A collaborative charging scheduling method for hybrid charging vehicles

Abstract

The present application relates to the field of wireless sensor networks, and in particular to a collaborative charging scheduling method for hybrid charging vehicles, comprising: constructing a wireless rechargeable sensor network model, including a base station, a hybrid charging vehicle and n sensor nodes; based on the location information between each sensor node with charging request, a bipartite graph is constructed, and the Euler distance between adjacent sensor nodes is used as the edge weight of the matching in the bipartite graph; wherein the actual distance between adjacent sensor nodes is less than or equal to the preset maximum flight distance of the drone; based on the KM maximum matching algorithm, the k matches, based on k The Euler distance between each pair of sensor nodes and the base station is calculated, and the sensor node with the closer distance in each pair is assigned to the wireless charging vehicle, while the sensor node with the farther distance is assigned to the wireless charging drone for charging. This application optimizes the charging task allocation through graph theory algorithms, improving the charging efficiency and automation level of wireless rechargeable sensor networks.

Description

Technical Field

[0001] The present application relates to the field of wireless sensor networks, and more specifically, to a collaborative charging scheduling method for hybrid charging vehicles. Background Art

[0002] Wireless Sensor Networks (WSNs) are collections of small, inexpensive, battery-powered nodes that monitor and collect data within a sensing area. As a key technology in the Internet of Things (IoT), WSNs are widely used in a variety of fields, including military, aerospace science and industry, intelligent transportation, precision agriculture, environmental monitoring, healthcare, security, industrial automation monitoring, smart grids, building structural condition monitoring, and home appliances.

[0003] From a network perspective, sensor nodes in WSNs mostly rely on energy-limited batteries for power. The unique, hazardous, and inaccessible nature of node deployment environments, coupled with the significant human, material, and financial costs associated with node replacement, makes extending the network's lifespan by replacing batteries significantly more challenging. In recent years, technologies related to wireless sensor networks have garnered significant attention, including power saving, target tracking, network data routing, network coverage, link strength, mobile data collection, and wireless charging mechanisms. Among these technologies, addressing sensor energy management plays a crucial role in wireless sensor network applications.

[0004] Existing technologies have adopted methods such as balancing power consumption loads, building power-efficient data collection methods, and utilizing mobile collectors to slow down energy consumption. However, since the total power of the entire WSN is still being depleted, these methods have not been able to substantially extend the life cycle of the WSN.

[0005] With the application of wireless charging technology to WSNs, wireless sensor networks equipped with rechargeable devices will become a development platform for many applications. Such networks are called Wireless Rechargeable Sensor Networks (WRSNs). Using wireless power transfer (WPT) technology to charge nodes can extend the lifespan of sensor nodes. In traditional WRSNs, wireless charging vehicles (WCVs) are typically used to charge nodes. WCVs move through the network according to their designed charging schedules and stop at sensor nodes that request charging to provide charging services. This approach, using wireless charging as a mobile vehicle, offers flexible charging options and effectively solves the network's energy challenges. However, in wireless sensor networks with complex deployment areas, numerous ground obstacles, and large areas, the movement of wireless charging vehicles is easily affected by these obstacles, and their slow speed can still cause some sensor nodes to die before they can be rescued. Furthermore, due to the high cost of wireless charging vehicles, multiple vehicles must be deployed to meet the charging needs of a large wireless sensor network, significantly increasing costs and hindering the application and development of WRSNs.

[0006] With the widespread adoption of drone technology in human life, coupled with their small size, low cost, flexible flight paths, and speeds of up to 289 km / h, some research has begun to consider using drones as mobile vehicles to charge sensor nodes. However, due to their small size and limited battery capacity, drones are significantly limited in their flight range, making them incapable of directly flying long distances to perform charging tasks. Furthermore, despite their low cost, drones carry limited power, making them unable to perform multiple, continuous charging tasks simultaneously and unable to independently complete all charging needs of the entire network. Therefore, how to design a collaborative charging scheduling method for hybrid charging vehicles that rationally allocates charging tasks, reduces the movement of wireless charging vehicles and unnecessary drone flights, minimizes the total time of each charging schedule, and extends the operating time and overall lifespan of the sensor network is an urgent problem that those skilled in the art need to solve. Summary of the Invention

[0007] In view of this, the present application provides a collaborative charging scheduling method for hybrid charging vehicles, which comprehensively considers the flight speed, flight distance and battery capacity of the UAV and adopts the modified KM algorithm to obtain the charging scheduling tasks of the wireless charging vehicle and the UAV.

[0008] The technical solutions provided in this application are as follows:

[0009] A collaborative charging scheduling method for a hybrid charging vehicle, wherein the hybrid charging vehicle comprises a wireless charging vehicle and a charging station mounted on the wireless charging vehicle. k ( k >1) A wireless charging drone, the method comprising:

[0010] S1. Construct a wireless rechargeable sensor network model, including a base station, the hybrid charging vehicle and n sensor nodes; the base station is used to receive location information and charging requests sent by each sensor node;

[0011] S2. Based on the location information of each sensor node with a charging request, a bipartite graph is constructed, and the Euler distance between adjacent sensor nodes is used as the edge weight of the matching in the bipartite graph; wherein the actual distance between the adjacent sensor nodes is less than or equal to the preset maximum flight distance of the UAV. D dmax ;

[0012] S3, select based on the KM maximum matching algorithm on the bipartite graph k matches, the k The sum of the weights of the matching paths is the largest;

[0013] S4. Based on the k The Euler distance between each pair of sensor nodes and the base station is matched, and the sensor node with a closer distance in each pair of sensor nodes is assigned to the wireless charging vehicle, and the sensor node with a farther distance is assigned to the wireless charging drone for charging each pair of sensor nodes.

[0014] In one possible implementation, in S1, building a wireless rechargeable sensor network model includes:

[0015] Establishing a two-dimensional plane coordinate system with the position of the base station as the origin;

[0016] Identify each sensor node s i The position coordinates of x i , y i );

[0017] Based on each sensor node s i The actual distance between each sensor node and between each sensor node and the base station is determined by the location coordinates of the sensor nodes.

[0018] In one possible implementation, in S2, constructing a bipartite graph includes:

[0019] S21, encoding the base station and each sensor node in the wireless rechargeable sensor network model, expressed as: {0, 1, 2, ..., n}; The generated array starts and ends with 0 to represent the base station, {1, 2, ..., n} represents a sensor node;

[0020] S22, for the sensor nodes that have charging requests, determining the actual distance between each sensor node;

[0021] S23: If the actual distance between the two sensor nodes is less than or equal to the preset maximum flight distance of the UAV D dmax , then the two sensor nodes are regarded as adjacent sensor nodes;

[0022] S24. Construct a bipartite graph based on the edge weight relationship between adjacent sensor nodes, expressed as:

[0023] G =( S , T , E );

[0024] Among them, the sensor node set T = S , E is the edge set, edge weight w [ i , j ] is the adjacent sensor node i and j The Euler distance between .

[0025] In one possible implementation, in S3, the KM maximum matching algorithm is used to select k matches, including:

[0026] S31. Initialize the collection S and T The sensor node number in S Each node i Top mark A [ i ] is the node i The maximum weight of the associated edge, T The top label of each node in B [ j ]=0;

[0027] S32, the edge weight w[ i , j ]The largest matching ( i , j ) into the matching setE m and delete the node i 、 j associated matching;

[0028] S33, repeat S32 until | E m |= k Or there are no relevant matching edges;

[0029] S34, if | E m |< k , then the bipartite graph is restored G , let the matching set E m In the association matching ( i , j )'s node top label A [ i ]-1, B [ j ]+1, repeat step S32;

[0030] S35. Output matching set E m .

[0031] In one possible implementation, S4 includes:

[0032] S41, calculate separately k Matching sensor nodes i 、 j Euler distance to base station 0 d i0 、 d j0 ;

[0033] S42, yes d i0 and d j0 For comparison, the sensor node in each pair of sensor nodes that is closer to the base station is assigned to the wireless charging vehicle, and the sensor node that is closer to the base station is assigned to the wireless charging drone;

[0034] S43, according to the location information of each sensor node assigned to the wireless charging vehicle, based on the principle of shortest distance priority, determine the charging path of the wireless charging vehicle, and k The corresponding sensor nodes in the matching are used as the release points of the charging drone. When the wireless charging car passes by, the charging drone is released to fly towards k The other sensor node in the matching pair.

[0035] Compared with the existing technology, the technical solution provided by this application has the following beneficial effects:

[0036] This technical solution proposes an innovative charging vehicle collaborative charging scheduling strategy for wireless rechargeable sensor networks. Through the deep integration of fine modeling and graph theory algorithms, it realizes efficient automation of network maintenance. First, a two-dimensional model including base stations, wireless charging vehicles, drones and sensor nodes is constructed according to the actual deployment environment, and the charging method and the maximum flight distance of drones are clarified, providing a solid foundation for the planning of subsequent charging tasks. On this basis, the maximum matching theory in graph theory is used to allocate charging tasks: by encoding the base stations and sensor nodes, the sensor nodes with charging requests in each round and their edge weight relationships are converted into a bipartite graph, and the optimal one is selected based on the modified KM maximum matching algorithm. k Matching is performed to ensure the weighted sum of the matching paths is maximized. Furthermore, by calculating the Euler distance from the sensor nodes in the matching set to the base station, charging tasks are rationally allocated, assigning sensor nodes closer to the base station to wireless charging vehicles and those farther away to drones. Simultaneously, the charging path for the wireless charging vehicle is designed using the principle of closest distance priority, and matching nodes are used as release points for drones, enabling efficient collaboration between drones and wireless charging vehicles. These technical approaches not only optimize the time required for each charging round but also significantly improve the efficiency of drones, enabling efficient charging of sensor nodes and significantly enhancing the operational efficiency and maintenance automation level of the entire wireless rechargeable sensor network. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0038] Figure 1 This is a flow chart of a collaborative charging scheduling method for hybrid charging vehicles applied to a wireless rechargeable sensor network provided in Example 1 of the present application.

[0039] Figure 2 This is a schematic diagram of a two-dimensional planar wireless rechargeable sensor network model provided in Example 1 of the present application.

[0040] Figure 3 This is a schematic diagram of the hybrid charging scheduling process in a wireless rechargeable sensor network provided in Example 1 of the present application.

[0041] Figure 4This is a flowchart of a method for constructing a bipartite graph based on location information between sensor nodes with charging requests provided in the first embodiment of the present application.

[0042] Figure 5 The bipartite graph converted from Example 1 of this application G The modified KM maximum matching algorithm is used to select k Flowchart of the matching method.

[0043] Figure 6 For the purpose of obtaining the k Flowchart of the allocation of charging tasks based on matching. DETAILED DESCRIPTION

[0044] The following will combine the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0045] Example 1

[0046] See also Figure 1 , is a flow chart of a collaborative charging scheduling method for hybrid charging vehicles applied to a wireless rechargeable sensor network provided in the first embodiment of the present application. Figure 1 As shown in , the specific implementation steps of the above method include:

[0047] Step 1: Build a wireless rechargeable sensor network model.

[0048] Specifically, the embodiment of the present application constructs a wireless rechargeable sensor network model on a two-dimensional plane based on the actual deployment environment, such as Figure 2 As shown in the figure, the wireless rechargeable sensor network model includes a base station, a single wireless charging vehicle, and a k ( k >1) Wireless charging drone and n ( n >1) sensor nodes. n Sensor nodes are evenly distributed in a two-dimensional space and are divided into energy-sufficient sensor nodes and charge-requesting nodes based on their remaining charge. Charge-requesting nodes can send charging requests to the base station. The base station responds to the charging requests from one or more sensor nodes and calls a wireless charging vehicle or wireless charging drone for charging.

[0049] Specifically, this application pre-sets the maximum flight distance of the drone D dmax, the battery capacity carried by the wireless charging drone e d , the energy consumed by wireless charging drone to charge a sensor node e max , Fixed flight speed of wireless charging drone v d , power consumption p d Decision, expressed as:

[0050] .

[0051] like Figure 3 As shown in , if the distance between the sensor node initiating the charging request and the base station is D dmax / 2, the wireless charging drone starting from the base station will charge the above sensor nodes. If the distance between the sensor node initiating the charging request and the base station is greater than D dmax / 2, the wireless charging vehicle or the wireless charging drone carried on the wireless charging vehicle will be charged according to the distribution of charging tasks. That is, for each sensor node, this application is based on the location of each sensor node and the flight range of the wireless charging drone. D dmax , on the two-dimensional plane of the deployed network, determine the circle with the sensor node as the center, D dmax / 2 of the flight range.

[0052] The wireless charging vehicle carries enough energy and is loaded with a fully charged charging drone. It departs from the base station in a "one-to-one" manner, that is, the charging vehicle can only charge one sensor node at a time. It charges the sensor nodes accurately at close range in turn according to the charging schedule. After completing the charging task, it returns to the base station.

[0053] The base station serves as a data receiver, receiving sensor data from each sensor node via multi-hop routing. This data includes the sensor node's location coordinates, remaining battery life, and charging requests. The base station also serves as a service station for charging vehicles, free from energy constraints, and can charge wireless charging vehicles and drones returning to the base station after completing their missions.

[0054] When building a wireless rechargeable sensor network model, the first step is to define the locations of the base station and sensor nodes. In this embodiment, a two-dimensional coordinate system is established with the base station location as the origin. The base station is deployed in the middle of the two-dimensional plane, with its location coordinates at (0, 0). n sensor nodes S ={ s 1, s2, ..., s n} is evenly distributed in two-dimensional space, s i Indicates the number i The corresponding position coordinates of the sensor node are expressed as ( x i , y i ). Based on the position coordinates of each sensor node, the actual distance between each sensor node and the actual distance between each sensor node and the base station can be determined.

[0055] Step 2: Based on the location information between each sensor node with charging request, a bipartite graph is constructed, and the Euler distance between adjacent sensor nodes is used as the edge weight of the matching in the above bipartite graph.

[0056] like Figure 4 As shown in , the above step 2 specifically also includes:

[0057] Step 21: Encode the base station and each sensor node in the wireless rechargeable sensor network model, and express it as: {0,1,2, ..., n}. The generated array starts and ends with 0 to represent the base station, {1, 2, ..., n} represents a sensor node.

[0058] Step 22: For the sensor nodes that have charging requests, determine the actual distances between the sensor nodes.

[0059] Step 23: If the actual distance between the two sensor nodes is less than or equal to the preset maximum flight distance of the UAV D dmax , then the two sensor nodes are regarded as adjacent sensor nodes.

[0060] Step 24: Construct a bipartite graph based on the edge weight relationship between adjacent sensor nodes, which is expressed as: G =( S , T , E ).

[0061] Among them, the sensor node set T=S , E is the edge set, edge weight w [ i , j ] is the adjacent sensor node i and j The Euler distance between .

[0062] Step 3: Select the KM maximum matching algorithm on the bipartite graph.k matches, and makes k The sum of the weights of the matching paths is the maximum.

[0063] like Figure 5 As shown in , based on the above wireless rechargeable sensor network model, the modified KM maximum matching algorithm is used to allocate the charging tasks of mixed vehicles, specifically including:

[0064] Step 31: Initialize the collection S and T The node numbers in S Each point i Top mark A [ i ] for i The maximum weight of the edge associated with the point, T The top mark of each point B [ j ]=0.

[0065] Step 32: w [ i , j ]The largest matching ( i , j ) into the matching set E m and delete the dot i , j Associated matches.

[0066] Step 33. Repeat step 32 until | E m |= k Or there are no relevant matching edges.

[0067] Step 34, if | E m |less than k , then the bipartite graph is restored G , let the matching set E m In the association matching ( i , j )'s node top label A [ i ]-1, B [ j ]+1, repeat step S32.

[0068] Step 35: Output matching set E m .

[0069] Step 4: Based on the above kThe Euler distance between each pair of sensor nodes and the base station is matched, and the sensor node with a closer distance in each pair of sensor nodes is assigned to the wireless charging vehicle, and the sensor node with a farther distance is assigned to the wireless charging drone for charging each pair of sensor nodes.

[0070] Specifically, if Figure 6 As shown in , the above step 4 specifically further includes:

[0071] Step 41: Calculate each pair of matches in the matching set ( i , j ) sensor nodes i 、 j Euler distance to base station 0 d i0 、d j0 ;

[0072] Step 42: For each pair of matching sensor nodes d i0 and d j0 For comparison, the sensor nodes that are closer to the base station are assigned to the wireless charging vehicle, and the sensor nodes that are farther away from the base station are assigned to the drone.

[0073] Step 43: According to the location information of each sensor node assigned to the wireless charging vehicle, the charging path of the wireless charging vehicle is determined based on the principle of shortest distance priority, and the charging path of the wireless charging vehicle is determined based on the shortest distance priority principle. k The corresponding sensor nodes in the matching are used as the release points of the charging drone. When the wireless charging car passes by, the charging drone is released to fly towards k The other sensor node in the matching pair.

[0074] Compared with the prior art, the technical solution provided by the embodiments of the present application has the following beneficial effects:

[0075] The technical solution of this embodiment proposes an innovative collaborative charging scheduling strategy for charging vehicles in wireless rechargeable sensor networks. Through the deep integration of sophisticated modeling and graph theory algorithms, it significantly improves the automation and efficiency of network maintenance.

[0076] First, based on the actual deployment environment, a two-dimensional model including base stations, wireless charging vehicles, drones, and sensor nodes was constructed. The "one-to-one" charging method and the maximum flight distance of drones were clarified to lay the foundation for subsequent planning. Then, the maximum matching concept of graph theory was used to allocate charging tasks for mixed vehicles: initially, the base stations and sensor nodes were encoded, and the sensor nodes with charging requests in each round and the edge weight relationships between them were converted into a bipartite graph. Then, based on this bipartite graph, the modified KM maximum matching algorithm was used to selectk Match, ensure This k The sum of the weights of the matching paths is the largest. And according to the selected k matches, calculate each pair of matches in the matching set ( i, j ) sensor nodes i, j Euler distance to base station 0 d i0 、 d j0 ; right d i0 and d j0 By comparing the distances between sensor nodes and the base station, the wireless charging vehicle is assigned to the sensor nodes closest to the base station, while the drone is assigned to the sensor nodes closest to the base station. This allows for the allocation of charging tasks. Subsequently, based on the principle of closest distance first, a greedy algorithm is used to design the vehicle's charging path. Specifically, the vehicle's starting point is the base station, and the nearest sensor nodes are greedily selected in sequence to form a Hamiltonian path to design the wireless charging vehicle's charging path.

[0077] And, according to the calculated matching points i As the release point of the drone, when the wireless charging car passes the matching node i Release the drone and fly directly to another matching node j These steps ensure that the charging time of each round is optimal, and can maximize the utilization rate of the drone and efficiently complete the charging task of the sensor nodes.

[0078] 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 demonstrates its potential for wide application in scenarios such as remote area monitoring and disaster emergency response.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. References to the same or similar parts between the various embodiments are sufficient. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For relevant parts, refer to the method description.

[0080] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A collaborative charging scheduling method for a hybrid charging vehicle, wherein the hybrid charging vehicle comprises a wireless charging vehicle and a charging station mounted on the wireless charging vehicle. k A wireless charging drone, k >1, characterized in that The method comprises: S1. Construct a wireless rechargeable sensor network model, including a base station, the hybrid charging vehicle and n sensor nodes; the base station is used to receive location information and charging requests sent by each sensor node; S2. Based on the location information of each sensor node with a charging request, a bipartite graph is constructed, and the Euler distance between adjacent sensor nodes is used as the edge weight of the matching in the bipartite graph; wherein the actual distance between the adjacent sensor nodes is less than or equal to D dmax / 2, D dmax Indicates the maximum flight distance of the drone; S3, select based on the KM maximum matching algorithm on the bipartite graph k matches, the k The sum of the weights of the matching paths is the largest; S4. Based on the k The Euler distance between each pair of sensor nodes and the base station is matched, and the sensor node with a closer distance in each pair of sensor nodes is assigned to the wireless charging vehicle, and the sensor node with a farther distance is assigned to the wireless charging drone for charging each pair of sensor nodes.

2. The method for cooperative charging scheduling of hybrid charging vehicles according to claim 1, characterized in that: In S1, a wireless rechargeable sensor network model is constructed, including: Establishing a two-dimensional plane coordinate system with the position of the base station as the origin; Identify each sensor node s i The position coordinates of x i , y i ); Based on each sensor node s i The actual distance between each sensor node and between each sensor node and the base station is determined by the location coordinates of the sensor nodes.

3. The method for cooperative charging scheduling of hybrid charging vehicles according to claim 1, characterized in that: In S2, constructing a bipartite graph includes: S21, encoding the base station and each sensor node in the wireless rechargeable sensor network model, expressed as: {0, 1, 2, ..., n }; The generated array starts and ends with 0 to represent the base station, {1, 2, ..., n } represents a sensor node; For sensor nodes with charging requests, determine the actual distance between each sensor node; If the actual distance between the two sensor nodes is less than or equal to the preset maximum flight distance of the UAV D dmax , then the two sensor nodes are regarded as adjacent sensor nodes; A bipartite graph is constructed based on the edge weight relationship between adjacent sensor nodes, which is expressed as: G =( S , T , E )； Among them, the sensor node set T = S , E is the edge set, edge weight w [ i , j ] is the adjacent sensor node i and j The Euler distance between .

4. The method for cooperative charging scheduling of hybrid charging vehicles according to claim 3, characterized in that: In S3, based on the KM maximum matching algorithm, k matches, including: S31. Initialize the collection S and T The sensor node number in S Each node i Top mark A [ i ] is the node i The maximum weight of the associated edge, T The top label of each node in B [ j ] = 0; S32, the edge weight w [ i , j ]The largest matching ( i , j ) into the matching set E m and delete the node i 、 j associated matching; S33, repeat S32 until | E m |= k Or there are no relevant matching edges; S34, if | E m |< k， Then the bipartite graph is restored G , let the matching set E m In the association matching ( i , j )'s node top label A [ i ]-1, B [ j ]+1, repeat step S32; S35. Output matching set E m .

5. The method for cooperative charging scheduling of hybrid charging vehicles according to claim 1, characterized in that: Said S4 includes: S41, calculate separately k Matching sensor nodes i 、 j Euler distance to the base station d i0 、 d j0 ; S42, yes d i0 and d j0 For comparison, the sensor node in each pair of sensor nodes that is closer to the base station is assigned to the wireless charging vehicle, and the sensor node that is closer to the base station is assigned to the wireless charging drone; S43, according to the location information of each sensor node assigned to the wireless charging vehicle, based on the principle of shortest distance priority, determine the charging path of the wireless charging vehicle, and k The corresponding sensor nodes in the matching are used as the release points of the wireless charging drone. When the wireless charging car passes by, the wireless charging drone is released to fly towards k The other sensor node in the matching pair.

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