CN115713222B - Unmanned aerial vehicle perception network charging scheduling method driven by utility - Google Patents

Unmanned aerial vehicle perception network charging scheduling method driven by utility Download PDF

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CN115713222B
CN115713222B CN202310025151.1A CN202310025151A CN115713222B CN 115713222 B CN115713222 B CN 115713222B CN 202310025151 A CN202310025151 A CN 202310025151A CN 115713222 B CN115713222 B CN 115713222B
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unmanned aerial
aerial vehicle
charging
utility
perceived
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CN115713222A (en
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徐佳
许琳昊
俞欣仕
孙俊
陈文斌
李德强
徐力杰
肖甫
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Nanjing University of Posts and Telecommunications
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention discloses a utility driven unmanned aerial vehicle perception network charging scheduling method, which comprises the following steps: acquiring an unmanned aerial vehicle, a charging facility and a point of interest set based on the running state of the unmanned aerial vehicle, and constructing an unmanned aerial vehicle perception network model; constructing a perceived value model and a charging power based on charging facilities according to perceived data quality of an unmanned aerial vehicle perceived network model, and constructing a charging and billing cost model; the problem of maximizing the perception utility of the formalized unmanned aerial vehicle under the period of the perception network system; and calling a utility-driven unmanned aerial vehicle sensing network charging scheduling algorithm to obtain a scheduling scheme of each unmanned aerial vehicle, and realizing the charging scheduling of the unmanned aerial vehicle. According to the invention, by considering two aspects of service quality and charging cost, a perception utility function is constructed, and the unmanned aerial vehicle short-term charging scheduling driven by the perception utility is utilized, so that an optimal scheduling scheme can be obtained in polynomial time, the charging scheduling benefit is high, and the method has remarkable advantages in the aspect of multi-round scheduling.

Description

Unmanned aerial vehicle perception network charging scheduling method driven by utility
Technical Field
The invention relates to the technical field of unmanned aerial vehicle perception network charging scheduling, in particular to a utility-driven unmanned aerial vehicle perception network charging scheduling method.
Background
In recent years, unmanned aerial vehicle sensing networks have become increasingly popular for use in power inspection, transportation, agriculture, and utility applications. Unmanned aerial vehicle perception has solved fixed and mobile perception equipment of on-vehicle unable to reach mill, enterprise, district, unit, scenic spot inner space, mountain region, river, forest, planting, the regional problem of carrying out daily perception of breeding, has greatly enlarged the perception scope. However, unmanned aerial vehicles require not only perception and communication, but also additional energy to sustain take-off, landing, flying and hovering, compared to conventional sensors. Unmanned aerial vehicles typically meet the power consumption of sensors, motors, i.e., other devices by being battery powered. Taking the rotary wing unmanned aerial vehicle Mavic Pro Platinum in the large area as an example, the rotary wing unmanned aerial vehicle is limited by the problems of high power consumption and battery capacity of a flight control system, the flight time is generally between 20 minutes and 30 minutes, and the short endurance time becomes a bottleneck problem of wide application of an unmanned aerial vehicle sensing network; taking inspection as an example, handling large batches of drones and their charging would become complex and laborious. Efficient charge scheduling schemes are key to extending the availability of the drone.
To address the challenges described above, unmanned aerial vehicle charging stations are used to autonomously charge unmanned aerial vehicles without human intervention. Both unmanned aerial vehicle wired charging stations and wireless charging stations have been put to practical use. However, under the condition that the number of charging stations is limited, it cannot be guaranteed that charging services are provided for each unmanned aerial vehicle at any time, which affects the execution of subsequent perception tasks of the unmanned aerial vehicle, and results in reduction of service quality of the unmanned aerial vehicle perception network. Therefore, there is a need to design efficient drone charging schedules to maintain continuous operation of the drone aware network. In the unmanned aerial vehicle perception network, high charging cost can be caused if only the perception quality of the unmanned aerial vehicle is considered, and only the charging cost is considered, so that the perception quality cannot reach the expectations.
Furthermore, most of the studies on unmanned aerial vehicle charging scheduling are from the viewpoint of single round scheduling, all the information is known by default, and there is no previous scheduling result, and whether this scheduling has an influence on the following scheduling is also not considered, but in fact, the scheduling of each unmanned aerial vehicle is influenced not only by the result of the previous scheduling, but also by other unmanned aerial vehicles and their subsequent allocation. Taking inspection as an example, if a single round scheduling scheme is applied to each round of inspection, overload of a charging station is likely to be caused, and even execution of subsequent inspection tasks of the unmanned aerial vehicle is affected.
Disclosure of Invention
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the utility-driven unmanned aerial vehicle perception network charging scheduling method provided by the invention solves the problems that the existing unmanned aerial vehicle charging scheduling mostly only considers single-round scheduling, has no previous scheduling result, does not consider the influence of previous scheduling on subsequent scheduling, cannot guarantee to provide charging service for each unmanned aerial vehicle at any time, influences the execution of subsequent perception and inspection tasks of the unmanned aerial vehicle, and causes the reduction of service quality of the unmanned aerial vehicle perception network.
In order to solve the technical problems, the invention provides the following technical scheme:
the embodiment of the invention provides a utility driven unmanned aerial vehicle perception network charging scheduling method, which comprises the following steps:
acquiring an unmanned aerial vehicle, a charging facility and a point of interest set based on the running state of the unmanned aerial vehicle, and constructing an unmanned aerial vehicle perception network model;
constructing a perception value model according to the perceived data quality of the unmanned aerial vehicle perception network model and establishing a charging and billing cost model based on the charging power of the charging facility;
formalizing a problem of maximizing perceived utility of the unmanned aerial vehicle under the period of a perceived network system;
and calling a utility-driven unmanned aerial vehicle sensing network charging scheduling algorithm to obtain a scheduling scheme of each unmanned aerial vehicle, and realizing the charging scheduling of the unmanned aerial vehicle.
As a preferable scheme of the utility driven unmanned aerial vehicle perceived network charging scheduling method, the invention comprises the following steps: the unmanned aerial vehicle set is expressed as:
Figure 321652DEST_PATH_IMAGE001
wherein, any unmanned aerial vehicle
Figure 583000DEST_PATH_IMAGE002
；
The interest points are all responsible for perception by at least one unmanned aerial vehicle, and the interest point set is expressed as:
Figure 97158DEST_PATH_IMAGE003
wherein, any interest point
Figure 226788DEST_PATH_IMAGE004
；
The utility that charges all is located a charging station, and unmanned aerial vehicle needs to fly to charge on the facility that charges, the facility collection that charges, the representation is:
Figure 634635DEST_PATH_IMAGE005
wherein any charging facility
Figure 191519DEST_PATH_IMAGE006
。
As a preferable scheme of the utility driven unmanned aerial vehicle perceived network charging scheduling method, the invention comprises the following steps: the man-machine perception network model comprises:
the unmanned aerial vehicle, the interest points and the charging facilities are uniformly distributed on a two-dimensional plane, the unmanned aerial vehicle is considered to be heterogeneous, and the unmanned aerial vehicle is assumed to reciprocate between the charging station and the sensing position in a flying state of a uniform speed straight line;
dispersing a section of continuous time into a plurality of time slots, when the electric quantity of the unmanned aerial vehicle is lower than a fixed threshold value, leaving the sensing position, flying to a charging station for charging, and returning to the sensing position again to execute a sensing task after full charge;
the time required for the drone to traverse between the charging station and the perceived location is expressed as:
Figure 192973DEST_PATH_IMAGE007
wherein,,
Figure 126294DEST_PATH_IMAGE008
for the distance between the perceived position of the drone and the charging station,
Figure 903495DEST_PATH_IMAGE009
the distance that unmanned aerial vehicle can fly in the unit time slot;
unmanned aerial vehicle adopts full charge mode at the charging station, unmanned aerial vehicle's charge demand at the charging station, represents as:
Figure 631279DEST_PATH_IMAGE010
wherein,,
Figure 854450DEST_PATH_IMAGE011
is the battery capacity of the unmanned aerial vehicle,
Figure 450516DEST_PATH_IMAGE012
for a fixed threshold of the battery capacity of the drone,
Figure 708322DEST_PATH_IMAGE013
the power consumption for the unmanned aerial vehicle to come and go between the charging station and the sensing position;
the time it takes for the drone to perform a perception task each time is expressed as:
Figure 872588DEST_PATH_IMAGE014
wherein,,
Figure 848634DEST_PATH_IMAGE015
is the working power of the unmanned aerial vehicle.
As a preferable scheme of the utility driven unmanned aerial vehicle perceived network charging scheduling method, the invention comprises the following steps: the perceptual value model comprises:
when unmanned aerial vehicle arrives the charging station, with the perception data that once only upload and carry, the produced perception value of unmanned aerial vehicle every round, represent as:
Figure 467965DEST_PATH_IMAGE016
wherein,,
Figure 845857DEST_PATH_IMAGE017
for the perceived quality that the unmanned aerial vehicle can produce for the point of interest in a unit time slot,
Figure 915444DEST_PATH_IMAGE018
a set of points of interest responsible for perception by the drone,
Figure 503420DEST_PATH_IMAGE019
is the weight coefficient of the interest point.
As a preferable scheme of the utility driven unmanned aerial vehicle perceived network charging scheduling method, the invention comprises the following steps: according to the charging power of the charging facility, a charging and billing cost model is established, which comprises the following steps:
the time occupied by the unmanned aerial vehicle in the time slot selection charging facility for charging is expressed as:
Figure 316655DEST_PATH_IMAGE020
wherein,,
Figure 283474DEST_PATH_IMAGE021
for charging power of charging facility, q is time slot and
Figure 169303DEST_PATH_IMAGE022
，
Figure 854362DEST_PATH_IMAGE023
for the charging demand of the unmanned aerial vehicle at the charging station,
Figure 471288DEST_PATH_IMAGE024
is the number of time slots;
the charging cost generated by charging the unmanned aerial vehicle at the time slot selection charging facility is expressed as:
Figure 558193DEST_PATH_IMAGE025
wherein,,
Figure 359796DEST_PATH_IMAGE026
the price is charged for a unit time slot of the charging facility.
As a preferable scheme of the utility driven unmanned aerial vehicle perceived network charging scheduling method, the invention comprises the following steps: formalizing a perceived utility maximization problem for the unmanned aerial vehicle perceived network system period, comprising:
the perceived utility obtained by the unmanned aerial vehicle in the time slot selecting charging facility for charging is expressed as:
Figure 532151DEST_PATH_IMAGE027
wherein,,
Figure 687189DEST_PATH_IMAGE028
the perceived value generated for each round of unmanned aerial vehicle,
Figure 503966DEST_PATH_IMAGE029
the time taken for the drone to charge at the time slot selection charging facility,
Figure 351837DEST_PATH_IMAGE030
for the time required for the drone to traverse between the charging station and the perceived location,
Figure 11488DEST_PATH_IMAGE031
time spent for the drone to perform a perception task each time;
the scheduling scheme is as follows:
Figure 970217DEST_PATH_IMAGE032
wherein,,
Figure 625189DEST_PATH_IMAGE033
a decision variable representing whether the unmanned aerial vehicle selects a charging facility for charging in the time slot;
the total perceived utility that the drone can obtain through the charge schedule throughout the cycle is expressed as:
Figure 643961DEST_PATH_IMAGE034
the problem of maximizing perceived utility under the period of the unmanned aerial vehicle perceived network system is formalized, expressed as:
Figure 790908DEST_PATH_IMAGE035
。
as a preferable scheme of the utility driven unmanned aerial vehicle perceived network charging scheduling method, the invention comprises the following steps: further comprises:
decision constraints, expressed as:
Figure 927229DEST_PATH_IMAGE036
wherein the decision variables
Figure 843233DEST_PATH_IMAGE037
Is a 0-1 variable;
for any charging facility, only one unmanned aerial vehicle can be provided with charging service constraint in any time slot, which is expressed as:
Figure 767326DEST_PATH_IMAGE038
for any unmanned aerial vehicle, only one charging facility can be selected for charging constraint in any time slot, which is expressed as:
Figure 401570DEST_PATH_IMAGE039
。
as a preferable scheme of the utility driven unmanned aerial vehicle perceived network charging scheduling method, the invention comprises the following steps: invoking a utility-driven unmanned aerial vehicle-aware network charging scheduling algorithm, comprising:
inputting unmanned aerial vehicle charging scheduling parameters;
the method comprises the steps of finding out the maximum time slot number and the minimum time slot number occupied by the fact that all unmanned aerial vehicles begin to charge from a certain time slot until returning to a charging station again to submit sensing data;
the maximum number of time slots is expressed as:
Figure 561156DEST_PATH_IMAGE040
the minimum number of time slots is expressed as:
Figure 597245DEST_PATH_IMAGE041
before setting up
Figure 692240DEST_PATH_IMAGE042
The maximum perceived utility of each time slot is
Figure 689146DEST_PATH_IMAGE043
Front (front)
Figure 793368DEST_PATH_IMAGE044
Optimal charge scheduling for individual timeslots
Figure 152805DEST_PATH_IMAGE045
Initializing slave
Figure 808915DEST_PATH_IMAGE046
To the point of
Figure 152171DEST_PATH_IMAGE047
Maximum perceived utility and optimal charge schedule of (a);
for the following
Figure 60084DEST_PATH_IMAGE048
Setting up
Figure 805186DEST_PATH_IMAGE049
Figure 615885DEST_PATH_IMAGE050
；
Updating
Figure 446438DEST_PATH_IMAGE051
When (when)
Figure 892463DEST_PATH_IMAGE052
When each time slot is calculated to obtain the front
Figure 616705DEST_PATH_IMAGE044
Maximum perceived utility of individual timeslots
Figure 489983DEST_PATH_IMAGE053
Optimal scheduling
Figure 807832DEST_PATH_IMAGE054
Continuing to update iteratively, otherwise, returning to the previous step
Figure 791969DEST_PATH_IMAGE055
Maximum perceived utility of individual timeslots
Figure 387029DEST_PATH_IMAGE056
And optimal charge schedule
Figure 431209DEST_PATH_IMAGE057
。
As a preferable scheme of the utility driven unmanned aerial vehicle perceived network charging scheduling method, the invention comprises the following steps: before the calculation
Figure 236354DEST_PATH_IMAGE044
Maximum perceived utility of individual timeslots
Figure 24181DEST_PATH_IMAGE058
Optimal scheduling
Figure 723016DEST_PATH_IMAGE059
Comprising:
before initialization
Figure 672517DEST_PATH_IMAGE060
Maximum perceived utility of individual timeslots
Figure 699379DEST_PATH_IMAGE061
Assume that from the last charge allocated starting time slot to time slot
Figure 664799DEST_PATH_IMAGE044
The number of occupied time slots is
Figure 93506DEST_PATH_IMAGE062
；
If it is
Figure 479488DEST_PATH_IMAGE063
When satisfied, assume that at the time slot
Figure 259225DEST_PATH_IMAGE064
Charging allocation is performed up to time slot
Figure 247910DEST_PATH_IMAGE044
Maximum perceived utility that can be produced
Figure 531124DEST_PATH_IMAGE065
For any unmanned aerial vehicle
Figure 353586DEST_PATH_IMAGE066
From time slots
Figure 230406DEST_PATH_IMAGE067
Selecting arbitrary charging devices
Figure 163727DEST_PATH_IMAGE068
Start charging until time slot
Figure 301448DEST_PATH_IMAGE060
The perceived utility that can be obtained is expressed as:
Figure 294811DEST_PATH_IMAGE069
based on the bipartite graph, the KM algorithm is adopted to solve the maximum weight matching of the bipartite graph
Figure 908195DEST_PATH_IMAGE070
By matching
Figure 379628DEST_PATH_IMAGE071
Performing charge distribution corresponding sensing effect as
Figure 637434DEST_PATH_IMAGE072
；
The bipartite graph is expressed as:
Figure 904162DEST_PATH_IMAGE073
wherein,,
Figure 614629DEST_PATH_IMAGE074
representation for any unmanned aerial vehicle
Figure 889752DEST_PATH_IMAGE075
Can be distributed to any charging facilities
Figure 267644DEST_PATH_IMAGE076
Aggregation of
Figure 461865DEST_PATH_IMAGE077
Representation of
Figure 925207DEST_PATH_IMAGE078
A corresponding set of side weights;
when (when)
Figure 738443DEST_PATH_IMAGE079
In this case, it is assumed that the time slot is from the start time slot to the time slot of the last charge allocation
Figure 970841DEST_PATH_IMAGE044
The number of the occupied optimal time slots is
Figure 86696DEST_PATH_IMAGE080
Time slot
Figure 771755DEST_PATH_IMAGE081
Maximum weight match of (2) is
Figure 388681DEST_PATH_IMAGE082
Updating
Figure 600219DEST_PATH_IMAGE083
,
Figure 277188DEST_PATH_IMAGE084
,
Figure 449544DEST_PATH_IMAGE085
Otherwise update
Figure 604582DEST_PATH_IMAGE086
Continuing iteration;
if it is
Figure 185473DEST_PATH_IMAGE087
If not, then according to
Figure 767764DEST_PATH_IMAGE088
And
Figure 161837DEST_PATH_IMAGE082
updating
Figure 245199DEST_PATH_IMAGE089
。
As a preferable scheme of the utility driven unmanned aerial vehicle perceived network charging scheduling method, the invention comprises the following steps: said basis is
Figure 41117DEST_PATH_IMAGE088
And
Figure 325468DEST_PATH_IMAGE082
updating
Figure 472415DEST_PATH_IMAGE089
Comprising:
initialization of
Figure 844622DEST_PATH_IMAGE090
And define decision time slot as
Figure 760625DEST_PATH_IMAGE091
；
If it is
Figure 684719DEST_PATH_IMAGE092
When satisfied, when
Figure 443596DEST_PATH_IMAGE093
，
Figure 744128DEST_PATH_IMAGE094
When updating
Figure 514638DEST_PATH_IMAGE095
，
Figure 875212DEST_PATH_IMAGE096
Otherwise update
Figure 370653DEST_PATH_IMAGE097
，
Figure 209296DEST_PATH_IMAGE098
The method comprises the steps of carrying out a first treatment on the surface of the Continuing iteration after completing the updateUpdating;
if it is
Figure 99892DEST_PATH_IMAGE092
If not, return
Figure 631367DEST_PATH_IMAGE089
。
Compared with the prior art, the invention has the beneficial effects that: according to the invention, by considering two aspects of service quality and charging cost, a perception utility function is constructed, and the unmanned aerial vehicle short-term charging scheduling driven by the perception utility is utilized, so that an optimal scheduling scheme can be obtained in polynomial time, the charging scheduling benefit is high, and the method has remarkable advantages in the aspect of multi-round scheduling.
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 a flowchart of a sensing scheme and a charging scheme of a utility driven unmanned aerial vehicle sensing network charging scheduling method according to an embodiment of the invention;
fig. 2 is a network model schematic diagram of a utility driven unmanned aerial vehicle aware network charging scheduling method according to an embodiment of the present invention;
fig. 3 is a flowchart of a charge scheduling algorithm of a utility driven unmanned aerial vehicle aware network charge scheduling method according to an embodiment of the present invention;
fig. 4 is a diagram illustrating a method for utility-driven unmanned aerial vehicle-aware network charge scheduling before solving according to an embodiment of the present invention
Figure 99258DEST_PATH_IMAGE099
Maximum perceived utility of individual timeslots
Figure 7171DEST_PATH_IMAGE100
Optimal scheduling
Figure 752273DEST_PATH_IMAGE101
Is a flow chart of the algorithm;
fig. 5 is a schematic diagram illustrating a utility-driven unmanned aerial vehicle-aware network charging scheduling method according to an embodiment of the present invention
Figure 454650DEST_PATH_IMAGE102
And
Figure 160569DEST_PATH_IMAGE103
updating
Figure 75435DEST_PATH_IMAGE104
Is an algorithm flow chart of (a).
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 to fig. 5, in an embodiment of the present invention, the embodiment provides a utility driven unmanned aerial vehicle perceived network charging scheduling method, including:
s1, acquiring an unmanned aerial vehicle, a charging facility and an interest point set based on the running state of the unmanned aerial vehicle, and constructing an unmanned aerial vehicle perception network model;
still further, the unmanned aerial vehicle set, expressed as:
Figure 940623DEST_PATH_IMAGE105
wherein, any unmanned aerial vehicle
Figure 938535DEST_PATH_IMAGE106
；
The interest points are all responsible for perception by at least one unmanned aerial vehicle, and the interest point set is expressed as:
Figure 256383DEST_PATH_IMAGE107
wherein, any interest point
Figure 240520DEST_PATH_IMAGE108
；
Charging facilities all are located in a charging station, and unmanned aerial vehicle needs to fly to charge on the charging facilities, and the charging facilities collection represents as:
Figure 960214DEST_PATH_IMAGE109
wherein any charging facility
Figure 112716DEST_PATH_IMAGE110
。
Still further, the human-machine-aware network model includes:
the unmanned aerial vehicle, the interest points and the charging facilities are uniformly distributed on a two-dimensional plane, the unmanned aerial vehicle is considered to be heterogeneous, and the unmanned aerial vehicle is assumed to come and go between the charging station and the sensing position in a flying state of a uniform speed straight line;
dispersing a section of continuous time into a plurality of time slots, when the electric quantity of the unmanned aerial vehicle is lower than a fixed threshold value, leaving the sensing position, flying to a charging station for charging, and returning to the sensing position again to execute a sensing task after full charge;
the time required for the drone to travel between the charging station and the perceived location is expressed as:
Figure 917861DEST_PATH_IMAGE111
wherein,,
Figure 705688DEST_PATH_IMAGE008
for the distance between the perceived position of the drone and the charging station,
Figure 545468DEST_PATH_IMAGE112
the distance that unmanned aerial vehicle can fly in the unit time slot;
unmanned aerial vehicle adopts full charge mode at the charging station, unmanned aerial vehicle's charge demand at the charging station, represents as:
Figure 885183DEST_PATH_IMAGE113
wherein,,
Figure 646465DEST_PATH_IMAGE114
is the battery capacity of the unmanned aerial vehicle,
Figure 503563DEST_PATH_IMAGE115
for a fixed threshold of the battery capacity of the drone,
Figure 542057DEST_PATH_IMAGE116
the power consumption for the unmanned aerial vehicle to come and go between the charging station and the sensing position;
the time it takes for the drone to perform a perceived task each time is expressed as:
Figure 928039DEST_PATH_IMAGE117
wherein,,
Figure 707776DEST_PATH_IMAGE118
is the working power of the unmanned aerial vehicle.
S2, constructing a perception value model according to the perceived data quality of the unmanned aerial vehicle perception network model and a charging cost model based on the charging power of the charging facility;
still further, the perceptual value model comprises:
when unmanned aerial vehicle arrives the charging station, with the perception data that once only upload carried, the produced perception value of unmanned aerial vehicle every round, represent as:
Figure 837406DEST_PATH_IMAGE119
wherein,,
Figure 245254DEST_PATH_IMAGE120
for the perceived quality that the unmanned aerial vehicle can produce for the point of interest in a unit time slot,
Figure 802137DEST_PATH_IMAGE121
a set of points of interest responsible for perception by the drone,
Figure 803591DEST_PATH_IMAGE122
is the weight coefficient of the interest point.
Further, according to the charging power of the charging facility, a charging and billing cost model is established, including:
the time occupied by the unmanned aerial vehicle in the time slot selecting charging facility for charging is expressed as:
Figure 851094DEST_PATH_IMAGE123
wherein,,
Figure 254393DEST_PATH_IMAGE124
for charging power of charging facility, q is time slot and
Figure 247757DEST_PATH_IMAGE125
，
Figure 470928DEST_PATH_IMAGE126
for the charging demand of the unmanned aerial vehicle at the charging station,
Figure 332574DEST_PATH_IMAGE055
is the number of time slots;
the charging cost generated by the unmanned aerial vehicle in the time slot selecting charging facility is expressed as:
Figure 590380DEST_PATH_IMAGE127
wherein,,
Figure 489066DEST_PATH_IMAGE128
the price is charged for a unit time slot of the charging facility.
S3, formalizing the problem of maximizing the perception utility of the unmanned aerial vehicle under the period of a perception network system;
further, formalizing the problem of maximizing perceived utility of an unmanned aerial vehicle under a perceived network system period includes:
the perceived utility obtained by the unmanned aerial vehicle in the time slot selecting charging facility for charging is expressed as:
Figure 74899DEST_PATH_IMAGE129
wherein,,
Figure 350022DEST_PATH_IMAGE130
the perceived value generated for each round of unmanned aerial vehicle,
Figure 727914DEST_PATH_IMAGE131
the time taken for the drone to charge at the time slot selection charging facility,
Figure 63080DEST_PATH_IMAGE132
for the time required for the drone to traverse between the charging station and the perceived location,
Figure 119898DEST_PATH_IMAGE133
time spent for the drone to perform a perception task each time;
the scheduling scheme is as follows:
Figure 198713DEST_PATH_IMAGE134
wherein,,
Figure 165532DEST_PATH_IMAGE135
a decision variable representing whether the unmanned aerial vehicle selects a charging facility for charging in the time slot;
the total perceived utility that the drone can obtain through the charge schedule throughout the cycle is expressed as:
Figure 779921DEST_PATH_IMAGE136
the problem of maximizing perceived utility under the period of the unmanned aerial vehicle perceived network system is formalized, expressed as:
Figure 730560DEST_PATH_IMAGE137
。
still further, still include:
decision constraints, expressed as:
Figure 347486DEST_PATH_IMAGE138
wherein the decision variables
Figure 434391DEST_PATH_IMAGE139
Is a 0-1 variable;
for any charging facility, only one unmanned aerial vehicle can be provided with charging service constraint in any time slot, which is expressed as:
Figure 704835DEST_PATH_IMAGE140
for any unmanned aerial vehicle, only one charging facility can be selected for charging constraint in any time slot, which is expressed as:
Figure 877190DEST_PATH_IMAGE141
。
s4, calling a utility-driven unmanned aerial vehicle sensing network charging scheduling algorithm to obtain a scheduling scheme of each unmanned aerial vehicle, and realizing charging scheduling of the unmanned aerial vehicle;
further, invoking a utility driven unmanned aerial vehicle-aware network charging scheduling algorithm, comprising:
inputting unmanned aerial vehicle charging scheduling parameters;
it should be noted that the input parameters include: unmanned aerial vehicle assembly
Figure 297807DEST_PATH_IMAGE142
Point of interest set
Figure 114585DEST_PATH_IMAGE143
Charging facility set
Figure 962455DEST_PATH_IMAGE144
Number of time slots
Figure 622107DEST_PATH_IMAGE145
Charge demand
Figure 580835DEST_PATH_IMAGE146
Any unmanned aerial vehicle
Figure 766966DEST_PATH_IMAGE147
Required time to and from charging station and perceived location
Figure 785738DEST_PATH_IMAGE148
Any unmanned aerial vehicle
Figure 667106DEST_PATH_IMAGE149
Time spent performing a sensing task each time
Figure 429526DEST_PATH_IMAGE150
Any unmanned aerial vehicle
Figure 719430DEST_PATH_IMAGE147
Perceived value generated by each round
Figure 909103DEST_PATH_IMAGE151
Charging facility
Figure 543347DEST_PATH_IMAGE152
Charging power of (2)
Figure 843878DEST_PATH_IMAGE153
Charging facility
Figure 473443DEST_PATH_IMAGE154
Charging price per unit time slot of (2)
Figure 834017DEST_PATH_IMAGE155
。
The method comprises the steps of finding out the maximum time slot number and the minimum time slot number occupied by the fact that all unmanned aerial vehicles begin to charge from a certain time slot until returning to a charging station again to submit sensing data;
the maximum number of slots, expressed as:
Figure 689977DEST_PATH_IMAGE156
the minimum number of slots, expressed as:
Figure 669566DEST_PATH_IMAGE157
before setting up
Figure 560162DEST_PATH_IMAGE158
The maximum perceived utility of each time slot is
Figure 91637DEST_PATH_IMAGE159
Front (front)
Figure 293948DEST_PATH_IMAGE158
Optimal charge scheduling for individual timeslots
Figure 936282DEST_PATH_IMAGE160
Initializing slave
Figure 681384DEST_PATH_IMAGE161
To the point of
Figure 383761DEST_PATH_IMAGE162
Maximum perceived utility and optimal charge schedule of (a);
for the following
Figure 588215DEST_PATH_IMAGE163
Setting up
Figure 34240DEST_PATH_IMAGE164
Figure 899428DEST_PATH_IMAGE165
；
Updating
Figure 772706DEST_PATH_IMAGE166
When (when)
Figure 684030DEST_PATH_IMAGE167
When each time slot is calculated to obtain the front
Figure 933746DEST_PATH_IMAGE042
Maximum perceived utility of individual timeslots
Figure 653440DEST_PATH_IMAGE159
Optimal scheduling
Figure 572986DEST_PATH_IMAGE168
Continuing to update iteratively, otherwise, returning to the previous step
Figure 112552DEST_PATH_IMAGE169
Maximum perceived utility of individual timeslots
Figure 900379DEST_PATH_IMAGE170
And optimal charge schedule
Figure 474580DEST_PATH_IMAGE171
。
Further, before calculation
Figure 814294DEST_PATH_IMAGE042
Maximum perceived utility of individual timeslots
Figure 106735DEST_PATH_IMAGE172
Optimal scheduling
Figure 432674DEST_PATH_IMAGE173
Comprising:
before initialization
Figure 126961DEST_PATH_IMAGE042
Maximum perceived utility of individual timeslots
Figure 904422DEST_PATH_IMAGE174
Assume that from the last charge allocated starting time slot to time slot
Figure 418580DEST_PATH_IMAGE042
The number of occupied time slots is
Figure 548210DEST_PATH_IMAGE175
；
If it is
Figure 97003DEST_PATH_IMAGE176
When satisfied, assume that at the time slot
Figure 778520DEST_PATH_IMAGE177
Charging allocation is performed up to time slot
Figure 514395DEST_PATH_IMAGE042
Maximum perceived utility that can be produced
Figure 447716DEST_PATH_IMAGE178
For any unmanned aerial vehicle
Figure 726382DEST_PATH_IMAGE179
From time slots
Figure 454167DEST_PATH_IMAGE180
Selecting arbitrary charging devices
Figure 208496DEST_PATH_IMAGE181
Start charging until time slot
Figure 679929DEST_PATH_IMAGE042
The perceived utility that can be obtained is expressed as:
Figure 62368DEST_PATH_IMAGE182
based on the bipartite graph, the KM algorithm is adopted to solve the maximum weight matching of the bipartite graph
Figure 695475DEST_PATH_IMAGE183
By matching
Figure 671521DEST_PATH_IMAGE184
Performing charge distribution corresponding sensing effect as
Figure 320546DEST_PATH_IMAGE185
；
Two figures, expressed as:
Figure 432859DEST_PATH_IMAGE186
wherein,,
Figure 768025DEST_PATH_IMAGE187
representation for any unmanned aerial vehicle
Figure 965788DEST_PATH_IMAGE188
Can be distributed to any charging facilities
Figure 169236DEST_PATH_IMAGE189
Aggregation of
Figure 870476DEST_PATH_IMAGE190
Representation of
Figure 376544DEST_PATH_IMAGE191
A corresponding set of side weights;
when (when)
Figure 936969DEST_PATH_IMAGE192
In this case, it is assumed that the time slot is from the start time slot to the time slot of the last charge allocation
Figure 553895DEST_PATH_IMAGE158
The number of the occupied optimal time slots is
Figure 640800DEST_PATH_IMAGE193
Time slot
Figure 317769DEST_PATH_IMAGE194
Maximum weight match of (2) is
Figure 614758DEST_PATH_IMAGE082
Updating
Figure 35375DEST_PATH_IMAGE195
,
Figure 976787DEST_PATH_IMAGE196
,
Figure 824657DEST_PATH_IMAGE197
Otherwise update
Figure 749888DEST_PATH_IMAGE198
Continuing iteration;
if it is
Figure 816938DEST_PATH_IMAGE199
If not, then according to
Figure 878435DEST_PATH_IMAGE200
And
Figure 631628DEST_PATH_IMAGE103
updating
Figure 903209DEST_PATH_IMAGE201
。
Further, according to
Figure 665629DEST_PATH_IMAGE202
And
Figure 581632DEST_PATH_IMAGE103
updating
Figure 36884DEST_PATH_IMAGE201
Comprising:
initialization of
Figure 405549DEST_PATH_IMAGE203
And define decision time slot as
Figure 581446DEST_PATH_IMAGE204
；
If it is
Figure 351956DEST_PATH_IMAGE205
When satisfied, when
Figure 712530DEST_PATH_IMAGE206
，
Figure 693125DEST_PATH_IMAGE207
When updating
Figure 531768DEST_PATH_IMAGE208
，
Figure 422363DEST_PATH_IMAGE096
Otherwise update
Figure 327740DEST_PATH_IMAGE209
，
Figure 405418DEST_PATH_IMAGE210
The method comprises the steps of carrying out a first treatment on the surface of the Continuing to iterate the updating after finishing the updating;
if it is
Figure 47751DEST_PATH_IMAGE205
If not, return
Figure 917487DEST_PATH_IMAGE201
。
Example 2
Referring to fig. 1 to 5, an embodiment of the present invention is shown, in which the utility-driven unmanned aerial vehicle perceived network charging scheduling method provided by the present invention is that a time complexity is
Figure 619864DEST_PATH_IMAGE211
Polynomial time optimization algorithm of (c).
Optimal solution to the problem of maximizing perceived utility if formalized
Figure 450417DEST_PATH_IMAGE212
The maximum benefit generated is
Figure 896442DEST_PATH_IMAGE213
Wherein, the method comprises the steps of, wherein,
Figure 371417DEST_PATH_IMAGE214
then
Figure 244695DEST_PATH_IMAGE215
Is the sub-problem, i.e. before
Figure 562543DEST_PATH_IMAGE216
Optimal solution of maximum benefit of each time slot, the maximum benefit generated is
Figure 812259DEST_PATH_IMAGE217
And (2) and
Figure 391008DEST_PATH_IMAGE218
。
further, the anti-evidence method is adopted for proving:
assuming that the optimal solution of the original problem is still
Figure 435187DEST_PATH_IMAGE219
But does not contain the optimal solution of the sub-problem, i.e. the sub-problem exists in solution
Figure 240332DEST_PATH_IMAGE220
So that the front part
Figure 28160DEST_PATH_IMAGE221
Benefit generated by each time slot
Figure 716542DEST_PATH_IMAGE222
And (2) and
Figure 931623DEST_PATH_IMAGE223
the method comprises the steps of carrying out a first treatment on the surface of the I.e. currently
Figure 83118DEST_PATH_IMAGE221
The time slots are not changed when the time slots are randomly planned
Figure 409058DEST_PATH_IMAGE224
Unmanned aerial vehicle state under and chargeThe status of the facility, and therefore the time slot
Figure 837765DEST_PATH_IMAGE225
The optimal allocation is still
Figure 489326DEST_PATH_IMAGE226
。
Further, the method comprises the steps of,
Figure 878850DEST_PATH_IMAGE227
，
Figure 8480DEST_PATH_IMAGE228
indicating that the original problem has a ratio
Figure 557273DEST_PATH_IMAGE229
A better solution, which contradicts the original assumption. Thus, the formalized perceptual utility maximization problem has optimal substructuring, i.e., the optimal solution of the original problem contains the optimal solution of its sub-problem. Therefore, the scheduling scheme obtained by the unmanned aerial vehicle perception network charging scheduling algorithm driven by the utility is the optimal scheme.
Example 3
Referring to fig. 2 to fig. 5, in order to better verify and explain the technical effects adopted in the method according to the present invention, the verification test in the embodiment is used to verify the actual effects of the method according to the present invention, which specifically includes:
as shown in FIG. 2, in a two-dimensional plane
Figure 114157DEST_PATH_IMAGE230
On, use
Figure 240244DEST_PATH_IMAGE231
Representing a collection of unmanned aerial vehicles,
Figure 907986DEST_PATH_IMAGE232
a set of points of interest is represented,
Figure 311286DEST_PATH_IMAGE233
indicating the collection of charging facilities, the number of time slots
Figure 412972DEST_PATH_IMAGE234
. The specific coordinates are shown in table 1:
TABLE 1 entity coordinates
Figure 901722DEST_PATH_IMAGE235
The relevant parameters of the unmanned aerial vehicle are set as follows:
Figure 373154DEST_PATH_IMAGE236
，
Figure 630960DEST_PATH_IMAGE237
the method comprises the steps of carrying out a first treatment on the surface of the The charging facility related parameters are set as follows:
Figure 919859DEST_PATH_IMAGE238
the method comprises the steps of carrying out a first treatment on the surface of the The charging time and the perceived utility corresponding to the charging of the unmanned aerial vehicle in the charging station by different charging facilities are shown in tables 2 and 3, the last decimal point of the actual result is reserved after rounding, and the rule is adopted in the embodiment.
Table 2:
Figure 895906DEST_PATH_IMAGE239
from the following components
Figure 639871DEST_PATH_IMAGE240
Actual number of time slots occupied for charging
Figure 893129DEST_PATH_IMAGE241
Figure 962716DEST_PATH_IMAGE242
Table 3:
Figure 160479DEST_PATH_IMAGE239
from the following components
Figure 98348DEST_PATH_IMAGE240
Sensing utility obtained by charging and timely returning to charging station
Figure 596325DEST_PATH_IMAGE243
Figure 836814DEST_PATH_IMAGE244
As shown in fig. 3, the specific process of the utility driven unmanned aerial vehicle perceived network charging scheduling algorithm is as follows:
a1: inputting parameters: unmanned aerial vehicle assembly
Figure 521873DEST_PATH_IMAGE245
Point of interest set
Figure 247121DEST_PATH_IMAGE246
Charging facility set
Figure 334026DEST_PATH_IMAGE247
Number of time slots
Figure 10995DEST_PATH_IMAGE248
As well as other parameters;
a2: finding out the maximum number of time slots occupied from when charging starts from a certain time slot until the time slots return to the charging station again to submit the sensing data in all unmanned aerial vehicles
Figure 307984DEST_PATH_IMAGE249
And minimum number of slots
Figure 463022DEST_PATH_IMAGE250
；
A3: before setting up
Figure 670012DEST_PATH_IMAGE251
The maximum perceived utility of each time slot is
Figure 127670DEST_PATH_IMAGE252
Front (front)
Figure 787321DEST_PATH_IMAGE251
Optimal charge scheduling for individual timeslots
Figure 746050DEST_PATH_IMAGE253
Initializing slave
Figure 541968DEST_PATH_IMAGE254
To the point of
Figure 685373DEST_PATH_IMAGE255
Maximum perceived utility and optimal charge scheduling for (a)
Figure 566741DEST_PATH_IMAGE256
Setting up
Figure 329161DEST_PATH_IMAGE257
Figure 619066DEST_PATH_IMAGE258
；
A4: updating
Figure 543159DEST_PATH_IMAGE259
；
A5: if it is
Figure 177403DEST_PATH_IMAGE260
Executing steps A6 to A7, otherwise executing step A8;
a6: before solving
Figure 477934DEST_PATH_IMAGE158
Maximum perceived utility of individual timeslots
Figure 638657DEST_PATH_IMAGE252
And charge schedule
Figure 733652DEST_PATH_IMAGE253
The method comprises the steps of carrying out a first treatment on the surface of the To be used for
Figure 855192DEST_PATH_IMAGE261
In the case of an example of this,
Figure 959414DEST_PATH_IMAGE263
step a6.1 is entered as shown in fig. 4;
a6.1: initializing the maximum perceived utility of the first 5 time slots
Figure 459797DEST_PATH_IMAGE264
Setting the time slot from the last charge allocated start time slot to time slot
Figure 991272DEST_PATH_IMAGE158
The number of occupied time slots is
Figure 334529DEST_PATH_IMAGE265
；
A6.2: if it is
Figure 242442DEST_PATH_IMAGE266
Executing steps A6.3 to A6.7, otherwise executing step A6.8;
a6.3: set in time slot
Figure 112178DEST_PATH_IMAGE267
Charging allocation is performed up to time slot
Figure 548976DEST_PATH_IMAGE158
Maximum perceived utility that can be produced
Figure 379528DEST_PATH_IMAGE268
For any unmanned aerial vehicle
Figure 193595DEST_PATH_IMAGE269
From time slots
Figure 793204DEST_PATH_IMAGE270
Selecting arbitrary charging devices
Figure 666482DEST_PATH_IMAGE271
The perceived utility available from starting charging until time slot 5 is
Figure 984331DEST_PATH_IMAGE272
Wherein
Figure 93101DEST_PATH_IMAGE273
Expressed as:
Figure 547216DEST_PATH_IMAGE274
to be used for
Figure 591395DEST_PATH_IMAGE275
In the case of an example of this,
Figure 271907DEST_PATH_IMAGE276
Figure 59734DEST_PATH_IMAGE277
i.e.
Figure 633935DEST_PATH_IMAGE278
Step A6.4 is entered;
a6.4: based on bipartite graph
Figure 849016DEST_PATH_IMAGE280
Wherein
Figure 734932DEST_PATH_IMAGE281
Representation for any unmanned aerial vehicle
Figure 326450DEST_PATH_IMAGE282
Can be distributed to any charging facilities
Figure 755158DEST_PATH_IMAGE283
Aggregation of
Figure 515041DEST_PATH_IMAGE285
Representation of
Figure 29199DEST_PATH_IMAGE286
Corresponding side weight set, adopting KM algorithm to solve maximum weight matching of bipartite graph
Figure 158829DEST_PATH_IMAGE287
The perception effect corresponding to the maximum match is as follows
Figure 566677DEST_PATH_IMAGE288
；
A6.5: if it is
Figure 389139DEST_PATH_IMAGE289
Executing the step A6.6, otherwise executing the step A6.7;
a6.6: setting the time slot from the last charge allocated start time slot to time slot
Figure 125014DEST_PATH_IMAGE042
The number of the occupied optimal time slots is
Figure 933701DEST_PATH_IMAGE290
Time slot
Figure 71421DEST_PATH_IMAGE291
Maximum weight match of (2) is
Figure 64785DEST_PATH_IMAGE292
Updating
Figure 819115DEST_PATH_IMAGE294
Figure 149602DEST_PATH_IMAGE296
；
A6.7: updating
Figure 407408DEST_PATH_IMAGE297
Returning to the step A6.2;
when (when)
Figure 571673DEST_PATH_IMAGE298
Repeating steps A6.3 to A6.7 to obtain
Figure 282140DEST_PATH_IMAGE299
,
Figure 665586DEST_PATH_IMAGE300
Step A6.8 is entered;
a6.8: according to
Figure 43477DEST_PATH_IMAGE301
And
Figure 113064DEST_PATH_IMAGE302
updating
Figure 701041DEST_PATH_IMAGE303
Step A6.8.1 is entered as shown in fig. 5;
a6.8.1: initialization of
Figure 514276DEST_PATH_IMAGE304
Defining decision time slots as
Figure 746674DEST_PATH_IMAGE305
；
A6.8.2: if it is
Figure 987162DEST_PATH_IMAGE306
Step A6.8.3 is performed, otherwise step A6.8.6 is performed;
a6.8.3: if it is
Figure 813167DEST_PATH_IMAGE305
And is also provided with
Figure 430093DEST_PATH_IMAGE307
Step A6.8.4 is performed, otherwise step A6.8.5 is performed;
a6.8.4: updating
Figure 251419DEST_PATH_IMAGE308
Returning to step A6.8.2;
A6.8.5：updating
Figure 53022DEST_PATH_IMAGE309
Returning to step A6.8.2;
a6.8.6: return to
Figure 225377DEST_PATH_IMAGE310
Wherein
Figure 380415DEST_PATH_IMAGE311
The balance being 0.
A7: returning to the step A4;
when (when)
Figure 587405DEST_PATH_IMAGE312
Repeating steps A6 to A7 to obtain
Figure 278018DEST_PATH_IMAGE313
Figure 937670DEST_PATH_IMAGE314
And
Figure 896399DEST_PATH_IMAGE316
wherein
Figure 82529DEST_PATH_IMAGE317
The rest are 0, and enter step A8;
a8: return to
Figure 101301DEST_PATH_IMAGE318
And
Figure 982669DEST_PATH_IMAGE319
the final result is
Figure 354876DEST_PATH_IMAGE321
The total perceived utility is 67.
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.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.

Claims (8)

1. The utility driven unmanned aerial vehicle perception network charging scheduling method is characterized by comprising the following steps of:
acquiring an unmanned aerial vehicle, a charging facility and a point of interest set based on the running state of the unmanned aerial vehicle, and constructing an unmanned aerial vehicle perception network model;
constructing a perception value model according to the perceived data quality of the unmanned aerial vehicle perception network model and establishing a charging and billing cost model based on the charging power of the charging facility;
formalizing a problem of maximizing perceived utility of the unmanned aerial vehicle under the period of a perceived network system;
invoking a utility-driven unmanned aerial vehicle sensing network charging scheduling algorithm to obtain a scheduling scheme of each unmanned aerial vehicle, and realizing charging scheduling of the unmanned aerial vehicle;
invoking a utility-driven unmanned aerial vehicle-aware network charging scheduling algorithm, comprising:
inputting unmanned aerial vehicle charging scheduling parameters;
the method comprises the steps of finding out the maximum time slot number and the minimum time slot number occupied by the fact that all unmanned aerial vehicles begin to charge from a certain time slot until returning to a charging station again to submit sensing data;
the maximum number of time slots is expressed as:
Figure QLYQS_1
the minimum number of time slots is expressed as:
Figure QLYQS_2
wherein,,
Figure QLYQS_3
selecting the time taken for the unmanned aerial vehicle to charge in the charging facility in the time slot, < >>
Figure QLYQS_4
For the time required for the unmanned aerial vehicle to travel between the charging station and the perceived location, < >>
Figure QLYQS_5
Time spent for the drone to perform a perception task each time;
let the maximum perceived utility of the first b time slots be U (b) Optimal charging schedule for the first b slots is X (b) Initializing a maximum perceived utility and an optimal charging schedule from b=1 to b=μ;
for the following
Figure QLYQS_6
Setting U (b) ＝0，/>
Figure QLYQS_7
Figure QLYQS_8
Updating b=b+1, and when b+.t+1 time slots, calculating to obtain maximum perceived utility U of the first b time slots (b) And optimal schedule X (b) Continuing to update iteratively, otherwise returning to the maximum perception utility U of the first T time slots (T) And optimal charge schedule X (T) ；
The maximum perceived utility U of the first b time slots is calculated (b) And optimal schedule X (b) Comprising:
maximum perceived utility U for initializing the first b timeslots (b) =0, assuming that the number of slots occupied from the start slot of the last charge allocation to slot b is t=1;
if it is
Figure QLYQS_9
When satisfied, assume that charge allocation is made in time slot b-t+1 up to the maximum perceived utility F that time slot b can produce (b-t+1,b) For any unmanned aerial vehicle v i E V selecting an arbitrary charging device l from time slots b-t+1 k The perceived utility that e L starts charging until slot b is available is expressed as:
Figure QLYQS_10
wherein Qi is the perceived value produced by each round of unmanned aerial vehicle, R k Charging price per time slot for charging facility, P k For the charging power of the charging facility,
Figure QLYQS_11
the charging demand of the unmanned aerial vehicle at the charging station is achieved;
based on the bipartite graph, adopting KM algorithm to solve maximum weight matching M of the bipartite graph (b-t+1,b) By matching M (b-t+1,b) The corresponding sensing effect of charging distribution is F (b-t+1,b) ；
The bipartite graph is expressed as:
G (b-t+1,b) ＝(V,L,E,f (b-t+1,b) )，
wherein e=v×L represents v for any unmanned aerial vehicle i E V can be distributed to any charging facility k E L, set
Figure QLYQS_12
Representing an edge weight set corresponding to the E;
when U is (b-t) +F (b-t+1,b) >U (b) In this case, it is assumed that the optimal number of slots from the start slot of the last charge allocation to slot b is t * Time slot t * Maximum weight match of M * Update t * ＝t,M * ＝M (b-t+1,b) ,U (b) ＝U (b-t*) +F (b-t*+1,b) Otherwise, updating t=t+1, and continuing iteration;
if it is
Figure QLYQS_13
If not, then according to X (b-t*) And M * Updating X (b) 。
2. The utility driven unmanned aerial vehicle aware network charging scheduling method of claim 1, wherein:
the unmanned aerial vehicle set is expressed as:
V＝{v 1 ,v 2 ,...,v n }
wherein, any unmanned aerial vehicle v i ∈V；
The interest points are all responsible for perception by at least one unmanned aerial vehicle, and the interest point set is expressed as:
O＝{o 1 ,o 2 ,...,o m }
wherein, any interest point o j ∈O；
The utility that charges all is located a charging station, and unmanned aerial vehicle needs to fly to charge on the facility that charges, the facility collection that charges, the representation is:
L＝{l 1 ,l 2 ,...l z }
wherein any charging facility l k ∈L。
3. The utility driven unmanned aerial vehicle aware network charging scheduling method of claim 1 or 2, wherein the unmanned aerial vehicle aware network model comprises:
the unmanned aerial vehicle, the interest points and the charging facilities are uniformly distributed on a two-dimensional plane, the unmanned aerial vehicle is considered to be heterogeneous, and the unmanned aerial vehicle is assumed to reciprocate between the charging station and the sensing position in a flying state of a uniform speed straight line;
dispersing a section of continuous time into a plurality of time slots, when the electric quantity of the unmanned aerial vehicle is lower than a fixed threshold value, leaving the sensing position, flying to a charging station for charging, and returning to the sensing position again to execute a sensing task after full charge;
the time required for the drone to traverse between the charging station and the perceived location is expressed as:
Figure QLYQS_14
wherein d i S is the distance between the perceived position of the unmanned aerial vehicle and the charging station i The distance that unmanned aerial vehicle can fly in the unit time slot;
unmanned aerial vehicle adopts full charge mode at the charging station, unmanned aerial vehicle's charge demand at the charging station, represents as:
Figure QLYQS_15
wherein,,
Figure QLYQS_16
is the battery capacity delta of the unmanned aerial vehicle i Is a fixed threshold value of the battery capacity of the unmanned aerial vehicle ρ i The power consumption for the unmanned aerial vehicle to come and go between the charging station and the sensing position;
the time it takes for the drone to perform a perception task each time is expressed as:
Figure QLYQS_17
wherein ρ is i Is the working power of the unmanned aerial vehicle.
4. The utility driven unmanned aerial vehicle perceived network charge scheduling method of claim 3, wherein the perceived value model comprises:
when unmanned aerial vehicle arrives the charging station, with the perception data that once only upload and carry, the produced perception value of unmanned aerial vehicle every round, represent as:
Figure QLYQS_18
wherein Q is ij For the perceived quality, O, of interest points generated by unmanned aerial vehicles in unit time slot i Interest point set for unmanned aerial vehicle to be responsible for perception, omega j Is the weight coefficient of the interest point.
5. The utility driven unmanned aerial vehicle aware network charging scheduling method of claim 4, wherein establishing a charging billing cost model based on charging power of a charging facility comprises:
the time occupied by the unmanned aerial vehicle in the time slot selection charging facility for charging is expressed as:
Figure QLYQS_19
wherein P is k For the charging power of the charging facility, q is the time slot and q e { 1..once., T },
Figure QLYQS_20
the charging demand of the unmanned aerial vehicle at the charging station is met, and T is the number of time slots;
the charging cost generated by charging the unmanned aerial vehicle at the time slot selection charging facility is expressed as:
Figure QLYQS_21
wherein R is k The price is charged for a unit time slot of the charging facility.
6. The utility driven unmanned aerial vehicle perceived network charge scheduling method of claim 5, wherein formalizing the perceived utility maximization problem for the unmanned aerial vehicle perceived network system cycle comprises:
the perceived utility obtained by the unmanned aerial vehicle in the time slot selecting charging facility for charging is expressed as:
Figure QLYQS_22
wherein Q is i The perceived value generated for each round of unmanned aerial vehicle,
Figure QLYQS_23
selecting the time taken for the unmanned aerial vehicle to charge in the charging facility in the time slot, < >>
Figure QLYQS_24
For the time required for the unmanned aerial vehicle to travel between the charging station and the perceived location, < >>
Figure QLYQS_25
Time spent for the drone to perform a perception task each time;
the scheduling scheme is as follows:
Figure QLYQS_26
wherein,,
Figure QLYQS_27
a decision variable representing whether the unmanned aerial vehicle selects a charging facility for charging in the time slot;
the total perceived utility that the drone can obtain through the charge schedule throughout the cycle is expressed as:
Figure QLYQS_28
the problem of maximizing perceived utility under the period of the unmanned aerial vehicle perceived network system is formalized, expressed as:
Figure QLYQS_29
7. the utility driven unmanned aerial vehicle aware network charging scheduling method of claim 6, further comprising:
decision constraints, expressed as:
Figure QLYQS_30
wherein the decision variables
Figure QLYQS_31
Is a 0-1 variable;
for any charging facility, only one unmanned aerial vehicle can be provided with charging service constraint in any time slot, which is expressed as:
Figure QLYQS_32
for any unmanned aerial vehicle, only one charging facility can be selected for charging constraint in any time slot, which is expressed as:
Figure QLYQS_33
8. the utility of claim 7The method for scheduling the network charging perceived by the driven unmanned aerial vehicle is characterized by comprising the following steps of (b-t*) And M * Updating X (b) Comprising:
initializing X (b) ＝X (b-t*) And defines the decision time slot as p=b-t * +1；
When p.noteq.b+1 is satisfied, when p=b-t * +1，(v i ,l k )∈M * When updating
Figure QLYQS_34
p=p+1, otherwise update
Figure QLYQS_35
p=p+1; continuing to iterate the updating after finishing the updating;
if p+.b+1 is not satisfied, then return X (b) 。
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