Cooperative charging scheduling method for hybrid charging carrier

Abstract

The invention relates to the field of wireless sensor networks, in particular to a cooperative charging scheduling method for a hybrid charging carrier, which comprises the following steps: constructing a wireless rechargeable sensor network model comprising a base station, a hybrid charging carrier and n sensor nodes; constructing a bipartite graph based on the position information among the sensor nodes with the charging requests, and taking the Euler distance between the adjacent sensor nodes as a matched edge weight in the bipartite graph; wherein the actual distance between the adjacent sensor nodes is smaller than or equal to the preset maximum flight distance of the unmanned aerial vehicle; k matches are selected based on a KM maximum matching algorithm, and based on the Euler distance between each pair of sensor nodes in the k matches and the base station, the sensor nodes with closer distance in each pair of sensor nodes are allocated to the wireless charging vehicle, and the sensor nodes with farther distance are allocated to the wireless charging unmanned aerial vehicle for charging. The charging task distribution is optimized through the graph theory algorithm, and the charging efficiency and the automation level of the wireless rechargeable sensor network are improved.

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 (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.

[0004] Existing technologies use methods such as balancing power consumption loads, building power-saving data collection methods, and using mobile collectors to slow down 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.

[0005] 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). The life of sensor nodes can be extended by charging them with the help of wireless power transfer (WPT) technology. In traditional WRSN, wireless charging vehicles (WCV) are generally used to charge 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. This method of using wireless charging as a mobile vehicle has a flexible charging form and effectively solves the energy problem of the network. However, in wireless sensor networks with complex deployment areas, many ground obstacles and large areas, the movement of wireless charging vehicles is easily affected by ground obstacles, and the moving speed is slow, which still causes some sensor nodes to die without timely rescue. In addition, due to the high cost of wireless charging vehicles, multiple units need to be deployed in wireless sensor networks with large areas to meet the charging needs of WRSN, which invisibly increases a lot of costs and restricts the application development of WRSN.

[0006] With the widespread application of drone technology in human life, coupled with the small size, low cost, flexible flight routes and maximum flight speed of 289 km / h of drones, 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 limited, and they cannot directly fly long distances to perform charging tasks over long distances. At the same time, drones are low in cost, but the power energy they carry is limited. They cannot perform multiple continuous charging tasks at the same time and cannot independently complete all charging needs of the entire network. Therefore, how to design a collaborative charging scheduling method for hybrid charging vehicles, reasonably allocate charging tasks, reduce the movement of wireless charging vehicles and the unnecessary flight of drones, minimize the total time of each charging scheduling, and extend the operating time and overall life of the sensor network is a problem that technicians in this field urgently 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 of the UAV and the battery capacity of the wireless charging vehicle, and adopts a modified KM algorithm to obtain the charging scheduling tasks of the wireless charging vehicle and the UAV.

[0008] The technical solutions provided by this application are as follows: A collaborative charging scheduling method for a hybrid charging vehicle, the hybrid charging vehicle comprising a wireless charging vehicle and a wireless charging vehicle mounted on the wireless charging vehicle k ( k >1) A wireless charging drone, the method comprising: S1. Constructing 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 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 the adjacent sensor nodes is less than or equal to the preset maximum flight distance of the UAV D dmax ; 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 maximum; S4. Based on the k The Euler distance from each pair of sensor nodes to the base station is matched, and the sensor nodes with closer distance in each pair of sensor nodes are assigned to the wireless charging vehicle, and the sensor nodes with farther distance are assigned to the wireless charging drone for charging each pair of sensor nodes.

[0009] In one possible implementation, in S1, building a wireless rechargeable sensor network model includes: 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 are expressed as ( x i , y i ); Based on each sensor node s i The location coordinates are used to determine the actual distance between each sensor node and between each sensor node and the base station.

[0010] In one possible implementation, 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; S22, for the sensor nodes with charging requests, determining the actual distances between the sensor nodes; 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; S24. Construct a bipartite graph based on the edge weight relationship between adjacent sensor nodes, 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 .

[0011] In one possible implementation, in S3, based on the KM maximum matching algorithm, select 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 match ( 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 restoredG , let the matching set E m In the association matching ( i , j ) A [ i ]-1, B [ j ]+1, repeat step S32; S35. Output matching set E m .

[0012] In one possible implementation, S4 includes: S41, calculate separately k Matching sensor nodes i , j Euler distance to base station 0 d i0 , d j0 ; S42, yes d i0 and d j0 For comparison, the sensor node closer to the base station in each pair of sensor nodes is assigned to the wireless charging vehicle, and the sensor node 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 charging drone. When the wireless charging car passes by, the charging drone is released to fly towards k The other sensor node in the match.

[0013] Compared with the prior art, the technical solution provided by this application has the following beneficial effects: 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, efficient and automated network maintenance is achieved. 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 the drone 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. kMatching is performed to ensure that the weight and the sum of the matching paths are maximized. Furthermore, by calculating the Euler distance from the sensor nodes in the matching set to the base station, the charging tasks are reasonably allocated, and the sensor nodes closer to the base station are allocated to the wireless charging vehicle, and the farther ones are allocated to the drone. At the same time, the charging path of the wireless charging vehicle is designed based on the principle of shortest distance priority, and the matching nodes are used as the release points of the drone to achieve efficient collaboration between the drone and the wireless charging vehicle. These technical means not only optimize the charging time of each round, but also significantly improve the use efficiency of the drone, thereby efficiently completing the charging task of the sensor nodes, and significantly improving the operation efficiency and maintenance automation level of the entire wireless rechargeable sensor network. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0015] Figure 1 A flowchart 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.

[0016] 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.

[0017] Figure 3 This is a schematic diagram of a hybrid charging scheduling process in a wireless rechargeable sensor network provided in Example 1 of the present application.

[0018] Figure 4 This 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.

[0019] Figure 5 The bipartite graph converted from Example 1 of the present application G The modified KM maximum matching algorithm is used to select k A matching method flow chart.

[0020] Figure 6 For the purpose of obtaining the k Flow chart of allocating charging tasks based on matching. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than 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 the present application.

[0022] Embodiment 1 See also Figure 1 , is a flow chart of a method for cooperative charging scheduling of 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: Step 1: Build a wireless rechargeable sensor network model.

[0023] 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 node. n The sensor nodes are evenly distributed in the two-dimensional space and are divided into energy-sufficient sensor nodes and charging request nodes based on their remaining power. The charging request node can send a charging request to the base station. The base station responds to the charging request of one or more sensor nodes and calls a wireless charging vehicle or wireless charging drone for charging.

[0024] 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 a 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: .

[0025] 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 charge according to the allocation of charging tasks. That is, for each sensor node, this application calculates 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 flight range.

[0026] 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.

[0027] The base station is used as a data receiving end to receive sensing data forwarded from each sensor node via multi-hop routing, including the location coordinates, remaining power and charging request of the sensor node. The base station also serves as a service station for charging vehicles. It is not limited by energy and can charge wireless charging vehicles and wireless charging drones that return to the base station after completing their missions.

[0028] In the process of building a wireless rechargeable sensor network model, it is first necessary to define the positions of the base station and the sensor nodes. In the embodiment of the present application, a two-dimensional coordinate system is established with the base station position as the coordinate origin, and the base station is deployed in the middle of the two-dimensional plane, and its position coordinate is (0,0). n Sensor Nodes S ={ s 1 , s 2 , ..., 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.

[0029] 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.

[0030] like Figure 4 As shown in, the above step 2 specifically also includes: Step 21: Encode 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, indicating a base station, {1, 2, ..., n} represents a sensor node.

[0031] Step 22: For the sensor nodes that have charging requests, determine the actual distances between the sensor nodes.

[0032] 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 , the two sensor nodes are regarded as adjacent sensor nodes.

[0033] Step 24: construct a bipartite graph based on the edge weight relationship between adjacent sensor nodes, expressed as: G =( S , T , E ).

[0034] 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 .

[0035] Step 3: Select based on the KM maximum matching algorithm on the bipartite graph k matches, and makes k The sum of the weights of the matching paths is maximum.

[0036] 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 the hybrid vehicles, which specifically includes: Step 31: Initialize the collection S and T The node numbers in S Each point i Top mark A [ i ] fori The maximum weight of the edge associated with the point, T The top mark of each point in B [ j ]=0.

[0037] Step 32: w [ i , j ]The largest match ( i , j ) into the matching set E m and delete the i , j Associated matches.

[0038] Step 33. Repeat step 32 until | E m |= k Or there are no relevant matching edges.

[0039] 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 ) A [ i ]-1, B [ j ]+1, repeat step S32.

[0040] Step 35: Output matching set E m .

[0041] Step 4: Based on the above k The Euler distance from each pair of sensor nodes to the base station is matched, and the sensor nodes with closer distance in each pair of sensor nodes are assigned to the wireless charging vehicle, and the sensor nodes with farther distance are assigned to the wireless charging drone for charging each pair of sensor nodes.

[0042] Specifically, Figure 6 As shown in , the above step 4 specifically also includes: Step 41: Calculate each pair of matches in the matching set ( i , j ) sensor node i , j Euler distance to base station 0 d i0 、d j0 ; 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.

[0043] Step 43: 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 match.

[0044] Compared with the prior art, the technical solution provided by the embodiment of the present application has the following beneficial effects: The technical solution of this embodiment proposes an innovative collaborative charging scheduling strategy for charging vehicles for wireless rechargeable sensor networks, which significantly improves the automation and efficiency of network maintenance through the deep integration of sophisticated modeling and graph theory algorithms.

[0045] First, based on the actual deployment environment, a two-dimensional model including base stations, wireless charging vehicles, drones, and sensor nodes was constructed, and the "one-to-one" charging method and the maximum flight distance of drones were clarified to lay the foundation for subsequent planning. Then, the concept of maximum matching in 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 the bipartite graph, the modified KM maximum matching algorithm was used to select k matches, ensuring This k The sum of the weights of the matching paths is the largest. And according to the selected k matches, and calculate each pair of matches in the matching set ( i, j ) sensor node i, j Euler distance to base station 0 d i0 , d j0 ; right d i0 and d j0By comparison, the sensor nodes closer to the base station are assigned to the wireless charging car, and the sensor nodes closer to the base station are assigned to the drone, so as to realize the allocation of charging tasks. Subsequently, combined with the allocation of the charging tasks, based on the principle of nearest distance priority, a greedy algorithm is used to design the charging path of the car. Specifically, the starting point of the car is taken as the base station, and the nearest sensor nodes are greedily selected in turn to form a Hamiltonian path to design the charging path of the wireless charging car.

[0046] 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.

[0047] 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.

[0048] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0049] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but rather 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 ( k >1) A wireless charging drone, characterized by, 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 the preset maximum flight distance of the UAV D dmax ; 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 base station 0 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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