CN113537722A - Scheduling planning method and application - Google Patents

Scheduling planning method and application Download PDF

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CN113537722A
CN113537722A CN202110698208.5A CN202110698208A CN113537722A CN 113537722 A CN113537722 A CN 113537722A CN 202110698208 A CN202110698208 A CN 202110698208A CN 113537722 A CN113537722 A CN 113537722A
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base station
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CN113537722B (en
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高阳
周琛淏
甘沛露
周支立
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Xian Jiaotong University
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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. The operator can remotely control the unmanned aerial vehicle through the 4G signal and charge the unmanned aerial vehicle through the base station, but the charging time and the charging position and the scheduling problem of patrol tasks need to be considered. The application provides a scheduling planning method, which comprises the steps of obtaining position information of a base station, constructing a base station communication relation graph, and grading the base station according to the relation graph; establishing a space-time convolutional network model, and solving an initial solution of the model; and obtaining a scheduling planning scheme according to the rating result and the initial solution. An appropriate schedule can be given.

Description

Scheduling planning method and application
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 unmanned plane for short, and is an 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 equipment. The personnel on the ground, the naval vessel or the mother aircraft remote control station can track, position, remotely control, telemeter and digitally transmit the personnel through equipment such as a radar. The aircraft can take off like a common airplane under the radio remote control or launch and lift off by a boosting rocket, and can also be thrown into the air by a mother aircraft for flying. During recovery, the aircraft can land automatically in the same way as the common aircraft landing process, and can also be recovered by a parachute or a barrier net for remote control. Can be repeatedly used for many times. The method is widely used for aerial reconnaissance, monitoring, communication, anti-submergence, electronic interference and the like.
Unmanned aerial vehicle patrols one direction that unmanned aerial vehicle used in the future, however, present unmanned aerial vehicle still can not realize independently long distance patrols, and the reason has two, and one is continuation of journey, and the other needs the operator. Can utilize the 4G signal to control and charge unmanned aerial vehicle. The operator can remotely control the unmanned aerial vehicle through the 4G signal and charge the unmanned aerial vehicle through the base station, but the charging time and the charging position and the scheduling problem of patrol tasks need to be considered.
Disclosure of Invention
1. Technical problem to be solved
Only small-scale problems can be processed based on the existing scheme, and results are difficult to obtain when large-scale problems are faced. And under the situation that the base station is used for providing signals and charging for the unmanned aerial vehicle, the number of the base stations is huge, the scale of the problem correspondingly shows exponential increase, and the existing scheme is difficult to meet. In addition, because the base station is very different from the airport, for example, the airport cannot be moved, but the base station is very easy to move, so that the flexibility is increased technically, great difficulty is brought to the scheduling problem, and the existing scheme is difficult to solve the scheduling problem with high flexibility. In order to solve the problems, the application provides a scheduling planning method and application.
2. Technical scheme
In order to achieve the above object, the present application provides a scheduling planning method, including obtaining base station location information, constructing a base station connectivity relationship diagram, and ranking the base stations according to the relationship diagram; establishing a space-time convolutional network model, and solving an initial solution of the model; and obtaining a scheduling planning scheme according to the rating result and the initial solution.
Another embodiment provided by the present application is: the base station position information comprises the longitude of the base station and the latitude of the base station, and the base station is divided into a tower base station and a rod base station.
Another embodiment provided by the present application is: and if the distance between the two base stations is smaller than the coverage radius of the base station signal, the two base stations are in direct contact, and a base station topological structure chart is manufactured according to the direct contact.
Another embodiment provided by the present application is: the rating uses the PageRank algorithm.
Another embodiment provided by the present application is: said ranking comprises giving each of said base stations an initial weight of 1, and then averaging each of said weights to the base stations having direct contact with said base station, the weight of said base station also being updated to the sum of the weights given to all base stations having direct contact with said base station; iterating the above processes, the weight of each base station is finally converged and tends to be stable; assuming that theta is a base station rating threshold value, and a decision variable related to a base station with a weight greater than theta is a first decision variable, the variation of the first decision variable can cause a large influence on the performance of the whole scheduling system, and the variation related to a base station with a weight less than theta is a second decision variable is difficult to cause a large influence on the performance of the whole scheduling system; the number of the first decision variables and the number of the second decision variables can be adjusted by adjusting the value of theta, and the first decision variables are optimized.
Another embodiment provided by the present application is: the space-time convolutional network model is a network flow model considering both time dimension and 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 by the present application is: and the time of the decision variable in the space-time convolutional network model is represented by the time of an arc, and the arc connects part of event nodes to form a closed loop.
Another embodiment provided by the present application is: the initial solution is found using 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 by the present application is: the unmanned aerial vehicle comprises three states which are respectively represented as a patrol arc, a charging arc and a transfer arc.
3. Advantageous effects
Compared with the prior art, the scheduling planning method and the application have the advantages that:
the scheduling planning method provided by the application is a method for scheduling planning of a base station-unmanned aerial vehicle system.
The scheduling planning method provided by the application can provide proper scheduling for the unmanned aerial vehicle maintenance path problem according to the cruising ability of the unmanned aerial vehicle, the maintenance ability of the airport and the execution condition of the flight and according to the appointed flight and the unmanned aerial vehicle.
The scheduling planning method 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 according to needs.
Drawings
FIG. 1 is a schematic flow chart of a scheduling planning method according to the present application;
FIG. 2 is a schematic diagram of a scheduling planning method according to the present application;
FIG. 3 is a schematic diagram of a base station connectivity diagram of the present application;
FIG. 4 is a schematic diagram of a spatio-temporal 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 it will be apparent to those skilled in the art from this detailed description that the present application can be practiced. Features from different embodiments may be combined to yield new embodiments, or certain features may be substituted for certain embodiments to yield yet further preferred embodiments, without departing from the principles of the present application.
Referring to fig. 1 to 4, the present application provides a scheduling planning method, including obtaining base station location information, constructing a base station connectivity graph, and ranking the base stations according to the graph; establishing a space-time convolutional network model, and solving an initial solution of the model; and obtaining a scheduling planning scheme according to the rating result and the initial solution.
Further, the base station location information includes a longitude of the base station and a latitude of the base station, and the base station is divided into a tower base station and a rod base station.
The base station position information is composed of longitude and latitude of the base station, the base station is divided into Tower base station and rod base station, English of the Tower base station is Tower-base station, English of the rod base station is Pole-base station. Wherein, tower basic station area is big usually, has the computer lab mostly, can carry out the extension of certain scale to the installation provides the equipment of electric energy for unmanned aerial vehicle. The rod-type base station does not necessarily exist in the shape of a rod, and some base stations are disguised as air conditioner outdoor units, loudspeakers and the like and placed on various buildings, so that the extension cannot be performed, and electric power cannot be supplied to the unmanned aerial vehicle. But rod-type basic station and tower basic station can both provide control signal for unmanned aerial vehicle. Since the signal coverage radius of the base station is limited and the drone is controlled continuously during the flight, the drone cannot fly from a base station to a base station with a signal coverage radius more than twice as large as the current base station without the aid of a third base station. Therefore, assuming that the distance between two base stations is smaller than the coverage radius of the signals of the base stations, the two base stations have direct contact, and a topology of one base station can be mapped according to the contact. Each base station is firstly given an initial weight of 1, then the weight of each base station is averagely given to the base station which is directly related to the base station, and the weight of the base station is updated to the sum of the weights given to all the base stations which are directly related to the base station. By iterating the above process, the weight of each base station is converged and tends to be stable. According to the core idea of PageRank, at this time, the variation of the decision variables associated with the base station with a larger weight can have a larger influence on the performance of the whole scheduling system, and the variation of the decision variables associated with the base station with a smaller weight cannot have a larger influence on the performance of the whole scheduling system, so that certain efficiency can be improved by concentrating the calculation resources to optimize the decision variables associated with the base station with a larger weight. Assuming that theta is a base station rating threshold value, and a decision variable related to a base station with a weight greater than theta is a first decision variable, the variation of the first decision variable can cause a large influence on the performance of the whole scheduling system, and the variation related to a base station with a weight less than theta is a second decision variable is difficult to cause a large influence on the performance of the whole scheduling system; the number of the first decision variables and the number of the second decision variables can be adjusted by adjusting the value of theta, and the first decision variables are optimized.
Further, the space-time convolutional network model is a network flow model considering both time dimension and 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 tasks are covered by adopting the routes.
Further, the time of the decision variable in the space-time convolutional network model is represented by the time of an arc, and the arc connects part of event nodes to form a closed loop.
Further, the initial solution is found using cplex.
The application also provides an application of the scheduling planning method, 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 which are respectively represented by three arcs as a patrol arc, a charging arc and a transfer arc.
Examples
Unmanned aerial vehicles are widely used in rescue and patrol. The research provides a patrol system scheme based on the unmanned aerial vehicle, namely, the unmanned aerial vehicle is remotely controlled by a communication base station through a mobile signal, and charging service is provided. This study introduces corresponding drone path issues and considers how to perform patrol missions, allocate facilities and schedule charging activities according to given locations and types. A mixed integer programming model and a heuristic method are presented.
Obtaining base station location
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 data of all data, including longitude and latitude.
Obtaining base station type
The base station type determines the operation that the base station can complete, the base station type is divided into Tower base station and rod base station, the Tower base station is English power-base station, and the rod base station is English Pole-base station. Wherein, tower basic station area is big usually, has the computer lab mostly, can carry out the extension of certain scale to the installation provides the equipment of electric energy for unmanned aerial vehicle. The rod-type base station does not necessarily exist in the shape of a rod, and some base stations are disguised as air conditioner outdoor units, loudspeakers and the like and placed on various buildings, so that the extension cannot be performed, and electric power cannot be supplied to the unmanned aerial vehicle. But rod-type basic station and tower basic station can both provide control signal for unmanned aerial vehicle.
Rating base stations
The base stations in different locations affect the overall scheduling to a different extent, so it is necessary to determine which base stations are important and which are not. The base station is graded by using a PageRank method, and two criteria are followed:
1) the base station capable of directly influencing a plurality of variables has great influence on the overall scheduling
2) The influence of the base station capable of indirectly influencing a plurality of variables on the overall scheduling is large
Designing a network
The problem is modeled by using a network flow model which simultaneously considers two dimensions of time and space. As shown in detail in fig. 4:
each base station is represented by a time axis, the time flow of the time axis is from the right, generally, for convenience of calculation, the starting point of the time axis is set to be zero, the end point of the time axis can be infinite or limited, but even if the length of the time axis is infinite, the duration of the unmanned aerial vehicle is limited, so that the time dimension parameter of the task in the whole network model does not exceed the duration of the unmanned aerial vehicle. In practice, the time line represented by each base station is continuous, but for the sake of calculation, it is sometimes possible to use discrete nodes to simplify the time line without significantly reducing the accuracy of the model. Each time point on each timeline is a potential event, and a drone starts or ends a certain state and can be regarded as an event. Each event node has a traffic balance, i.e. the amount flowing into this node is equal to the amount flowing out of this node. It can be vividly understood that the drone will not disappear from the sky. The unmanned aerial vehicle has three states, which are described by three arcs, namely a patrol arc, a charging arc and a transfer arc. The arc of states, where patrol and transition, is in the same time direction as the time line, and the charging arc is in the opposite time direction to the time line. Thus, the power of the drone is greatly correlated with the position of the time dimension in which the drone is located. Arcs connect a portion of the nodes to form a closed loop, enabling patrolling schedules that can be continuously executed. An example is shown in figure 4.
Giving an initial solution
According to fig. 4, a mathematical model is established, all arcs are considered as a decision variable, each decision variable representing the time of the arc and the corresponding operation, whether there are drones to complete, and the number of drones. For example, a patrol arc issued by area1 from time zero represents patrol from time zero, and the value of the corresponding variable represents the number of drones executing the task, if the value of the variable is 2, there are two drones executing the task, and if the value of the variable is 0, there is no drone executing the task. From the above description, the decision variables should be non-negative integer type variables. After the mathematical model is established, relevant data are brought in, and the objective function is changed into the quantity of the optimized charging stations instead of the total cost, and at the moment, because the objective function is simpler, the solver can provide a better solution in a shorter time. Taking this solution as the initial solution, the next solution can be performed.
Give a final solution
When the final solution is obtained, the initial solution is obtained by cplex, and the grades of the areas where different base stations are located are also obtained. According to the rating, some areas needing to be optimized with emphasis can be identified, namely areas with higher rating. According to the rating, the operation in the region 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 performance similar to or even better than that of long-time accurate solving can be obtained in a very short time. Since the initial solution is feasible, after setting some operations equal to the initial solution, this problem is certainly feasible, and after all there is 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 the performance of the solution, increasing the solution time if the threshold needs to be lowered to improve the performance of the solution.
According to the space-time convolutional network model, if one or more closed loops are obtained, which are in accordance with the inflow and outflow balance, a patrol schedule which can be continuously executed is obtained. If all patrol tasks are covered by a series of closed loops that are balanced with incoming and outgoing flows, this means that a patrol schedule is obtained that can be continuously executed to meet patrol requirements.
The present application relates to a model architecture for mathematically modeling scheduling problems; an existing space-time network convolution model is a network model considering 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 tasks are covered by the routes, so that a good effect can be achieved.
The application also relates to a rating algorithm to scale down a problem; all variables are optimized indifferently, which is a waste of computing power, so that it is understood which variables can play a key role, and which variables are not important to be optimized, and the optimization of scheduling is very helpful.
Although the present application has been described above with reference to specific embodiments, those skilled in the art will recognize that many changes may be made in the configuration and details of the present application within the principles and scope of the present application. The scope of protection of the application is determined by the appended claims, and all changes that come within the meaning and range of equivalency of the technical features are intended to be embraced therein.

Claims (10)

1. A method of scheduling planning, characterized by: the method comprises the steps of obtaining position information of a base station, constructing a base station communication relation graph, and grading the base station according to the relation graph; establishing a space-time convolutional network model, and solving an initial solution of the model; and obtaining a scheduling planning scheme according to the rating result and the initial solution.
2. The method of claim 1, wherein: the base station position information comprises the longitude of the base station and the latitude of the base station, and the base station is divided into a tower base station and a rod base station.
3. The method of claim 1, wherein: and if the distance between the two base stations is smaller than the coverage radius of the base station signal, the two base stations are in direct contact, and a base station topological structure chart is manufactured according to the direct contact.
4. The method of claim 3, wherein: the rating uses the PageRank algorithm.
5. The method of claim 4, wherein: said ranking comprises giving each of said base stations an initial weight of 1, and then averaging each of said weights to the base stations having direct contact with said base station, the weight of said base station also being updated to the sum of the weights given to all base stations having direct contact with said base station; iterating the above processes, the weight of each base station is finally converged and tends to be stable; assuming that theta is a base station rating threshold value, and a decision variable related to a base station with a weight greater than theta is a first decision variable, the variation of the first decision variable can cause a large influence on the performance of the whole scheduling system, and the variation related to a base station with a weight less than theta is a second decision variable is difficult to cause a large influence on the performance of the whole scheduling system; the number of the first decision variables and the number of the second decision variables can be adjusted by adjusting the value of theta, and the first decision variables are optimized.
6. The method of claim 1, wherein: the space-time convolutional network model is a network flow model considering both time dimension and 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.
7. The method of claim 6, wherein: and the time of the decision variable in the space-time convolutional network model is represented by the time of an arc, and the arc connects part of event nodes to form a closed loop.
8. The method of claim 1, wherein: the initial solution is found using cplex.
9. An application of a scheduling planning method, characterized by: applying the dispatch planning method of any one of claims 1-8 to traffic planning, logistics delivery, or drone scheduling.
10. Use of the scheduling planning method of claim 9 wherein: the unmanned aerial vehicle comprises three states which are respectively represented as a patrol arc, a charging arc and a transfer arc.
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