Unmanned aerial vehicle charging scheduling method, product and equipment based on wireless sensor network

Abstract

The invention discloses an unmanned aerial vehicle charging scheduling method based on a wireless sensor network, a product and equipment, and belongs to the technical field of wireless charging. The method comprises the following steps: S1, constructing a two-dimensional plane wireless rechargeable sensor network model; s2, on the basis of the wireless rechargeable sensor network model, using a hierarchical clustering algorithm to solve clusters and calculate the center of each cluster, and obtaining the deployment number and deployment positions of wireless charging panels; s3, based on the deployment number and deployment positions of the wireless charging panels, calculating a path of the unmanned aerial vehicle from each sensor node to any other sensor node by adopting an A star algorithm, thereby obtaining a static routing table of the unmanned aerial vehicle; and S4, obtaining a multi-hop charging scheduling path of the unmanned aerial vehicle in combination with the static routing table of the unmanned aerial vehicle according to the charging request of the sensor node. According to the invention, through reasonable charging path planning, the flight distance and time of the unmanned aerial vehicle are reduced, the charging efficiency is maximized, and the operation time of the sensor network is prolonged.

Description

Technical Field

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

[0002] Wireless Sensor Networks (WSN) are a collection of a large number of small, inexpensive battery-powered nodes that can monitor and collect data in the 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 care, security protection, industrial automation monitoring, smart grid, building structure status monitoring, home applications, etc.

[0003] From the perspective of network composition, most sensor nodes in WSN rely on energy-limited batteries for power supply. The particularity, danger and inaccessibility of the node deployment environment, as well as the huge cost of manpower, material and financial resources caused by node replacement, make it much more difficult to extend the network life cycle by replacing batteries. In recent years, relevant technologies for wireless sensor networks have received extensive attention, including power saving, target tracking, network data routing, network coverage issues, network link strength, mobile data collection, wireless charging mechanisms, etc. Among these related technologies, how to solve the energy problem of sensors plays a pivotal role in the application of wireless sensor networks. Existing technologies use methods such as balancing power consumption loads, building power-saving data collection methods, and using mobile collectors to slow down the speed of energy consumption. However, since the total power of the entire WSN is still being consumed, these methods have not been able to substantially extend the life cycle of the WSN.

[0004] With the application of wireless charging technology to WSN, wireless sensor networks with rechargeable devices will become the development platform for many applications. Such networks are called wireless rechargeable sensor networks (WRSN). Charging sensor nodes with the help of wireless power transfer (WPT) technology can extend the life of sensor nodes. In traditional WRSN, wireless charging vehicles (WCV) are generally used to charge sensor nodes. WCV moves in the network according to its designed charging scheduling path and stops at the sensor nodes that request charging to provide charging services. In addition, drone technology has been widely used in people's lives in recent years. In order to break through the limitations of drone battery capacity, many technologies for wireless charging of drones have also been developed. Consider adding a charger to the drone to provide instant charging services for WRSN, and use a wireless charging board to provide a new wireless charging technology for drones. Therefore, how to fully and collaboratively utilize the wireless charging technology 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 future development of the Internet of Things.

[0005] With the widespread use of drone technology in human life, coupled with the small size, low cost, flexible flight routes and flight speed of up to 289 km / h, some studies have begun to consider using drones as mobile vehicles to charge sensor nodes. However, due to the small size of drones and the limited battery capacity they carry, the flight distance is greatly restricted, and they cannot directly fly long distances to perform long-distance charging tasks. At the same time, drones are low in cost, but the power energy they carry is limited, and they cannot perform multiple continuous charging tasks at the same time. Therefore, they cannot independently complete all the charging needs of the entire network.

[0006] The development of wireless charging pad technology provides a new technology to solve the endurance problem of wireless charging drones. By simply installing wireless charging equipment on the fuselage, the drone can land at any time to replenish its 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 set of wireless charging pads is designed. The model uses wireless charging drones to provide charging services for sensor nodes, and the wireless charging pad provides active charging for wireless charging drones, overcoming the disadvantage that wireless charging drones cannot fly long distances, thereby extending the life cycle of WRSN.

[0007] Therefore, how to design a UAV charging scheduling method assisted by charging plates for wireless rechargeable sensor networks, how to reasonably deploy the minimum number of wireless charging plates, design a UAV charging scheduling plan based on real-time charging requests in the network, reduce unnecessary flights of UAVs, complete each charging scheduling work in the shortest time, and extend the operating time and overall life of the sensor network are issues that technical personnel in this field urgently need to solve. Summary of the invention

[0008] The present invention aims to solve one of the technical problems in the above-mentioned related art at least to a certain extent.

[0009] To this end, the purpose of the present invention is to provide a UAV charging scheduling method, product and equipment 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 UAV, maximize the charging efficiency of each round of charging tasks, extend the operating time and overall life of the sensor network, and reduce maintenance costs.

[0010] In order to solve the above-mentioned technical problems, the present invention is achieved as follows: An embodiment of the present invention provides a method for charging and scheduling a UAV based on a wireless sensor network, the method comprising: S1. Construct a two-dimensional planar wireless rechargeable sensor network model; S2. Based on the wireless rechargeable sensor network model, a hierarchical clustering algorithm is used to solve clusters and calculate the center of each cluster to obtain the number and location of wireless charging pads. S3, based on the number and location of wireless charging pads deployed, the A-star algorithm is used to calculate the path of the drone from each sensor node to any other sensor node, thereby obtaining the static routing table of the drone; S4. According to the charging request of the sensor node and the static routing table of the UAV, the multi-hop charging scheduling path of the UAV is obtained.

[0011] In addition, the UAV charging scheduling method based on wireless sensor network according to the present invention may also have the following additional technical features: In some of the embodiments, the contents of constructing the wireless rechargeable sensor network model in step S1 include: defining the locations of base stations and sensor nodes, and determining the maximum flight distance of the drone.

[0012] In some implementations, the content of step S2 includes: S21, encoding the base station and sensor nodes in the wireless rechargeable sensor network model; S22, calculating the Euler distance between each sensor node to obtain a distance matrix; S23, based on the distance matrix, using the hierarchical clustering algorithm to calculate the clustering of sensor nodes and obtain the number of clusters; S24. Based on the calculated clusters, calculate the center of each cluster and use it as the deployment location of the corresponding charging plate.

[0013] In some implementations, the content of step S23 includes: S231, initializing clustering, treating each sensor node as a separate cluster; S232, finding two clusters with the closest Euler distance in the distance matrix and merging them to form a new cluster; S233, recalculating the Euler distance between the new cluster and other clusters according to the farthest neighbor method, and updating 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 is the maximum flight distance of the drone.

[0014] In some implementations, the content of step S24 includes: S241, using the mean method to calculate the center of each cluster in turn; S242, judging whether the distance between any two cluster centers meets the requirement, and if so, drawing an edge between the two cluster centers, thereby constructing a first graph consisting 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 corresponding edges to make it a connected graph.

[0015] In some implementations, the content of step S3 includes: S31. Set up two arrays open and close ; Among them, the array open Used to store data related to the charging pads that the drone may pass through, array close Used to store the data related to the charging board that has passed; set the current starting point sensor node to the first i nodes, and the endpoint sensor node is set as j nodes; and i =0, j =1; S32, store the current starting point sensor node into the array open middle; S33, traversing array open , find FThe node with the smallest value is used as the node to be processed currently; F The value is defined as: F = 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 end point; S34. Add the node to be processed to the array close middle; S35, traverse a charging board node that the node to be processed currently can reach, determine whether it has been passed, and ensure that the relevant data of the charging board node is stored in the array close Medium or array open In the process, determine whether the current path traversing the charging board node is a better path; if so, proceed to the next step, otherwise, continue traversing; S36, determine whether the current traversal node can reach the end node; if so, add the path to the static routing table, otherwise, return to S35; S37, Judgment j Is it less than n , n is the total number of sensor nodes; if so, j = j +1, clear the array open and arrays close , and jump to S32; otherwise, go to the next step; S38, judgment i Is it less than n -1, if yes, make i = i +1, clear the array open and arrays close , and jump to S32; otherwise, end the A star algorithm.

[0016] In some implementations, in step S35, the method for determining the nodes that the node to be processed can reach is: Determine whether the node to be processed currently 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 is ≤ D dmax / 2, the charging board node is judged to be a reachable node; If it is a charging board node, the Euler distance from this node to a charging board node is ≤ D dmax , then the charging board node is determined to be a reachable node; D dmax is the maximum flight distance of the drone.

[0017] In some of the 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 closest distance first; The charging board node to be passed between two sensor nodes is searched in the static routing table of the drone and inserted between the two sensor nodes, thereby obtaining the multi-hop charging scheduling path of the drone.

[0018] An embodiment of the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the unmanned aerial vehicle charging scheduling method based on a wireless sensor network as described in any of the above items.

[0019] An embodiment of the present invention also provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the unmanned aerial vehicle charging scheduling method based on a wireless sensor network as described in any one of the above items.

[0020] Compared with the prior art, the present invention has at least the following beneficial effects: In an embodiment of the present invention, a wireless sensor network-based UAV charging scheduling method is provided, which minimizes the number of deployed charging plates, optimizes the deployment positions of charging plates, and optimizes the multi-hop flight paths of wireless charging UAVs through accurate calculation of charging plate deployment methods, thereby maximizing the overall charging efficiency of the UAVs and minimizing the total time to complete a charging task, thereby extending the operating time and overall life of the sensor network and reducing maintenance costs.

[0021] In the embodiment of the present invention, the provided UAV charging scheduling method based on wireless sensor network comprehensively considers the advantages and disadvantages of UAV's fast flight speed but limited flight distance, and requires the assistance of charging plates to reach all sensor nodes in the sensing area to complete the charging task; the present invention ensures the rationality of network deployment and the consistency of actual application scenarios through the construction of a planar wireless rechargeable sensor network model. In the subsequent steps, not only the optimal deployment of charging plates is paid attention to, but also the multi-hop flight of UAVs with the assistance of charging plates is considered, and the multi-hop flight route is optimized to minimize the completion time of the charging task, thereby improving the operation efficiency of the overall system.

[0022] In the embodiment of the present invention, the provided UAV charging scheduling method based on wireless sensor network introduces a hierarchical clustering charging board deployment method; since the K-means clustering algorithm needs to pre-specify the number of clusters, the hierarchical clustering algorithm can dynamically adjust the number of clusters according to the distribution of sensor nodes, which 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, which is more suitable for multi-level charging board deployment requirements; therefore, this method combines the hierarchical clustering algorithm to deploy the charging board; this method fully considers the disadvantage of the limited flight distance of the UAV, and in a bottom-up manner, the sensor nodes are grouped according to the geographical location to form multiple clusters, and the center point of each cluster is calculated 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 sensor nodes within the coverage range and reduce the flight distance of the UAV. The deployment method reasonably deploys the charging board, minimizes the number of deployed charging boards, maximizes the utilization rate of the charging board, and reduces the waste of resources of the charging board.

[0023] In the embodiment of the present invention, the provided unmanned aerial vehicle charging scheduling method based on wireless sensor network adopts A star algorithm to plan and calculate the charging scheduling path of the unmanned aerial vehicle. A star algorithm is a heuristic search algorithm. Compared with the disadvantages of low computational efficiency of Dijkstra algorithm in complex environment, and genetic algorithm, although it can find the global optimal solution, has high computational complexity and is not suitable for real-time scheduling, A star algorithm can find the approximate optimal solution in a short time through heuristic search, significantly improves the computational efficiency, and is more suitable for real-time scheduling. A star algorithm finds the optimal path from the base station to each sensor node with charging request by comprehensively considering the path length and heuristic estimation value. After the charging board is deployed, the static routing table generated by A star algorithm can provide multi-hop charging scheduling path for unmanned aerial vehicle, ensuring that unmanned aerial vehicle can efficiently complete multiple charging tasks. A star algorithm can quickly find the optimal solution for the design of unmanned aerial vehicle charging scheduling from a large number of feasible flight scheduling routes, ensuring that unmanned aerial vehicle completes the task with the shortest path and the least energy consumption. A star algorithm can adapt to complex environment, meet the dynamic scheduling requirements under the on-demand charging architecture of wireless rechargeable sensor network, adjust charging scheduling in real time, and improve the flexibility and robustness of charging task completion.

[0024] In an embodiment of the present invention, a UAV charging scheduling method based on a wireless sensor network is provided, which realizes global optimization from charging board deployment to UAV path planning by combining a hierarchical clustering algorithm and an A-star algorithm. The hierarchical clustering algorithm is used to calculate the number of deployments and location coordinates of charging boards, and the A-star 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 charging efficiency, reduce deployment costs, and extend the endurance of UAVs, providing reliable technical support for the efficient operation of wireless rechargeable sensor networks.

[0025] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flowchart of a method for charging and scheduling a UAV with the assistance of a charging board according to an embodiment of the present invention; Figure 2 A schematic diagram of a two-dimensional planar wireless rechargeable sensor network model disclosed in an embodiment of the present invention; Figure 3 A flowchart of obtaining a charging board deployment using a hierarchical clustering algorithm disclosed in one embodiment of the present invention; Figure 4 A flowchart of calculating clusters of sensor nodes using a hierarchical clustering algorithm disclosed in one embodiment of the present invention; Figure 5 A flowchart of calculating each cluster center as a charging plate deployment location disclosed in one embodiment of the present invention; Figure 6 A deployment diagram based on a charging board disclosed in an embodiment of the present invention G p Based on this, the A* algorithm is used to plan the multi-hop charging flight path of the drone. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The embodiments of the present invention are described in detail below through specific embodiments and application scenarios in conjunction with the accompanying drawings.

[0029] In some embodiments of the present invention, a UAV charging scheduling method based on a wireless sensor network is provided, in which static charging plates are reasonably deployed according to the limited flight distance of the UAV, and a related flight network is designed according to the advantages and disadvantages of the UAV, and a wireless multi-hop charging scheduling path design is performed for the UAV, thereby obtaining the charging scheduling of the UAV.

[0030] See also Figure 1 As shown, in some embodiments of the present invention, the steps of the drone charging scheduling method include: Step 1: construct a two-dimensional planar wireless rechargeable sensor network model; the wireless rechargeable sensor network model includes: a base station, a single wireless charging drone and several wireless charging plates, and a group of sensor nodes.

[0031] See also Figure 2 As shown, in this step, a two-dimensional planar wireless rechargeable sensor network model is constructed, including: defining the location of base stations and sensor nodes, a single wireless charging drone, several wireless charging pads, and determining the maximum flight distance of the drone D dmax , for each sensor node, based on the location coordinates of each sensor node and the maximum flight distance of the UAV D dmax , the battery capacity carried by the wireless charging drone e d , the energy consumed by wireless charging drones to charge a single sensor node e sn , Fixed flight speed of wireless charging drone v d , power consumption p d Decisions are made on the two-dimensional plane of the deployed network. D dmax It is expressed as: .

[0032] Base Station s 0 It is deployed in the middle of the two-dimensional plane, and its position coordinates are (0, 0). As a data sink, it collects sensing data forwarded from each sensor node via multi-hop routing, and can monitor the coordinate information and remaining power information of the sensor node in real time. The base station also serves as a service station for drones. It is not limited by energy and can charge drones that return to the base station after completing their missions.

[0033] n Sensor Nodes S ={ s 1 , s 2, ..., s n} is evenly distributed in two-dimensional space, s i Indicates the number i The sensor node, its corresponding coordinate position ( x i , y i ). For each sensor node with charging request, the drone provides charging service.

[0034] When the drone docks on the charging pad, the drone can automatically connect to the charging pad to replenish its own energy.

[0035] A single drone starts from the base station and uses a "one-to-one" method to provide close-range charging services to sensor nodes in turn according to the charging schedule. After completing a power request, it needs to go to the nearby charging board to replenish energy. When all charging tasks are completed, it finally returns to the base station.

[0036] Step 2: Based on the wireless rechargeable sensor network model, a hierarchical clustering algorithm is used to solve the clusters, and the center of each cluster is calculated to obtain the number of wireless charging pads deployed. K and deployment location.

[0037] See also Figure 3 As shown, in this step, based on the wireless rechargeable sensor network model, the sensor nodes are clustered using a hierarchical clustering algorithm, and the center of the cluster is calculated to obtain the number of wireless charging pads deployed. K and location coordinates, including: Step 21, encoding the base station and sensor nodes in the wireless rechargeable sensor network model; including: The base stations and sensor nodes are numbered as {0,1,2,..., n}, the generated array starts and ends with 0 to represent the base station, {1,2,..., n} represents sensor node; Step 22, calculate the Euler distance between sensor nodes to obtain a distance matrix; Step 23: Based on the transformed distance matrix, the hierarchical clustering algorithm is used to calculate the clustering of sensor nodes and obtain the number of clusters. K ;like Figure 4 As shown, including: 1) Initialize clustering, each sensor node is treated as a separate cluster; 2) Find the two clusters with the closest Euler distance in the distance matrix and merge them to form a new cluster; 3) Recalculate the Euler distance between the new cluster and other clusters using the farthest neighbor method and update the distance matrix; The distance matrix is ​​the Euclidean distance matrix: D =[ d ij ]; in, d ij For two sensor nodes i and j The Euler distance between them is calculated as: ; in, , Sensor nodes i , j The horizontal axis of , Sensor nodes i , j The vertical coordinate of .

[0038] In this step, the farthest neighbor method means that when calculating the distance between two clusters, the maximum distance between all point pairs in the two clusters is considered, that is, the distance between the two farthest points in the two clusters is the inter-cluster distance of the two clusters, that is: Cd IJ =max({ d ij}); in, 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 The logic behind the farthest neighbor method is that by using the maximum distance between clusters as a criterion, it can ensure that the final cluster center as the static charging board deployment location can ensure that the Euler distance from each sensor node in the cluster to the center is less than or equal to D dmax / 2, ensuring that the drone can freely travel between the sensor node and the charging board.

[0039] 4) Repeat steps 2) and 3) above until each distance value in the distance matrix is ​​greater than D dmax / 2.

[0040] Step 24: Based on the calculated Kclusters, calculate the center of each cluster as the deployment location of the charging board; Figure 5 As shown, including: 1) Cluster the C 1 , C 2 , …, C k The mean method is used to calculate the center of each cluster c 1 , c 2 , …, c k ; 2) If the center c i and Center c j The distance between D dmax , then the center c i and Center c j There is an edge between e ij ( e ij E ), and finally construct the graph G p =( C , E ),in, C is the node set, that is, the charging board set ∪ base station 0; E is an edge set; 3) Judgment G p = ( C,E ) is connected, if not connected, add a center point c to the set C and the corresponding edge e To the edge E , making it a connected graph.

[0041] judge G p = ( C,E ) is connected, a depth-first search is used. Starting from base station 0, visit the node and mark it as visited. Then recursively search for G p Visit all the unvisited adjacent charging plates of the node in . If all charging plates are visited, the graph is connected; otherwise, the graph is not connected. G p If not connected, add a center pointc and the corresponding edges to make it a connected graph. Specifically, find the minimum distance between the nodes of two disconnected parts d ij ,like and , then on the line segment ij Center c , Add a charging board node at and , then on the line segment ij Two three-point points, , Add a charging board node at each location; and so on.

[0042] In the present invention, the sensor nodes are randomly deployed in the deployment area; based on the deployment of the sensor nodes, the cluster center set is calculated. C Each center point c 1 , c 2 , …, c k Deploy the wireless charging plate in the desired location.

[0043] Step 3: Combined with the deployment location of the wireless charging board, 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; Figure 6 As shown, including: Step 31: Set up two arrays open and close ,in open Store charging pad points that may be passed by, close Store the charging board points that have been passed and set the starting point sensor node number i The initial value is 0, and the endpoint sensor node number is j , the initial value is 1; Step 32: Set the starting point as the sensor node i , added to open In array; Step 33: Traversal open , find F The node with the smallest value is used as the current node to be processed; Step 34: Add the node to close middle; Step 35: Process all the charging points that can be reached from this point as follows: If the accessible charging pad point is already in close , then ignore it; otherwise, If the accessible charging pad point is not open , add it to open , and set the current node as its parent node, record the node's F , R and H value; If the accessible charging pad point is already in open In, use R Value as a reference to check whether this path is better; smaller R The charging point with a value of indicates that this is a better path, so its parent node is set to the current node and its R and F value; In this step, the accessible charging pad points are defined as: If the current node is a sensor node, the Euler distance from the node to a charging board node is less than or equal to D dmax / 2, then the charging board node is called a reachable charging board point; if the current node is a charging board node, then the Euler distance from the node to a charging board node is less than or equal to D dmax , then the charging board node is called a reachable charging board point; Above F The value is defined as: F = R + H ; in, R is the distance from the starting point to the charging board point (this distance refers to the total length of the path from the starting point to the current point, which is the sum of the lengths of the edges passed through), H is the Manhattan distance from this point to the end point (target sensor node); where node i , j Manhattan distance between dm ij The calculation is as follows: .

[0044] Step 36: Check whether the current node can reach the end point j ; If yes, the path has been found and added to the static routing table; otherwise, jump to step 35; Step 37: If j Value less than n ,but j Add 1, clear open and close Array, jump to step 32; Step 38: If i Value less thann -1, then i Add 1, set j for i +1, clear open and close Array, jump to step 32; 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; 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 .

[0045] 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.

[0046] 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 of each round is optimal, and can maximize the charging efficiency of the drone and efficiently complete the charging task of 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 network's adaptability to complex scenarios, and shows the potential for wide application in scenarios such as remote area monitoring and disaster emergency response.

[0047] Several difficulties and bottlenecks in the development process of the present invention are as follows: 1) Uneven distribution of sensor nodes: The distribution of sensor nodes on the two-dimensional plane may be uneven, resulting in unsatisfactory clustering results and affecting the deployment effect of the charging board.

[0048] The present invention overcomes this problem by adopting a hierarchical clustering algorithm, which can dynamically adjust the clustering results according to the actual distribution of nodes, adapt to uneven distribution, and ensure the rationality of the deployment of charging boards.

[0049] 2) The problem of adaptability to dynamic environments: The energy state of sensor nodes changes anytime and anywhere, which requires dynamic adjustment of path planning.

[0050] The present invention overcomes this problem by using a hierarchical clustering algorithm to adaptively adjust clustering results according to dynamic changes of nodes. The A-star algorithm can adjust path planning in real time to adapt to dynamic environments.

[0051] 3) Complexity of path planning: In complex environments, the path planning of drones needs to consider energy consumption and time cost, and the computational complexity is high.

[0052] The present invention overcomes this problem by adopting the A-star algorithm to reduce the computational complexity through heuristic search and quickly find the optimal path.

[0053] 4) Charging board deployment cost: The number and location of charging boards directly affect the deployment cost. How to reduce the deployment cost while ensuring charging efficiency is a difficult point.

[0054] The present invention overcomes this problem by optimizing the number and location of charging plates through a hierarchical clustering algorithm, thereby reducing redundant deployment and lowering deployment costs.

[0055] 5) The problem of drone endurance: The endurance of drones is limited. How to complete multiple charging tasks within the limited endurance time is a difficult point.

[0056] The present invention overcomes this problem by optimizing the path planning through the A-star algorithm on the basis of assisting the UAV in replenishing energy with the help of the charging board, thereby reducing the flight distance and energy consumption of the UAV and extending the flight time.

[0057] The parts of the present invention that are not described in detail may refer to the prior art or are known to those skilled in the art, and this embodiment does not limit this and will not be described in detail here.

[0058] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A charging scheduling method for unmanned aerial vehicle based on wireless sensor network, characterized in that: The method comprises: S1. Construct a two-dimensional planar wireless rechargeable sensor network model; S2. Based on the wireless rechargeable sensor network model, a hierarchical clustering algorithm is used to solve clusters and calculate the center of each cluster to obtain the number and location of wireless charging pads. S3, based on the number and location of wireless charging pads deployed, the A-star algorithm is used to calculate the path of the drone from each sensor node to any other sensor node, thereby obtaining the static routing table of the drone; S4. According to the charging request of the sensor node and the static routing table of the UAV, the multi-hop charging scheduling path of the UAV is obtained.

2. The method for charging and scheduling unmanned aerial vehicles based on wireless sensor networks according to claim 1 is characterized in that: The contents of building the wireless rechargeable sensor network model in step S1 include: defining the locations of base stations and sensor nodes, and determining the maximum flight distance of the drone.

3. The method for charging and scheduling unmanned aerial vehicles based on wireless sensor networks according to claim 1 is characterized in that: The content of step S2 includes: S21, encoding the base station and sensor nodes in the wireless rechargeable sensor network model; S22, calculating the Euler distance between each sensor node to obtain a distance matrix; S23, based on the distance matrix, using the hierarchical clustering algorithm to calculate the clustering of sensor nodes and obtain the number of clusters; S24. Based on the calculated clusters, calculate the center of each cluster and use it as the deployment location of the corresponding charging plate.

4. The method for charging and scheduling unmanned aerial vehicle based on wireless sensor network according to claim 3 is characterized in that: The content of step S23 includes: S231, initializing clustering, treating each sensor node as a separate cluster; S232, finding two clusters with the closest Euler distance in the distance matrix and merging them to form a new cluster; S233, recalculating the Euler distance between the new cluster and other clusters according to the farthest neighbor method, and updating 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 is the maximum flight distance of the drone.

5. The method for charging and scheduling unmanned aerial vehicle based on wireless sensor network according to claim 3 is characterized in that: The contents of step S24 include: S241, using the mean method to calculate the center of each cluster in turn; S242, judging whether the distance between any two cluster centers meets the requirement, and if so, drawing an edge between the two cluster centers, thereby constructing a first graph consisting 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 corresponding edges to make it a connected graph.

6. The method for charging and scheduling unmanned aerial vehicles based on wireless sensor networks according to claim 1, characterized in that: The content of step S3 includes: S31. Set up two arrays open and close ; Among them, the array open Used to store data related to the charging pads that the drone may pass through, array close Used to store the data related to the charging board that has passed; set the current starting point sensor node to the first i nodes, and the endpoint sensor node is set as j nodes; and i =0, j =1; S32, store the current starting point sensor node into the array open middle; S33, traversing array open , find F The node with the smallest value is used as the node to be processed currently; F The value is defined as: F = 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 end point; S34. Add the node to be processed to the array close middle; S35, traverse a charging board node that the node to be processed currently can reach, determine whether it has been passed, and ensure that the relevant data of the charging board node is stored in the array close Medium or array open In the process, determine whether the current path traversing the charging board node is a better path; if so, proceed to the next step, otherwise, continue traversing; S36, determine whether the current traversal node can reach the end node; if so, add the path to the static routing table, otherwise, return to S35; S37, Judgment j Is it less than n , n is the total number of sensor nodes; if so, j = j +1, clear the array open and arrays close , and jump to S32; otherwise, go to the next step; S38, judgment i Is it less than n -1, if yes, make i = i +1, clear the array open and arrays close , and jump to S32; otherwise, end the A star algorithm.

7. The method for charging and scheduling unmanned aerial vehicle based on wireless sensor network according to claim 6, characterized in that: In step S35, the method for determining the nodes that the node to be processed can reach is: Determine whether the node to be processed currently 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 is ≤ D dmax / 2, the charging board node is judged to be a reachable node; If it is a charging board node, the Euler distance from this node to a charging board node is ≤ D dmax , then the charging board node is determined to be a reachable node; D dmax is the maximum flight distance of the drone.

8. The method for charging and scheduling unmanned aerial vehicles based on wireless sensor networks according to claim 1, characterized in that: In step S4, according to the charging requests of the sensor nodes in each round, the sensor nodes with charging requests are sorted according to the principle of closest distance first; The charging board node to be passed between two sensor nodes is searched in the static routing table of the drone and inserted between the two sensor nodes, thereby obtaining the multi-hop charging scheduling path of the drone.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the wireless sensor network-based drone charging scheduling method described in any one of claims 1 to 8 are implemented.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the wireless sensor network-based drone charging scheduling method as described in any one of claims 1 to 6.

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