CN113537722B - Scheduling planning method and application - Google Patents
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Abstract
The application belongs to the technical field of unmanned aerial vehicles, and particularly relates to a scheduling planning method and application. An operator can remotely control the unmanned aerial vehicle through a 4G signal and charge by means of the base station, but the charging time position and the scheduling problem of patrol tasks are considered. The application provides a scheduling planning method, which comprises the steps of obtaining base station position information, constructing a base station communication relation diagram, and grading the base stations according to the relation diagram; establishing a space-time convolution network model, and solving an initial solution of the model; and obtaining a scheduling planning scheme according to the grading result and the initial solution. A suitable schedule can be given.
Description
Technical Field
The application belongs to the technical field of unmanned aerial vehicles, and particularly relates to a scheduling planning method and application.
Background
The unmanned plane is called as unmanned plane for short, and is a unmanned plane operated by radio remote control equipment and a self-contained program control device. The machine has no cockpit, but is provided with an automatic pilot, a program control device and other devices. Personnel on the ground, ships or on a mother machine remote control station track, position, remote control, telemetere and digital transmission through radar and other equipment. Can take off like a common plane under radio remote control or launch and lift off by using a boosting rocket, and can also be brought into the air by a master machine to put in flight. When recovered, the aircraft can automatically land in the same way as the landing process of a common aircraft, and can also be recovered by a parachute or a barrier net for remote control. Can be repeatedly used for a plurality of times. The method is widely used for air reconnaissance, monitoring, communication, anti-diving, electronic interference and the like.
Unmanned aerial vehicle patrol is one direction of unmanned aerial vehicle application in the future, however, current unmanned aerial vehicle can not realize autonomous long-distance patrol yet, and the reason is two, one is continuous voyage, and the other is that an operator is required. The 4G signal can be utilized to control and charge the drone. An operator can remotely control the unmanned aerial vehicle through a 4G signal and charge by means of the base station, but the charging time position and the scheduling problem of patrol tasks are considered.
Disclosure of Invention
1. Technical problem to be solved
Based on the current scheme, only small-scale problems can be processed, and the result is often difficult to obtain in the face of large-scale problems. Under the situation that the base stations are used for providing signals and charging for the unmanned aerial vehicle, the number of the base stations is huge, the scale of the problem is correspondingly increased exponentially, and the current scheme is difficult to meet. In addition, because the base station and the airport have great differences, for example, the airport cannot be moved, but the base station is easy to move, the technology has strong flexibility, great difficulty is brought to the scheduling problem, and the current scheme is difficult to solve for the highly flexible scheduling problem. Aiming at the problems, the application provides a scheduling planning method and application.
2. Technical proposal
In order to achieve the above purpose, the present application provides a scheduling planning method, which includes obtaining base station location information, constructing a base station communication relation diagram, and grading the base stations according to the relation diagram; establishing a space-time convolution network model, and solving an initial solution of the model; and obtaining a scheduling planning scheme according to the grading result and the initial solution.
Another embodiment provided herein is: the base station location information includes longitude of the base station and latitude of the base station, and the base station is divided into a tower base station and a bar base station.
Another embodiment provided herein is: and if the distance between the two base stations is smaller than the coverage radius of the base station signals, the two base stations are in direct connection, and a base station topological structure diagram is manufactured according to the direct connection.
Another embodiment provided herein is: the evaluation uses the PageRank algorithm.
Another embodiment provided herein is: the evaluation comprises giving each of the base stations an initial weight of 1, and then giving each of the weights on average to a base station having direct contact with the base station, the weight of the base station also being updated to the sum of the weights given to all base stations having direct contact with the base station; iterating the above process, wherein the weight of each base station can be converged finally and tend to be stable; assuming θ is a base station rating threshold, and a decision variable related to a base station with a weight greater than θ is a first decision variable, so that the variation of the first decision variable can have a larger influence on the performance of the whole scheduling system, and the variation related to a base station with a weight less than θ is a second decision variable, so that the variation is difficult to have a larger influence on the performance of the whole scheduling system; the number of the first decision variable and the second decision variable can be adjusted by adjusting the value of θ, and the first decision variable is optimized.
Another embodiment provided herein is: the space-time convolutional network model is a network flow model which simultaneously considers a time dimension and a space dimension, a series of sustainable routes are derived by constructing one or more closed loops in the time dimension and the space dimension, and the routes are adopted to cover tasks.
Another embodiment provided herein is: the decision variables are represented by the time of arcs in the space-time convolutional network model, and the arcs connect part of event nodes to form a closed loop.
Another embodiment provided herein is: the initial solution is obtained by cplex.
The application also provides an application of the scheduling planning method, which is characterized in that: the scheduling planning method is applied to traffic planning, logistics distribution or unmanned aerial vehicle scheduling.
Another embodiment provided herein is: the unmanned aerial vehicle comprises three states, namely a patrol arc, a charging arc and a transfer arc, which are respectively represented by three arcs.
3. Advantageous effects
Compared with the prior art, the scheduling planning method and the application have the beneficial effects that:
the scheduling planning method provided by the application is a method for scheduling and planning a base station-unmanned aerial vehicle system.
The scheduling planning method is used for scheduling the unmanned aerial vehicle maintenance path problem, and proper scheduling can be given according to the appointed flight and the unmanned aerial vehicle according to the endurance capacity of the unmanned aerial vehicle, the maintenance capacity of an airport and the execution condition of the flight.
The scheduling planning method provided by the application can provide a feasible scheme with excellent performance in a short time, has extremely high timeliness, and can adjust trade-off between the precision and timeliness of the scheme as required.
Drawings
FIG. 1 is a flow chart of a scheduling method of the present application;
FIG. 2 is a schematic diagram of a scheduling method of the present application;
FIG. 3 is a schematic illustration of base station connectivity of the present application;
fig. 4 is a schematic diagram of a space-time convolutional network model of the present application.
Detailed Description
Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings, and according to these detailed descriptions, those skilled in the art can clearly understand the present application and can practice the present application. Features from various embodiments may be combined to obtain new implementations or to replace certain features from certain embodiments to obtain other preferred implementations without departing from the principles of the present application.
Referring to fig. 1-4, the present application provides a scheduling planning method, which includes obtaining base station location information, constructing a base station communication relation diagram, and grading the base stations according to the relation diagram; establishing a space-time convolution network model, and solving an initial solution of the model; and obtaining a scheduling planning scheme according to the grading result and the initial solution.
Further, the base station location information includes longitude of the base station and latitude of the base station, and the base station is divided into a tower base station and a bar base station.
The base station position information consists of longitude and latitude of the base station, the types of the base station are Tower base stations and rod base stations, english of the Tower base stations is Tower-base station, and English of the rod base stations is hole-base station. The tower base station generally occupies a large area, mostly has a machine room, can be expanded in a certain scale, and is provided with equipment for providing electric energy for the unmanned aerial vehicle. The rod-type base station does not necessarily exist in the rod shape, but is disguised to be placed on various buildings in the shapes of an air conditioner external unit, a loudspeaker and the like, so that extension cannot be performed, and power cannot be provided for the unmanned aerial vehicle. But both the stick base station and the tower base station can provide control signals for the drone. Since the signal coverage radius of a base station is limited and the drone is to be controlled continuously during the flight, the drone cannot fly from one base station to a base station that is more than twice the coverage radius of the signal of the current base station without the aid of a third base station. It is assumed that two base stations have a direct connection when the distance between them is smaller than the radius of coverage of the base station's signal, from which connection a map of the topology of one base station can be made. Each base station is first given an initial weight of 1, then the weight of each base station is given on average to the base station with which it has direct contact, and the weight of this base station is also updated to the sum of the weights given to all base stations with which it has direct contact. The above process is iterated, and the weight of each base station can be converged finally and tend to be stable. According to the core idea of PageRank, the variation of the decision variables related to the base stations with larger weights can cause larger influence on the performance of the whole scheduling system, while the variation of the decision variables related to the base stations with smaller weights hardly causes larger influence on the performance of the whole scheduling system, so that the optimization of the decision variables related to the base stations with larger weights by the centralized computing resource can improve certain efficiency. Assuming θ is a base station rating threshold, and a decision variable related to a base station with a weight greater than θ is a first decision variable, so that the variation of the first decision variable can have a larger influence on the performance of the whole scheduling system, and the variation related to a base station with a weight less than θ is a second decision variable, so that the variation is difficult to have a larger influence on the performance of the whole scheduling system; the number of the first decision variable and the second decision variable can be adjusted by adjusting the value of θ, and the first decision variable is optimized.
Further, the space-time convolutional network model is a network flow model which simultaneously considers a time dimension and a space dimension, a series of sustainable routes are derived by constructing one or more closed loops in the time dimension and the space dimension, and the routes are adopted to cover tasks.
Further, the decision variables in the space-time convolutional network model are represented by the time of arcs, and the arcs connect part of event nodes to form a closed loop.
Further, the initial solution is obtained by cplex.
The application of the scheduling planning method is also provided, and the scheduling planning method is applied to traffic planning, logistics distribution or unmanned aerial vehicle scheduling.
Further, the unmanned aerial vehicle comprises three states, namely a patrol arc, a charging arc and a transfer arc, which are respectively represented by three arcs.
Examples
Unmanned aerial vehicles are widely used in rescue and patrol. The present study proposes a patrol system scheme based on unmanned aerial vehicle, namely, utilize communication base station to control unmanned aerial vehicle through mobile signal remote control to provide the service of charging. The present study introduces the corresponding drone path problem and considers how to perform patrol tasks, allocate facilities, and schedule charging activities according to a given location and type. A mixed integer programming model and a heuristic method are presented.
Acquiring base station position
The location of the base station is an important factor in determining the path of the drone, so the base station location is the most basic of all data, including longitude and latitude.
Acquiring base station type
The base station type determines the operation which can be completed by the base station, the base station type is divided into a Tower base station and a rod base station, the English of the Tower base station is a Tower-base station, and the English of the rod base station is a hole-base station. The tower base station generally occupies a large area, mostly has a machine room, can be expanded in a certain scale, and is provided with equipment for providing electric energy for the unmanned aerial vehicle. The rod-type base station does not necessarily exist in the rod shape, but is disguised to be placed on various buildings in the shapes of an air conditioner external unit, a loudspeaker and the like, so that extension cannot be performed, and power cannot be provided for the unmanned aerial vehicle. But both the stick base station and the tower base station can provide control signals for the drone.
Ranking base stations
The extent to which base stations in different locations affect the overall schedule is different, so it is necessary to determine which base stations are important to the overall schedule and which are not. The present application utilizes the PageRank approach to rank base stations, following two criteria:
1) Base stations capable of directly affecting multiple variables have a large impact on overall scheduling
2) Base station capable of indirectly influencing multiple variables has great influence on overall scheduling
Designing a network
The present application models problems using a network flow model that considers both time and space dimensions. As shown in detail in fig. 4:
each base station is represented by a time axis, the time flow direction of the time axis is from right, generally, for convenience of calculation, the starting point of the time axis is set to zero, and the end point of the time axis can be infinite or finite, but even if the length of the time axis is infinite, the duration of the unmanned aerial vehicle is finite, so that the parameter of the time dimension of the task in the whole network model cannot exceed the duration of the unmanned aerial vehicle. In practice, the time line represented by each base station is continuous, but for ease of calculation it is sometimes possible to simplify the time line with discrete nodes without significantly degrading the accuracy of the model. Each point in time on each timeline is a potential event, and the start or end of a state of the drone may be considered an event. Each event node has a traffic balance, i.e. the amount flowing into the node is equal to the amount flowing out of the node. It can be understood visually that the unmanned aerial vehicle will not vanish due to the lack of credit. The unmanned aerial vehicle has three states, and is described by three arcs respectively, namely a patrol arc, a charging arc and a transfer arc. Wherein the direction of time of the arc of patrol and transition states is the same as the direction of the time line, and the direction of time of the charging arc is opposite to the direction of time of the time axis. In this way, the electric quantity of the unmanned aerial vehicle has great relevance with the position of the time dimension where the unmanned aerial vehicle is located. Some arcs connect some nodes to form a closed loop, so that patrol schedule which can be continuously executed can be obtained. An example is shown in fig. 4.
Giving an initial solution
According to fig. 4, a mathematical model will be built, all arcs being considered as one decision variable, each decision variable representing the time this arc is in, and the corresponding operation, whether there are drones to complete, and the number of drones. For example, a patrol arc from time zero, area1, represents patrol starting from time zero, and the value of the corresponding variable represents the number of unmanned aerial vehicles performing the task, if the value of the variable is 2, two unmanned aerial vehicles perform the task, and if the value of the variable is 0, no unmanned aerial vehicle performs the task. According to the above description, the decision variables should be non-negative integer variables. After the mathematical model is built, the relevant data are brought in and the objective function is changed to optimize the number of charging stations instead of the total cost, at which time the solver can give a better solution in a shorter time due to the simpler objective function. Taking this solution as the initial solution, the next solution can be performed.
Giving the final solution
When the final solution is performed, the initial solution is already obtained by using cplex, and the ratings of the areas where different base stations are located are also obtained. According to the ratings, areas needing to be optimized with emphasis can be identified, namely areas with higher ratings. According to the rating, the operation in the area with the rating lower than a certain threshold value is set to be equal to the initial solution, and then the cplex is used for solving, so that a scheme with similar performance or even better performance than long-time accurate solving can be obtained in an extremely short time. Since the initial solution is feasible, setting some operations to be equivalent to after the initial solution, the problem must also be feasible, after all, at least the initial solution. While different thresholds will result in different solution times and performance of the solution, higher thresholds will result in shorter solution times and some degradation in performance of the solution, if the performance of the solution is to be improved, the threshold needs to be reduced and the solution time is to be increased.
Based on the space-time convolutional network model, if one or more closed loops are obtained that meet the inflow-outflow balance, this means that a patrol schedule is obtained that can be continuously executed. If the patrol task is covered by a series of closed loops which meet the balance of the inflow and outflow, the patrol schedule which can be continuously executed and meets the patrol requirement is obtained.
The present application relates to a model framework for mathematical modeling of scheduling problems; an existing space-time network convolution model is a network model which considers both time dimension and space dimension, and a series of sustainable routes are derived by constructing one or more closed loops in the time dimension and the space dimension, and the routes are used for covering tasks, so that a good effect can be achieved.
The application also relates to a rating algorithm to reduce the problem size; the optimization of all variables is indiscriminately wasteful of computational effort, so that it is clear which variables can play a key role, and which variables are irrelevant, and it is helpful to optimize the scheduling.
Although the present application has been described with reference to particular embodiments, those skilled in the art will appreciate that many modifications are possible in the principles and scope of the disclosure. The scope of the application is to be determined by the appended claims, and it is intended that the claims cover all modifications that are within the literal meaning or range of equivalents of the technical features of the claims.
Claims (7)
1. A scheduling planning method is characterized in that: the method comprises the steps of obtaining base station position information, constructing a base station communication relation diagram, and grading the base stations according to the relation diagram; establishing a space-time convolution network model, and solving an initial solution of the model; obtaining a scheduling planning scheme according to the grading result and the initial solution; the evaluation adopts a PageRank algorithm; the evaluation comprises giving each of the base stations an initial weight of 1, and then giving each of the weights on average to a base station having direct contact with the base station, the weight of the base station also being updated to the sum of the weights given to all base stations having direct contact with the base station; iterating the above process, wherein the weight of each base station can be converged finally and tend to be stable; θ is a base station rating threshold, and a decision variable related to a base station with a weight greater than θ is a first decision variable, so that the variation of the first decision variable can cause a larger influence on the performance of the whole scheduling system, and a decision variable related to a base station with a weight less than θ is a second decision variable, and the variation of the second decision variable is difficult to cause a larger influence on the performance of the whole scheduling system; the number of the first decision variable and the second decision variable can be adjusted by adjusting the value of θ, and the first decision variable is optimized.
2. The method of claim 1, wherein: the base station location information includes longitude of the base station and latitude of the base station, and the base station is divided into a tower base station and a bar base station.
3. The method of claim 1, wherein: when the distance between the two base stations is smaller than the coverage radius of the base station signals, the two base stations are in direct connection, and a base station topological structure diagram is manufactured according to the direct connection.
4. The method of claim 1, wherein: the space-time convolutional network model is a network flow model which simultaneously considers a time dimension and a space dimension, a series of sustainable routes are derived by constructing one or more closed loops in the time dimension and the space dimension, and the routes are adopted to cover tasks.
5. The method of claim 4, wherein: the decision variables are represented by the time of arcs in the space-time convolutional network model, and the arcs connect part of event nodes to form a closed loop.
6. The method of claim 1, wherein: the initial solution is obtained by cplex.
7. An application of a scheduling planning method is characterized in that: application of the scheduling method of any one of claims 1 to 5 to traffic planning, logistics distribution or unmanned aerial vehicle scheduling.
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