CN115759505B - Task-oriented multi-mobile charging vehicle scheduling method - Google Patents

Task-oriented multi-mobile charging vehicle scheduling method Download PDF

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CN115759505B
CN115759505B CN202310030877.4A CN202310030877A CN115759505B CN 115759505 B CN115759505 B CN 115759505B CN 202310030877 A CN202310030877 A CN 202310030877A CN 115759505 B CN115759505 B CN 115759505B
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sensor
charging
path
utility
trolley
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CN115759505A (en
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徐佳
申辰雷
张毅铭
范露露
张一宁
徐力杰
鲁蔚锋
肖甫
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a task-oriented multi-mobile charging vehicle dispatching method, which comprises the following steps: acquiring and determining the position and performance parameters of the wireless chargeable sensor and the charging vehicle, and establishing a wireless chargeable sensor network model according to the acquired parameters; setting a charging utility function of the mobile chargeable device; formalizing a task-oriented charging scheduling problem; the sum of the utility of the sensor charging is maximized, and the path and trolley set with the maximum utility are updated. The invention can closely connect event monitoring with wireless charging, formalize task-oriented charging scheduling problem and maximize the sum of the utility of sensor charging; the task-oriented charge scheduling algorithm is provided, the algorithm meets the calculation effectiveness and the good approximation ratio, and a plurality of mobile chargers are used, so that the work of a large-scale wireless sensor network can be met.

Description

Task-oriented multi-mobile charging vehicle scheduling method
Technical Field
The invention relates to the technical field of wireless chargeable sensor networks, in particular to a task-oriented multi-mobile charging vehicle dispatching method.
Background
In recent years, wireless energy transfer technology based on strong magnetic resonance is considered as a breakthrough technology for prolonging the service life of a sensor in a wireless chargeable sensor network. The sensor of the wireless chargeable sensor network is a wireless sensor network formed by charging without wire connection. Each sensor is provided with a receiving device which can receive the electric quantity transmitted by the wireless charger. Electromagnetic waves are less affected by the surroundings, so potential safety hazards, such as leakage of wires due to aging, and more robust and safe networks, are avoided.
Currently, it is not feasible to cover all sensors with the charger under a large area, since the number of chargers is usually limited. In this case, the mobile chargeable wireless sensor network can greatly reduce the charger deployment cost, which is applied to charge the sensors in the wireless sensor network, and periodically schedule the mobile charging vehicles so that the network can run permanently. However, with the current research, implementation effects cannot be considered from the aspects of mobile cost and charging cost, and resource waste in actual operation is easily caused, so how to consider task-oriented charging scheduling problems in a mobile rechargeable wireless sensor network, and simultaneously consider mobile cost and charging cost of rechargeable equipment, and maximizing charging utility under limited energy constraint is a problem to be solved urgently.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the invention solves the technical problems that the implementation effect cannot be considered from the aspects of the moving cost and the charging cost at present, and the resource waste and the easy loss in the actual operation are easily caused.
In order to solve the technical problems, the invention provides the following technical scheme: a task-oriented multi-mobile charging vehicle scheduling method comprises the following steps:
acquiring and determining the position and performance parameters of the wireless chargeable sensor and the charging vehicle, and establishing a wireless chargeable sensor network model according to the acquired parameters;
setting a charging utility function of the mobile chargeable device;
formalizing a task-oriented charging scheduling problem;
the sum of the utility of the sensor charging is maximized, and the path and trolley set with the maximum utility are updated.
As a preferable scheme of the task-oriented multi-mobile charging vehicle dispatching method, the invention obtains and determines the position and the performance parameters of the wireless chargeable sensor, and comprises,
setting up
Figure 307884DEST_PATH_IMAGE001
For a given complete undirected graph,
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 678822DEST_PATH_IMAGE002
in order to be a base station,
Figure 425062DEST_PATH_IMAGE003
in order for the set of sensors to be a set of sensors,
Figure 248661DEST_PATH_IMAGE004
is a set of points of interest, wherein
Figure 105759DEST_PATH_IMAGE005
Each sensor monitors an interest point, performs random event capturing tasks, each interest point is covered by at least one sensor, an edge exists between the sensors and the base station, and the set of the edges is set as
Figure 65624DEST_PATH_IMAGE006
；
Monitoring the same point of interest
Figure 513923DEST_PATH_IMAGE007
Is uniform in sensor type, monitors the same point of interest
Figure 559240DEST_PATH_IMAGE007
The charged sensors are assembled into
Figure 954449DEST_PATH_IMAGE008
，
Figure 34400DEST_PATH_IMAGE008
The battery capacities of the sensors in (a) are all
Figure 122442DEST_PATH_IMAGE009
The perceived power is all
Figure 655055DEST_PATH_IMAGE010
。
As a preferable scheme of the task-oriented multi-mobile charging vehicle dispatching method, the invention obtains and determines the charging vehicle position and the performance parameters, including,
scheduling
Figure 119534DEST_PATH_IMAGE011
The charging trolley is
Figure 53992DEST_PATH_IMAGE012
The sensor in (a) provides a charging service,
Figure 312935DEST_PATH_IMAGE011
the set of vehicles is represented as:
Figure 828449DEST_PATH_IMAGE013
is provided with
Figure 831040DEST_PATH_IMAGE011
Different types of charging trolleys of vehicles and sensors
Figure 620005DEST_PATH_IMAGE014
And
Figure 315428DEST_PATH_IMAGE015
the edge weight between them is
Figure 822633DEST_PATH_IMAGE016
，
Figure 363336DEST_PATH_IMAGE017
Indicating mobile charging trolley
Figure 272386DEST_PATH_IMAGE018
In the sensor
Figure 873131DEST_PATH_IMAGE014
And
Figure 867632DEST_PATH_IMAGE015
the travel energy consumption between;
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 212026DEST_PATH_IMAGE019
representation sensor
Figure 975583DEST_PATH_IMAGE014
And
Figure 12809DEST_PATH_IMAGE015
the distance between the two plates is set to be equal,
Figure 229026DEST_PATH_IMAGE017
is a trolley
Figure 377111DEST_PATH_IMAGE018
Energy consumption for moving unit distance and trolley
Figure 995174DEST_PATH_IMAGE018
At the sensor set
Figure 937722DEST_PATH_IMAGE008
Medium sensor
Figure 641236DEST_PATH_IMAGE014
Is that the charging energy consumption is
Figure 828897DEST_PATH_IMAGE020
，
Figure 832625DEST_PATH_IMAGE021
For vehicles
Figure 211654DEST_PATH_IMAGE018
Is used for the charging efficiency of the battery,
Figure 136885DEST_PATH_IMAGE022
mobile charging trolley
Figure 626772DEST_PATH_IMAGE018
Is the battery capacity of
Figure 219427DEST_PATH_IMAGE023
。
As a preferable scheme of the task-oriented multi-mobile charging vehicle dispatching method, the method for establishing the wireless chargeable sensor network model comprises the following steps of,
definition of the definition
Figure 769357DEST_PATH_IMAGE024
Indicating mobile charging trolley
Figure 447463DEST_PATH_IMAGE018
Is provided;
wherein the method comprises the steps of
Figure 475462DEST_PATH_IMAGE025
Is a trolley
Figure 922624DEST_PATH_IMAGE018
On the path
Figure 377876DEST_PATH_IMAGE026
Except for base stations
Figure 543278DEST_PATH_IMAGE027
Number of sensors serviced;
trolley
Figure 374968DEST_PATH_IMAGE018
On the path
Figure 942216DEST_PATH_IMAGE026
The energy consumption is
Figure 568369DEST_PATH_IMAGE028
；
Wherein the method comprises the steps of
Figure 221067DEST_PATH_IMAGE029
Each mobile charging trolley
Figure 623492DEST_PATH_IMAGE018
Is a path of (a)
Figure 514088DEST_PATH_IMAGE026
The energy consumption in the water heater cannot exceed the energy consumption of the water heaterBundles, i.e. bundles
Figure 576722DEST_PATH_IMAGE030
。
As a preferable scheme of the task-oriented multi-mobile charging vehicle dispatching method of the invention, wherein the setting of the charging utility function of the mobile chargeable device comprises,
setting each interest point
Figure 716716DEST_PATH_IMAGE007
The arrival time of the random event accords with the Poisson distribution, so that one interest point
Figure 155787DEST_PATH_IMAGE007
At intervals of time
Figure 432048DEST_PATH_IMAGE031
The number of random events arriving internally is
Figure 400004DEST_PATH_IMAGE032
The method comprises the steps of carrying out a first treatment on the surface of the From the probability function of poisson distribution
Figure 27294DEST_PATH_IMAGE033
Wherein, the method comprises the steps of, wherein,
Figure 4478DEST_PATH_IMAGE034
is the point of interest
Figure 135245DEST_PATH_IMAGE007
The arrival intensity of the random event;
for a set of sensors
Figure 274102DEST_PATH_IMAGE008
In (a) and a sensor in (b) of the same
Figure 388689DEST_PATH_IMAGE014
The monitoring interest points are
Figure 638404DEST_PATH_IMAGE007
By using
Figure 889257DEST_PATH_IMAGE035
Representation capable of overlaying points of interest
Figure 464595DEST_PATH_IMAGE007
And the number of sensors charged;
interest point
Figure 800898DEST_PATH_IMAGE007
Is of utility as
Figure 621349DEST_PATH_IMAGE036
，
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 726708DEST_PATH_IMAGE037
is that
Figure 472947DEST_PATH_IMAGE008
The battery capacity of the sensor in (a),
Figure 30968DEST_PATH_IMAGE010
is that
Figure 153645DEST_PATH_IMAGE008
The perceived power of the sensor in (a),
for a set of sensors
Figure 113510DEST_PATH_IMAGE008
Each of the sensors of (a)
Figure 30651DEST_PATH_IMAGE014
The utility of charging is:
Figure 341546DEST_PATH_IMAGE038
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 2335DEST_PATH_IMAGE039
and is also provided with
Figure 816707DEST_PATH_IMAGE040
Belonging to
Figure 170328DEST_PATH_IMAGE041
Namely:
Figure 702941DEST_PATH_IMAGE042
。
as a preferable scheme of the task-oriented multi-mobile charging vehicle dispatching method, the formalized task-oriented charging dispatching problem comprises that,
given a picture
Figure 167420DEST_PATH_IMAGE001
；
Setting the problem as in the graph
Figure 836299DEST_PATH_IMAGE043
Middle is
Figure 360821DEST_PATH_IMAGE011
Vehicle trolley finding
Figure 380729DEST_PATH_IMAGE011
Different paths of the strip sensors;
the goal is to maximize the sum of the charging utilities of all paths under limited energy constraints, expressed as:
Figure 878926DEST_PATH_IMAGE044
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 667890DEST_PATH_IMAGE045
the representation being different from
Figure 363314DEST_PATH_IMAGE046
Is provided.
As a preferable scheme of the task-oriented multi-mobile charging vehicle dispatching method, the method for maximizing the sum of the utility of the sensor charging comprises the following steps of,
make the trolley with found path integrated as
Figure 604940DEST_PATH_IMAGE047
The set of paths that have been found are
Figure 411222DEST_PATH_IMAGE048
The number of paths that have been found is
Figure 320272DEST_PATH_IMAGE049
；
Initialization of
Figure 921017DEST_PATH_IMAGE050
；
For the given graph
Figure 915518DEST_PATH_IMAGE001
Structure of
Figure 259912DEST_PATH_IMAGE011
Personal auxiliary graph
Figure 23468DEST_PATH_IMAGE051
Wherein, the method comprises the steps of, wherein,
Figure 795115DEST_PATH_IMAGE052
be used for seeking removal dolly that charges
Figure 276912DEST_PATH_IMAGE018
Is a path of (a)
Figure 424997DEST_PATH_IMAGE026
；
Drawing of the figure
Figure 43060DEST_PATH_IMAGE043
The weight of the middle edge is
Figure 251187DEST_PATH_IMAGE053
；
For any one path
Figure 721745DEST_PATH_IMAGE026
By using
Figure 407942DEST_PATH_IMAGE054
The cost of representing the path is also known as the sum of edge weights,
Figure 146090DEST_PATH_IMAGE055
；
and for the number of found paths is
Figure 259540DEST_PATH_IMAGE049
And judging.
As a preferable scheme of the task-oriented multi-mobile charging vehicle dispatching method, the judging comprises the steps of,
if the number of paths has been found
Figure 450350DEST_PATH_IMAGE049
Greater than
Figure 940237DEST_PATH_IMAGE011
The scheduling is finished, and the found path set is returned as
Figure 532892DEST_PATH_IMAGE048
。
As a preferable scheme of the task-oriented multi-mobile charging vehicle dispatching method, the judging further comprises,
if the number of paths has been found
Figure 82822DEST_PATH_IMAGE049
Less than
Figure 760928DEST_PATH_IMAGE011
Will currently have found
Figure 523348DEST_PATH_IMAGE049
The bar path is represented as
Figure 970510DEST_PATH_IMAGE056
；
Order the
Figure 691341DEST_PATH_IMAGE057
Indicating charging trolley
Figure 856743DEST_PATH_IMAGE058
Is provided with a path for the path of (a),
Figure 954012DEST_PATH_IMAGE059
and is also provided with
Figure 255681DEST_PATH_IMAGE060
；
For all of
Figure 147413DEST_PATH_IMAGE061
On-map by single path utility maximization algorithm
Figure 301576DEST_PATH_IMAGE062
Find a starting point and an ending point to be s paths
Figure 671378DEST_PATH_IMAGE026
So that the path is
Figure 93132DEST_PATH_IMAGE026
Utility of (C)
Figure 155766DEST_PATH_IMAGE063
Maximize and meet
Figure 30181DEST_PATH_IMAGE064
；
The graph is provided with
Figure 469253DEST_PATH_IMAGE062
Each of the sensors of (a)
Figure 479934DEST_PATH_IMAGE014
The service utility of (a) is:
Figure 713469DEST_PATH_IMAGE065
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 340760DEST_PATH_IMAGE040
belonging to
Figure 52364DEST_PATH_IMAGE008
；
Selecting a path with the greatest utility
Figure 448710DEST_PATH_IMAGE066
The corresponding trolley is
Figure 587567DEST_PATH_IMAGE067
The method comprises the following steps:
Figure 702154DEST_PATH_IMAGE068
；
updating a set of paths that have been found
Figure 483028DEST_PATH_IMAGE069
Updating a set of carts that have found a path
Figure 733881DEST_PATH_IMAGE070
Updating the number of paths that have been found
Figure 810683DEST_PATH_IMAGE071
And return to execute the number of paths already found for the pair as
Figure 146987DEST_PATH_IMAGE049
And judging.
As a preferable scheme of the task-oriented multi-mobile charging vehicle dispatching method, the single-path utility maximum algorithm comprises the following steps of,
a1: make the current path already find
Figure 465973DEST_PATH_IMAGE072
Personal sensor
Figure 571332DEST_PATH_IMAGE073
The set of sensors already found in the current path is
Figure 317571DEST_PATH_IMAGE074
Initializing a current path
Figure 875591DEST_PATH_IMAGE075
；
A2: judging the current path
Figure 998268DEST_PATH_IMAGE026
Whether the cost of (2) exceeds the energy constraint
Figure 958134DEST_PATH_IMAGE023
If (if)
Figure 875274DEST_PATH_IMAGE076
Step A3 is executed, otherwise, the path with the greatest utility is found
Figure 186170DEST_PATH_IMAGE026
Ending the algorithm;
a3: setting all sensors
Figure 846958DEST_PATH_IMAGE077
The calculated marginal utility is expressed as:
Figure 661331DEST_PATH_IMAGE078
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 14952DEST_PATH_IMAGE079
belonging to
Figure 547564DEST_PATH_IMAGE008
；
Calculation according to nearest neighbor algorithm of tourist problem
Figure 746464DEST_PATH_IMAGE080
And (3) with
Figure 680922DEST_PATH_IMAGE081
；
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 724487DEST_PATH_IMAGE080
expressed in a collection
Figure 478817DEST_PATH_IMAGE074
The sensor in the process calculates the path cost by using the nearest neighbor algorithm of the travel business problem, and the sensor
Figure 746987DEST_PATH_IMAGE082
Is the marginal cost of
Figure 535952DEST_PATH_IMAGE083
；
A4: selecting a sensor with the largest marginal utility to marginal cost ratio
Figure 965796DEST_PATH_IMAGE082
The method comprises the following steps:
Figure 473001DEST_PATH_IMAGE084
；
a5: judging path cost
Figure 279283DEST_PATH_IMAGE081
Whether or not energy constraints are exceeded
Figure 922754DEST_PATH_IMAGE085
If not, the process is performedSensor joining current path
Figure 789078DEST_PATH_IMAGE086
And executing the step A6; otherwise, the algorithm ends;
a6: traversal is obtained according to nearest neighbor algorithm of tourist problem
Figure 518000DEST_PATH_IMAGE074
The shortest closed path of all sensors in (a)
Figure 127973DEST_PATH_IMAGE026
Updating the number of currently selected sensors
Figure 891530DEST_PATH_IMAGE087
And returns to continue to step A2.
The invention has the beneficial effects that: the invention provides a task-oriented multi-mobile charging vehicle dispatching method, which can closely connect event monitoring with wireless charging, formalize task-oriented charging dispatching problems and maximize the sum of the utility of sensor charging; the task-oriented charge scheduling algorithm is provided, the algorithm meets the calculation effectiveness and the good approximation ratio, and a plurality of mobile chargers are used, so that the work of a large-scale wireless sensor network can be met.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is an overall flow chart of a task-oriented multi-mobile charging vehicle scheduling method provided by an embodiment of the invention;
fig. 2 is a schematic diagram of a network model in a task-oriented multi-mobile charging vehicle scheduling method according to an embodiment of the present invention;
FIG. 3 is a flowchart of a method for dispatching multiple mobile charging vehicles in a task-oriented method for dispatching multiple mobile charging vehicles according to an embodiment of the present invention;
fig. 4 is a flowchart of a single-path utility maximization algorithm in a task-oriented multi-mobile charging vehicle scheduling method according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1, 3 and 4, in one embodiment of the present invention, a task-oriented multi-mobile charging vehicle scheduling method is provided, including:
s1: acquiring and determining the position and performance parameters of the wireless chargeable sensor and the charging vehicle, and establishing a wireless chargeable sensor network model according to the acquired parameters;
further, wireless chargeable sensor location and performance parameters are obtained and determined, including,
setting up
Figure 663176DEST_PATH_IMAGE001
For a given complete undirected graph,
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 144973DEST_PATH_IMAGE002
in order to be a base station,
Figure 293058DEST_PATH_IMAGE003
in order for the set of sensors to be a set of sensors,
Figure 645542DEST_PATH_IMAGE004
is a set of points of interest, wherein
Figure 588090DEST_PATH_IMAGE005
Each sensor monitors an interest point, performs random event capturing tasks, each interest point is covered by at least one sensor, an edge exists between the sensors and the base station, and the set of the edges is set as
Figure 793069DEST_PATH_IMAGE006
；
Monitoring the same point of interest
Figure 744844DEST_PATH_IMAGE007
Is uniform in sensor type, monitors the same point of interest
Figure 217414DEST_PATH_IMAGE007
The charged sensors are assembled into
Figure 596443DEST_PATH_IMAGE008
，
Figure 787252DEST_PATH_IMAGE008
The battery capacities of the sensors in (a) are all
Figure 277140DEST_PATH_IMAGE009
The perceived power is all
Figure 869795DEST_PATH_IMAGE010
。
Further, battery car position and performance parameters are obtained and determined, including,
scheduling
Figure 419725DEST_PATH_IMAGE011
The charging trolley is
Figure 832252DEST_PATH_IMAGE012
The sensor in (a) provides a charging service,
Figure 125830DEST_PATH_IMAGE011
the set of vehicles is represented as:
Figure 572992DEST_PATH_IMAGE013
is provided with
Figure 28244DEST_PATH_IMAGE011
Different types of charging trolleys of vehicles and sensors
Figure 193646DEST_PATH_IMAGE014
And
Figure 25336DEST_PATH_IMAGE015
the edge weight between them is
Figure 327004DEST_PATH_IMAGE016
，
Figure 218737DEST_PATH_IMAGE017
Indicating mobile charging trolley
Figure 605856DEST_PATH_IMAGE018
In the sensor
Figure 742701DEST_PATH_IMAGE014
And
Figure 164455DEST_PATH_IMAGE015
the travel energy consumption between;
it should be noted that since the charging carts are of different types, there are different travel energy consumption and charging energy consumption, and it is necessary to specify the different travel energy consumption.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 227089DEST_PATH_IMAGE019
representation sensor
Figure 101504DEST_PATH_IMAGE014
And
Figure 9418DEST_PATH_IMAGE015
the distance between the two plates is set to be equal,
Figure 285678DEST_PATH_IMAGE017
is a trolley
Figure 519213DEST_PATH_IMAGE018
Energy consumption for moving unit distance and trolley
Figure 880925DEST_PATH_IMAGE018
At the sensor set
Figure 858108DEST_PATH_IMAGE008
Medium sensor
Figure 254454DEST_PATH_IMAGE014
Is that the charging energy consumption is
Figure 658891DEST_PATH_IMAGE020
，
Figure 242319DEST_PATH_IMAGE021
For vehicles
Figure 23193DEST_PATH_IMAGE018
Is used for the charging efficiency of the battery,
Figure 274046DEST_PATH_IMAGE022
mobile charging trolley
Figure 849383DEST_PATH_IMAGE018
Is the battery capacity of
Figure 654528DEST_PATH_IMAGE023
。
Further, a wireless chargeable sensor network model is established, including,
definition of the definition
Figure 973514DEST_PATH_IMAGE024
Indicating mobile charging trolley
Figure 78874DEST_PATH_IMAGE018
Is provided;
wherein the method comprises the steps of
Figure 326578DEST_PATH_IMAGE025
Is a trolley
Figure 150177DEST_PATH_IMAGE018
On the path
Figure 7275DEST_PATH_IMAGE026
Except for base stations
Figure 232720DEST_PATH_IMAGE027
Number of sensors serviced;
trolley
Figure 149860DEST_PATH_IMAGE018
On the path
Figure 195176DEST_PATH_IMAGE026
The energy consumption is
Figure 855965DEST_PATH_IMAGE088
；
Wherein the method comprises the steps of
Figure 935916DEST_PATH_IMAGE029
Each mobile charging trolley
Figure 23958DEST_PATH_IMAGE018
Is a path of (a)
Figure 290991DEST_PATH_IMAGE026
The energy consumption in (a) cannot exceed the energy constraint, i.e
Figure 755471DEST_PATH_IMAGE030
。
S2: setting a charging utility function of the mobile chargeable device;
still further, setting a charge utility function of the mobile chargeable device, comprising,
setting each interest point
Figure 689929DEST_PATH_IMAGE007
The arrival time of the random event accords with the Poisson distribution, so that one interest point
Figure 948872DEST_PATH_IMAGE007
At intervals of time
Figure 968780DEST_PATH_IMAGE031
The number of random events arriving internally is
Figure 236951DEST_PATH_IMAGE032
The method comprises the steps of carrying out a first treatment on the surface of the From the probability function of poisson distribution
Figure 25915DEST_PATH_IMAGE033
Wherein, the method comprises the steps of, wherein,
Figure 455759DEST_PATH_IMAGE034
is the point of interest
Figure 458570DEST_PATH_IMAGE007
The arrival intensity of the random event;
it should be noted that the more sensors monitoring the same point of interest, the greater the probability of capturing a random event, but the marginal utility is decreasing, and the more charging carts are used, the more sensors can be charged, in order to improve the ability of the sensors to capture a random event, it is necessary to schedule a plurality of mobile charging carts to wirelessly charge the sensors, thus setting the following sensor representation:
for a set of sensors
Figure 264852DEST_PATH_IMAGE008
In (a) and a sensor in (b) of the same
Figure 908323DEST_PATH_IMAGE014
The monitoring interest points are
Figure 774647DEST_PATH_IMAGE007
By using
Figure 503569DEST_PATH_IMAGE035
Representation capable of overlaying points of interest
Figure 847963DEST_PATH_IMAGE007
And the number of sensors charged;
interest point
Figure 345940DEST_PATH_IMAGE007
Is of utility as
Figure 383166DEST_PATH_IMAGE036
，
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 599384DEST_PATH_IMAGE037
is that
Figure 747468DEST_PATH_IMAGE008
The battery capacity of the sensor in (a),
Figure 365532DEST_PATH_IMAGE010
is that
Figure 573659DEST_PATH_IMAGE008
The perceived power of the sensor in (a),
for a set of sensors
Figure 277173DEST_PATH_IMAGE008
Each of the sensors of (a)
Figure 228948DEST_PATH_IMAGE014
The utility of charging is:
Figure 701518DEST_PATH_IMAGE038
namely:
Figure 80547DEST_PATH_IMAGE042
。
it should be noted that,
Figure 271357DEST_PATH_IMAGE089
this function describes that the more sensors are charged within the same point of interest coverage, the less marginal utility the charging achieves, i.e., the function encourages access to sensors monitoring new points of interest, the utility of each path being the sum of the utilities of each sensor on the path, i.e.
Figure 495665DEST_PATH_IMAGE090
。
S3: formalizing a task-oriented charging scheduling problem;
still further, formalizing task-oriented charge scheduling problems, including,
given a picture
Figure 589785DEST_PATH_IMAGE001
；
Setting the problem as in the graph
Figure 874136DEST_PATH_IMAGE043
Middle is
Figure 552242DEST_PATH_IMAGE011
Vehicle trolley finding
Figure 845820DEST_PATH_IMAGE011
Different paths of the strip sensors;
the goal is to maximize the sum of the charging utilities of all paths under limited energy constraints, expressed as:
Figure 292982DEST_PATH_IMAGE044
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 13813DEST_PATH_IMAGE045
the representation being different from
Figure 913636DEST_PATH_IMAGE046
Is provided.
It should be noted that constraint (2) ensures the vehicle
Figure 745326DEST_PATH_IMAGE018
Is a path of (a)
Figure 312573DEST_PATH_IMAGE026
Is not more than the battery capacity of the vehicle
Figure 938727DEST_PATH_IMAGE091
Constraint (3) ensures that the sensors in each path are not identical, i.e. the paths of any 2 different carts are disjoint from each other.
S4: the sum of the utility of the sensor charging is maximized, and the path and trolley set with the maximum utility are updated.
Further, maximizing the sum of the utility of the sensor charges, including,
make the trolley with found path integrated as
Figure 325846DEST_PATH_IMAGE047
The set of paths that have been found are
Figure 961226DEST_PATH_IMAGE048
The number of paths that have been found is
Figure 382980DEST_PATH_IMAGE049
；
Initialization of
Figure 445614DEST_PATH_IMAGE050
；
For a given graph
Figure 320029DEST_PATH_IMAGE001
Structure of
Figure 493522DEST_PATH_IMAGE011
Personal auxiliary graph
Figure 769782DEST_PATH_IMAGE051
，
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 504782DEST_PATH_IMAGE052
be used for seeking removal dolly that charges
Figure 866494DEST_PATH_IMAGE018
Is a path of (a)
Figure 843677DEST_PATH_IMAGE026
；
Drawing of the figure
Figure 974444DEST_PATH_IMAGE043
The weight of the middle edge is
Figure 113301DEST_PATH_IMAGE053
；
For any one path
Figure 962308DEST_PATH_IMAGE026
By using
Figure 743183DEST_PATH_IMAGE054
The cost of representing the path is also known as the sum of edge weights,
Figure 728456DEST_PATH_IMAGE055
；
and for the number of found paths is
Figure 303794DEST_PATH_IMAGE049
And judging.
Further, the judging includes,
if the number of paths has been found
Figure 640097DEST_PATH_IMAGE049
Greater than
Figure 959083DEST_PATH_IMAGE011
The scheduling is finished, and the found path set is returned as
Figure 64443DEST_PATH_IMAGE048
。
Still further, the judging further comprises,
if the number of paths has been found
Figure 810682DEST_PATH_IMAGE049
Less than
Figure 634281DEST_PATH_IMAGE011
Will currently have found
Figure 491379DEST_PATH_IMAGE049
The bar path is represented as
Figure 451245DEST_PATH_IMAGE056
；
Order the
Figure 633964DEST_PATH_IMAGE057
Indicating charging trolley
Figure 413701DEST_PATH_IMAGE058
Is provided with a path for the path of (a),
Figure 575955DEST_PATH_IMAGE059
and is also provided with
Figure 655906DEST_PATH_IMAGE060
；
For all of
Figure 743948DEST_PATH_IMAGE061
On-map by single path utility maximization algorithm
Figure 276560DEST_PATH_IMAGE062
Find a starting point and an ending point to be s paths
Figure 741040DEST_PATH_IMAGE026
So that the path is
Figure 675498DEST_PATH_IMAGE026
Utility of (C)
Figure 934441DEST_PATH_IMAGE063
Maximize and meet
Figure 954349DEST_PATH_IMAGE064
；
Drawing of the figure
Figure 956940DEST_PATH_IMAGE062
Each of the sensors of (a)
Figure 745905DEST_PATH_IMAGE014
The service utility of (a) is:
Figure 175749DEST_PATH_IMAGE092
；
selecting a path with the greatest utility
Figure 948533DEST_PATH_IMAGE066
The corresponding trolley is
Figure 489236DEST_PATH_IMAGE067
The method comprises the following steps:
Figure 398286DEST_PATH_IMAGE068
；
updating a set of paths that have been found
Figure 999032DEST_PATH_IMAGE093
Updating a set of carts that have found a path
Figure 993532DEST_PATH_IMAGE070
Updating the number of paths that have been found
Figure 833532DEST_PATH_IMAGE071
And return execution of the number of paths already found as
Figure 597088DEST_PATH_IMAGE049
And judging.
Still further, a single path utility maximization algorithm, comprising,
a1: make the current path already find
Figure 634314DEST_PATH_IMAGE072
Personal sensor
Figure 850532DEST_PATH_IMAGE073
The set of sensors already found in the current path is
Figure 998617DEST_PATH_IMAGE074
Initializing a current path
Figure 616680DEST_PATH_IMAGE075
；
A2: judging the current path
Figure 824807DEST_PATH_IMAGE026
Whether the cost of (2) exceeds the energy constraint
Figure 528321DEST_PATH_IMAGE023
If (if)
Figure 948938DEST_PATH_IMAGE076
Step A3 is executed, otherwise, the path with the greatest utility is found
Figure 687087DEST_PATH_IMAGE026
Ending the algorithm;
a3: setting all sensors
Figure 66116DEST_PATH_IMAGE077
The calculated marginal utility is expressed as:
Figure 256926DEST_PATH_IMAGE094
；
calculation according to nearest neighbor algorithm of tourist problem
Figure 12392DEST_PATH_IMAGE080
And (3) with
Figure 73889DEST_PATH_IMAGE081
；
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure 623819DEST_PATH_IMAGE080
expressed in a collection
Figure 301925DEST_PATH_IMAGE074
The sensor in the process calculates the path cost by using the nearest neighbor algorithm of the travel business problem, and the sensor
Figure 595503DEST_PATH_IMAGE082
Is the marginal cost of
Figure 544130DEST_PATH_IMAGE083
；
A4: selecting a sensor with the largest marginal utility to marginal cost ratio
Figure 999382DEST_PATH_IMAGE082
The method comprises the following steps:
Figure 164784DEST_PATH_IMAGE095
；
a5: judging path cost
Figure 996474DEST_PATH_IMAGE096
Whether or not energy constraints are exceeded
Figure 563721DEST_PATH_IMAGE085
If not, adding the sensor to the current path
Figure 189875DEST_PATH_IMAGE086
And executing the step A6; otherwise, the algorithm ends;
a6: traversal is obtained according to nearest neighbor algorithm of tourist problem
Figure 842573DEST_PATH_IMAGE074
The shortest closed path of all sensors in (a)
Figure 477954DEST_PATH_IMAGE026
Updating the number of currently selected sensors
Figure 634129DEST_PATH_IMAGE087
And returns to continue to step A2.
Example 2
Referring to fig. 1-4, the present embodiment provides an application scenario of a task-oriented multi-mobile charging vehicle dispatching method, as shown in fig. 2, with the goal of maximizing the sum of charging utilities of all charging devices, and solving a dispatching scheme of the multi-mobile charging vehicle; the task-oriented multi-mobile charging vehicle dispatching method in the wireless sensor network comprises the following steps, specifically shown in fig. 3:
s1: acquiring and determining the position and performance parameters of the wireless chargeable sensor and the charging vehicle, and establishing a wireless chargeable sensor network model according to the acquired parameters, wherein the process is as follows:
to extend the life of the sensor network, 2 mobile charging carts are scheduled from the base station
Figure 696762DEST_PATH_IMAGE027
Starting, accessing a selected part of sensors, wirelessly charging the sensors at the positions of the sensors, and returning to the base station after completing the charging task
Figure 571178DEST_PATH_IMAGE097
。
Order the
Figure 10249DEST_PATH_IMAGE098
A given complete undirected graph;
wherein the method comprises the steps of
Figure 286510DEST_PATH_IMAGE099
In order to be a base station,
Figure 520045DEST_PATH_IMAGE100
a set of sensors to be charged up,
Figure 914379DEST_PATH_IMAGE101
for a set of points of interest, where sensors 1 and 2 monitor point of interest 1 and sensor 3 monitors point of interest 2.
To dispatch 2 charging trolleys as
Figure 891563DEST_PATH_IMAGE102
The sensor service in (2) charging trolleys are of different types, and therefore have different travel cost and charging cost, and the sensor
Figure 22330DEST_PATH_IMAGE103
And
Figure 426766DEST_PATH_IMAGE104
the edge weight between them is
Figure 275774DEST_PATH_IMAGE105
Indicating mobile charging trolley
Figure 791069DEST_PATH_IMAGE018
In the sensor
Figure 41921DEST_PATH_IMAGE103
And
Figure 617259DEST_PATH_IMAGE104
cost of travel between, wherein
Figure 953563DEST_PATH_IMAGE106
Representation sensor
Figure 272548DEST_PATH_IMAGE103
And
Figure 377908DEST_PATH_IMAGE104
the distance between the two plates is set to be equal,
Figure 858568DEST_PATH_IMAGE107
is a trolley
Figure 682167DEST_PATH_IMAGE018
Energy consumption per unit distance of movement. Vehicle with a vehicle body having a vehicle body support
Figure 804844DEST_PATH_IMAGE018
At the sensor set
Figure 499130DEST_PATH_IMAGE108
In (a) and a sensor in (b) of the same
Figure 416271DEST_PATH_IMAGE103
The charging energy consumption of (2) is as follows:
Figure 727166DEST_PATH_IMAGE109
。
the information for each charge cart is shown in table 1:
table 1: charging trolley information table
Figure 889420DEST_PATH_IMAGE110
The distance between the sensors and the required power information are shown in table 2:
table 2: distance between sensors and required electric quantity information meter
Figure 969371DEST_PATH_IMAGE111
The calculation can be as follows:
Figure 322992DEST_PATH_IMAGE112
by using
Figure 121184DEST_PATH_IMAGE113
A collection of 2 carts is shown,
Figure 585663DEST_PATH_IMAGE114
indicating mobile charging trolley
Figure 254542DEST_PATH_IMAGE018
Wherein
Figure 779064DEST_PATH_IMAGE115
Is a trolley
Figure 798973DEST_PATH_IMAGE018
On the path
Figure 801564DEST_PATH_IMAGE026
Except for base stations
Figure 590528DEST_PATH_IMAGE002
Number of sensors serviced. Vehicle with a vehicle body having a vehicle body support
Figure 285952DEST_PATH_IMAGE116
On the path
Figure 527577DEST_PATH_IMAGE026
The cost of (a) is
Figure 333859DEST_PATH_IMAGE117
Figure 242910DEST_PATH_IMAGE118
Wherein
Figure 339261DEST_PATH_IMAGE119
By using
Figure 333762DEST_PATH_IMAGE120
Representing battery capacity of mobile charging carts, each mobile charging cart
Figure 678155DEST_PATH_IMAGE116
Is a path of (a)
Figure 441712DEST_PATH_IMAGE026
Cannot exceed its energy constraint, i.e.
Figure 213359DEST_PATH_IMAGE121
. Order the
Figure 695156DEST_PATH_IMAGE122
。
S2: the charging utility function of the mobile chargeable device is determined as follows:
assume that each point of interest
Figure 843240DEST_PATH_IMAGE007
The arrival time of random event accords with poisson distribution, so that one interest point is arranged at time interval
Figure 461303DEST_PATH_IMAGE123
The number of random events arriving internally is
Figure 669431DEST_PATH_IMAGE124
From the probability function of poisson distribution
Figure 372945DEST_PATH_IMAGE125
Wherein
Figure 59141DEST_PATH_IMAGE126
Is the point of interest
Figure 797290DEST_PATH_IMAGE007
The intensity of arrival of random events, in this example
Figure 910739DEST_PATH_IMAGE127
。
For each sensor
Figure 101549DEST_PATH_IMAGE014
Assume that the monitoring interest point is
Figure 591436DEST_PATH_IMAGE007
Interest point
Figure 184092DEST_PATH_IMAGE007
Is of utility as
Figure 235487DEST_PATH_IMAGE128
Assuming that the sensors monitoring the same point of interest are homogenous, i.e., the battery capacity and power consumption of the sensors are the same, then for each sensor
Figure 913593DEST_PATH_IMAGE103
The charging effect is that
Figure 941592DEST_PATH_IMAGE129
In the present example
Figure 388753DEST_PATH_IMAGE130
Figure 109585DEST_PATH_IMAGE131
. This function describes that the more sensors that are charged within the same point of interest coverage, the less marginal utility is obtained by charging, i.e., the function encourages access to sensors that monitor new points of interest.
S3: formalized task-oriented charge scheduling problems, the process is as follows:
given a picture
Figure 540566DEST_PATH_IMAGE132
The problem studied by the present invention is how to schedule a plurality of mobile charging carts to charge sensors in a wireless chargeable sensor network, i.e. in the figure
Figure 372256DEST_PATH_IMAGE133
Find 2 paths for 2 carts
Figure 673924DEST_PATH_IMAGE134
Each path is from a base station
Figure 565657DEST_PATH_IMAGE097
Go out and finally return to base station
Figure 952776DEST_PATH_IMAGE097
The utility of each path is the sum of the utility of each sensor on the path, i.e
Figure 322577DEST_PATH_IMAGE135
. Each sensor can only be served by one mobile charging trolley, each sensor monitors an interest point, each interest point has random event arrival, the sensor executes the task of random event capturing, and the aim is to improve the capability of the sensor to execute the task of random event capturing under the limited energy constraint and maximize the overall charging utility. The invention refers to the problem of task-oriented charge scheduling, and can be expressed as the following expression:
Figure 9911DEST_PATH_IMAGE136
restraint (2) ensures vehicle
Figure 72544DEST_PATH_IMAGE018
Is a path of (a)
Figure 946960DEST_PATH_IMAGE026
Is not more than the battery capacity of the vehicle
Figure 386031DEST_PATH_IMAGE023
Constraint (3) ensures that the sensors in each path are not identical.
S4: the sum of the utility of the sensor charging is maximized, and the path and trolley set with the maximum utility are updated.
Make the set of mobile charging trolleys as
Figure 163757DEST_PATH_IMAGE137
The trolley set with found path is
Figure 397292DEST_PATH_IMAGE138
The set of paths that have been found are
Figure 759003DEST_PATH_IMAGE139
The number of paths that have been found is
Figure 470607DEST_PATH_IMAGE140
. For a given graph
Figure 132533DEST_PATH_IMAGE141
First, 2 auxiliary graphs are constructed
Figure 536969DEST_PATH_IMAGE142
Wherein
Figure 385976DEST_PATH_IMAGE143
Be used for seeking removal dolly that charges
Figure 901271DEST_PATH_IMAGE018
Is a path of (a)
Figure 152124DEST_PATH_IMAGE026
The edges in the graph are weighted as
Figure 727462DEST_PATH_IMAGE144
For any one path
Figure 63765DEST_PATH_IMAGE026
By using
Figure 382751DEST_PATH_IMAGE145
The cost of representing the path is also known as the sum of edge weights,
Figure 488110DEST_PATH_IMAGE146
。
the number of paths that have been found currently
Figure 499929DEST_PATH_IMAGE147
Execution continues.
Respectively in the graph according to the single-path utility maximization algorithm
Figure 559414DEST_PATH_IMAGE148
Find a starting point and an ending point to be s paths
Figure 682091DEST_PATH_IMAGE149
So that the path is
Figure 641957DEST_PATH_IMAGE149
Utility of (C)
Figure 293518DEST_PATH_IMAGE150
Maximize and meet
Figure 604413DEST_PATH_IMAGE151
. Drawing of the figure
Figure 530781DEST_PATH_IMAGE152
Each of the sensors of (a)
Figure 345153DEST_PATH_IMAGE014
Is used as the service utility of
Figure 698774DEST_PATH_IMAGE153
。
Calculated to obtain
Figure 231387DEST_PATH_IMAGE154
。
Selecting a path with the greatest utility
Figure 430287DEST_PATH_IMAGE155
Corresponding to trolley 1.
Updating a set of paths that have been found
Figure 364745DEST_PATH_IMAGE156
Updating a set of carts that have found a path
Figure 154846DEST_PATH_IMAGE157
Updating the number of paths that have been found
Figure 909176DEST_PATH_IMAGE158
And return execution of the number of paths already found as
Figure 177346DEST_PATH_IMAGE049
And judging.
In this embodiment, repeating step S4 may obtain that the path corresponding to the cart 1 is
Figure 966310DEST_PATH_IMAGE159
The corresponding path of the trolley 2 is
Figure 180777DEST_PATH_IMAGE160
。
Further, in the figure
Figure 687982DEST_PATH_IMAGE161
For example, as in FIG. 4, a single path utility maximization algorithm withThe method comprises the following steps:
a1: the set of sensors already found in the current path is
Figure 228684DEST_PATH_IMAGE162
Initializing a current path
Figure 137735DEST_PATH_IMAGE163
；
A2: current path
Figure 4059DEST_PATH_IMAGE026
Is not more than the energy constraint
Figure 732981DEST_PATH_IMAGE164
Continuing execution;
a3: for all sensors
Figure 342954DEST_PATH_IMAGE165
；
Calculate its marginal utility
Figure 106511DEST_PATH_IMAGE166
；
Calculation based on approximation algorithm of traveller's problem
Figure 878157DEST_PATH_IMAGE167
And (3) with
Figure 625534DEST_PATH_IMAGE168
，
Figure 773618DEST_PATH_IMAGE167
Expressed in a collection
Figure 391681DEST_PATH_IMAGE169
The sensor in the road cost calculated by using the approximation algorithm of the traveller problem is the sensor
Figure 334230DEST_PATH_IMAGE170
Is the marginal cost of
Figure 804787DEST_PATH_IMAGE171
；
Calculated to obtain
Figure 756563DEST_PATH_IMAGE172
；
A4: selecting a sensor 1 with the largest marginal utility to marginal cost ratio;
a5: path cost
Figure 229133DEST_PATH_IMAGE173
Without exceeding the trolley battery energy 40, the sensor is added to the current path
Figure 608161DEST_PATH_IMAGE174
；
A6: traversal is obtained according to nearest neighbor algorithm of tourist problem
Figure 64550DEST_PATH_IMAGE175
The shortest closed path of all sensors in (a)
Figure 554438DEST_PATH_IMAGE176
Updating the number of currently selected sensors
Figure 147093DEST_PATH_IMAGE177
Continuing to execute the step A2;
in this embodiment, steps A2-A6 are repeatedly performed to obtain a path
Figure 697023DEST_PATH_IMAGE178
。
Further, assume that the single path utility maximum algorithm approximation ratio is
Figure 375129DEST_PATH_IMAGE179
The approximate ratio of the task-oriented charge scheduling algorithm of step S4 is
Figure 668707DEST_PATH_IMAGE180
：
And (3) proving: assuming that there is an optimal solution to the problem
Figure 850290DEST_PATH_IMAGE181
Let this
Figure 571121DEST_PATH_IMAGE182
The set of the optimal paths is
Figure 2102DEST_PATH_IMAGE183
I.e.
Figure 833792DEST_PATH_IMAGE184
At the same time
Figure 636925DEST_PATH_IMAGE185
. Output by algorithm
Figure 794237DEST_PATH_IMAGE011
The set of the paths is
Figure 181356DEST_PATH_IMAGE186
The method comprises the following steps: updating a set of paths that have been found
Figure 816737DEST_PATH_IMAGE187
In (1) performing
Figure 238491DEST_PATH_IMAGE188
The next time the obtained front
Figure 301125DEST_PATH_IMAGE188
The set of the paths is
Figure 175540DEST_PATH_IMAGE189
I.e.
Figure 349032DEST_PATH_IMAGE190
. Assuming that the algorithm is currently already obtained
Figure 359714DEST_PATH_IMAGE188
Path
Figure 593249DEST_PATH_IMAGE189
For any one of
Figure 954960DEST_PATH_IMAGE188
，
Figure 932143DEST_PATH_IMAGE191
By using
Figure 328490DEST_PATH_IMAGE192
Representing a current diagram
Figure 732926DEST_PATH_IMAGE193
The path of greatest mid-margin utility, i.e
Figure 316354DEST_PATH_IMAGE194
Assume that the algorithm invokes a single path utility maximum algorithm approximation ratio of
Figure 864273DEST_PATH_IMAGE195
Therefore, it is
Figure 115125DEST_PATH_IMAGE196
Thus, there are:
Figure 690463DEST_PATH_IMAGE197
for any one
Figure 761187DEST_PATH_IMAGE188
And
Figure 80173DEST_PATH_IMAGE198
，
Figure 185533DEST_PATH_IMAGE199
and is also provided with
Figure 931772DEST_PATH_IMAGE200
The method comprises the following steps:
Figure 755371DEST_PATH_IMAGE201
then there are:
Figure 878048DEST_PATH_IMAGE202
thereby, can obtain:
Figure 103493DEST_PATH_IMAGE203
to sum up, solution obtained by S4 overall method
Figure 20633DEST_PATH_IMAGE204
The utility of (2) is as follows:
Figure 65950DEST_PATH_IMAGE205
the last inequality uses the mathematical inequality
Figure 726738DEST_PATH_IMAGE206
Thus, the approximate ratio of the task-oriented charge scheduling algorithm of step S2 is demonstrated.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims (4)

1. The task-oriented multi-mobile charging vehicle scheduling method is characterized by comprising the following steps of:
acquiring and determining the position and performance parameters of the wireless chargeable sensor and the charging vehicle, and establishing a wireless chargeable sensor network model according to the acquired parameters;
wireless chargeable sensor location and performance parameters are obtained and determined, including,
setting G= (V.u.O.u.s, E) as a given undirected graph,
where s is the base station, v= { V 1 ,…,v n And O= { O) is a sensor set 1 ,…,o m Each sensor monitors one interest point, performs random event capturing task, and each interest point is covered by at least one sensor, one edge exists between the sensors and the base station, and the edge set is E;
monitoring the same interest point o j Is uniform in sensor type, monitors the same point of interest o j The charged sensor set is V j ，V j Each battery capacity of the sensor in (a) is b j The perceived power is beta j ；
Acquiring and determining charging car position and performance parameters, including,
k charging trolleys are scheduled to provide charging service for the sensors in the sensor set V, and the K trolley set is expressed as R= { R 1 ,…,r K }；
Let K charge carts of different types, sensor v i And v i′ The edge weight between the two is c k (v i ,v i′ )＝d(v i ,v i′ )·α k ；
Wherein d (v) i ,v i′ ) Representing sensor v i And v i′ Distance between alpha k Is a trolley r k Energy consumption of moving unit distance, trolley r k At the same point of interest o j Charged sensor set V j Middle sensor v i Is that the charging energy consumption is
Figure QLYQS_1
γ k For vehicles r k Charging efficiency of 0<γ k <1, mobile charging trolley r k Is B k ；
Setting a charging utility function of the mobile chargeable device;
the setting of the charging utility function of the mobile chargeable device includes,
setting each interest point o j The arrival time of the random event accords with the poisson distribution, so that one interest point o j The number of random events arriving within the time interval t is X j (t); from the probability function of poisson distribution
Figure QLYQS_2
Wherein lambda is j For the interest point o j The arrival intensity of the random event;
for sensor set V j Sensor v in (a) i The monitoring interest point is o j By |V j The representation can cover the point of interest o j And the number of sensors charged;
let point of interest o j Is of utility as
Figure QLYQS_3
Wherein b j Is V (V) j Battery capacity, beta, of each sensor in (a) j Is V (V) j The perceived power of each sensor in (a),
for the sensor set V j Each sensor v of (2) i The utility of charging is:
Figure QLYQS_4
wherein, |V j I is not less than 1 and v i Belonging to V j ；
Namely:
Figure QLYQS_5
u(P k ) Representing the utility of each path;
formalizing a task-oriented charging scheduling problem;
maximizing the sum of the utility of sensor charging, and updating the path and trolley set with the maximum utility;
the sum of the effects of maximizing sensor charging, including,
make the trolley with found path integrated as
Figure QLYQS_6
The set of paths that have been found is +.>
Figure QLYQS_7
The number of paths that have been found is K';
initialization of
Figure QLYQS_8
K′＝0；
For a given graph g= (V u O u { s }, E), K auxiliary graphs G are constructed 1 ,G 2 ,…,G K ，
Wherein G is k For finding mobile charging trolleys r k Path P of (2) k ；
The edges in graph G are weighted as
Figure QLYQS_9
For any one path P k By w k (P k ) The cost of representing the path is also known as the sum of edge weights,
Figure QLYQS_10
judging the number of the found paths as K';
the determination may further comprise the step of,
if the number of the paths K 'which are found is smaller than K, the paths K' which are found currently are expressed as
Figure QLYQS_11
Order the
Figure QLYQS_12
Representing charging trolley->
Figure QLYQS_13
Path of 1.ltoreq.q j K is more than or equal to 1 and j is more than or equal to K';
for all of
Figure QLYQS_14
Graph G through single path utility maximization algorithm k Find a start point and an end point to be s path P k So that the path P k Utility of->
Figure QLYQS_15
Maximizing and meeting the requirement of the trolley r k In path P k Energy consumption w (P) k )≤B k ；
The graph G k Each sensor v of (2) i The service utility of (a) is:
Figure QLYQS_16
wherein v is i Belonging to V j ；
Selecting a path with the greatest utility
Figure QLYQS_17
The corresponding trolley is +.>
Figure QLYQS_18
Namely:
Figure QLYQS_19
updating a set of paths that have been found
Figure QLYQS_20
Updating a set of carts that have found a path
Figure QLYQS_21
Updating the number of found paths K ' =k ' +1, and returning to the step of performing the judgment on the number of found paths being K ';
the single path utility maximization algorithm, comprising,
a1: having the current path find n sensors
Figure QLYQS_22
The set of sensors already found in the current path is L, initializing the current path +.>
Figure QLYQS_23
A2: judging the current path P k Whether the cost of (2) exceeds the energy constraint B k If w (P) k )<B k Step A3 is executed, otherwise, the path P with the greatest utility is found k Ending the algorithm;
a3: setting all sensors
Figure QLYQS_24
The computational marginal utility is expressed as:
Figure QLYQS_25
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_26
belonging to V j ；
Calculating TSP (L) and TSP (L) respectively according to nearest neighbor algorithm of tourist problem
Figure QLYQS_27
Where TSP (L) represents the path cost calculated by the sensor in set L using the nearest neighbor algorithm of the travel business problem, then the sensor
Figure QLYQS_28
Is +.>
Figure QLYQS_29
A4: selecting a sensor with the largest marginal utility to marginal cost ratio
Figure QLYQS_30
Namely:
Figure QLYQS_31
a5: judging path cost
Figure QLYQS_32
Whether or not energy constraint B is exceeded k If not, the sensor is added to the current path +.>
Figure QLYQS_33
And executing the step A6; otherwise, the algorithm ends;
a6: obtaining shortest closed path P traversing all sensors in L according to nearest neighbor algorithm of tourist problem k The currently selected sensor number n=n+1 is updated and the procedure returns to continue step A2.
2. The task oriented multi-mobile charging vehicle dispatching method of claim 1, wherein said establishing a wireless chargeable sensor network model comprises,
definition of the definition
Figure QLYQS_34
Indicating mobile charging trolley r k Is provided;
wherein q is k Is a trolley r k In path P k Except for the number of sensors served by base station s;
trolley r k In path P k The energy consumption is
Figure QLYQS_35
Wherein the method comprises the steps of
Figure QLYQS_36
Each mobile charging trolley r k Path P of (2) k The energy consumption in (a) cannot exceed the energy constraint, i.e. w (P) k )≤B k 。
3. The task-oriented multi-mobile charging car scheduling method of claim 2, wherein said formalizing task-oriented charging scheduling problem comprises,
giving a graph G= (V.u.O.u.s, E);
the method comprises the steps that K paths with different sensors are found for K trolleys in a graph G;
the goal is to maximize the sum of the charging utilities of all paths under limited energy constraints, expressed as:
Figure QLYQS_37
s.t.w(P k )≤B k ,1≤k≤K (2)
Figure QLYQS_38
where k' represents a trolley other than k.
4. The task oriented multi-mobile battery car scheduling method of claim 3, wherein said determining comprises,
if the number of the found paths K' is greater than K, the scheduling is finished, and the found paths are returned to be gathered into
Figure QLYQS_39
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