CN113993175A - Unmanned aerial vehicle communication switching method, system, equipment and storage medium - Google Patents

Unmanned aerial vehicle communication switching method, system, equipment and storage medium Download PDF

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CN113993175A
CN113993175A CN202111243723.0A CN202111243723A CN113993175A CN 113993175 A CN113993175 A CN 113993175A CN 202111243723 A CN202111243723 A CN 202111243723A CN 113993175 A CN113993175 A CN 113993175A
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unmanned aerial
aerial vehicle
switching
qoe
parameter
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CN113993175B (en
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武青
任鑫
李小翔
王�华
王恩民
刘溟江
姚中原
钱开荣
牛晨辉
周国栋
张宇
马强
王有超
祝金涛
吴昊
吕亮
朱俊杰
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Huaneng Clean Energy Research Institute
Clean Energy Branch of Huaneng International Power Jiangsu Energy Development Co Ltd Clean Energy Branch
Huaneng International Power Jiangsu Energy Development Co Ltd
Shengdong Rudong Offshore Wind Power Co Ltd
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Huaneng Clean Energy Research Institute
Clean Energy Branch of Huaneng International Power Jiangsu Energy Development Co Ltd Clean Energy Branch
Huaneng International Power Jiangsu Energy Development Co Ltd
Shengdong Rudong Offshore Wind Power Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/08—Reselecting an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/32—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data

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Abstract

The invention discloses an unmanned aerial vehicle communication switching method, a system, equipment and a storage medium, S1, determining switching parameters according to the position and the speed of an unmanned aerial vehicle; s2, adjusting the switching parameters by using a nonlinear exponential DPSO algorithm; s3, judging whether the adjusted switching parameters meet the QoE of the unmanned aerial vehicle video transmission; if not, the process goes to S2, and if so, the adjusted handover parameter is used as the parameter of the current communication handover of the unmanned aerial vehicle and is used as the initial value of the next non-linear exponential DPSO algorithm. Satisfy video transmission QoE index, promote unmanned aerial vehicle and patrol and examine video transmission efficiency when patrolling and examining, improve unmanned aerial vehicle and patrol and examine efficiency.

Description

Unmanned aerial vehicle communication switching method, system, equipment and storage medium
Technical Field
The invention belongs to the field of wireless communication, and relates to an unmanned aerial vehicle communication switching method, system, equipment and storage medium.
Background
The offshore wind power station has wide range, complex environment, difficult access and difficult selection of traffic equipment, and greatly hinders the maintenance of operation and maintenance personnel. At present, operation and maintenance personnel often adopt modes such as unmanned aerial vehicle patrols and examines to carry out the operation and maintenance detection of fan. When the unmanned aerial vehicle carries out video inspection, the requirement on the communication bandwidth is high, and because the wind power plant fan interval is large, the flying distance of the unmanned aerial vehicle is far away when the unmanned aerial vehicle flies and inspects, so that the communication link is switched during the communication of the unmanned aerial vehicle. In traditional communication switching process, unmanned aerial vehicle video communication quality can not be guaranteed, leading to that unmanned aerial vehicle patrols and examines efficiency reduction.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a communication switching method, a system, equipment and a storage medium for an unmanned aerial vehicle, which meet QoE (quality of experience) indexes of video transmission, improve video transmission efficiency during unmanned aerial vehicle routing inspection and improve the routing inspection efficiency of the unmanned aerial vehicle.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
an unmanned aerial vehicle communication switching method comprises the following processes:
s1, determining switching parameters according to the position and the speed of the unmanned aerial vehicle;
s2, adjusting the switching parameters by using a nonlinear exponential DPSO algorithm;
s3, judging whether the adjusted switching parameters meet the QoE of the unmanned aerial vehicle video transmission; if not, the process goes to S2, and if so, the adjusted handover parameter is used as the parameter of the current communication handover of the unmanned aerial vehicle and is used as the initial value of the next non-linear exponential DPSO algorithm.
Preferably, the specific process of S2 is:
s21, updating the position and the speed of the unmanned aerial vehicle, and calculating the QoE of the video transmission of the unmanned aerial vehicle under different parameters;
s22, dynamically adjusting the acceleration and the weight factor through iteration;
and S23, recording the optimal value of a single unmanned plane and the global optimal value of all unmanned planes in each iteration.
Further, the formula for updating the position and the speed of the unmanned aerial vehicle is as follows:
vi＝ω·vi+c1·rabd(0,1)·(pi-xi)+c2·rand(0,1)·(pg-xi)
wherein p isiCorresponding switching parameter, p, for the ith unmanned aerial vehicle when the optimal video QoE is satisfiedgAnd corresponding switching parameters are provided for all the unmanned aerial vehicles in the wind power plant when the unmanned aerial vehicles meet the QoE video. rabd (0,1) is randomly selected within 0-1, omega is a weight factor, c1And c2Are all accelerations.
Still further, c1And c2The iterative calculation process of (a) is as follows:
Figure BDA0003320177800000021
Figure BDA0003320177800000022
where iter is the current iteration number and iter _ max is the maximum iteration number.
Still further, the iterative computation process of the weight factor ω is:
Figure BDA0003320177800000023
where iter is the current iteration number and iter _ max is the maximum iteration number.
Preferably, the QoE of video transmission is calculated as follows:
QoEvideo＝4.23-0.0672Tinit-0.742Frebuf-0.106Trebuf
wherein T isinit、TrebufAnd FrebufRespectively, initial play-out delay, average rebuffering time and rebuffering frequency.
An unmanned aerial vehicle communication switching system, comprising:
the switching parameter determining unit is used for determining switching parameters according to the position and the speed of the unmanned aerial vehicle;
the switching parameter adjusting unit is used for adjusting switching parameters by using a nonlinear exponential DPSO algorithm;
the switching parameter judgment application unit is used for judging whether the adjusted switching parameters meet the QoE of the video transmission of the unmanned aerial vehicle; if not, the process goes to S2, and if so, the adjusted handover parameter is used as the parameter of the current communication handover of the unmanned aerial vehicle and is used as the initial value of the next non-linear exponential DPSO algorithm.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the drone communication handoff method as in any one of the above when executing the computer program.
A computer-readable storage medium, storing a computer program which, when executed by a processor, implements the steps of the drone communication handoff method of any one of the above.
Compared with the prior art, the invention has the following beneficial effects:
the invention uses DPSO algorithm to switch the network of the inspection unmanned aerial vehicle, and the purpose of the algorithm is as follows: in the flight process of the unmanned aerial vehicle, a group of combination parameters which enable the video transmission QoE optimization value of the unmanned aerial vehicle to be the highest are selected within a certain time according to a plurality of parameters in the flight process, including the combination of the flight speed, the acceleration, the position, the base station parameters and the like of the unmanned aerial vehicle. Finally, the unmanned aerial vehicle meets the QoE requirement of video transmission in the whole inspection process, the video transmission efficiency of the unmanned aerial vehicle during inspection is improved, and the inspection efficiency of the unmanned aerial vehicle is improved.
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FIG. 1 is a schematic diagram of inspection of an unmanned aerial vehicle in an offshore wind farm according to the present invention;
FIG. 2 is a schematic view of the fan external wireless network coverage of the present invention;
FIG. 3 is a flow chart of a handover algorithm of the present invention;
FIG. 4 is a diagram of a hardware architecture of a handover algorithm of the present invention;
FIG. 5 is a schematic diagram of an electronic structure according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
as shown in FIG. 1, the present invention begins with the establishment of a wireless coverage facility at an offshore wind farm. Because offshore wind farm keeps away from the coast, ordinary public base station can't cover offshore wind farm, consequently every offshore wind farm can set up independent looped netowrk and connect every fan to booster station, as shown in fig. 2, solitary fan looped netowrk only is used for transmitting the fan signal, patrols and examines that unmanned aerial vehicle can't insert wherein. Aiming at the scene that the unmanned aerial vehicle flies in the wind power plant, AP devices are deployed outside a fan, each AP is physically marked, and the AP devices are accessed into a wind power plant ring network through a switch to complete the coverage of the offshore wind power plant.
Unmanned aerial vehicle patrols and examines the in-process at the wind-powered electricity generation field, and the AP that serves unmanned aerial vehicle can constantly change, consequently at the in-process that changes, needs carry out the switching of AP, has very big influence to unmanned aerial vehicle video transmission's QoE at this in-process.
The method comprises the steps of deploying an AP device outside each fan by utilizing a well deployed fan ring network of an offshore wind farm, accessing the AP device to the fan ring network through a switch, and labeling each AP. The wind turbine looped network is converged in the booster station, and the AC controller at the booster station completes control over all APs in the looped network, so that wireless network coverage of the whole wind power plant is completed.
As shown in fig. 3, the communication switching method of the offshore wind power inspection unmanned aerial vehicle specifically comprises the following steps:
and determining initial parameters of the network according to the working parameters of all APs in the wind power plant network. The QoE (Quality of Experience) model is determined as follows:
QoEvideo＝4.23-0.0672Tinit-0.742Frebuf-0.106Trebuf
wherein T isinit,Trebuf,FrebufRepresenting the initial playout delay, average rebuffering time, and rebuffering frequency, respectively.
The switching action is performed by adjusting the switching parameters by using a nonlinear exponential DPSO (Discrete Particle Swarm Optimization) Algorithm. The specific principle is as follows:
firstly, calculating the QoE of video transmission under the flight parameters of the leading unmanned aerial vehicle, and calculating T according to a video transmission sensorinit,Trebuf,Frebuf。
The DSPO algorithm adjusts the flight parameters of the unmanned aerial vehicle, including the speed, position, acceleration and the like of the unmanned aerial vehicle. Wherein, p of unmanned aerial vehicleiIs defined as: and the ith unmanned aerial vehicle passes through the video QoE maximum value calculated by all the switching parameters when the current iteration number is reached.
The optimal value for the global drone is defined as: and when all the unmanned planes are in the current iteration number, the maximum value of the video QoE is calculated through all the switching parameters.
Wherein unmanned aerial vehicle's position is among this optimization setting:
xi＝(xi1,…,xiM)
wherein xi1,…,xiMThe positions of the unmanned aerial vehicles from the 1 st moment to the M th moment.
The speed of the unmanned aerial vehicle is:
vi＝(vi1,…,viM)
wherein v isi1,…,viMThe speed of the unmanned aerial vehicle in the 1 st to M th moments.
Updating the position and the speed of the unmanned aerial vehicle by using a DPSO algorithm, and recording the QoE of the video transmission of the unmanned aerial vehicle at each iteration:
vi＝ω·vi+c1·rand(0,1)·(pi-xi)+c2·rand(0,1)
x′i＝xi+xi
rand (0,1) is a random number within 0-1. Omega is an inertia weight, the invention provides a formula for adjusting omega along with the dynamic change of iteration times:
Figure BDA0003320177800000051
where iter is the current iteration number and iter _ max is the maximum iteration number.
c1，c2All acceleration, the proportion of the unmanned aerial vehicle towards the global optimal direction and the self optimal direction is determined, and the invention provides a formula for calculating c1 and c2
Figure BDA0003320177800000061
Figure BDA0003320177800000062
Where iter is the current iteration number and iter _ max is the maximum iteration number.
The QoE of video transmission is an evaluation scoring mode, the QoE is divided into 5 grades from high to low, the evaluation result of the video to be evaluated, which is not damaged or cannot be perceived by video damage degree, is excellent, and the QoE can be divided into 5 grades; the damage degree is not serious, but the evaluation result of the video to be evaluated is good, and 4 points can be obtained; the evaluation result of the video to be evaluated with slight damage is general, and 3 points can be obtained; the evaluation result of the video to be evaluated with serious video damage degree is poor, and 2 points can be obtained; the evaluation result of the video to be evaluated with very serious video damage degree is very poor, and the score can be 1. Therefore, if the QoE of the unmanned aerial vehicle polling video is to be met, the QoEvideo should be greater than or equal to 3.
Judging whether the QoE of the unmanned aerial vehicle video transmission after the switching parameters are adjusted is more than or equal to 3; if not, the process goes to S2, and if so, the adjusted handover parameter is used as the parameter of the current communication handover of the unmanned aerial vehicle and is used as the initial value of the next non-linear exponential DPSO algorithm.
In order to ensure QoE improvement and balance of the drone, the DPSO handover algorithm adjusts handover parameters every 10 seconds while the drone is moving. And the adjusted switching parameters are used as initial values of a next DPSO algorithm, and the DPSO algorithm calculates QoE under different switching parameters through a video QoE model according to the position and the speed of the unmanned aerial vehicle at the moment. And the DPSO algorithm dynamically adjusts the flight speed and acceleration of the unmanned aerial vehicle according to the iteration times to finally obtain the highest QoE of the unmanned aerial vehicle within 10 seconds, determines the switching parameters of the unmanned aerial vehicle, and uses the obtained result as the initial value of the DPSO algorithm next time to complete the dynamic adjustment of the switching parameters in the process of user movement so as to ensure the QoE improvement and balance in the flight process of the unmanned aerial vehicle.
As shown in fig. 4, the switching algorithm hardware structure includes a network establishment module, an unmanned aerial vehicle video transmission module, and an unmanned aerial vehicle polling switching algorithm module, which are connected in sequence.
As shown in fig. 5, the electronic device comprises a memory 50 for storing a computer program; a processor 51, configured to implement the steps of the drone handoff communication method as mentioned in any of the above embodiments when executing a computer program. In some embodiments, the electronic device may further include a display 52, an input/output interface 53, a communication interface 34 or network interface, a power supply 55, and a communication bus 56. The display 52 and the input/output interface 53, such as a Keyboard (Keyboard), belong to a user interface, and the optional user interface may also include a standard wired interface, a wireless interface, and the like.
The unmanned aerial vehicle communication switching system of the invention comprises:
and the switching parameter determining unit is used for determining switching parameters according to the position and the speed of the unmanned aerial vehicle.
And the switching parameter adjusting unit is used for adjusting the switching parameters by using a nonlinear exponential DPSO algorithm.
The switching parameter judgment application unit is used for judging whether the adjusted switching parameters meet the QoE of the video transmission of the unmanned aerial vehicle; if not, the process goes to S2, and if so, the adjusted handover parameter is used as the parameter of the current communication handover of the unmanned aerial vehicle and is used as the initial value of the next non-linear exponential DPSO algorithm.
The computer device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the unmanned aerial vehicle communication switching method.
The computer-readable storage medium of the present invention stores a computer program, which when executed by a processor implements the steps of the unmanned aerial vehicle communication handover method as described.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (9)

1. An unmanned aerial vehicle communication switching method is characterized by comprising the following processes:
s1, determining switching parameters according to the position and the speed of the unmanned aerial vehicle;
s2, adjusting the switching parameters by using a nonlinear exponential DPSO algorithm;
s3, judging whether the adjusted switching parameters meet the QoE of the unmanned aerial vehicle video transmission; if not, the process goes to S2, and if so, the adjusted handover parameter is used as the parameter of the current communication handover of the unmanned aerial vehicle and is used as the initial value of the next non-linear exponential DPSO algorithm.
2. The unmanned aerial vehicle communication switching method of claim 1, wherein the specific process of S2 is as follows:
s21, updating the position and the speed of the unmanned aerial vehicle, and calculating the QoE of the video transmission of the unmanned aerial vehicle under different parameters;
s22, dynamically adjusting the acceleration and the weight factor through iteration;
and S23, recording the optimal value of a single unmanned plane and the global optimal value of all unmanned planes in each iteration.
3. The unmanned aerial vehicle communication handover method of claim 2, wherein the formula for updating the position and the speed of the unmanned aerial vehicle is as follows:
vi＝ω·vi+c1·rand(0，1)·(pi-xi)+c2·rand(0，1)·(pg-xi)
wherein p isiCorresponding switching parameter, p, for the ith unmanned aerial vehicle when the optimal video QoE is satisfiedgAnd corresponding switching parameters are provided for all the unmanned aerial vehicles in the wind power plant when the unmanned aerial vehicles meet the QoE video. rand (0,1) is randomly selected within 0-1, omega is a weight factor, c1And c2Are all accelerations.
4. The UAV communication handover method of claim 3, wherein c is1And c2The iterative calculation process of (a) is as follows:
Figure FDA0003320177790000011
Figure FDA0003320177790000012
where iter is the current iteration number and iter _ max is the maximum iteration number.
5. The unmanned aerial vehicle communication switching method of claim 3, wherein the iterative calculation process of the weight factor ω is as follows:
Figure FDA0003320177790000021
where iter is the current iteration number and iter _ max is the maximum iteration number.
6. The unmanned aerial vehicle communication handover method of claim 1, wherein the QoE of video transmission is calculated by:
QoEVideo＝4.23-0.0672Tinit-0.742Frebuf-0.106Trebuf
wherein T isinit、TrebufAnd FrebufRespectively, initial play-out delay, average rebuffering time and rebuffering frequency.
7. An unmanned aerial vehicle communication switching system, comprising:
the switching parameter determining unit is used for determining switching parameters according to the position and the speed of the unmanned aerial vehicle;
the switching parameter adjusting unit is used for adjusting switching parameters by using a nonlinear exponential DPSO algorithm;
the switching parameter judgment application unit is used for judging whether the adjusted switching parameters meet the QoE of the video transmission of the unmanned aerial vehicle; if not, the process goes to S2, and if so, the adjusted handover parameter is used as the parameter of the current communication handover of the unmanned aerial vehicle and is used as the initial value of the next non-linear exponential DPSO algorithm.
8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the drone communication handoff method of any one of claims 1 to 6.
9. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the steps of the drone communication handover method according to any one of claims 1 to 6.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103279793A (en) * 2013-04-25 2013-09-04 北京航空航天大学 Task allocation method for formation of unmanned aerial vehicles in certain environment
CN106990792A (en) * 2017-05-23 2017-07-28 西北工业大学 Mix the multiple no-manned plane collaboration sequential coupling task distribution method of gravitation search algorithm
CN110232492A (en) * 2019-04-01 2019-09-13 南京邮电大学 A kind of multiple no-manned plane cotasking dispatching method based on improvement discrete particle cluster algorithm
CN111884829A (en) * 2020-06-19 2020-11-03 西安电子科技大学 Method for maximizing multi-unmanned aerial vehicle architecture income

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103279793A (en) * 2013-04-25 2013-09-04 北京航空航天大学 Task allocation method for formation of unmanned aerial vehicles in certain environment
CN106990792A (en) * 2017-05-23 2017-07-28 西北工业大学 Mix the multiple no-manned plane collaboration sequential coupling task distribution method of gravitation search algorithm
CN110232492A (en) * 2019-04-01 2019-09-13 南京邮电大学 A kind of multiple no-manned plane cotasking dispatching method based on improvement discrete particle cluster algorithm
CN111884829A (en) * 2020-06-19 2020-11-03 西安电子科技大学 Method for maximizing multi-unmanned aerial vehicle architecture income

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