CN113885555A - Multi-machine task allocation method and system for power transmission line dense channel routing inspection - Google Patents
Multi-machine task allocation method and system for power transmission line dense channel routing inspection Download PDFInfo
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Abstract
The invention provides a multi-machine task allocation method and system for power transmission line dense channel routing inspection, and relates to the technical field of multi-unmanned aerial vehicle task allocation. In the scene of routing inspection of the dense channel of the power transmission line, in consideration of the fact that the unmanned aerial vehicle needs to supplement electric quantity in task execution, a multi-machine task allocation model is innovatively provided, and the routing inspection task and the charging task of the unmanned aerial vehicle are jointly optimized; an improved genetic algorithm is designed based on the multi-machine task allocation model provided by the invention, and a multi-level chromosome mixed coding, double population selection and chromosome correction module is added in the algorithm, so that a high-quality task allocation scheme can be quickly obtained, and a flight path for each unmanned aerial vehicle to execute tasks and charge is planned on the basis.
Description
Technical Field
The invention relates to the technical field of multi-unmanned aerial vehicle task allocation, in particular to a multi-machine task allocation method and system for power transmission line dense channel routing inspection.
Background
In recent years, under the promotion of national policies, the construction of power grid infrastructure in China realizes high-speed development. With the expansion of the distribution range and the increase of the distribution density of the power transmission line, the line equipment is exposed to the outdoor environment for a long time to operate, so that factors such as natural disasters, animal and plant invasion, artificial external force damage and the like generate losses of different degrees on the line equipment, power accidents occur occasionally, and the production life and social operation of people are seriously influenced. In order to maintain the normal operation of the transmission line, the power grid company needs to perform fine inspection and inspection on the power tower and the line regularly. Because the transmission line that is the main with traditional artifical patrolling and examining is patrolled and examined and is had the personal safety risk height, is patrolled and examined inefficiency and with high costs the scheduling problem, unmanned aerial vehicle patrols and examines and becomes the normalized means that electric power was patrolled and examined gradually.
Present transmission line patrols and examines mainly through patrolling and examining personnel and control an unmanned aerial vehicle or single unmanned aerial vehicle and independently patrol and examine and realize, but in the face of the transmission line of the intensive passageway of extra-high voltage, the limited duration of single unmanned aerial vehicle is difficult to accomplish alone that a large amount is many, the transmission line that the distance is far away patrols and examines the task, it receives very big restriction to patrol and examine the ability, utilize many unmanned aerial vehicles to patrol and examine the intensive passageway in coordination and can fully release unmanned aerial vehicle in the aspect of patrolling and examining the application potentiality, very big promotion is patrolled and examined efficiency.
However, due to the limitation of power technology, the endurance time of the existing unmanned aerial vehicle is limited, when the unmanned aerial vehicle executes a large number of power transmission line routing inspection tasks with long distance, the unmanned aerial vehicle needs to be charged in time to meet the endurance requirement, so that under the scene of routing inspection of the power transmission line dense channels with cooperation of multiple unmanned aerial vehicles, the difference of endurance between each unmanned aerial vehicle and the position of the charging station of the unmanned aerial vehicle when the unmanned aerial vehicle is formed into a formation and routing inspection are considered, the charging station and the target task point are distributed for the unmanned aerial vehicle, the access sequence of the multiple unmanned aerial vehicles to the task point and the charging station is optimized, and the shortest total time for completing the task by the whole unmanned aerial vehicle formation is the problem which needs to be solved urgently.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a multi-machine task allocation method and a multi-machine task allocation system for transmission line dense channel routing inspection, and solves the problems that in a scene of transmission line dense channel routing inspection with cooperation of multiple unmanned aerial vehicles, charging stations and target task points are allocated to the unmanned aerial vehicles by considering the difference of continuous voyage among the unmanned aerial vehicles and the positions of the charging stations of the unmanned aerial vehicles during formation routing inspection of the unmanned aerial vehicles, and the access sequence of the multiple unmanned aerial vehicles to the task points and the charging stations is optimized, so that the total time for completing tasks by the whole unmanned aerial vehicles in formation is shortest.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, a multi-machine task allocation method facing transmission line dense channel routing inspection is provided, and the method comprises the following steps:
acquiring position information of a task point and a charging station and formation information of unmanned aerial vehicles; wherein, unmanned aerial vehicle formation information includes:
the flight speed of the unmanned aerial vehicle;
flight time of the unmanned aerial vehicle from each charging station to each task point;
the flight time of the unmanned aerial vehicle between each task point;
the duration of each unmanned aerial vehicle in the unmanned aerial vehicle formation before the start of the routing inspection is prolonged;
constructing a multi-machine task allocation model facing the power transmission line dense channel inspection based on the task points, the charging station position information and the unmanned aerial vehicle formation information;
and solving the multi-machine task allocation model by adopting a preset algorithm to obtain a task allocation scheme for unmanned aerial vehicle formation.
Further, the objective function of the multi-machine task allocation model is as follows:
wherein,
k is the unmanned aerial vehicle number;
c is the number of the charging station;
i. j is a task point number;
u is a set of drones;
c is a charging station set;
t is a set of task points;
v is a constant, representing the flight rate of the drone.
Further, the constraints of the multi-machine task allocation model include:
constraint 1: after starting from a charging station, the unmanned aerial vehicle must visit a task point
Constraint 2: a task point can only be accessed once
Constraint 3: after the unmanned aerial vehicle finishes the task, the unmanned aerial vehicle must return to one of the charging stations
Constraint 4: unmanned aerial vehicle surplus power flight distance constraint
Constraint 5: constraint that charging time of kth unmanned aerial vehicle satisfies follow-up flight time
Wherein,
and indicating the duration of the k unmanned plane before the start of the patrol.
Further, the flight duration of each unmanned aerial vehicle from each charging station to each task point is calculated based on the position of the charging station, the position of the task point and the flight speed of the unmanned aerial vehicle to obtain:
wherein, tciRepresenting the flight time of the unmanned aerial vehicle from the c charging station to the i task point;
and x and y respectively represent coordinate values under a plane coordinate system;
v represents the flight speed of the drone;
the flight time of each unmanned aerial vehicle between each task point is calculated and obtained based on the positions of the two task points and the flight speed of the unmanned aerial vehicle:
wherein i, j represents a task point sequence number, i ≠ j, tijRepresenting the flight time of the unmanned aerial vehicle from the ith task point to the jth task point;
the current endurance time of each unmanned aerial vehicle is calculated based on the remaining capacity and the flight power of the unmanned aerial vehicle to obtain:
wherein,
purepresenting the flight power of the drone.
Further, the solving of the multi-machine task allocation model by using a preset algorithm to obtain a task allocation scheme for unmanned aerial vehicle formation comprises:
s1, acquiring preset parameters of the genetic algorithm;
s2, generating chromosomes by adopting a multi-level integer coding mode, and constructing an initial population;
s3, regarding the current population as a parent population, and calculating the fitness value of each chromosome in the population based on the multi-machine task allocation model;
s4, judging the feasibility of the solution corresponding to the chromosome in the current population by adopting an infeasible solution judgment method; if the determination is passed, the routine proceeds to S6; otherwise, go to S5;
s5, correcting chromosomes which do not pass feasibility judgment by adopting an infeasible solution correction method;
s6, updating chromosomes in the current population by adopting a preset updating operation to generate a child population;
s7, judging whether the iteration times reach the maximum iteration times; if not, increasing the iteration number by 1, and returning to S3;
and S8, calculating the fitness value of each chromosome in the offspring population, and outputting the optimal path planning scheme corresponding to the chromosome with the optimal fitness in the offspring population as the task allocation scheme of the unmanned aerial vehicle formation.
Further, the generating of the chromosome by using the multi-stage integer coding mode includes:
the first line of the chromosome represents the drone number;
the second row of the chromosome represents the charging station number for the unmanned aerial vehicle to charge, wherein 0 represents that the unmanned aerial vehicle does not need to be charged;
the third row of the chromosome represents the number of task points and the length of the chromosome corresponds to the number of task points.
Further, the calculating the fitness value of the chromosome includes:
the inverse of the objective function of the feasible chromosomes is taken as the fitness function.
Further, the infeasible solution determination method includes:
taking chromosomes which do not meet any constraint conditions as infeasible solutions, and defining the objective function of the chromosomes as an infinite value M;
infeasible solution correction method:
and calculating the objective function value of the generated chromosome, and regenerating the chromosome when the objective function value is larger than M until the objective function value of the generated chromosome is smaller than M.
Further, the updating operation includes:
in the crossover operation, a two-point crossover method is used for the first and second rows of chromosomes, and a two-point sequential crossover method is used for the third row of chromosomes:
in the mutation operation, single-point mutation is adopted for the first line and the second line of the chromosome, and two adjacent fragments are exchanged for the third line of the chromosome;
in the selection operation, an elite retention strategy is adopted for the initial population, and a roulette strategy is adopted for the cross-mutated population.
In a second aspect, a multi-machine task allocation system for power transmission line dense channel inspection is provided, where the system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method when executing the computer program.
(III) advantageous effects
The invention provides a multi-machine task allocation method and system for power transmission line dense channel routing inspection. Compared with the prior art, the method has the following beneficial effects:
according to the unmanned aerial vehicle formation task optimization method, under the scene of power transmission line dense channel inspection with cooperation of multiple unmanned aerial vehicles, the difference of electric quantity supplement required in task execution of the unmanned aerial vehicles, the continuation of the journey between each unmanned aerial vehicle when the unmanned aerial vehicles form an inspection and the charging station position of the unmanned aerial vehicle are considered, a multi-vehicle task allocation model is innovatively provided, the inspection task and the charging task of the unmanned aerial vehicles are optimized in a combined mode, the charging station and the target task point can be allocated to the unmanned aerial vehicles by solving the model, the access sequence of the multiple unmanned aerial vehicles to the task point and the charging station is optimized, and the total time of the whole unmanned aerial vehicle formation to complete the task is shortest.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a power transmission line dense channel inspection scene with multiple unmanned aerial vehicles in cooperation;
FIG. 2 is a schematic diagram of the coding of the improved genetic algorithm proposed by the present invention;
FIG. 3 is a path diagram corresponding to the encoding of FIG. 2;
FIG. 4 is a schematic diagram of the crossover operation of the improved genetic algorithm proposed by the present invention;
FIG. 5 is a schematic diagram of a mutation operation of the improved genetic algorithm proposed by the present invention;
FIG. 6 is a graph of orthogonal experimental parameter analysis in a validation experiment of the present invention;
FIG. 7 is a line graph of the data size and solution time relationship of the algorithm in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a multi-machine task allocation method and a multi-machine task allocation system for power transmission line dense channel inspection, and solves the problems that under the scene of power transmission line dense channel inspection in cooperation with multiple unmanned aerial vehicles, the charging station and the target task point are allocated for the unmanned aerial vehicles by considering the difference of endurance between each unmanned aerial vehicle and the position of the charging station of the unmanned aerial vehicles when the unmanned aerial vehicles are formed into a team for inspection, the access sequence of the multiple unmanned aerial vehicles to the task points and the charging station is optimized, and the total time for the whole unmanned aerial vehicles to form the complete tasks is shortest.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
dense passageway is total a plurality of shaft towers, task point promptly, and the unmanned aerial vehicle formation that current many unmanned aerial vehicle constitute takes off from a fixed honeycomb, has patrolled and examined all shaft towers to accomplish and patrol and examine the task, one of them unmanned aerial vehicle can visit a plurality of task points in flight once. When there is not enough electric quantity to charge at the in-process of accomplishing the task, unmanned aerial vehicle can fly to the fixed charging station of a plurality of and charge.
As shown in figure 1, the regional 2 unmanned aerial vehicles that total, 2 charging stations and 10 shaft towers, No. 1 unmanned aerial vehicle takes off from No. 1 charging station now, patrols and examines 1, 2, 3, 4 shaft towers in succession, descends to No. 1 charging station and charges, is full of the electricity back, takes off again and examines 5, 6 shaft towers in succession, descends at No. 2 charging stations that the distance is nearer at last. When No. 1 unmanned aerial vehicle carried out and patrolled and examined the task, No. 2 unmanned aerial vehicle took off from No. 2 charging stations, patrolled and examined 7, 8, 9, behind No. 10 shaft towers in succession, descended at No. 2 charging stations that are nearer apart from.
Under this scene, the difference of continuation of the journey between every unmanned aerial vehicle when unmanned aerial vehicle formation patrols and examines, unmanned aerial vehicle charging station position, unmanned aerial vehicle charge time length need be considered to distribute charging station and task point for unmanned aerial vehicle to optimize many unmanned aerial vehicles's the visit order to task point and charging station, make whole unmanned aerial vehicle formation accomplish the task total time the shortest.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Example 1:
the invention provides a multi-machine task allocation method for intensive channel routing inspection of a power transmission line, which comprises the following steps:
acquiring position information of a task point and a charging station and formation information of unmanned aerial vehicles; wherein, unmanned aerial vehicle formation information includes:
the flight speed of the unmanned aerial vehicle;
flight time of the unmanned aerial vehicle from each charging station to each task point;
the flight time of the unmanned aerial vehicle between each task point;
the duration of each unmanned aerial vehicle in the unmanned aerial vehicle formation before the start of the routing inspection is prolonged;
constructing a multi-machine task allocation model facing the power transmission line dense channel inspection based on the task points, the charging station position information and the unmanned aerial vehicle formation information;
and solving the multi-machine task allocation model by adopting a preset algorithm to obtain a task allocation scheme for unmanned aerial vehicle formation.
The beneficial effect of this embodiment does:
according to the unmanned aerial vehicle formation task optimization method, under the scene of power transmission line dense channel inspection with cooperation of multiple unmanned aerial vehicles, the difference of electric quantity supplement required in task execution of the unmanned aerial vehicles, the continuation of the journey between each unmanned aerial vehicle when the unmanned aerial vehicles form an inspection and the charging station position of the unmanned aerial vehicle are considered, a multi-vehicle task allocation model is innovatively provided, the inspection task and the charging task of the unmanned aerial vehicles are optimized in a combined mode, the charging station and the target task point can be allocated to the unmanned aerial vehicles by solving the model, the access sequence of the multiple unmanned aerial vehicles to the task point and the charging station is optimized, and the total time of the whole unmanned aerial vehicle formation to complete the task is shortest.
The following describes the implementation process of the embodiment of the present invention in detail:
Specifically, the method comprises the following steps:
the task point location information includes: the number of task points and their coordinates.
The charging station location information includes: the number of charging stations and their coordinates.
The performance parameters of all the unmanned planes in the formation of the unmanned planes are consistent, namely the flight power puThe parameters of the flying speed v, the charging power and the like are all constants.
Due to the coordinates (x) of each task pointi,yi) I ∈ T, coordinates (x) of each charging stationc,yc) C belongs to C and the flight speed v of the unmanned aerial vehicle is a known quantity, and the flight time t of the unmanned aerial vehicle from each charging station to each task point can be calculated according to the following formulaci；
Similarly, the flight time t of the unmanned aerial vehicle between two task points i and jijI ≠ j, which can be calculated according to the following formulaObtaining:
and the duration of the kth unmanned aerial vehicle in the formation of unmanned aerial vehicles before the start of patrol
Can be according to unmanned aerial vehicle's residual capacity
And the flight power p of the unmanned aerial vehicleuThe formula is as follows:
when the unmanned aerial vehicle is specifically implemented, the duration of the unmanned aerial vehicle before the start of patrol
Flight time t of unmanned aerial vehicle between each task pointijTime t of flight of the unmanned aerial vehicle from each charging station to each task pointciAnd the two-dimensional matrix can be used for storage.
Duration of endurance before start of patrol inspection through unmanned aerial vehicle
And the flight time t of the unmanned aerial vehicle from each charging station to each task pointciAnd whether the residual electric quantity of the unmanned aerial vehicle supports the unmanned aerial vehicle to fly to the task point or not can be judged.
And 2, constructing a multi-machine task allocation model facing the power transmission line dense channel inspection based on the task points, the charging station position information and the unmanned aerial vehicle formation information.
Specifically, after the data are acquired, a mathematical model for solving the technical problem, namely a multi-machine task allocation model (MU-TSP-C model), can be constructed based on the data.
The model comprises an objective function and a constraint condition, wherein:
the objective function is:
wherein,
k is the unmanned aerial vehicle number;
c is the number of the charging station;
i. j is a task point number;
u is a set of drones;
c is a charging station set;
t is a set of task points;
v is a constant, representing the flight rate of the drone.
And the MU-TSP-C model contains 5 constraints:
constraint 1: a drone must visit a mission point after departing from a charging station. The mathematical expression is as follows:
constraint 2: a task point can only be accessed once. The mathematical expression is as follows:
constraint 3: the drone must return to one of the charging stations after completing the mission. The mathematical expression is as follows:
constraint 4: and (5) restraining the flying distance of the residual electric quantity of the unmanned aerial vehicle. The mathematical expression is as follows:
constraint 5: the charging time of the kth unmanned aerial vehicle is to satisfy the constraint of the follow-up flight time. The mathematical expression is as follows:
and at this point, the construction of a multi-machine task allocation model is completed.
And 3, solving the multi-machine task allocation model by adopting a preset algorithm to obtain a task allocation scheme for unmanned aerial vehicle formation.
For the solution of the multi-machine task allocation model, the existing solution algorithm can be preset in a program, and the solution is called. However, the invention further considers the solving efficiency under the scene of multi-unmanned aerial vehicle cooperative transmission line dense channel routing inspection, and provides an optimal path planning scheme for solving the obtained multi-machine task allocation model by adopting a multi-level chromosome mixed coding and a dual population selection improved genetic algorithm (CH & DS-GA) to obtain multi-unmanned aerial vehicle charging station and task point allocation.
The improved genetic algorithm (CH & DS-GA) proposed by the present invention is described in detail below:
(1) chromosome coding mode:
one chromosome represents one possible solution according to genetic algorithms. Aiming at a specific application scene of the invention, a multi-level chromosome coding mode is designed, and as shown in fig. 2, the first line of the chromosome represents the unmanned aerial vehicle number; the second row represents the charging station number for the unmanned aerial vehicle to charge, wherein 0 represents a virtual charging station, which represents that the unmanned aerial vehicle does not need to charge; the third row represents the number of task points and the length of the chromosome corresponds to the number of task points.
The path planning scheme obtained after chromosome decoding in fig. 2 is shown in fig. 3, and the total number of routing inspection tasks is 8, namely nodes 1-8. The polling task is executed by 3 unmanned aerial vehicles:
no. 1 unmanned aerial vehicle goes to carry out No. 7 tasks earlier, goes No. 1 charging station again and charges, carries out No. 1 and No. 2 tasks in proper order at last, returns No. 2 charging stations at last again.
No. 2 unmanned aerial vehicle goes to carry out No. 6 and No. 5 tasks earlier, and the completion goes No. 2 charging station to charge, goes No. 8 task point executive task at last, returns No. 3 charging stations at last.
No. 3 unmanned aerial vehicle directly goes to carry out No. 4 and No. 3 task, returns No. 1 charging station at last again.
(2) Fitness function:
the target function is the inspection time when the multiple unmanned aerial vehicles cooperatively finish the power grid dense channel, the larger the individual fitness according to the genetic algorithm is, the better the inspection time is, and the smaller the inspection time is, the better the inspection time is, and the reciprocal of the target function of the feasible chromosome is converted into a fitness function value by adopting a reciprocal method. In this case, the smaller the value of the objective function is, the larger the fitness value is, and the better the solution is represented.
The chromosome updating operation in the genetic algorithm comprises the following steps: selecting, crossing and mutating, wherein:
(3) and (3) cross operation:
through the cross operation among chromosomes, the gene sequence of the chromosomes is changed, the population diversity can be increased, and the global search capability of the genetic algorithm is improved. Regarding the meaning represented by three rows of the chromosome, as shown in fig. 4, a two-point intersection method is adopted for uav and charge, considering that the coding of uav and charge is repeatable, and the coding of task requires no repetition and omission, and all task points can be kept; and a two-point sequence crossing method is adopted for task, so that all task points can be reserved.
(4) Mutation operation:
mutation operations generate new chromosomes by changing genes or gene positions in the chromosomes, and increase the diversity of the population so as to avoid the algorithm from falling into local optimization. Similar to the cross, as shown in FIG. 5, a single point variation is used for uav and charge; and carrying out fragment mutation on the task, namely exchanging two adjacent fragments of the task.
(5) And (3) population selection operation:
the designed genetic algorithms share two selection strategies: elite reservation and roulette selection. An elite retention strategy is adopted for the initial population, and a roulette strategy is adopted for the population after cross variation.
Firstly, the fitness of all individuals in the population is calculated and arranged from big to small. Selecting part of individuals with highest fitness (the scheme with the least inspection time) as an elite reservation strategy according to the proportion;
fitness is calculated as the selection probability, and chromosomes with high fitness values have higher probability to be selected and passed to the next generation of roulette selection strategy.
The population updating is carried out through the two strategies, so that the optimal individual can be inherited to the next generation, the algorithm has better global search capability, and the trapping of a local optimal solution is avoided.
(6) Chromosome correction:
during initial generation, crossing and mutation of chromosomes, the resulting chromosomes may produce an infeasible solution.
An infeasible solution discrimination method:
in the algorithm, a solution that does not satisfy any constraint condition is taken as an infeasible solution, and its objective function is defined as an infinite value M.
Infeasible solution correction method:
by calculating the objective function value for the chromosome, and when the objective function value is greater than M, it is returned to regeneration until the resulting solution is less than M, this way ensuring that the generated chromosome is viable.
In summary, the improved genetic algorithm (CH & DS-GA) solving step proposed by the present invention comprises:
s1, acquiring preset parameters of the genetic algorithm; including cross probability, mutation probability, population size, maximum iteration number, etc.
S2, generating chromosomes by adopting a multi-level integer coding mode, and constructing an initial population;
s3, regarding the current population as a parent population, and calculating the fitness value of each chromosome in the population based on the multi-machine task allocation model;
s4, judging the feasibility of the solution corresponding to the chromosome in the current population by adopting an infeasible solution judgment method; if the determination is passed, the routine proceeds to S6; otherwise, go to S5;
s5, correcting chromosomes which do not pass feasibility judgment by adopting an infeasible solution correction method;
s6, updating chromosomes in the current population by adopting a preset updating operation to generate a child population;
s7, judging whether the iteration times reach the maximum iteration times; if not, increasing the iteration number by 1, and returning to S3;
and S8, calculating the fitness value of each chromosome in the offspring population, and outputting the optimal path planning scheme corresponding to the chromosome with the optimal fitness in the offspring population as the task allocation scheme of the unmanned aerial vehicle formation.
In order to verify the effects of the MU-TSP-C model and the CH & DS-GA algorithm of the embodiment of the invention, an algorithm efficiency comparison experiment is given as follows:
comparative experiments constructed a typical multiple drone TSP scenario with limited charging stations,
the relevant performance parameters of the unmanned aerial vehicle are as follows:
the flight speed of the unmanned aerial vehicle is 25km/h
The flight power of the unmanned aerial vehicle is 114.25W
The capacity of the unmanned aerial vehicle battery is 50Wh
The parameters of the improved algorithm under the experimental data are optimized by adopting a Taguchi orthogonal experimental method, and the obtained parameter analysis result is shown in FIG. 6, so that the optimal parameter results under the experimental data can be determined to be 0.6 of cross probability, 0.8 of variation probability and 200 of population scale. Under this parameter, the improved Genetic Algorithm (GA) and the exhaustive algorithm (exhaustible algorithm) herein were run to solve the experimental data 10 times, respectively. The solution results are shown in table 1:
TABLE 1
Wherein, the gap calculation formula is as follows:
as can be seen from table 1, on the basis that both solve the same small-scale test example, compared with the exhaustive algorithm, the improved genetic algorithm provided by the present invention has the operation effect close to the optimal solution on the basis that the operation time is greatly reduced, i.e., the approximately optimal solution can be found in a shorter time. The effectiveness of the algorithm of the invention in solving multiple drones TSP with limited charging stations on a small scale is illustrated.
To further illustrate the performance of the algorithm, the algorithm performance experiment is further given:
firstly, generating positions on a plane, assuming 3 charging stations, wherein the inspection range is a circle with one of the charging stations as a circle center and a radius of 10km, the unmanned aerial vehicle takes off from the charging stations, and task points are randomly distributed in the circle (except charging points). 10 groups of data with different scales are generated, 10 groups of data are solved respectively by using the algorithm, each group of experiments are operated for 20 times, the task scale and the algorithm solving time are shown in a table 2, wherein 2U-10T-3C represents 2 unmanned aerial vehicles with 10 tasks and 3 charging stations.
TABLE 2
| Numbering | Task | Algorithm runtime | |
| 1 | 2U-10T-3C | 19.6 | |
| 2 | 4U-20T-3C | 43.5 | |
| 3 | 6U-30T-3C | 72.4 | |
| 4 | 8U-40T-3C | 106.5 | |
| 5 | 10U-50T-3C | 145.4 | |
| 6 | 12U-60T-3C | 188.5 | |
| 7 | 14U-70T-3C | 236.8 | |
| 8 | 16U-80T-3C | 289.3 | |
| 9 | 18U-90T-3C | 353.2 | |
| 10 | 20U-100T-3C | 426.8 |
From the data in table 2, a line graph with the horizontal axis as the task scale and the vertical axis as the algorithm running time is drawn, as shown in fig. 7, where "Linear" represents a Linear straight line with "y ═ 5 × x", it can be seen that as the data scale increases, the solution time of the algorithm of the present embodiment shows a stable Linear increase situation, and the requirement of the large-scale multi-drone TSP with limited charging stations on the solution time is met, and experiments show that the algorithm of the present embodiment has good performance.
Example 2
The invention also provides a multi-machine task allocation system for the power transmission line dense channel inspection, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of the method when executing the computer program.
It can be understood that the multi-machine task allocation system for power transmission line dense channel inspection provided by the embodiment of the present invention corresponds to the multi-machine task allocation method for power transmission line dense channel inspection, and the explanation, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the multi-machine task allocation method for power transmission line dense channel inspection, and are not described herein again.
In summary, compared with the prior art, the invention has the following beneficial effects:
in the scene of routing inspection of the dense channel of the power transmission line, in consideration of the fact that the unmanned aerial vehicle needs to supplement electric quantity in task execution, a multi-machine task allocation model is innovatively provided, and the routing inspection task and the charging task of the unmanned aerial vehicle are jointly optimized; an improved genetic algorithm is designed based on the multi-machine task allocation model provided by the invention, and a multi-level chromosome mixed coding, double population selection and chromosome correction module is added in the algorithm, so that a high-quality task allocation scheme can be quickly obtained, and a flight path for each unmanned aerial vehicle to execute tasks and charge is planned on the basis.
It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform. With this understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments. In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A multi-machine task allocation method for power transmission line dense channel inspection is characterized by comprising the following steps:
acquiring position information of a task point and a charging station and formation information of unmanned aerial vehicles; wherein, unmanned aerial vehicle formation information includes:
the flight speed of the unmanned aerial vehicle;
flight time of the unmanned aerial vehicle from each charging station to each task point;
the flight time of the unmanned aerial vehicle between each task point;
the duration of each unmanned aerial vehicle in the unmanned aerial vehicle formation before the start of the routing inspection is prolonged;
constructing a multi-machine task allocation model facing the power transmission line dense channel inspection based on the task points, the charging station position information and the unmanned aerial vehicle formation information;
and solving the multi-machine task allocation model by adopting a preset algorithm to obtain a task allocation scheme for unmanned aerial vehicle formation.
2. The multi-machine task allocation method for the power transmission line dense channel inspection according to claim 1, wherein an objective function of the multi-machine task allocation model is as follows:
wherein,
k is the unmanned aerial vehicle number;
c is the number of the charging station;
i. j is a task point number;
u is a set of drones;
c is a charging station set;
t is a set of task points;
v is a constant, representing the flight rate of the drone.
3. The multi-machine task allocation method for the power transmission line dense channel inspection according to claim 2, wherein the constraint conditions of the multi-machine task allocation model include:
constraint 1: after starting from a charging station, the unmanned aerial vehicle must visit a task point
Constraint 2: a task point can only be accessed once
Constraint 3: after the unmanned aerial vehicle finishes the task, the unmanned aerial vehicle must return to one of the charging stations
Constraint 4: unmanned aerial vehicle surplus power flight distance constraint
Constraint 5: constraint that charging time of kth unmanned aerial vehicle satisfies follow-up flight time
Wherein,
and indicating the duration of the k unmanned plane before the start of the patrol.
4. The multi-machine task allocation method for the power transmission line dense channel inspection according to claim 1, wherein the flight time of each unmanned aerial vehicle from each charging station to each task point is calculated based on the charging station position, the task point position, and the flight speed of the unmanned aerial vehicle to obtain:
wherein, tciRepresenting the flight time of the unmanned aerial vehicle from the c charging station to the i task point;
and x and y respectively represent coordinate values under a plane coordinate system;
v represents the flight speed of the drone;
the flight time of each unmanned aerial vehicle between each task point is calculated and obtained based on the positions of the two task points and the flight speed of the unmanned aerial vehicle:
wherein i, j represents a task point sequence number, i ≠ j, tijRepresenting the flight time of the unmanned aerial vehicle from the ith task point to the jth task point;
the current endurance time of each unmanned aerial vehicle is calculated based on the remaining capacity and the flight power of the unmanned aerial vehicle to obtain:
wherein,
purepresenting the flight power of the drone.
5. The multi-machine task allocation method for the transmission line dense channel inspection according to claim 1, wherein the step of solving the multi-machine task allocation model by using a preset algorithm to obtain the task allocation scheme for the formation of the unmanned aerial vehicles comprises the following steps:
s1, acquiring preset parameters of the genetic algorithm;
s2, generating chromosomes by adopting a multi-level integer coding mode, and constructing an initial population;
s3, regarding the current population as a parent population, and calculating the fitness value of each chromosome in the population based on the multi-machine task allocation model;
s4, judging the feasibility of the solution corresponding to the chromosome in the current population by adopting an infeasible solution judgment method; if the determination is passed, the routine proceeds to S6; otherwise, go to S5;
s5, correcting chromosomes which do not pass feasibility judgment by adopting an infeasible solution correction method;
s6, updating chromosomes in the current population by adopting a preset updating operation to generate a child population;
s7, judging whether the iteration times reach the maximum iteration times; if not, increasing the iteration number by 1, and returning to S3;
and S8, calculating the fitness value of each chromosome in the offspring population, and outputting the optimal path planning scheme corresponding to the chromosome with the optimal fitness in the offspring population as the task allocation scheme of the unmanned aerial vehicle formation.
6. The multi-machine task allocation method for power transmission line dense channel inspection according to claim 5, wherein the generating of the chromosome by using a multi-stage integer coding mode comprises:
the first line of the chromosome represents the drone number;
the second row of the chromosome represents the charging station number for the unmanned aerial vehicle to charge, wherein 0 represents that the unmanned aerial vehicle does not need to be charged;
the third row of the chromosome represents the number of task points and the length of the chromosome corresponds to the number of task points.
7. The multi-machine task allocation method for power transmission line dense channel inspection according to claim 6, wherein the calculating the fitness value of the chromosome comprises:
the inverse of the objective function of the feasible chromosomes is taken as the fitness function.
8. The multi-machine task allocation method for the intensive channel inspection of the power transmission line according to claim 5, wherein the infeasible solution discrimination method comprises the following steps:
taking chromosomes which do not meet any constraint conditions as infeasible solutions, and defining the objective function of the chromosomes as an infinite value M;
infeasible solution correction method:
and calculating the objective function value of the generated chromosome, and regenerating the chromosome when the objective function value is larger than M until the objective function value of the generated chromosome is smaller than M.
9. The multi-machine task allocation method for power transmission line dense channel inspection according to claim 5, wherein the updating operation comprises:
in the crossover operation, a two-point crossover method is used for the first and second rows of chromosomes, and a two-point sequential crossover method is used for the third row of chromosomes:
in the mutation operation, single-point mutation is adopted for the first line and the second line of the chromosome, and two adjacent fragments are exchanged for the third line of the chromosome;
in the selection operation, an elite retention strategy is adopted for the initial population, and a roulette strategy is adopted for the cross-mutated population.
10. A multi-machine task distribution system for power transmission line dense channel inspection, the system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 9.
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