeable sensor network model includes: a base station, a single wireless charging unmanned aerial vehicle, several wireless charging boards, and a group of sensor nodes.

[0068] Please refer to Figure 2 As shown, in this step, constructing a two-dimensional plane wireless rechargeable sensor network model includes: defining the positions of the base station and sensor nodes, a single wireless charging unmanned aerial vehicle, several wireless charging boards, and determining the maximum flight distance of the unmanned aerial vehicle D dmax , for each sensor node, based on the position coordinates of each sensor node and the maximum flight distance of the unmanned aerial vehicle D dmax , the battery capacity carried by the wireless charging unmanned aerial vehicle ed The energy consumed by the wireless charging drone to charge a single sensor node e sn The fixed flight speed of the wireless charging drone v d Power consumption p d It is determined on the two-dimensional plane where the network is deployed. D dmax It is expressed as:

[0069] .

[0070] Base station s 0 is deployed in the middle of the two-dimensional plane, and its position coordinates are (0, 0). As a data sink, it collects the sensed data forwarded by multi-hop routing from each sensor node and can monitor the coordinate information and remaining battery information of the sensor nodes in real time. The base station also serves as a service station for the drones. Without energy limitations, it can charge the drones that return to the base station after completing their tasks.

[0071] n A number of sensor nodes S ={ s 1, s 2,..., s n} are evenly distributed in the two-dimensional space. s i denotes the sensor node numbered i , and its corresponding coordinate position ([[]] x i , y i ). For each sensor node with a charging request, charging services are provided by the drone.

[0072] When the drone docks on the charging board, it can automatically connect to the charging board to replenish its own energy.

[0073] A single drone departs from the base station and uses a "one-to-one" method to provide short-range charging services to the sensor nodes in sequence according to the charging schedule. After completing a charging request, it needs to go to the adjacent charging board to replenish energy. After all charging tasks are completed, it finally returns to the base station.

[0074] Step 2: Based on the described wireless rechargeable sensor network model, use the hierarchical clustering algorithm to solve the clustering, calculate the center of each cluster, and obtain the number of deployments K and deployment locations of the wireless charging boards.

[0075] Please refer to Figure 3As shown, in this step, based on the wireless rechargeable sensor network model, the hierarchical clustering algorithm is used to cluster the sensor nodes, and the center of the cluster is calculated to obtain the number of deployed wireless charging boards K and their location coordinates, including:

[0076] Step 21: Encode the base station and sensor nodes in the wireless rechargeable sensor network model; including:

[0077] Number the base station and sensor nodes as {0, 1, 2,..., n}, and the generated array with 0 at both ends represents the base station, and {1, 2,..., n} represents the sensor nodes;

[0078] Step 22: Calculate the Euler distance between sensor nodes to obtain a distance matrix;

[0079] Step 23: Based on the transformed distance matrix, use the hierarchical clustering algorithm to calculate the clustering of sensor nodes and obtain the number of clusters K ; As Figure 4 shown, including:

[0080] 1) Initialize the clustering, with each sensor node as a separate cluster;

[0081] 2) Find the two clusters with the closest Euler distance in the distance matrix and merge them to form a new cluster;

[0082] 3) Recalculate the Euler distance between the new cluster and other clusters according to the farthest neighbor method and update the distance matrix;

[0083] The distance matrix is the Euclidean distance matrix:

[0084] D = d ij ;

[0085] Among them, d ij is the Euler distance between two sensor nodes i and j , and its calculation formula is:

[0086] ;

[0087] Among them, , are the abscissas of sensor nodes i , j respectively, , are the abscissas of sensor nodes i , jThe ordinate of

[0088] In this step, the farthest neighbor method means that when calculating the distance between two clusters, the maximum value of the distances between all pairs of points in the two clusters is considered. That is, the distance between the two farthest points in the two clusters is the inter-cluster distance between the two clusters, namely:

[0089] Cd IJ = max({ d ij )

[0090] Among them, Cd IJ refers to the distance between two clusters I and J; d ij refers to any two sensor nodes located in two clusters I and J respectively i , j the distance between;

[0091] The logic behind the farthest neighbor method is that using the maximum distance between clusters as a measurement standard can ensure that the final clustering center as the static charging board deployment location can guarantee that the Euler distance from each sensor node in the clustering to this center can meet less than or equal to D dmax / 2, ensuring that the UAV can freely travel back and forth between the sensor node and the charging board.

[0092] 4) Repeat the above steps 2) and 3) until each distance value in the distance matrix is greater than D dmax / 2.

[0093] Step 24. Based on the K clusters obtained by calculation, calculate the center of each cluster as the deployment location of the charging board; as Figure 5 shown, including:

[0094] 1) Calculate the center of each cluster in turn for clusters C 1, C 2,..., C k using the mean method to calculate the center of each cluster c 1, c 2,..., c k ;

[0095] 2) If the distance between the center c i and the center c j is less than or equal to D dmax , then the center ci and the center c j there is an edge between e ij ( e ij E ) and finally construct the graph G p =( C , E ), where C is the node set, that is, the charging board set ∪ base station 0; E is the edge set;

[0096] 3) Judge G p = ( C,E ) whether it is connected. If it is not connected, add the center point c to the set C and the corresponding edge e to the edge set E to make it a connected graph.

[0097] Judge G p = ( C,E ) whether it is connected, and then use the depth-first search method. Starting from base station 0, visit this node and mark it as visited. Then recursively visit all unvisited adjacent charging board points of this node in G p . If all charging boards are visited, the graph is connected; otherwise, the graph is not connected. If G p is not connected, add the center point c and the corresponding edge to make it a connected graph. Specifically, find the minimum distance d ij between the nodes of the two unconnected parts. If and , then add a charging board node at the center ij of the line segment c , ; if and , then add a charging board node at each of the two trisection points ij of the line segment , ; and so on.

[0098] In the present invention, the sensor nodes are randomly deployed in the deployment area; on the basis of the deployment of the sensor nodes, in each center point in the calculated clustering center set C ​c 1, c 2, …, c k deploy a wireless charging board at the position of.

[0099] Step 3: Combine the obtained deployment positions of the wireless charging boards, and use the A* algorithm to design the paths of the drones from each sensor node to any other sensor node, so as to obtain the static routing table of the drones; as Figure 6 shown, including:

[0100] Step 31: Set two arrays open and close , where open stores the charging board points that may be passed through, close stores the charging board points that have been passed through. Let the starting sensor node number i have an initial value of 0, and let the ending sensor node number be j , with an initial value of 1;

[0101] Step 32: Set the starting point as the sensor node i , and add it to the open array;

[0102] Step 33: Traverse open , and find the node with the smallest F value, and take it as the current node to be processed;

[0103] Step 34: Add this node to close ;

[0104] Step 35: Process all reachable charging board points starting from this point as follows:

[0105] If the reachable charging board point is already in close , then ignore it; otherwise,

[0106] If the reachable charging board point is not in open , add it to open , and set the current node as its parent node, and record the F , R and H values of this node;

[0107] If the reachable charging board point is already in open , use the R value as a reference to check whether this path is better; a charging board point with a smaller R value indicates a better path, then set its parent node as the current node, and recalculate its R and F values;

[0108] In this step, the reachable charging board points are defined as:

[0109] If the current node is a sensor node, the Euler distance from this node to a certain charging board node is less than or equal to D dmax / 2, then this charging board node is called a reachable charging board point; if the current node is a charging board node, the Euler distance from this node to a certain charging board node is less than or equal to D dmax , then this charging board node is called a reachable charging board point;

[0110] The above F value is defined as:

[0111] F = R + H ;

[0112] Among them, R is the distance from the starting point to this charging board point (this distance refers to the total length of the path from the starting point to the current point, that is, the sum of the lengths of the passed edges), H is the Manhattan distance from this point to the end point (the target sensor node); among them, the Manhattan distance i , j between nodes dm ij is calculated as follows:

[0113] .

[0114] Step 36: Check whether the current node can reach the end point j ; if it can, the path has been found at this time and added to the static routing table; otherwise, jump to step 35;

[0115] Step 37: If the j value is less than n , then j is incremented by 1, the open and close arrays are cleared, and jump to step 32;

[0116] Step 38: If the i value is less than n -1, then i is incremented by 1, set j to i +1, the open and close arrays are cleared, and jump to step 32;

[0117] Step 4: According to the charging request of the sensor node in each round, combined with the static routing table of the UAV, the multi-hop charging scheduling path of the UAV is obtained;

[0118] According to the charging request of each round of sensor nodes, the sensor nodes with charging request are sorted in the charging sequence according to the principle of closest distance first, starting from base station 0, and then i , j , ... the end point is also base station 0, and combined with the static routing table of the drone, the sensor node is found in the table i , j The charging point to be passed between p z , …, and inserted between the two nodes to obtain the multi-hop charging scheduling path of the drone .

[0119] This paper proposes an innovative UAV charging scheduling strategy with the assistance of charging boards for wireless rechargeable sensor networks. Through the deep integration of fine modeling, graph theory, machine learning and heuristic algorithms, the automation and efficiency of network maintenance are significantly improved.

[0120] First, based on the actual deployment environment, a two-dimensional model including a base station, a single wireless charging drone, a sensor node, and a wireless charging plate was constructed. The "one-to-one" charging method and the maximum flight distance of the drone were clarified to lay the foundation for subsequent planning. Then, the hierarchical clustering algorithm in machine learning was used to calculate the number and location of wireless charging plates: initially, the base station and sensor nodes were encoded, and the Euler distance between the sensor nodes was calculated to obtain a distance matrix. Then, based on the generated distance matrix, the hierarchical clustering algorithm was used to calculate the clustering of the sensor nodes to obtain the number of clusters. K ; and based on the calculated K Clusters are formed, and the mean method is used to calculate the center of each cluster as the deployment location of the charging board; then, the A-star algorithm is used to design the path of the drone from each sensor node to any other sensor node, so as to obtain the static routing table of the drone flight; finally, according to the charging request of each round of sensor nodes, the sensor nodes with charging requests are sorted in the charging sequence according to the principle of shortest distance first, starting from base station 0, and then in turn. i , j , ... the end point is also base station 0, and combined with the static routing table of the drone, the sensor node is found in the table i , j The charging point to be passed between p z , …, and inserted between the two nodes to obtain the multi-hop charging scheduling path of the drone These steps ensure that the charging time for each round is optimal, and can maximize the charging efficiency of the drone, efficiently completing the charging task for the sensor nodes. It not only effectively extends the continuous operation time of the network, but also expands the application scale of the network, improves the adaptability of the network to complex scenarios, and demonstrates broad application potential in scenarios such as remote area monitoring and disaster emergency response.

[0121] The following are several difficulties and bottlenecks in the R & D process of the present invention:

[0122] 1) Uneven distribution of sensor nodes: The distribution of sensor nodes in the two-dimensional plane may be uneven, resulting in an unsatisfactory clustering result and affecting the deployment effect of the charging board.

[0123] The way the present invention overcomes this is to adopt a hierarchical clustering algorithm, which can dynamically adjust the clustering result according to the actual distribution of the nodes, adapt to the uneven distribution, and ensure the rationality of the charging board deployment.

[0124] 2) Problem of adaptability to dynamic environment: The energy state of sensor nodes changes at any time and place, resulting in the need for dynamic adjustment of path planning.

[0125] The way the present invention overcomes this is to adopt a hierarchical clustering algorithm to adaptively adjust the clustering result according to the dynamic changes of the nodes, and the A* algorithm can adjust the path planning in real time to adapt to the dynamic environment.

[0126] 3) Complexity of path planning: In a complex environment, the path planning of the drone needs to consider energy consumption and time cost, with a high computational complexity.

[0127] The way the present invention overcomes this is to adopt the A* algorithm, which reduces the computational complexity through heuristic search and quickly finds the optimal path.

[0128] 4) Deployment cost of charging board: The number and location of the deployed charging boards directly affect the deployment cost. How to reduce the deployment cost while ensuring the charging efficiency is a difficult point.

[0129] The way the present invention overcomes this is to optimize the number and location of the deployed charging boards through a hierarchical clustering algorithm, reduce redundant deployments, and lower the deployment cost.

[0130] 5) Problem of the endurance of the drone: The endurance of the drone is limited. How to complete multiple charging tasks within the limited endurance time is a difficult point.

[0131] The way the present invention overcomes this is, on the basis of the charging board assisting the drone to replenish energy, to optimize the path planning through the A* algorithm, reduce the flight distance and energy consumption of the drone, and extend the endurance time.

[0132] For the parts not detailed in the present invention, reference can be made to the prior art or they are well-known technologies to those skilled in the art. This embodiment does not limit them and will not be described in detail herein.

[0133] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the purpose of the present invention and the scope protected by the claims, and all of them belong to the protection scope of the present invention.

Claims

1. A drone charging scheduling method based on a wireless sensor network, characterized in that, The method includes: S1. Construct a two-dimensional planar wireless rechargeable sensor network model; S2. Based on the wireless rechargeable sensor network model, use the hierarchical clustering algorithm to solve the clustering and calculate the center of each cluster to obtain the number and deployment locations of wireless charging boards; The content of step S2 includes: S21. Encode the base station and sensor nodes in the wireless rechargeable sensor network model; S22. Calculate the Euler distance between each sensor node to obtain a distance matrix; S23. Based on the distance matrix, use the hierarchical clustering algorithm to calculate the clustering of sensor nodes and obtain the number of clusters; S24. Based on the calculated clustering, calculate the center of each cluster and use it as the deployment location of the corresponding charging board; S3. Based on the number and deployment locations of wireless charging boards, use the A* algorithm to calculate the path of the drone from each sensor node to any other sensor node, so as to obtain the static routing table of the drone; The content of step S3 includes: S31. Set two arrays open and close ; among them, the array open is used to store the data related to the charging boards that the UAV may pass through, and the array close is used to store the data related to the charging boards that have been passed through; set the current starting sensor node as the i th node, and the ending sensor node as the j th node; and make i = 0, j = 1; S32. Store the current starting sensor node into the array open ; S33. Traverse the array open and find F the node with the smallest value as the node to be processed currently; F The definition of the value is: F = R + H ; R is the distance from the starting point to the current node, H and is the Manhattan distance from the current node to the end point; S34. Add the node to be processed currently to the array close ; S35. Traverse the charging plate nodes that the current node to be processed can reach, determine whether they have been passed through, and ensure that the relevant data of the charging plate nodes are stored in an array close or an array open ; Determine whether the path of the currently traversed charging plate node is a better path; if so, proceed to the next step, otherwise, continue traversing; S36. Determine whether the currently traversed node can reach the end node; if so, add this path to the static routing table, otherwise, return to S35; S37. Determine j whether it is less than n , n where is the total number of sensor nodes; if so, set j = j +1, clear array open and array close , and jump to S32; otherwise, proceed to the next step; S38, Judge i Is less than n -1. If so, make i = i +1, clear the array open and the array close , and jump to S32; otherwise, end the A* algorithm; S4. According to the charging requests of sensor nodes and combined with the static routing table of the drone, obtain the multi-hop charging scheduling path of the drone.

2. The method for charging scheduling of an unmanned aerial vehicle based on a wireless sensor network according to claim 1, wherein The content of constructing the wireless rechargeable sensor network model in step S1 includes: defining the locations of the base station and sensor nodes and determining the maximum flight distance of the drone.

3. The method for charging scheduling of an unmanned aerial vehicle based on a wireless sensor network according to claim 1, wherein The content of step S23 includes: S231. Initialize the clustering, and regard each sensor node as a separate cluster; S232. Find the two clusters with the closest Euler distance in the distance matrix and merge them to form a new cluster; S233. Recalculate the Euler distance between the new cluster and other clusters according to the farthest neighbor method and update the distance matrix; S234. Repeat S232 and S233 until the Euler distance between every two clusters in the distance matrix is greater than D dmax / 2, D dmax which is the maximum flight distance of the UAV.

4. The method for charging scheduling of an unmanned aerial vehicle based on a wireless sensor network according to claim 1, wherein The content of step S24 includes: S241. Calculate the center of each cluster in turn by the mean method; S242. Determine whether the distance between any two cluster centers meets the requirements. If so, draw an edge between these two cluster centers to construct a first graph composed of a sensor node set and an edge set; S243. Determine whether the first graph is connected. If so, proceed to the next step; otherwise, add a center point and the corresponding edge to make it a connected graph.

5. The method for charging scheduling of an unmanned aerial vehicle based on a wireless sensor network according to claim 1, wherein In step S35, the way to determine the nodes that the currently processed node can reach is: Determine whether the currently processed node is a sensor node or a charging board node; If it is a sensor node, the Euler distance from this node to a charging board node ≤ D dmax / 2, then it is determined that this charging board node is a reachable node; If it is a charging board node, the Euler distance from this node to a certain charging board node ≤ D dmax , then determine that this charging board node is a reachable node; D dmax It is the maximum flight distance of the drone.

6. The method for charging and scheduling of an unmanned aerial vehicle based on a wireless sensor network according to claim 1, wherein, In step S4, according to the charging requests of sensor nodes in each round, sort the sensor nodes with charging requests for charging according to the principle of nearest distance first; Find the charging board nodes to pass through between two sensor nodes in the static routing table of the drone and insert them between the two sensor nodes to obtain the multi-hop charging scheduling path of the drone.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method for charging scheduling of a drone based on a wireless sensor network described in any one of claims 1-6.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for charging scheduling of an unmanned aerial vehicle based on a wireless sensor network according to any one of claims 1-6.

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