Method and system for reducing energy loss working duration of equipment in rechargeable sensor network

Abstract

The invention discloses a method and system for reducing equipment energy loss working duration in a rechargeable sensor network, and the method comprises the steps: firstly, taking the minimization of the working duration of charging equipment as a target, and taking the condition that the charging equipment can charge all sensor nodes in a cluster at least at one resident point at the same time as a constraint condition; clustering and fusing sensor nodes in the wireless rechargeable sensor network by using a cluster fusion algorithm; secondly, the position of a resident point of each cluster is adjusted with the purpose of minimizing the energy loss of charging equipment; then, with the aim of minimizing the residence duration of the charging equipment at the residence point, charging output power is set for each sensor node in the cluster; and finally, the base station constructs a charging path, and the charging equipment charges the sensor nodes in each cluster at the optimal resident point according to the power setting table in the charging path sequence. According to the method, the energy loss and the working duration of the charging equipment can be optimized at the same time, and the method has higher applicability.

Description

Technical Field

[0001] The present invention belongs to the field of rechargeable sensor networks, and in particular relates to a method and a system for reducing energy loss and working time of equipment in a rechargeable sensor network. Background Art

[0002] With the development of the Internet of Things, rechargeable sensor networks have derived a wide range of application scenarios, such as environmental monitoring, medical care, military reconnaissance, etc. Compared with general sensor networks, sensor nodes in rechargeable sensor networks have higher reliability because they can be continuously recharged by mobile charging devices. Therefore, the design of charging scheduling methods for charging devices will significantly affect the performance of rechargeable sensor networks.

[0003] Wireless charging technology based on magnetic coupling resonance can enable the charging device to charge sensor nodes within a certain range at the same time, that is, a one-to-many charging mode. This mode can effectively improve the charging efficiency and save the moving time and energy consumption of the charging device.

[0004] Most of the existing charging scheduling schemes based on the one-to-many charging mode in the rechargeable sensor network only consider the mobile energy loss of the charging equipment and use it as the optimization target, while almost no consideration is given to the energy loss caused by the charging process. In addition, the working time of the charging equipment has also been ignored in many studies. However, minimizing the working time while completing the charging task is beneficial for the charging equipment to have time to return to the base station to replenish enough energy and prepare for the next charging scheduling. In addition, there is currently no efficient charging scheduling scheme that simultaneously considers the optimization of the energy loss and working time of the charging equipment. Summary of the invention

[0005] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a method for reducing the working time of energy loss of equipment in a rechargeable sensor network.

[0006] Technical solution: To achieve the above purpose, the technical solution adopted by the present invention is:

[0007] A method for reducing the working time of energy loss of equipment in a rechargeable sensor network, wherein the equipment is a movable charging device that provides energy replenishment for sensor nodes in a wireless form in the rechargeable sensor network, comprising the following steps:

[0008] Step S1, collect the rechargeable sensor network information and the charging device information. Based on the charging device information and the rechargeable sensor network information, with the goal of minimizing the working time of the charging device, and with the constraint that the charging device can charge all sensor nodes in the cluster at least at one resident point at the same time, the sensor nodes in the wireless rechargeable sensor network are clustered and fused using a cluster fusion algorithm to obtain a fused cluster set and a resident point set.

[0009] Step S2, based on the fused cluster set and the residence point set, with the goal of minimizing the energy loss of the charging equipment and the charging demand of the sensor node as a constraint, the residence point position of each cluster is adjusted to obtain the optimal residence point set.

[0010] Step S3, based on the optimal residence point set, with the goal of minimizing the residence time of the charging device at the residence point and with the maximum charging output power of the charging device as a constraint, the charging output power is set for each sensor node in the cluster to obtain a power setting table set.

[0011] Step S4: Based on the optimal resident point set and the power setting table set, the base station constructs a charging path, and the charging device charges the sensor nodes in each cluster at the optimal resident point according to the power setting table in the order of the charging path.

[0012] Preferably: the method for obtaining the fused cluster set and the stationary point set in step S1 is:

[0013] Initialization, C s ={s1, s2, ..., s N} is the initial sensor node set, s i represents sensor node i; cluster set C B = {B1, B2, ..., B N}, B i represents cluster i, B i ={s i}, i∈[1,N], N is the total number of sensor nodes;

[0014] Step S101: Let the sensor node set C′ s =C s ;

[0015] Step S102, view C' s All sensor nodes in the cluster form a cluster, and the station point r s The position is C′ s The geometric center of the polygon formed by all sensor nodes in the cluster is used to calculate the charging device for each sensor node s in the cluster. i The charging demand E i Charging time Then the residence time of the charging device is s i ∈C′ s .

[0016] Step S103, select charging time Sensor nodes k , judge k To the stop point r s Distance Is it less than d? max , d max The longest distance for wireless energy transmission for charging devices:

[0017] Step S1031, if Let D s C B Remove C′ s The remaining cluster sets of the nodes in the middle are constructed respectively. s ∪{C′ s} and {{s k}}∪D s ∪{C′ s \s k} traverse the TSP path of each cluster to obtain the path length L s , L s&k , the moving time is Where v is the moving speed of the charging device. s \s k , execute step S102 to obtain the charging device residence time t s\k , if satisfied Then execute step S104. Otherwise, execute step S105.

[0018] Step S1032, if Execute step S104.

[0019] Step S104, let C′ s =C′ s \s k , return to step S102.

[0020] Step S105, merging clusters, setting B = UB i , where s i ∈B i And s i ∈C′ s , delete the cluster set C B B i , add the merged cluster B to C B . C s =C s -C′ s , if C s If it is not empty, return to step S101. Otherwise, end and obtain the fused cluster set C B and the set of station points C r ={r0, r1, ..., r M , r M+1}, where r0 = r M+1 represents the base station location, M is the cluster set C BThe number of clusters in the p The location is cluster B p The geometric center of the polygon formed by all sensor nodes in , p∈[1,M].

[0021] Preferably: in step S102, the charging device is calculated as each sensor node s in the cluster. i The charging demand E i Charging time The formula is:

[0022]

[0023] in, The charging device is used for each sensor node s in the cluster. i The charging demand E i Charging time, E i For sensor nodes s i The charging demand, For sensor nodes s i To the stop point r s The distance, |C′ s | for C s The number of sensor nodes in ′, P max is the maximum charging output power of the charging device, is the charging efficiency, according to the Friis transmission equation:

[0024]

[0025] Among them G t , Gr, η, L P , λ, α are constants.

[0026] Preferably, the method for obtaining the optimal set of dwelling points in step S2 is:

[0027] For the cluster set C B Each cluster B m , m∈[1,M], and a polygon M is constructed with all sensor nodes in the cluster as vertices m .

[0028] For the set of station points C r Each station point r in m , use the ray method to determine whether the station point is outside the polygon, and set the objective function to:

[0029]

[0030] Among them, f(r m ) is the objective function, τ(r m ) indicates that the charging device is at the station point rm Cluster B m The energy loss caused by charging all sensor nodes in the u Cluster B m Sensor nodes u The charging demand, n m Cluster B m The number of sensor nodes in For sensor nodes s u To the stop point r m distance.

[0031] Based on the polygon M m , set the boundary constraint Q m for:

[0032]

[0033] Among them, (x m ,y m ) is the dwell point r m The position coordinates, x min =min{x u}, x max =max{x u},y min =min{y u},y max =max{y u},(x u ,y u ) is the sensor node S u Position coordinates, s u ∈B m .

[0034] For the set of station points C r Each station point r in m , based on the objective function f(r m ) and the boundary constraint Q m , use the quasi-Newton method L-BFGS-B method with boundary constraints to calculate the optimal residence point r′ m , thus obtaining the optimal set of residence points C′ e ={r0, r′1, …, r′ M , r M+1}.

[0035] Preferably: Step 3 is a method for obtaining a power setting table set:

[0036] For the optimal residence point set C′ r Each optimal residence point r′ m , the charging equipment is cluster B m Each sensor node su Set charging output power for:

[0037]

[0038] The derivation process of the formula is as follows:

[0039] Assume that the charging equipment is cluster B m The charging power allocated to each sensor node is Optimal dwelling point r′ m The distances to each sensor node are In order to minimize the residence time t of the charging equipment at the residence point m , the charging time of each sensor node should be made as equal as possible.

[0040]

[0041] There are

[0042]

[0043] So we can get

[0044]

[0045] then,

[0046]

[0047] So the power allocation table Then we get the power setting table set C T ={T1, T2, ..., T M}.

[0048] Preferably: the specific method of step 4 is: based on the optimal residence point set C' r and power setting table set C T The base station constructs a TSP path that traverses all the best residence points, and the charging equipment is located at each best residence point r in turn according to the charging path sequence. m According to the power setting table T m Cluster B m Charging of sensor nodes in the

[0049] Preferred: residence point r s The position is C′ s The geometric center of the polygon formed by all sensor nodes and the residence point r p The location is cluster B p The calculation method of the geometric center of the polygon formed by all sensor nodes in is:

[0050] For any sensor node set C with H sensor nodes, H>0, the location coordinates (x, y) of the station point r are calculated as follows: Where (x h ,y h ) is the sensor node ss h The position coordinates, s h ∈C.

[0051] Preferably: at the beginning of step S1, each sensor node in the wireless rechargeable sensor network forms a cluster, there is only one sensor node in the cluster, and the station point position is the sensor node position.

[0052] Another object of the present invention is to provide a system for reducing the working time of energy loss of equipment in a rechargeable sensor network, which is used to implement the method for reducing the working time of energy loss of equipment in the rechargeable sensor network, including a collection unit, a fused cluster set and a resident point set acquisition unit, an optimal resident point set acquisition unit, a power setting table set acquisition unit, and a charging control unit, wherein:

[0053] The collection unit is used to collect the rechargeable sensor network information and the charging device information.

[0054] The fused cluster set and resident point set obtaining unit is used to cluster and fuse the sensor nodes in the wireless rechargeable sensor network using a cluster fusion algorithm based on the charging device information and the rechargeable sensor network information with the goal of minimizing the working time of the charging device and with the constraint that the charging device can charge all sensor nodes in the cluster at at least one resident point at the same time, so as to obtain the fused cluster set and resident point set.

[0055] The optimal residence point set acquisition unit is used to adjust the residence point position of each cluster based on the fused cluster set and residence point set, with the goal of minimizing the energy loss of the charging device and the charging demand of the sensor node as a constraint condition, to obtain the optimal residence point set.

[0056] The power setting table set obtaining unit is used to set the charging output power for each sensor node in the cluster based on the optimal residence point set, with the goal of minimizing the residence time of the charging device at the residence point and with the maximum charging output power of the charging device as a constraint condition, to obtain a power setting table set.

[0057] The charging control unit is used for constructing a charging path based on the optimal residence point set and the power setting table set, and the charging device charges the sensor nodes in each cluster at the optimal residence point according to the power setting table in the order of the charging path.

[0058] Another object of the present invention is to provide a computer system, comprising a memory and a processor, wherein the memory is used to store a computer program / instruction, and the processor is used to execute the computer program / instruction to implement the method for reducing the working time of energy loss of equipment in the rechargeable sensor network.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] First, with the goal of minimizing the working time of the charging device and the constraint that the charging device can charge all sensor nodes in the cluster at at least one dwelling point at the same time, the cluster fusion algorithm is used to cluster and fuse the sensor nodes in the wireless rechargeable sensor network to obtain the fused cluster set and dwelling point set. Then, based on the fused cluster set and dwelling point set, with the goal of minimizing the energy loss of the charging device and the charging demand of the sensor node as the constraint, the dwelling point position of each cluster is adjusted to obtain the optimal dwelling point set. After that, with the goal of minimizing the dwelling time of the charging device at the dwelling point and the constraint of the maximum charging output power of the charging device, the charging output power is set for each sensor node in the cluster to obtain the power setting table set. Finally, based on the optimal dwelling point set and the power setting table set, the base station constructs the charging path, and the charging device charges the sensor nodes in each cluster at the optimal dwelling point according to the power setting table in the order of the charging path. By adopting the method of the present invention, the problem of minimizing the energy loss and working time of the charging equipment is split into two parts, first optimizing the moving time, then optimizing the energy loss, and finally optimizing the residence time, thereby reducing the complexity of problem solving. Secondly, the proposed cluster fusion algorithm can ensure that there is at least one residence point in the fused cluster under the condition of meeting the optimization target according to the greedy strategy so that the charging device can charge all nodes in the cluster. In addition, the quasi-Newton method L-BFGS-B method with boundary constraints is used to adjust the location of the residence point, thereby minimizing the energy loss of the charging equipment, and has efficient convergence and robustness, reducing the search time. Finally, according to the charging characteristics, the proposed charging power setting method can minimize the residence time of the charging device to the greatest extent. . BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is an execution flow chart of a method for reducing energy loss and working time of equipment in the rechargeable sensor network.

[0062] Figure 2 This is a schematic diagram of cluster distribution in the network before and after the cluster fusion algorithm is executed. (a) shows that each sensor node forms a cluster before the cluster fusion algorithm is executed, and (b) shows that some clusters are fused into one cluster after the cluster fusion algorithm is executed.

[0063] Figure 3 is a schematic diagram of the adjustment of the residence point; Table 1 is an example of the information of the sensor nodes and residence points within the cluster.

[0064] Figure 4 It is a schematic diagram of the charging output power set by the charging device for each sensor node in the cluster at the optimal residence point.

[0065] Figure 5 It is a schematic diagram of the charging path constructed by the base station to traverse all the optimal staying points. DETAILED DESCRIPTION

[0066] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0067] This embodiment provides a method for reducing the working time of energy loss of a device in a rechargeable sensor network, wherein the device is a movable charging device that provides energy replenishment for sensor nodes in a wireless form in the rechargeable sensor network, and comprises the following steps:

[0068] Step S1, collect the rechargeable sensor network information and the charging device information, the charging device information includes the working time, and the rechargeable sensor network information includes the sensor nodes. Based on the charging device information and the rechargeable sensor network information, with the goal of minimizing the working time of the charging device, and with the constraint that the charging device can charge all the sensor nodes in the cluster at least at one resident point at the same time, the sensor nodes in the wireless rechargeable sensor network are clustered and fused using a cluster fusion algorithm to obtain a fused cluster set and a resident point set.

[0069] Step S2, based on the fused cluster set and the residence point set, with the goal of minimizing the energy loss of the charging equipment and the charging demand of the sensor node as a constraint, the residence point position of each cluster is adjusted to obtain the optimal residence point set.

[0070] Step S3, based on the optimal residence point set, with the goal of minimizing the residence time of the charging device at the residence point and with the maximum charging output power of the charging device as a constraint, the charging output power is set for each sensor node in the cluster to obtain a power setting table set.

[0071] Step S4: Based on the optimal resident point set and the power setting table set, the base station constructs a charging path, and the charging device charges the sensor nodes in each cluster at the optimal resident point according to the power setting table in the order of the charging path.

[0072] In some specific embodiments, the execution process of the method is as follows Figure 1As shown, it includes: initially, each sensor node in the wireless rechargeable sensor network forms a cluster, there is only one sensor node in the cluster, and the resident point position is the sensor node position. With the goal of minimizing the working time of the charging device, and with the constraint that the charging device can charge all sensor nodes in the cluster at at least one resident point at the same time, the cluster fusion algorithm is executed to obtain the fused cluster set and resident point set. Next, the quasi-Newton method L-BFGS-B method with boundary constraints is used to adjust each resident point to minimize the resident time of the charging device at the resident point. Then, according to the optimal resident point set, the charging output power is set for each sensor node in the cluster to minimize the resident time of the charging device. Finally, the base station constructs a TSP path that traverses all the optimal resident points, and the charging device charges the sensor nodes in the cluster at each optimal resident point in turn according to the power setting table in the order of the charging path.

[0073] In some specific embodiments, the method for obtaining the fused cluster set and the stationary point set in step S1 is:

[0074] s i represents sensor node i, sensor node set C s ={s1, s2, ..., s N}, B i represents cluster i, cluster set C B = {B1, B2, ..., B N}, initially B i ={s i}, i∈[1,N], N is the total number of sensor nodes. Let sensor node s i The charging demand is E i , the maximum charging output power of the charging device is P max .

[0075] Step S101: Let the sensor node set C′ s =C s , C s is the initial set of sensor nodes.

[0076] Step S102, view C' s All sensor nodes in the cluster form a cluster, and the station point r s The position is C′ s The geometric center of the polygon formed by all sensor nodes in the cluster is used to calculate the charging device for each sensor node s in the cluster. i The charging demand E i Charging time Then the residence time of the charging device is s i ∈C′ s .

[0077] Step S103, select charging time Sensor nodes k , judge k To the stop point r s Distance Is it less than d? max , d max The longest distance for wireless energy transmission for charging devices:

[0078] Step S1031, if Let D s C B Remove C′ s The remaining cluster sets of the nodes in the middle are constructed respectively. s ∪{C′ s} and {{s k}}∪D s ∪{C′ s \s k} traverse the TSP path of each cluster to obtain the path length L s , L s&k , the moving time is Where v is the moving speed of the charging device. s \s k , execute step S102 to obtain the charging device residence time t s\k , if satisfied Then execute step S104. Otherwise, execute step S105.

[0079] Step S1032, if Execute step S104.

[0080] Step S104, let C′ s =C′ s \s k , return to step S102.

[0081] Step S105, merging clusters, setting B = UB i , where s i ∈B i And s i ∈C′ s , delete the cluster set C B B i , add the merged cluster B to C B . C s =C s -C′ s , if C s If it is not empty, return to step S101. Otherwise, end and obtain the fused cluster set C B and the set of station points Cr ={r0, r1, ..., r M , r M+1}, where r0 = r M+1 represents the base station location, M is the cluster set C B The number of clusters in the p The location is cluster B p The geometric center of the polygon formed by all sensor nodes in , p∈[1,M].

[0082] Preferably: in step S102, the charging device is calculated as each sensor node s in the cluster. i The charging demand E i Charging time The formula is:

[0083]

[0084] in, The charging device is used for each sensor node s in the cluster. i The charging demand E i Charging time, E i For sensor nodes s i The charging demand, For charging efficiency, For sensor nodes s i To the stop point r s The distance, |C′ s | for C s The number of sensor nodes in ′, P max The maximum charging output power of the charging device.

[0085] by Figure 2 For example, 24 sensor nodes are deployed in a network area of ​​any shape and size. Before executing the cluster fusion algorithm, each sensor node is clustered separately, that is, there are 24 clusters, such as Figure 2 (a) After executing the cluster fusion algorithm, some clusters contain multiple sensor nodes, and the resident point is the geometric center of the nodes in the cluster, as shown in Figure 2 As shown in (b), there are 15 clusters at this time.

[0086] In another embodiment, the method for obtaining the optimal set of dwelling points in step S2 is:

[0087] For the cluster set C B Each cluster B m , m∈[1,M], and a polygon M is constructed with all sensor nodes in the cluster as vertices m .

[0088] For the set of station points C r Each station point r inm , use the ray method to determine whether the station point is outside the polygon, and set the objective function to:

[0089]

[0090] Among them, f(r m ) is the objective function, τ(r m ) indicates that the charging device is at the station point r m Cluster B m The energy loss caused by charging all sensor nodes in the u Cluster B m Sensor nodes u The charging demand, n m Cluster B m The number of sensor nodes in For sensor nodes s u To the stop point r m distance.

[0091] Based on the polygon M m , set the boundary constraint Q m for:

[0092]

[0093] Among them, (x m ,y m ) is the dwell point r m The position coordinates, x min =min{x u}, x max =max{x u},y min =min{y u},y max =max{y u},(x u ,y u ) is the sensor node S u Position coordinates, s u ∈B m .

[0094] For each station point r in the station point set C m , based on the objective function f(r m ) and the boundary constraint Q m , use the quasi-Newton method L-BFGS-B method with boundary constraints to calculate the optimal residence point r′ m , thus obtaining the optimal set of residence points C′ r ={r0, r1, ..., r′ M , r M+1}.

[0095] by Figure 3 For example, suppose cluster B m There are four sensor nodes s1, s2, s3, and s4, and the station point is r m , their coordinates are shown in Table 1. According to the adjustment method, there is an objective function f(r m ) and boundary constraints Qm: Finally, the adjusted optimal dwelling point r′ is obtained m The location coordinates are (4.1, 4.2). Charging all nodes in the cluster at this residence point can minimize the energy loss of the charging device.

[0096] Table 1

[0097] Location coordinates <![CDATA[Energy demand E i > <![CDATA[S1]]> (1.0，4.5) 100Joule <![CDATA[S2]]> (4.5，6.0) 170 Joule <![CDATA[S3]]> (6.0，4.0) 180Joule <![CDATA[S4]]> (3.0，1.0) 50Joule <![CDATA[r m ]]> (3.4，3.6) -

[0098] In another embodiment, the method of obtaining the power setting table set in step 3 is as follows:

[0099] For the optimal residence point set C′ r Each optimal residence point r m , the charging equipment is cluster B m Each sensor node s u Set charging output power for:

[0100]

[0101] Power Setting Table Then we get the power setting table set C T ={T1, T2, ..., T M}.

[0102] by Figure 4 Cluster B m Take the four sensor nodes s1, s2, s3, and s4 as an example. According to the distance from the optimal station point to each sensor node and the energy demand of each sensor node, the charging output power set by the charging device for each sensor node is obtained by using the calculation method.

[0103] In another embodiment, the dwell point r s The position is C′ s The geometric center of the polygon formed by all sensor nodes and the residence point r p The location is cluster B p The calculation method of the geometric center of the polygon formed by all sensor nodes in is:

[0104] For any sensor node set C with H sensor nodes, H>0, the location coordinates (x, y) of the station point r are calculated as follows: Where (x h ,y h ) is the sensor node s j The position coordinates, s h ∈C.

[0105] In another embodiment, the specific method of step 4 is: based on the optimal residence point set C′ r and power setting table set c T The base station constructs a TSP path that traverses all the best residence points, and the charging equipment is located at each best residence point r′ in turn according to the order of the charging path. m According to the power setting table T m Cluster B m Charging the sensor nodes in the

[0106] by Figure 5 For example, the constructed TSP path starts from the base station, passes through the resident points of 15 clusters in the network, charges the sensor nodes in the cluster at each resident point according to the power setting table and energy requirements, and finally returns to the base station.

[0107] In another embodiment, a system for reducing the working time of energy loss of equipment in a rechargeable sensor network is provided, which is used to implement the method for reducing the working time of energy loss of equipment in the rechargeable sensor network, including a collection unit, a fused cluster set and a resident point set acquisition unit, an optimal resident point set acquisition unit, a power setting table set acquisition unit, and a charging control unit, wherein:

[0108] The collection unit is used to collect the rechargeable sensor network information and the charging device information.

[0109] The fused cluster set and resident point set obtaining unit is used to cluster and fuse the sensor nodes in the wireless rechargeable sensor network using a cluster fusion algorithm based on the charging device information and the rechargeable sensor network information with the goal of minimizing the working time of the charging device and with the constraint that the charging device can charge all sensor nodes in the cluster at at least one resident point at the same time, so as to obtain the fused cluster set and resident point set.

[0110] The optimal residence point set acquisition unit is used to adjust the residence point position of each cluster based on the fused cluster set and residence point set, with the goal of minimizing the energy loss of the charging device and the charging demand of the sensor node as a constraint condition, to obtain the optimal residence point set.

[0111] The power setting table set obtaining unit is used to set the charging output power for each sensor node in the cluster based on the optimal residence point set, with the goal of minimizing the residence time of the charging device at the residence point and with the maximum charging output power of the charging device as a constraint condition, to obtain a power setting table set.

[0112] The charging control unit is used for constructing a charging path based on the optimal residence point set and the power setting table set, and the charging device charges the sensor nodes in each cluster at the optimal residence point according to the power setting table in the order of the charging path.

[0113] In another embodiment, a computer system is provided, including a memory and a processor, wherein the memory is used to store a computer program / instruction, and the processor is used to execute the computer program / instruction to implement the method for reducing the working time of energy loss of equipment in the rechargeable sensor network.

[0114] The present invention aims to minimize the working time of the charging device, takes the charging device being able to charge all sensor nodes in the cluster at least at one residence point at the same time as a constraint condition, and uses a cluster fusion algorithm to cluster and fuse the sensor nodes in the wireless rechargeable sensor network; secondly, with the goal of minimizing the energy loss of the charging device, the residence point position of each cluster is adjusted; then, with the goal of minimizing the residence time of the charging device at the residence point, the charging output power is set for each sensor node in the cluster; finally, the base station constructs a charging path, and the charging device charges the sensor nodes in each cluster according to the power setting table at the optimal residence point in the order of the charging path. As an efficient charging scheduling scheme, the present invention can simultaneously optimize the energy loss and working time of the charging device, and has stronger applicability.

[0115] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for reducing the working time of energy loss of equipment in a rechargeable sensor network, wherein the equipment is a mobile charging device that provides energy replenishment for sensor nodes in a wireless form in the rechargeable sensor network. It is characterized in that The following steps are involved: Step S1, collecting rechargeable sensor network information and charging equipment information; According to the charging device information and the rechargeable sensor network information, the goal is to minimize the working time of the charging device, and the charging device can charge all sensor nodes in the cluster at least at one residence point at the same time as the constraint condition. The sensor nodes in the wireless rechargeable sensor network are clustered and fused using the cluster fusion algorithm to obtain the fused cluster set and residence point set. Step S2, based on the fused cluster set and the residence point set, with the goal of minimizing the energy loss of the charging equipment and the charging demand of the sensor node as a constraint, the residence point position of each cluster is adjusted to obtain the optimal residence point set; Step S3, based on the optimal residence point set, with the goal of minimizing the residence time of the charging device at the residence point and with the maximum charging output power of the charging device as a constraint, setting the charging output power for each sensor node in the cluster to obtain a power setting table set; Step S4: Based on the optimal resident point set and the power setting table set, the base station constructs a charging path, and the charging device charges the sensor nodes in each cluster at the optimal resident point according to the power setting table in the order of the charging path.

2. The method for reducing the working time of energy loss of equipment in the rechargeable sensor network according to claim 1, Features: The method for obtaining the fused cluster set and the station point set in step S1 is: Initialization, C s ={s 1 ,s 2 , ..., s N } is the initial sensor node set, s i represents sensor node i; cluster set C B = {B 1 , B 2 , …, B N }, B i represents cluster i, B i ={s i }, i∈[1,N], N is the total number of sensor nodes; Step S101: Let the sensor node set C′ s =C s ; Step S102, view C' s All sensor nodes in the cluster form a cluster, and the station point r s The position is C′ s The geometric center of the polygon formed by all sensor nodes in the cluster is used to calculate the charging device for each sensor node s in the cluster. i The charging demand E i Charging time Then the residence time of the charging device is s i ∈C′ s ; Step S103, select charging time Sensor nodes k , judge k To the stop point r s Distance Is it less than d? max , d max The longest distance for wireless energy transmission for charging devices: Step S1031, if Let D s C B Remove C′ s The remaining cluster sets of the nodes in the D s ∪{C′ s } and {{s k }}∪D s ∪{C′ s \s k } traverse the TSP path of each cluster to obtain the path length L s , L s&k , the moving time is Where v is the moving speed of the charging device; for C′ s \s k , execute step S102 to obtain the charging device residence time t s\k , if satisfied Then execute step S104; otherwise execute step S105; Step S1032, if Execute step S104; Step S104, let C′ s =C′ s \s k , return to step S102; Step S105, merging clusters, setting B = ∪B i , where s i ∈B i And s i ∈C′ s , delete the cluster set C B B i , add the merged cluster B to C B ; C s =C s -C′ s , if C s If it is not empty, return to step S101; otherwise, end and obtain the fused cluster set C B and the set of station points C r = {r 0 ,r 1 ,…,r M ,r M+1 }, where r 0 =r M+1 represents the base station location, M is the cluster set C B The number of clusters in the p The location is cluster B p The geometric center of the polygon formed by all sensor nodes in , p∈[1,M].

3. The method for reducing the working time of equipment energy loss in the rechargeable sensor network according to claim 2, Features: In step S102, the charging device is calculated as each sensor node s in the cluster. i The charging demand E i Charging time The formula is: in, The charging device is used for each sensor node s in the cluster. i The charging demand E i Charging time, E i For sensor nodes s i The charging demand, For charging efficiency, For sensor nodes s i To the stop point r s The distance, |C' s | for C s The number of sensor nodes in ', P max The maximum charging output power of the charging device.

4. The method for reducing the working time of energy loss of equipment in a rechargeable sensor network according to claim 3, Features: The method for obtaining the optimal set of dwelling points in step S2 is: For the cluster set C B Each cluster B m , m∈[1,M], and a polygon M is constructed with all sensor nodes in the cluster as vertices m ; For the set of station points C r Each station point r in m , use the ray method to determine whether the station point is outside the polygon, and set the objective function to: Among them, f(r m ) is the objective function, τ(r m ) indicates that the charging device is at the station point r m Cluster B m The energy loss caused by charging all sensor nodes in the u Cluster B m Sensor nodes u The charging demand, n m Cluster B m The number of sensor nodes in For sensor nodes s u To the stop point r m distance; Based on the polygon M m , set the boundary constraint Q m for: Among them, (x m ,y m ) is the dwell point r m The position coordinates, x min =min{x u }, x max =max{x u },y min =min{y u },y max =max{y u },(x u ,y u ) is the sensor node S u Position coordinates, s u ∈B m ; For the set of station points C r Each station point r in m , based on the objective function f(r m ) and the boundary constraint Q m , use the quasi-Newton method L-BFGS-B method with boundary constraints to calculate the optimal residence point r′ m , thus obtaining the optimal set of residence points C′ r = {r 0 , r 1 ′,…,r′ M , r M+1 }.

5. The method for reducing the working time of energy loss of equipment in a rechargeable sensor network according to claim 4, Features: Step 3: Method for obtaining a power setting table set: For the optimal residence point set C′ r Each optimal residence point r′ m , the charging equipment is cluster B m Each sensor node s u Set charging output power for: Power Setting Table Then we get the power setting table set C T ={T 1 ,T 2 ,…,T M }.

6. The method for reducing the working time of energy loss of equipment in a rechargeable sensor network according to claim 5, Features: The specific method of step 4 is: based on the optimal residence point set C′ r and power setting table set C T The base station constructs a TSP path that traverses all the best residence points, and the charging equipment is located at each best residence point r′ in turn according to the order of the charging path. m According to the power setting table T m Cluster B m Charging the sensor nodes in the 7. The method for reducing the working time of energy loss of equipment in a rechargeable sensor network according to claim 6, Features: Stay point s The position is C′ s The geometric center of the polygon formed by all sensor nodes and the residence point r p The location is cluster B p The calculation method of the geometric center of the polygon formed by all sensor nodes in is: For any sensor node set C with H sensor nodes, H>0, the location coordinates (x, y) of the station point r are calculated as follows: Where (x h ,y h ) is the sensor node s h The position coordinates, s h ∈C.

8. The method for reducing the working time of energy loss of equipment in a rechargeable sensor network according to claim 7, Features: At the beginning of step S1, each sensor node in the wireless rechargeable sensor network forms a cluster, there is only one sensor node in the cluster, and the location of the resident point is the location of the sensor node.

9. A system for reducing the working time of equipment energy loss in a rechargeable sensor network. Features: The method for reducing the working time of energy loss of equipment in a rechargeable sensor network according to any one of claims 1 to 8 comprises a collection unit, a fused cluster set and a resident point set acquisition unit, an optimal resident point set acquisition unit, a power setting table set acquisition unit, and a charging control unit, wherein: The collection unit is used to collect the rechargeable sensor network information and the charging device information; The fused cluster set and resident point set obtaining unit is used to cluster and fuse the sensor nodes in the wireless rechargeable sensor network using a cluster fusion algorithm based on the charging device information and the rechargeable sensor network information, with the goal of minimizing the working time of the charging device and the constraint that the charging device can charge all sensor nodes in the cluster at at least one resident point at the same time, to obtain the fused cluster set and resident point set; The optimal residence point set obtaining unit is used to adjust the residence point position of each cluster based on the fused cluster set and the residence point set, with the goal of minimizing the energy loss of the charging device and the charging demand of the sensor node as a constraint condition, to obtain the optimal residence point set; The power setting table set obtaining unit is used to set the charging output power for each sensor node in the cluster based on the optimal residence point set, with the goal of minimizing the residence time of the charging device at the residence point and with the maximum charging output power of the charging device as a constraint condition, to obtain the power setting table set; The charging control unit is used for constructing a charging path based on the optimal residence point set and the power setting table set, and the charging device charges the sensor nodes in each cluster at the optimal residence point according to the power setting table in the order of the charging path.

10. A computer system, It is characterized in that It comprises a memory and a processor, wherein the memory is used to store computer programs / instructions; and the processor is used to execute the computer programs / instructions to implement the method for reducing the working time of energy loss of equipment in a rechargeable sensor network as described in any one of claims 1-8.

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