CN115460668A - Method and system for planning data evacuation transmission path between unmanned aerial vehicles - Google Patents

Method and system for planning data evacuation transmission path between unmanned aerial vehicles Download PDF

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CN115460668A
CN115460668A CN202211115229.0A CN202211115229A CN115460668A CN 115460668 A CN115460668 A CN 115460668A CN 202211115229 A CN202211115229 A CN 202211115229A CN 115460668 A CN115460668 A CN 115460668A
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翟临博
朱秀敏
高星霞
杨峰
赵景梅
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Abstract

The invention discloses a method and a system for planning a data evacuation transmission path between unmanned aerial vehicles, electronic equipment and a computer readable storage medium, belonging to the technical field of mobile communication; constructing a multi-objective fitness function for minimizing all evacuation data transmission time and energy consumption of all unmanned aerial vehicles, and constraint conditions of the multi-objective fitness function; selecting a temporary relay unmanned aerial vehicle according to the storage space and the data transmission time of the unmanned aerial vehicle, and establishing an initial data transmission link; solving the multi-target fitness function under the constraint condition based on the temporary relay unmanned aerial vehicle until an optimal solution is found; and deploying the optimal data evacuation transmission path according to the optimal solution. When the data unmanned aerial vehicle gives an alarm, the optimal data evacuation path can be selected, and the total data evacuation time and the energy consumption of the unmanned aerial vehicle are minimized. The problem of exist among the prior art "unmanned aerial vehicle is too far away from the basic station, and there is the wasting of resources and data loss risk in the transmission data" is solved.

Description

Method and system for planning data evacuation transmission path between unmanned aerial vehicles
Technical Field
The application relates to the technical field of mobile communication, in particular to a method and a system for planning a data evacuation transmission path between unmanned aerial vehicles.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
With the development of the internet of things (IoT), the demand of users is increasing, and the demand for reducing data delay is also increasing. Unmanned Aerial Vehicles (UAVs) have been widely used in internet of things applications, such as data acquisition, disaster detection, etc., due to their easy deployment and high flexibility.
However, the energy, memory and communication resources of the drones are limited, which affects the service life of the multi-drone auxiliary network. When the drones are low in energy in the multi-drone secondary network, the data collected by the drones should be evacuated to the base station within time limits. Therefore, it is desirable to minimize the time for data to be evacuated to avoid data loss.
In terms of data evacuation, most research is mainly focused on cloud data centers, namely main network infrastructures, a combined emergency data and service evacuation scheme under given early warning time constraints, and a network protection scheme is an optimized network which is mainly focused on natural disaster resistance according to implementation time. Considering that natural disasters threaten data safety of the network data center, network operators need an effective emergency backup plan to evacuate endangered data. Unmanned aerial vehicle's low energy is equivalent to natural disasters, and in case unmanned aerial vehicle can not work because of the low energy, the ground data that unmanned aerial vehicle collected will lose to influence many unmanned aerial vehicle auxiliary network's performance. Considering the mobility characteristics of the unmanned aerial vehicle, the research on the data evacuation plan of the unmanned aerial vehicle auxiliary network is of great significance.
Disclosure of Invention
In order to solve the defects of the prior art, the application provides a method, a system, electronic equipment and a computer-readable storage medium for planning a data evacuation transmission path between unmanned aerial vehicles, and the data transmission time is minimized while the low energy consumption is ensured.
In a first aspect, the application provides a method for planning a data evacuation transmission path between unmanned aerial vehicles;
a method for planning a data evacuation transmission path between unmanned aerial vehicles comprises the following steps:
constructing a multi-objective fitness function for minimizing all evacuation data transmission time and energy consumption of all unmanned aerial vehicles, and constraint conditions thereof;
selecting a temporary relay unmanned aerial vehicle according to the storage space and the data transmission time of the unmanned aerial vehicle, and establishing an initial data transmission link;
solving the multi-target fitness function under the constraint condition based on the temporary relay unmanned aerial vehicle until an optimal solution is found; and deploying the optimal data evacuation transmission path according to the optimal solution.
In a second aspect, the application provides a system for planning a data evacuation transmission path between unmanned aerial vehicles;
an inter-drone data evacuation transmission path planning system comprising:
a multi-objective fitness function construction module configured to: constructing a multi-target fitness function for minimizing all evacuation data transmission time and energy consumption of all unmanned aerial vehicles, and constraint conditions of the multi-target fitness function;
a temporary relay selection module configured to: selecting a temporary relay unmanned aerial vehicle according to the storage space and the data transmission time of the unmanned aerial vehicle, and establishing an initial data transmission link;
a data evacuation transmission optimal path deployment module configured to: solving the multi-target fitness function under the constraint condition based on the initial data transmission link until an optimal solution is found; and deploying the optimal data evacuation transmission path according to the optimal solution.
In a third aspect, the present application provides an electronic device;
an electronic device comprises a memory, a processor and computer instructions stored on the memory and running on the processor, wherein when the computer instructions are run by the processor, the steps of the inter-unmanned aerial vehicle data evacuation transmission path planning method are completed.
In a fourth aspect, the present application provides a computer-readable storage medium;
a computer readable storage medium for storing computer instructions, which when executed by a processor, perform the steps of the above method for planning a data evacuation transmission path between unmanned aerial vehicles.
Compared with the prior art, the beneficial effects of this application are:
1. according to the method and the system, the energy consumption of the unmanned aerial vehicle and the data evacuation transmission time are minimized, and the problems that the resource is wasted and the data is lost due to the fact that the service area of the unmanned aerial vehicle is far away from the ground base station and the time is limited when the data is directly transmitted are avoided;
2. based on load balance of the relay unmanned aerial vehicle and time constraint of the data unmanned aerial vehicle, a data temporary transfer point selection (DTSPS) algorithm is designed to select proper relay unmanned aerial vehicle to temporarily store data, and data loss is avoided;
3. the application provides an MDEPACS algorithm to search a plurality of data evacuation paths from a selected relay unmanned aerial vehicle to a base station, in the MDEPACS algorithm, a hybrid data transmission mode of the relay unmanned aerial vehicle is designed, and a new solution structure construction is provided, wherein the solution comprises a mode of the relay unmanned aerial vehicle and each data path;
4. the application provides a new pheromone updating rule based on a non-dominant solution so as to ensure that an ant colony can quickly find an optimal evacuation path.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
Fig. 1 is a schematic view of a scenario provided in an embodiment of the present application;
fig. 2 is a schematic flowchart provided in an embodiment of the present application.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
In the prior art, multi-hop communication of the unmanned aerial vehicle only considers hop-by-hop routing, and the maneuverability of the unmanned aerial vehicle is not fully utilized; therefore, the application provides a method for planning a data evacuation transmission path between unmanned aerial vehicles.
A method for planning a data evacuation transmission path between unmanned aerial vehicles comprises the following steps:
constructing a multi-objective fitness function for minimizing all evacuation data transmission time and energy consumption of all unmanned aerial vehicles, and constraint conditions thereof;
selecting a temporary relay unmanned aerial vehicle according to the storage space and the data transmission time of the unmanned aerial vehicle, and establishing an initial data transmission link;
solving the multi-target fitness function under the constraint condition based on the temporary relay unmanned aerial vehicle until an optimal solution is found; and deploying the optimal data evacuation transmission path according to the optimal solution.
Further, the multi-objective fitness function is
Figure BDA0003845259810000051
f 1 For the first goal, minimizing all evacuation data transmission time; f. of 2 For the second objective, namely minimizing the energy consumption of all drones;
Figure BDA0003845259810000052
wherein the content of the first and second substances,
Figure BDA0003845259810000053
for the size of the data carried by the nth data drone,
Figure BDA0003845259810000054
is a data unmanned plane UD n With relay unmanned UR m The rate of transmission of the data between the two,
Figure BDA0003845259810000055
for relaying unmanned aerial vehicle UR m Bandwidth resource of P n To transmit power, N 0 As noise power, ρ m Is the channel gain.
Figure BDA0003845259810000056
For the mth relaying all data, R, received by the drone m,j For relaying unmanned aerial vehicle UR m With relay unmanned UR j The data transfer rate between.
Figure BDA0003845259810000057
For the flight time of the mth relay unmanned aerial vehicle to reach the base station communication point, R m,b For relaying unmanned aerial vehicle UR m A data transmission rate with a Base Station (BS);
Figure BDA0003845259810000058
wherein the content of the first and second substances,
Figure BDA0003845259810000059
is the energy consumed by hovering the relay drone when receiving data, P H Is the hovering power of the drone,
Figure BDA00038452598100000510
is the hover energy consumed by the relaying drone when transmitting data to other drones,
Figure BDA00038452598100000511
is the amount of energy that is consumed by the flight,
Figure BDA00038452598100000512
the energy consumed when the relay unmanned aerial vehicle and the base station transmit data is used.
Further, the constraint conditions include that the total flow passing through any link cannot exceed the maximum capacity of the link, the flow proportion of all data distributed by the relay unmanned aerial vehicle to be transmitted approaches the proportion of the residual flow in all the capacity, the flow proportion of each data obtained by the relay unmanned aerial vehicle approaches the proportion of the transmission data volume, the transmission time of each data does not exceed a data time threshold, the residual electric quantity of each relay unmanned aerial vehicle is larger than the early warning electric quantity, the sum of the bandwidth resources distributed by the relay unmanned aerial vehicle is 1, and the relay unmanned aerial vehicle selects a hovering mode and a flight mode at the same time.
Further, according to the storage space and the data transmission time of the unmanned aerial vehicle, selecting a temporary relay unmanned aerial vehicle, and establishing an initial data transmission link includes:
all the data unmanned aerial vehicles randomly select one relay unmanned aerial vehicle for data transmission, and all the data are placed according to load balance constraint;
and updating the relay unmanned aerial vehicle according to the time constraint.
Further, according to the time constraint, updating the relay drone includes:
according to the relative remaining time of data transmission and the threshold value of the data transmission time, finding out the temporary relay unmanned aerial vehicle with the minimum relative remaining time;
and selecting the relay unmanned aerial vehicle according to the load balance constraint and the minimum relative remaining time, and establishing a data transmission link.
Further, based on the temporary relay unmanned aerial vehicle, solving the multi-target fitness function under the constraint condition until an optimal solution is found comprises the following steps:
constructing a data transmission path according to the state transition rule and the mode of each unmanned aerial vehicle through an ant colony algorithm;
updating local pheromones in the process of constructing a data transmission path according to pheromone updating rules;
when all ants in the ant colony reach the destination, forming a solution, and solving a multi-target fitness function based on the solution;
and carrying out the next iteration until the evaluation of the multi-target fitness function is stable.
Further, the local pheromone updating rule is as follows:
Figure BDA0003845259810000071
wherein xi epsilon (0, 1) is the local pheromone evaporation coefficient.
Next, a method for planning a data evacuation transmission path between unmanned aerial vehicles according to the present embodiment will be described in detail with reference to fig. 1-2.
The embodiment provides a method for planning a data evacuation transmission path between unmanned aerial vehicles.
A method for planning a data evacuation transmission path between unmanned aerial vehicles comprises the following steps:
s1, initializing the position, storage space and electric quantity of an unmanned aerial vehicle, the size of data carried by the unmanned aerial vehicle, a data time threshold value, transmission bandwidth required by the data, modeling evacuation data transmission time and an unmanned aerial vehicle energy consumption multi-target problem, namely constructing a multi-target fitness function for minimizing all evacuation data transmission time and energy consumption of all unmanned aerial vehicles and constraint conditions thereof;
illustratively, if there are N data drones that need to evacuate data to the base station and M relay drones with sufficient electric power, the decision variable of the multi-objective fitness function is the bandwidth resource allocation decision P = [ ρ ] N×M ,ρ M×M ,ρ M×1 ]Wherein, in the step (A),
Figure BDA0003845259810000072
representing the bandwidth resource allocation between the data drone and the relay drone,
Figure BDA0003845259810000073
indicating bandwidth resource allocation when relaying drone data transmissions,
Figure BDA0003845259810000074
bandwidth allocation during data transmission of the relay unmanned aerial vehicle and the base station is represented; mode selection decision for drone is X = { X N×M ,X M×M }，X N×M ＝{x n,m Denotes the selection decision of the data drone, X M×M ＝{x m,j ,x m,b Denotes the mode selection decision of the relay drone.
The first objective is to minimize all evacuation data transmission times:
Figure BDA0003845259810000081
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003845259810000082
for the size of the data carried by the nth data drone,
Figure BDA0003845259810000083
is a data unmanned plane UD n With relay unmanned UR m The rate of transmission of the data between the two,
Figure BDA0003845259810000084
is composed ofRelay unmanned plane UR m Bandwidth resource of P n To transmit power, N 0 As noise power, p m In order to be the gain of the channel,
Figure BDA0003845259810000085
allocating bandwidth resources between the data unmanned aerial vehicle and the relay unmanned aerial vehicle;
Figure BDA0003845259810000086
Figure BDA0003845259810000087
for the mth relaying all data, R, received by the drone m,j For relaying unmanned aerial vehicle UR m With relay unmanned UR j Inter data transfer rate, x m,j Is a binary variable, x m,j =1 relay drone UR m With relay unmanned UR j Establishing a data transmission link;
Figure BDA0003845259810000088
flight time, R, for the mth relay unmanned aerial vehicle to reach the base station communication point m,b For relaying unmanned plane UR m Data transmission rate, x, with a Base Station (BS) m,bs Is a binary variable, x m,bs =1 relay drone UR m A data link is established with the base station BS.
The second objective is to minimize the energy consumption of all drones:
Figure BDA0003845259810000089
wherein the content of the first and second substances,
Figure BDA00038452598100000810
is the energy consumed by hovering the relay drone when receiving data, P H Is the hovering power of the drone,
Figure BDA00038452598100000811
to relay the time of hover when the drone receives data,
Figure BDA00038452598100000812
Figure BDA00038452598100000813
is the hover energy consumed by the relaying drone when transmitting data to other drones,
Figure BDA00038452598100000814
is the time of hover when the relaying drone transmits data to the other drone,
Figure BDA00038452598100000815
is the amount of energy that is consumed by the flight,
Figure BDA00038452598100000816
Figure BDA00038452598100000817
the energy consumed when the relay unmanned aerial vehicle and the base station transmit data is used.
In summary, the multi-objective fitness function is
Figure BDA00038452598100000818
s.t.
Figure BDA00038452598100000819
Figure BDA0003845259810000091
Figure BDA0003845259810000092
Figure BDA0003845259810000093
Figure BDA0003845259810000094
Figure BDA0003845259810000095
Figure BDA0003845259810000096
Figure BDA0003845259810000097
Constraint (1) ensures that the total flow through any link cannot exceed its maximum capacity; constraint (2) to ensure under-the-trunk-UAV UB m The flow proportion distributed to all the transmitted data approaches the proportion of the residual capacity to all the capacity so as to realize load distribution balance; constraint (3) ensures that each data is relaying the drone UR m The obtained flow proportion approaches the proportion of the data quantity transmitted by the flow proportion to realize the quick transmission of the total backup data; constraints (4) ensure that the data rate between drones does not exceed the maximum transmission rate; constraint (5) means that the time of each data transmission does not exceed its time threshold; constraint (6) indicates that the residual electric quantity of each backup unmanned aerial vehicle is greater than the early warning electric quantity
Figure BDA0003845259810000098
Constraint (7) represents the sum of the allocated bandwidth resources of one relay drone is 1; constraint (8) indicates that each relay drone cannot select both modes simultaneously.
S2, according to the storage space and the data transmission time of the unmanned aerial vehicle, a data temporary relay point selection algorithm (DTSPS) is designed to select the temporary relay unmanned aerial vehicle, so that the data unmanned aerial vehicle selects the relay unmanned aerial vehicle with sufficient electric quantity, and an initial data transmission link is established to ensure that data cannot be lost. The method comprises the following specific steps:
s201, allThe data drone of (2) randomly selects one relay drone in set UR' i Recording the connection condition of each relay unmanned aerial vehicle, including the received data volume and the serial number of the data unmanned aerial vehicle;
s202, performing preliminary adjustment on data according to load balance constraint (namely constraint (2)); exemplary, for data unmanned UR i For example, first, according to the judgment, whether the load balance constraint is satisfied is judged, and if not, UR 'is preferentially deleted' i If all the data which do not satisfy the time constraint (i.e. constraint (5)) satisfy the time constraint, one data is randomly deleted until the load balancing constraint condition is satisfied. The deleted data information is stored in the set U1, and the relay unmanned aerial vehicle except the relay unmanned aerial vehicle meeting the load balance constraint is selected to perform data transmission. Until all data are placed according to the load balancing condition.
S203, after the initial distribution is finished, performing redistribution on the basis of meeting all constraint conditions to minimize transmission time; due to the parallel transmission, the data transmission time on the same backup drone is the same, and to reduce this time, the backup drone needs to make the best spectrum allocation.
The specific steps are to find the data with the minimum relative remaining time (set as UD) l ∈UR′ m ) To UD l Redistributing the transmitted data, respectively selecting other relay unmanned aerial vehicles, and selecting the unmanned aerial vehicle with the minimum transmission time under the constraint of meeting load balance; wherein the reallocation opportunity of each piece of data is evaluated Relative Remaining Time (RRT), the relative remaining time of the ith piece of data on the mth relay drone being defined as:
Figure BDA0003845259810000101
wherein, RRT l,m A relative difference value between the time of the first data drone transmitting to the mth relay drone and its transmission time threshold; RRT (remote resistance test) l,m The smaller the data, the greater the chance that the data will be transferred.
Note that once data is transferred, the spectrum allocation of some relay drones changes, and at this time, the data transmission time also changes; therefore, multiple iterations through the DTSPS algorithm are required, each of which finds the data with the smallest relative remaining time to be redistributed until the total transmission time decreases to a certain value and no longer changes.
S3, optimizing a data evacuation transmission path by using a multidata evacuation path ant colony optimization system (MDEPACS) algorithm based on a result of the DTSPS algorithm (namely, based on a finally selected intermediate unmanned aerial vehicle serving as a temporary relay unmanned aerial vehicle), and searching an optimal data evacuation transmission path from the temporary relay unmanned aerial vehicle to a base station; solving the multi-target fitness function under the constraint condition until an optimal solution is found; deploying an optimal data evacuation transmission path according to the optimal solution; the method comprises the following specific steps:
s301, the temporary relay unmanned aerial vehicle is used as a new data unmanned aerial vehicle to carry out mode initialization and data transmission path initialization of the data unmanned aerial vehicle, parameters in the MDEPACS algorithm are initialized, the parameters comprise the number of solutions, pheromone weight and heuristic information weight, pheromone initial values and heuristic information initial values on each side are calculated, and the current iteration number is set to be 0.
Illustratively, there are M patterns of drones and corresponding paths in each ant. The paths are different for different mode strategies. The probability of mode selection is related to the remaining time and energy of the data on the drone, the mode initialization of all relay drones:
Figure BDA0003845259810000111
Figure BDA0003845259810000112
wherein r is 0 (x i )∈[0,1]. σ = -0.1, coefficient.
Figure BDA0003845259810000113
And x i ∈[0,60]Determined by the distance of each drone from the base station and the remaining energy of each drone. The solution of the algorithm is denoted sol k ＝{path 1 ,…,path R Each path includes the selected relay drone node and the mode of the relay drone, which may be denoted as
Figure BDA0003845259810000114
S302, randomly placing each ant in the ant group at an initial point, and gradually constructing a path by using the sub-ants in each ant according to a state transition rule and the mode of each unmanned aerial vehicle until each sub-ant reaches a destination;
the construction of the path depends on a state transition rule, the rule is determined by heuristic information and pheromones between nodes of the unmanned aerial vehicles, a pseudo-random proportion rule is adopted, and the transition probability of the ith unmanned aerial vehicle of the kth ant from the node u to the node v is as follows:
Figure BDA0003845259810000115
wherein all is k,i Set of available adjacent nodes, allow, for node u k,i ＝{UR-tabu k,i }。
Figure BDA00038452598100001210
Represents the concentration of pheromone between the ith unmanned plane node u and the ith unmanned plane node v,
Figure BDA0003845259810000121
representing heuristic information between ith unmanned aerial vehicle nodes u and v; wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003845259810000122
Figure BDA0003845259810000123
Figure BDA0003845259810000124
Figure BDA0003845259810000125
an initial pheromone value of
Figure BDA0003845259810000126
f′ v ，
Figure BDA0003845259810000127
Three normalized values are defined.
In order to prevent the falling into the local optimum,
Figure BDA0003845259810000128
and (3) pheromones are left for links passed by the data path on the ith unmanned aerial vehicle in the kth ant, and the pheromones only contribute to data path exploration (only path) performed by the ant after the modes of the unmanned aerial vehicles are known. In order to avoid mutual interference of different data transmission in the path searching process, unique pheromone and heuristic information, a transverse starting point and a longitudinal end point are set for each backup unmanned aerial vehicle.
The local pheromone update rule is as follows:
Figure BDA0003845259810000129
wherein xi epsilon (0, 1) is the local pheromone evaporation coefficient.
All ants in the ant colony arrive at the end point, one solution (ant colony) is formed, and the corresponding total data evacuation transmission time and the energy consumption (objective function value) of the unmanned aerial vehicle are recorded.
S303, obtaining N solutions by the N ant colonies, and storing the obtained non-dominant solutions in an archive according to the pareto dominance and the fitness value; if the archive has space, the new non-dominated solution is added directly, if the archive is full, a grid mechanism is initiated, and the carousel read is used to delete the old solution in the archive and make room for the new non-dominated solution.
S304, the global pheromone is updated according to the non-dominant solution in the archive. And carrying out segmentation processing on the updated file by using a grid mechanism, randomly selecting a non-dominant solution in each segment, and carrying out global updating by using the mean value of pheromones.
S305, each ant colony releases one pheromone according to the result obtained in S304 to update the global pheromone. After the update is completed, the current number of iterations G is incremented by 1. Ants find the optimum according to the global pheromone in the next iteration. Wherein, in the global pheromone updating rule, only the global optimal ant is allowed to release pheromones along the path. Unlike the local pheromone update rule, global pheromone update is performed only after ants complete the path construction process of multiple unmanned aerial vehicle data in their own solutions. And determines whether it is a non-dominant solution by comparison. Assuming that there are N _ a non-dominant solutions in the archive in the current iteration, the global pheromone is updated by the non-dominant solutions. Firstly, the solutions in the file are sorted in an ascending order according to a target value of time consumption, then sorted in an ascending order according to a target value of energy consumption of the unmanned aerial vehicle, and one dominant solution in the two lists which is in the former sequence at the same time is selected for global updating; wherein, for the pheromone on each node in the solution of the archive:
τ(v i ,u i )＝(1-ρ)·τ(v i ,u i )+ρ·Δτ b (v i ,u i )
Figure BDA0003845259810000131
Δτ b (v i ,u i ) Is the pheromone released by the termite son in the selected optimal solution. When all the mother ants complete their respective paths, we rank the mother ant's resultant based on the fitness function. Only the current optimal solution and (w-1) the optimal solution can release the pheromone. The more advanced path length rankThe larger the amount of information released by the ants is, the weight (w-k) plays a role in amplifying the concentration difference of pheromones of different paths.
And S306, when the function evaluation is stable, the algorithm is terminated, and the optimal data evacuation transmission path is deployed.
Example two
The embodiment discloses a system for planning an unmanned aerial vehicle data evacuation transmission path, which comprises:
a multi-objective fitness function construction module configured to: constructing a multi-objective fitness function for minimizing all evacuation data transmission time and energy consumption of all unmanned aerial vehicles, and constraint conditions thereof;
a temporary relay selection module configured to: selecting a temporary relay unmanned aerial vehicle according to the storage space and the data transmission time of the unmanned aerial vehicle, and establishing an initial data transmission link;
a data evacuation transmission optimal path deployment module configured to: solving the multi-target fitness function under the constraint condition based on the initial data transmission link until an optimal solution is found; and deploying the optimal data evacuation transmission path according to the optimal solution.
It should be noted that the above multi-objective fitness function building module, the temporary relay point selecting module, and the data evacuation transmission optimal path deploying module correspond to the steps in the first embodiment, and the above modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the contents disclosed in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
EXAMPLE III
The third embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer instruction stored in the memory and running on the processor, where the computer instruction is executed by the processor to complete the steps of the first embodiment.
Example four
A fourth embodiment of the present invention provides a computer-readable storage medium, configured to store computer instructions, where the computer instructions, when executed by a processor, perform the steps of the first embodiment.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.
In the foregoing embodiments, the description of each embodiment has an emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions in other embodiments.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. A method for planning a data evacuation transmission path between unmanned aerial vehicles is characterized by comprising the following steps:
constructing a multi-target fitness function for minimizing all evacuation data transmission time and energy consumption of all unmanned aerial vehicles, and constraint conditions of the multi-target fitness function;
selecting a temporary relay unmanned aerial vehicle according to the storage space and the data transmission time of the unmanned aerial vehicle, and establishing an initial data transmission link;
solving the multi-target fitness function under the constraint condition based on the temporary relay unmanned aerial vehicle until an optimal solution is found; and deploying the optimal data evacuation transmission path according to the optimal solution.
2. The method according to claim 1, wherein the multi-objective fitness function is
Figure FDA0003845259800000011
f 1 For the first goal, i.e., minimizing all evacuation data transmission time; f. of 2 For the second objective, namely minimizing the energy consumption of all drones;
Figure FDA0003845259800000012
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003845259800000013
Figure FDA0003845259800000014
for the size of the data carried by the nth data drone,
Figure FDA0003845259800000015
is a data unmanned plane UD n With relay unmanned UR m The rate of transmission of the data between the two,
Figure FDA0003845259800000016
for relaying unmanned plane UR m Bandwidth resource of P n To transmit power, N 0 As noise power, p m Is the channel gain.
Figure FDA0003845259800000017
Figure FDA0003845259800000018
For the mth relaying all data, R, received by the drone m, For relaying unmanned plane UR m With relay unmanned UR j The data transfer rate of each.
Figure FDA0003845259800000019
Figure FDA00038452598000000110
Flight time, R, for the mth relay unmanned aerial vehicle to reach the base station communication point m, For relaying unmanned plane UR m A data transmission rate with a Base Station (BS);
Figure FDA0003845259800000021
wherein the content of the first and second substances,
Figure FDA0003845259800000022
is the energy consumed by hovering when the relay drone receives data, P H Is the hovering power of the drone,
Figure FDA0003845259800000023
the relay unmanned aerial vehicle transmits dataGiven the hover energy consumed by other drones,
Figure FDA0003845259800000024
is the amount of energy that is consumed by the flight,
Figure FDA0003845259800000025
the energy consumed when the relay unmanned aerial vehicle and the base station transmit data is used.
3. The method according to claim 1, wherein the constraints include that the total flow through any link cannot exceed the maximum capacity, the proportion of the flow allocated by the relay drone to all the transmitted data approaches the proportion of the residual flow to all the capacity, the proportion of the flow obtained by each data at the relay drone approaches the proportion of the transmitted data volume, the transmission time of each data does not exceed the data time threshold, the residual capacity of each relay drone is greater than the early warning power, the proportion of the bandwidth resources allocated by the relay drone is 1, and the relay drones select the hovering mode and the flight mode at the same time.
4. The method according to claim 1, wherein the selecting the temporary relay drone according to the storage space and the data transmission time of the drone, and the establishing the initial data transmission link includes:
all the data unmanned aerial vehicles randomly select one relay unmanned aerial vehicle for data transmission, and all the data are placed according to load balance constraint;
and updating the relay unmanned aerial vehicle according to the time constraint.
5. The method of claim 4, wherein said updating the relay drones based on time constraints comprises:
according to the relative residual time of data transmission and the threshold value of the data transmission time, finding out a temporary relay unmanned aerial vehicle with the minimum relative residual time;
and selecting the relay unmanned aerial vehicle according to the load balance constraint and the minimum relative remaining time, and establishing a data transmission link.
6. The method according to claim 1, wherein solving the multi-objective fitness function under constraint conditions until an optimal solution is found based on the temporary relay drone includes:
constructing a data transmission path according to the state transition rule and the mode of each unmanned aerial vehicle through an ant colony algorithm;
updating local pheromones in the process of constructing a data transmission path according to pheromone updating rules;
when all ants in the ant colony reach the destination, forming a solution, and solving the multi-target fitness function based on the solution;
and carrying out the next iteration until the evaluation of the multi-target fitness function is stable.
7. The method according to claim 6, wherein the inter-UAV data evacuation transmission path planning step,
storing the obtained non-dominant solution in an archive according to the obtained solution and the pareto dominance and fitness value;
if the files have residual space, directly adding the non-dominated solution into the files; if the file has no remaining space, the roulette method is used to delete the old non-dominant solution and place the new non-dominant solution.
8. The utility model provides a data evacuation transmission system between unmanned aerial vehicle, characterized by includes:
a multi-objective fitness function construction module configured to: constructing a multi-objective fitness function for minimizing all evacuation data transmission time and energy consumption of all unmanned aerial vehicles, and constraint conditions thereof;
a temporary relay selection module configured to: selecting a temporary relay unmanned aerial vehicle according to the storage space and the data transmission time of the unmanned aerial vehicle, and establishing an initial data transmission link;
a data evacuation transmission optimal path deployment module configured to: solving the multi-target fitness function under the constraint condition based on the initial data transmission link until an optimal solution is found; and deploying the optimal data evacuation transmission path according to the optimal solution.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the steps of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the steps of any one of claims 1 to 7.
CN202211115229.0A 2022-09-14 2022-09-14 Method and system for planning data evacuation transmission path between unmanned aerial vehicles Pending CN115460668A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116132354A (en) * 2023-02-23 2023-05-16 中国科学院软件研究所 Unmanned aerial vehicle cluster networking transmission path optimization method and system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116132354A (en) * 2023-02-23 2023-05-16 中国科学院软件研究所 Unmanned aerial vehicle cluster networking transmission path optimization method and system
CN116132354B (en) * 2023-02-23 2024-03-22 中国科学院软件研究所 Unmanned aerial vehicle cluster networking transmission path optimization method and system

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