CN110300418B - Space-time scheduling algorithm for charging according to needs in wireless chargeable sensor network - Google Patents

Space-time scheduling algorithm for charging according to needs in wireless chargeable sensor network Download PDF

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CN110300418B
CN110300418B CN201910486461.7A CN201910486461A CN110300418B CN 110300418 B CN110300418 B CN 110300418B CN 201910486461 A CN201910486461 A CN 201910486461A CN 110300418 B CN110300418 B CN 110300418B
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charging
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time
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CN110300418A (en
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石俊
胡凡君
王少华
潘科
李留文
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Lijiang Power Supply Bureau of Yunnan Power Grid Co Ltd)
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/56—Allocation or scheduling criteria for wireless resources based on priority criteria
    • H04W72/566—Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power

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Abstract

The invention provides a space-time scheduling algorithm for charging as required in a wireless chargeable sensor network. The invention establishes a wireless power supply model of a sensor node; according to the wireless power supply model of the sensor node, executing a basic path algorithm to obtain a basic charging path; executing a local optimization algorithm to further enlarge the charging remaining time by optimizing the basic charging path; executing a node deletion algorithm to reduce the running time by adjusting the charging queue on the premise of keeping the sensor node alive, and reducing the number of dying nodes by deleting the low-efficiency nodes in the charging path; and executing a global optimization algorithm to perform global optimization on the high-efficiency charging path to obtain an output optimal charging path and the number of dead nodes. The invention carries out local and global optimization on the basic charging path, and realizes the minimization of the number of dead nodes and the maximization of the energy efficiency of the system.

Description

Space-time scheduling algorithm for charging according to needs in wireless chargeable sensor network
Technical Field
The invention belongs to the technical field of wireless rechargeable sensor networks, and particularly relates to a space-time scheduling algorithm for charging as required in a wireless rechargeable sensor network.
Background
Cooperative charging of a wirelessly rechargeable sensor network has been a focus of research. With the development of wireless power transmission technology, a wireless charging automobile can be used for charging the sensor network, so that the service life of the network is prolonged. Obviously, the charging must be performed before the sensor node dies, so the scheduling problem of the wireless charging automobile is very important.
Existing cooperative charging techniques typically employ two schemes, a deterministic scheme and a non-deterministic scheme. The deterministic scheme ignores system factors such as node positions and energy consumption rate, and is not suitable for large-scale wireless sensor networks. The non-deterministic scheme employs an on-demand charging system, but there are two drawbacks at present, the contradiction between local optimization and global optimization, and the shortest hamiltonian ring is not the optimal solution. In order to solve the above problems, the present invention provides a space-time scheduling algorithm for a charge-on-demand system.
Disclosure of Invention
The invention provides a space-time scheduling algorithm based on an on-demand charging system, aiming at solving the problems that the existing non-deterministic charging scheduling scheme conflicts with local optimization and global optimization, and the shortest Hamiltonian ring is not the optimal charging strategy. The specific scheme of the algorithm is as follows: a space-time scheduling algorithm of a wireless sensor network charging-on-demand system is composed of four parts, including a basic path algorithm, a local optimization algorithm, a node deletion algorithm and a global optimization algorithm. The basic path algorithm selects the E charging requests meeting the requirements from the charging requests of all the wireless sensor nodes to form a charging task application queue, and sequences according to the time priority of the sensor nodes to generate a basic charging path. And calling a local optimization algorithm to generate a local optimization charging path, and adding the charging request which does not meet the requirements into a discarding application queue. Generating a feasible charging path through a basic path algorithm and a local optimization algorithm, calling a node deletion algorithm to delete the low-efficiency nodes in the feasible charging path, generating a high-efficiency charging path, and adding the low-efficiency nodes into a discard application queue. And finally, calling a global optimization algorithm, inserting a part to discard the sensor nodes in the application queue, and outputting a global optimal charging path. The algorithm can achieve minimization of the number of dead nodes and maximization of system energy efficiency.
The scheduling algorithm of the invention can construct a system charging path and realize global charging optimization.
The demand-based charging scheduling algorithm comprises the following steps: the wireless chargeable sensor node sends a charging request, and after the wireless chargeable vehicle receives the node charging request, the wireless chargeable vehicle calculates an optimized charging path scheme. The method specifically comprises the following steps:
step 1: establishing a wireless power supply model of a sensor node;
step 2: according to the wireless power supply model of the sensor node, executing a basic path algorithm to obtain a basic charging path;
and step 3: performing a local optimization algorithm to further maximize charge remaining time by optimizing a basic charge path;
and 4, step 4: executing a node deletion algorithm to reduce the running time by adjusting the charging queue on the premise of keeping the sensor node alive, and reducing the number of dying nodes by deleting the low-efficiency nodes in the charging path;
and 5: and executing a global optimization algorithm to perform global optimization on the high-efficiency charging path to obtain an output optimal charging path and the number of dead nodes.
Preferably, the establishing of the wireless power supply model of the sensor node in the step 1 is as follows:
setting the total number of the sensor nodes in the system to be N, and when the residual electric quantity of the nodes is lower than a threshold value, sending a request to the charging automobile; the automobile responds to the request and drives to the node for charging; neglecting the charging time of the automobile, setting the running speed of the automobile to be constant V and the energy consumed by the automobile per unit distance to be constant VD(ii) a If the residual electric quantity of the node is reduced to 0 before the automobile is charged, the node is dead;
as a multi-objective optimization problem, the primary goal is to minimize the number of dead nodes, and the secondary goal is to maximize charging efficiency;
thus defining SN(i) For the state of sensor node i, each sensor node has twoThe individual states, survival is 0 and death is 1, then the node state can be expressed as:
Figure BDA0002085548760000021
wherein i ∈ [0, N ∈ >]，SN(i) For the ith sensor node, T denotes the current time, TDThe death period of a node i is pointed out, N is the number of sensor nodes, and i is an integer;
the scheduling target model is:
Figure BDA0002085548760000022
Figure BDA0002085548760000023
wherein N isDRepresents the total number of dead nodes;
eta: the charging efficiency of the vehicle;
ee: represents the energy consumed by the sensor;
VD: the energy consumed by the automobile per unit distance is constant;
ntask: the total number of charging tasks completed;
|Qopt(j) l: representing the number of the remaining total nodes after the optimization in the task j;
Dnk,nk+1(j) representing the distance between the nk node and the nk +1 node in the task j;
the constraint conditions are as follows:
Figure BDA0002085548760000031
TR(i)＝TD(i)-T
Figure BDA0002085548760000032
Figure BDA0002085548760000033
wherein the content of the first and second substances,
ti: the time when the car arrives at the ith node;
TR (i): the remaining time of death of node i;
TD (i): time of death of node i;
EC: the remaining energy of the node;
vm: energy consumed by node i per unit time
When the wireless electric automobile receives a charging request, firstly, a charging waiting queue Q is recordedWPerforming the following steps; then generating a task j, wherein the task j comprises the front e node requests in the charging waiting queue; each task j corresponds to a charging task queue QM(j) Specifically, a charging waiting queue and a charging task queue;
optionally, constructing a basic charging path; it can be seen that QWIs large enough, but QM(j) Is small;
from QM(j) Find out an optimal charging path Qopt(j) Then the number of dead nodes can be expressed as
Figure BDA0002085548760000034
Wherein, | QM(j) | is the number of nodes in the jth charging task queue, | Qopt(j) L is the number of nodes in the optimal charging queue; the first goal is to minimize dead nodes by making | Qopt(j) Maximizing the | value;
the second objective is to minimize the electric vehicle driving distance.
Preferably, the executing basic path algorithm in step 2 is:
first, the slave charging queue QWThe front e-charging request is selected and added into a task queue QM(j) Performing the following steps; each timeA charging request riAll correspond to a temporal priority level PT(i) From node death time TD(i) Determining; the shorter the death time, the higher the time priority; these charging requests are then in accordance with PT(i) Sequencing to establish a charging path ring pi ═ pi1,2,π2,3,...π∈-1,∈,π∈，1)；πi,jRepresents the charging path from node i to node j; then judging whether the charging path pi meets space-time constraint;
when a node i dies, which means that the node i cannot be charged before the death time, the charging path is planned again as much as possible to save the dying node; node n incapable of being chargedaMust advance in the optimized charging queue; scanning node by node to find the optimal position; can insert only when two conditions are met;
nbafter charging, naIs still alive;
at nbRear insert naOther nodes cannot die; otherwise only Q is adjustedB(j) One node in the system cannot get a feasible charging scheme; this is a local optimization algorithm that must be invoked to optimize the current path QB(j) (ii) a Once the optimum Q is reachedB(j) Determine but naNo position can be inserted, then naWill be put into QP(ii) a Then naThe charging request of the node will be ignored by the wireless charging car.
Preferably, the executing of the local optimization algorithm in step 3 further enlarges the charging remaining time by optimizing the basic charging path by:
tend to reserve more charge time for subsequent charge queues; each sub-path is part of a charging path, then the target can be written as
max MA(Π)＝min(TR(i))i∈Π,
Wherein T isR(i) Representing the time remaining at node i, n being a specific optimized line, MA(Π) represents the shortest remaining time of the node; current path QB(j) Derived from the basic path algorithm by first finding out the node iMinimum residual time TR(i) Then seeking a maximization scheme of the shortest remaining time based on the current path;
obviously, advancing node i may increase the minimum remaining time; advancing the time increased after node i to be
Figure BDA0002085548760000041
By minimizing TC(i) The driving distance of the wireless charging to the automobile can be minimized; the optimization algorithm of the current path is as follows; the process of the local optimization algorithm is as follows: first, the node n with the shortest remaining time is selected from the current pathcInserted into any neighboring node; recording the increased time TC(i) Finally, select the minimum TC(i) The charging path of (1).
Preferably, the executing node deletion algorithm in step 4 is as follows:
to check whether node i is an inefficient node, the following principle is proposed:
Di,i+1＞Di-1,i+1and Di-1,i＞Di-1,i+1；
the angle of the two adjusting nodes is smaller than 90; obtaining an optimized path Q after a basic path algorithm and a local optimization algorithmB(j) And a series of discarded nodes QP(ii) a Deleting Q by a node deletion algorithmB(j) The charging performance is further improved by the low-efficiency nodes;
and (3) node deletion algorithm process: finding out 3 nodes a, b and c each time, and verifying whether the intermediate node b is an inefficient node; if so, node b is dropped into QPIn the middle, the path directly connects the nodes a and c.
Preferably, the executing of the global optimization algorithm in step 5 globally optimizes the efficient charging path as follows:
after the inefficient node deletion, a more efficient charging path Q will resultH(j) (ii) a The queue is approximately a circular path, and the wireless charging automobile has shorter driving distance; and the charging request in the path mayThe charging is completed in advance greatly, and more charging time is reserved; however, the inefficient nodes cannot be charged and will die; therefore, it is necessary to re-slave QPInserting some nodes into the charging path to minimize the number of dead nodes;
at QPIn (2), there are two types of nodes:
discarded nodes in the basic path algorithm;
deleting inefficient nodes in the algorithm by the nodes; and the first node is arranged in front of the second node, the low-efficiency node can reduce the charging performance; selecting some adjacent nodes to be inserted into Q againopt(j) In a queue; to ensure minimization of the driving distance, T is minimizedCTo perform an insertion algorithm;
the process of the global optimization algorithm is as follows: first, select QPThe first node i, then inserted into the optimal position in the charging path, i.e. minimizing TCThe position of (a); if the node i can not be inserted, the node i is directly deleted; finally, the algorithm returns a value of Qopt(j) For optimal charging path and NDThe number of dead nodes.
The beneficial results of the invention are: and local and global optimization is carried out on the basic charging path by considering the space-time characteristics of charging scheduling aiming at the on-demand charging system of the wireless charging sensor network. But to achieve the functionality of minimizing the number of dead nodes and maximizing the energy efficiency of the system.
Drawings
FIG. 1: is a flow chart of the method of the present invention;
FIG. 2: an application queue model diagram of the charging on demand system of the invention;
FIG. 3: is a basic path algorithm flow chart of the invention;
FIG. 4: a local optimization algorithm flow chart of the invention;
FIG. 5: a node deletion algorithm flow chart of the invention;
FIG. 6: is a flow chart of the global optimization algorithm of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a space-time scheduling algorithm of a wireless sensor network charging-on-demand system is composed of four parts, including a basic path algorithm, a local optimization algorithm, a node deletion algorithm, and a global optimization algorithm. The basic path algorithm selects the E charging requests meeting the requirements from the charging requests of all the wireless sensor nodes to form a charging task application queue, and sequences according to the time priority of the sensor nodes to generate a basic charging path. And calling a local optimization algorithm to generate a local optimization charging path, and adding the charging request which does not meet the requirements into a discarding application queue. Generating a feasible charging path through a basic path algorithm and a local optimization algorithm, calling a node deletion algorithm to delete the low-efficiency nodes in the feasible charging path, generating a high-efficiency charging path, and adding the low-efficiency nodes into a discard application queue. And finally, calling a global optimization algorithm, inserting a part to discard the sensor nodes in the application queue, and outputting a global optimal charging path.
As shown in fig. 2, the present embodiment is such a scenario. The same type of sensor nodes are randomly distributed in a free space, and one wireless electric automobile is used for charging the sensor nodes. The chargeable area is defined as a rectangle in which the electric vehicle can receive a charging request. The wirelessly chargeable sensor network has N sensor nodes. After the sensor node sends out charging applications, the wireless charging automobile records the charging applications and adds the charging applications into a charging waiting queue. The electric vehicle then selects a charging application and travels to the node to charge it. Therefore, this charging scheduling problem can be abstracted as a multi-objective optimization problem, i.e. to ensure node survival as much as possible, minimizing the number of dead nodes is the primary goal of scheduling, and the second goal is to maximize the system charging efficiency.
The following describes the embodiments of the present invention with reference to fig. 1 to 6:
step 1: establishing a wireless power supply model of a sensor node;
the establishment of the wireless power supply model of the sensor node in the step 1 comprises the following steps:
assuming that the total number of sensor nodes in the system is N-100, when the remaining capacity of the nodes is lower than the threshold value of 30%, a request can be sent to the charging automobile. The vehicle responds to the request and travels to the node for charging. Neglecting the charging time of the automobile, setting the running speed of the automobile to be constant as V ═ 5m/s, and the energy consumed by the automobile per unit distance to be constant as VD. If the residual electric quantity of the node is reduced to 0 before the automobile is charged, the node is dead;
as a multi-objective optimization problem, the primary goal is to minimize the number of dead nodes, and the secondary goal is to maximize charging efficiency;
thus defining SN(i) For the state of sensor node i, each sensor node has two states, survival is 0 and death is 1, then the node state can be expressed as:
Figure BDA0002085548760000071
wherein i ∈ [0, N ∈ >]，SN(i) For the ith sensor node, T denotes the current time, TDThe death period of a node i is pointed out, N is the number of sensor nodes, and i is an integer;
the scheduling target model is:
Figure BDA0002085548760000072
Figure BDA0002085548760000073
wherein N isDRepresents the total number of dead nodes;
eta: the charging efficiency of the vehicle;
ee: represents the energy consumed by the sensor;
VD: the energy consumed by the automobile per unit distance is constant;
ntask: the total number of charging tasks completed;
|Qopt(j) l: representing the number of the remaining total nodes after the optimization in the task j;
Dnk,nk+1(j) representing the distance between the nk node and the nk +1 node in the task j;
the constraint conditions are as follows:
Figure BDA0002085548760000074
TR(i)＝TD(i)-T
Figure BDA0002085548760000081
Figure BDA0002085548760000082
wherein the content of the first and second substances,
ti: the time when the car arrives at the ith node;
TR (i): the remaining time of death of node i;
TD (i): time of death of node i;
EC: the remaining energy of the node;
vm: energy consumed by node i per unit time
When the wireless electric automobile receives a charging request, firstly, a charging waiting queue Q is recordedWIn (1). Then, a task j is generated, and the task j comprises the front epsilon node requests in the charging waiting queue. Each task j corresponds to a charging task queue QM(j) Specifically, a charging waiting queue and a charging task queue;
optionally, a basic charging path is constructed. It can be seen that，QWIs large enough, but QM(j) Is smaller.
From QM(j) Find out an optimal charging path Qopt(j) Then the number of dead nodes can be expressed as
Figure BDA0002085548760000083
Wherein, | QM(j) | is the number of nodes in the jth charging task queue, | Qopt(j) L is the number of nodes in the optimal charging queue; the first goal is to minimize dead nodes by making | Qopt(j) Maximizing the | value;
the second aim is to minimize the driving distance of the electric vehicle;
step 2: executing a basic path algorithm to obtain a basic charging path;
the basic path algorithm executed in step 2 is as follows:
as shown in fig. 3, first, the slave charging queue QWThe front e-charging request is selected and added into a task queue QM(j) Performing the following steps; each charging request riAll correspond to a temporal priority level PT(i) From node death time TD(i) Determining; the shorter the death time, the higher the time priority. These charging requests are then in accordance with PT(i) Sequencing to establish a charging path ring pi ═ pi1,2,π2,3,...π∈-1,∈,π∈，1)。πi,jRepresenting the charging path from node i to node j. Then judging whether the charging path pi meets space-time constraint;
when a node i dies, meaning that the node i cannot be charged before the death time, the charging path is planned again to save the dying node. Node n incapable of being chargedaIt must be advanced in the optimized charging queue. The best position is found by scanning node by node. Two conditions are met to enable insertion. n isbAfter charging, naIs still alive; at nbRear insert naAnd then the death of other nodes can not be caused. Otherwise only Q is adjustedB(j) One node in the system cannot get a feasible charging scheme. This is a local optimization algorithm that must be invoked to optimize the current path QB(j) In that respect Once the optimum Q is reachedB(j) Determine but naNo position can be inserted, then naWill be put into QP. Then naThe charging request of the node is ignored by the wireless charging automobile;
and step 3: executing a local optimization algorithm to further enlarge the charging remaining time by optimizing the basic charging path;
as shown in fig. 4, the local optimization algorithm executed in step 3 further enlarges the charging remaining time by optimizing the basic charging path by:
more charge time tends to be reserved for subsequent charge queues. Each sub-path is part of a charging path, then the target can be written as
max MA(Π)＝min(TR(i))i∈Π,
Wherein T isR(i) Representing the time remaining at node i, n being a specific optimized line, MA(Π) represents the shortest remaining time of the node. Current path QB(j) The shortest residual time T of the node i is found out firstly by the basic path algorithmR(i) Then, a maximization scheme of the shortest remaining time is sought based on the current path.
Obviously, advancing node i may increase the minimum remaining time. Advancing the time increased after node i to be
Figure BDA0002085548760000091
By minimizing TC(i) The travel distance to the vehicle for wireless charging can be minimized. The optimization algorithm of the current path is as follows. The process of the local optimization algorithm is as follows: first, the node n with the shortest remaining time is selected from the current pathcInserted into any neighboring node. Recording the increased time TC(i) Finally, select the minimum TC(i) The charging path of (1).
And 4, step 4: executing a node deletion algorithm to reduce the running time by adjusting the charging queue on the premise of keeping the sensor node alive, and reducing the number of dying nodes by deleting the low-efficiency nodes in the charging path;
as shown in fig. 5, the executing node deletion algorithm in step 4 is:
to check whether node i is an inefficient node, the following principle is proposed:
Di,i+1＞Di-1,i+1and Di-1,i＞Di-1,i+1；
the angle of the two adjusting nodes is less than 90. Obtaining an optimized path Q after a basic path algorithm and a local optimization algorithmB(j) And a series of discarded nodes QP. Deleting Q by a node deletion algorithmB(j) The charging performance is further improved by the low-efficiency nodes.
And (3) node deletion algorithm process: each time 3 nodes a, b, c are found, it is verified whether the intermediate node b is an inefficient node. If so, node b is dropped into QPIn the middle, the path directly connects the nodes a and c.
And 5: executing a global optimization algorithm to perform global optimization on the efficient charging path;
as shown in fig. 6, the global optimization algorithm executed in step 5 globally optimizes the efficient charging path as follows:
after the inefficient node deletion, a more efficient charging path Q will resultH(j) In that respect This queue is approximately a circular path, with wireless charging cars having shorter travel distances. And the charging request in the path can be completed in advance greatly, so that more charging time is reserved. However, the inefficient nodes cannot be charged and will die. Therefore, it is necessary to re-slave QPInserting some nodes into the charging path minimizes the number of dead nodes.
At QPIn (2), there are two types of nodes:
discarded nodes in the basic path algorithm;
the node deletes the inefficient node in the algorithm. And the first node is arranged in front of the second node, the low-efficiency nodeCharging performance may also be degraded. Selecting some adjacent nodes to be inserted into Q againopt(j) In a queue. To ensure minimization of the driving distance, T is minimizedCTo perform the insertion algorithm.
The process of the global optimization algorithm is as follows: first, select QPThe first node i, then inserted into the optimal position in the charging path, i.e. minimizing TCThe position of (a). If the node i cannot be inserted, it is directly deleted. Finally, the algorithm returns a value of Qopt(j) For optimal charging path and NDThe number of dead nodes.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (1)

1. A space-time scheduling method for charging on demand in a wireless chargeable sensor network is characterized by comprising the following steps:
step 1: establishing a wireless power supply model of a sensor node;
step 2: according to the wireless power supply model of the sensor node, executing a basic path algorithm to obtain a basic charging path;
and step 3: performing a local optimization algorithm to further maximize charge remaining time by optimizing a basic charge path;
and 4, step 4: executing a node deletion algorithm to reduce the running time by adjusting the charging queue on the premise of keeping the sensor node alive, and reducing the number of dying nodes by deleting the low-efficiency nodes in the charging path;
and 5: executing a global optimization algorithm to perform global optimization on the efficient charging path to obtain an output optimal charging path and the number of dead nodes;
in step 3, the local optimization algorithm is executed to further maximize the remaining charging time by optimizing the basic charging path:
tend to reserve more charge time for subsequent charge queues; each sub-path is part of a charging path, then the target is written as
max MA(Π)＝min(TR(i))i∈Π,
Wherein T isR(i) Representing the time remaining at node i, n being a specific optimized line, MA(Π) represents the shortest remaining time of the node; current path QB(j) The shortest residual time T of the node i is found out firstly by the basic path algorithmR(i) Then seeking a maximization scheme of the shortest remaining time based on the current path;
obviously, node i is increased by the shortest remaining time in advance; advancing the time increased after node i to be
Figure FDA0003489413590000011
Wherein v represents the running speed of the wireless charging automobile;
Dx,irepresents the distance from location x to node i;
Di,yrepresents the distance of node i to location y;
by minimizing TC(i) Minimizing the driving distance from the wireless charging to the automobile; the optimization algorithm of the current path is as follows; the process of the local optimization algorithm is as follows: first, the node n with the shortest remaining time is selected from the current pathcInserted into any neighboring node; recording the increased time TC(i) Finally, select the minimum TC(i) The charging path of (1);
the executing node deleting algorithm in the step 4 is as follows:
to check whether node i is an inefficient node, the following principle is proposed:
Di,i+1＞Di-1,i+1and Di-1,i＞Di-1,i+1；
the angle of the two adjusting nodes is smaller than 90; obtaining an optimized path Q after a basic path algorithm and a local optimization algorithmB(j) And a series of discarded nodes QP(ii) a Deleting Q by a node deletion algorithmB(j) The charging performance is further improved by the low-efficiency nodes;
and (3) node deletion algorithm process: finding out 3 nodes a, b and c each time, and verifying whether the intermediate node b is an inefficient node; if so, node b is dropped into QPIn the method, a path directly connects nodes a and c;
in step 5, the global optimization algorithm is executed to globally optimize the efficient charging path as follows:
after the inefficient node deletion, a more efficient charging path Q will resultH(j) (ii) a The queue is a circular path, and the wireless charging automobile has shorter driving distance; moreover, the charging request in the path is greatly completed in advance, so that more charging time is reserved; however, the inefficient nodes cannot be charged and will die; therefore, it is necessary to re-slave QPInserting some nodes into the charging path to minimize the number of dead nodes;
at QPIn (2), there are two types of nodes:
discarded nodes in the basic path algorithm;
deleting inefficient nodes in the algorithm by the nodes; and the first node is arranged in front of the second node, and the low-efficiency node can reduce the charging performance; selecting some adjacent nodes to be inserted into Q againopt(j) In a queue; to ensure minimization of the driving distance, T is minimizedCTo perform an insertion algorithm;
the process of the global optimization algorithm is as follows: first, select QPThe first node i, then inserted into the optimal position in the charging path, i.e. minimizing TCThe position of (a); if the node i can not be inserted, the node i is directly deleted; finally, the algorithm returns a value of Qopt(j) For optimal charging path and NDThe number of dead nodes;
the establishment of the wireless power supply model of the sensor node in the step 1 comprises the following steps:
setting the total number of sensor nodes in the system to be N, and sending a request to the charging automobile when the residual electric quantity of the nodes is lower than a threshold value; the automobile responds to the request and drives to the node for charging; neglecting the charging time of the automobile, setting the running speed of the automobile to be constant V and the energy consumed by the automobile per unit distance to be constant VD(ii) a If the residual electric quantity of the node is reduced to 0 before the automobile is charged, the node is dead;
as a multi-objective optimization problem, the primary goal is to minimize the number of dead nodes, and the secondary goal is to maximize charging efficiency;
thus defining SN(i) For the state of sensor node i, each sensor node has two states, survival is 0 and death is 1, then the node state is expressed as:
Figure FDA0003489413590000031
wherein i ∈ [0, N ∈ >]，SN(i) For the ith sensor node, T denotes the current time, TDThe death period of a node i is pointed out, N is the number of sensor nodes, and i is an integer;
the scheduling target model is:
Figure FDA0003489413590000032
Figure FDA0003489413590000033
wherein N isDRepresents the total number of dead nodes;
eta: the charging efficiency of the vehicle;
ee: represents the energy consumed by the sensor;
VD: the energy consumed by the automobile per unit distance is constant;
Ntask: the total number of charging tasks completed;
|Qopt(j) l: representing the number of the remaining total nodes after the optimization in the task j;
Dnk,nk+1(j) representing the distance between the nk node and the nk +1 node in the task j;
the constraint conditions are as follows:
Figure FDA0003489413590000034
TR(i)＝TD(i)-T
Figure FDA0003489413590000041
Figure FDA0003489413590000042
wherein the content of the first and second substances,
ti: the time when the car arrives at the ith node;
TR (i): the remaining time of death of node i;
TD (i): time of death of node i;
EC: the remaining energy of the node;
vm: energy consumed by node i per unit time
When the wireless electric automobile receives a charging request, firstly, a charging waiting queue Q is recordedWPerforming the following steps; then generating a task j, wherein the task j comprises the front e node requests in the charging waiting queue; each task j corresponds to a charging task queue QM(j) Specifically, a charging waiting queue and a charging task queue;
from QM(j) Find out an optimal charging path Qopt(j) Then the number of dead nodes is expressed as
Figure FDA0003489413590000043
Wherein, | QM(j) | is the number of nodes in the jth charging task queue, | Qopt(j) L is the number of nodes in the optimal charging queue; the first goal is to minimize dead nodes by making | Qopt(j) Maximizing the | value;
the second aim is to minimize the driving distance of the electric vehicle;
the basic path algorithm executed in step 2 is as follows:
first, the slave charging queue QWThe front e-charging request is selected and added into a task queue QM(j) Performing the following steps; each charging request riAll correspond to a temporal priority level PT(i) From node death time TD(i) Determining; the shorter the death time, the higher the time priority; these charging requests are then in accordance with PT(i) Ordering to establish a specific optimized line pi ═ pi (pi)1,2,π2,3,...π∈-1,∈,π∈，1)；πi,jRepresents the charging path from node i to node j; then judging whether the charging path pi meets space-time constraint;
when a node i dies, meaning that the node i cannot be charged before the death time, replanning the charging path as much as possible to save the dying node; node n incapable of being chargedaMust advance in the optimized charging queue; scanning node by node to find the optimal position; can insert only when two conditions are met;
nbafter charging, naIs still alive;
at nbRear insert naOther nodes cannot die; otherwise only Q is adjustedB(j) One node in the system cannot get a feasible charging scheme; this is a local optimization algorithm that must be invoked to optimize the current path QB(j) (ii) a Once the optimum Q is reachedB(j) Determine but naWithout position insertion, then naWill be put into QP(ii) a Then naCharging requests of the nodes are ignored by the wireless charging automobile, QPIndicating that the charging request can ignore the queue.
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