Heterogeneous unmanned aerial vehicle scheduling method and device considering airport charging compatibility and collaborative operation, electronic equipment and storage medium

Abstract

The invention discloses a heterogeneous unmanned aerial vehicle scheduling method and device considering airport charging compatibility and collaborative operation, electronic equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicles. The method comprises the following steps: representing a task scene to obtain the state of an unmanned aerial vehicle at each passing flight point; and constructing an unmanned aerial vehicle scheduling model by taking meeting of unmanned aerial vehicle flight consistency and energy consumption, all task point service requirements and intelligent airport charging as constraints and taking waiting time weighted sum minimization of all task points as a target, and solving to obtain an unmanned aerial vehicle optimal scheduling scheme. According to the method, the type requirement of the task point on the cooperative operation unmanned aerial vehicle is considered, and the type limitation of the intelligent airport on the rechargeable unmanned aerial vehicle is also considered, so that the synchronous arrival of the unmanned aerial vehicle at the task point and the charging decision of the intelligent airport are brought into the traditional unmanned aerial vehicle task allocation and path planning problem; the energy consumption waste can be further reduced, and the applicability of the unmanned aerial vehicle in various scenes is improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle technology, and in particular to a heterogeneous unmanned aerial vehicle scheduling method, device, electronic device and storage medium that take into account airport charging and battery replacement compatibility and collaborative operation. Background Art

[0002] In recent years, drones have experienced significant growth and are becoming an important enabler for services such as public safety, IoT applications, remote healthcare, and situational awareness. Although drones have many advantages such as high mobility, low cost, and easy deployment, their flight time is usually short, which greatly limits their application in large geographical areas.

[0003] In order to overcome the above shortcomings of drones, in-depth research is needed in terms of hardware and methods. Among them, in terms of hardware, smart airports can provide charging and battery replacement services for drones. UAV smart airports are ground equipment with functions such as drone storage, smart standby, automatic charging or replacement of drone batteries, wireless communication, automatic battery maintenance, UPS power failure protection, fault self-test, and take-off condition detection. In terms of methods, it is necessary to optimize the scheduling plan of drone systems including smart airports.

[0004] At present, there are some problems when heterogeneous multi-UAVs perform tasks: (1) When heterogeneous multi-UAVs provide services for multiple mission points, different mission points may require multiple types of UAVs to work together. Therefore, it is necessary to reasonably arrange the UAV task allocation and flight trajectory, and try to achieve the synchronous arrival of UAVs at the mission point to avoid some UAVs arriving too early and causing energy waste in circling while waiting. (2) When using smart airports to charge and replace UAVs, different types of smart airports may have restrictions on the types of UAVs that can be charged and replaced. In the scenario where heterogeneous UAVs perform multiple tasks together, it is necessary to optimize the charging and replacement decisions of smart airports at the same time so that all UAVs can meet the energy consumption constraints. Summary of the invention

[0005] In order to solve the problems mentioned in the background technology, the present invention provides a heterogeneous UAV scheduling method, device, electronic device and storage medium that consider airport charging and battery replacement compatibility and collaborative operation.

[0006] The present invention is achieved through the following technical solutions.

[0007] In a first aspect, the present invention provides a heterogeneous UAV scheduling method considering airport charging and battery swapping compatibility and collaborative operation, comprising: Characterize the mission scenarios where heterogeneous multiple drones provide services for multiple mission points distributed in the mission area, multiple smart airports provide multiple charging and battery replacement for drones, different smart airports have restrictions on the types of drones that can be charged and replaced, and different mission points have requirements for drone types; Determine the decision variables according to the mission scenario, solve the UAV state transition equation, and obtain the state of the UAV at each flight point; According to the status of the drone at each flight point, the drone scheduling model is constructed with the goal of minimizing the weighted waiting time of all mission points, meeting the flight consistency and energy consumption of the drone, the service requirements of all mission points, and the charging and swapping of smart airports as constraints; The genetic algorithm is used to solve the UAV scheduling model and obtain the optimal UAV scheduling solution.

[0008] Furthermore, the task scenario is specifically characterized as follows: After all drones are numbered, they form a drone number set: , In the formula, N express n A set of drone numbers; Mission points and smart airports are collectively referred to as the flight points of the drone, and all flight points are numbered to form a flight point number set: , In the formula, express n + m A set of flight point numbers, of which the first n The flight points are n A smart airport, m The flight points are m Task points, and numbered j The drone is numbered j Take off from the smart airport and return to the smart airport after completing all tasks; Calculate the distance between flight points: , In the formula, Indicates the number The flight point and number are The distance between the flight points, where and Respectively indicate the number The flight point and number are The two-dimensional coordinates of the flight point; All drone types are numbered to form a drone type number set: , In the formula, P express p A collection of drone type numbers; Number jThe type number of the drone is ,in, ; Number j The flight speed of the drone is ; Number j The energy consumption per unit time of the UAV is ; Number j The hovering energy consumption per unit time of the UAV is ; Number j The energy limit of the drone is ; Number j The lower limit of the energy of the UAV is ; Number i The service time of the task point is ; Number i The importance weight of the task point is ; Number i Mission point service drone vector ,in, , Indicates the number i How many task points do you need to be numbered? k Types of drones to be serviced; Number j The number set of drone types that can be charged and replaced at smart airports is ,in, ; Number j The number of smart airport pairs is k The time required for charging and replacing the battery of the drone type is , among which, when hour, ,when hour, .

[0009] Furthermore, the number is j The scheduling decision variables of the UAV are: , In the formula, Indicates the number j Does the flight path of the drone contain The flight point and number are The edge between the flight points, when =1, indicating that the number isj The flight path of the drone contains The flight point and number are The edge between the flight points, when =0, it means not included.

[0010] Furthermore, the drone state transfer equation is specifically: Number j The set of numbers of all the flight points passed by the drone is: , In the formula, Indicates the number j The set of numbers of all the flight points that the drone passes through, Indicates the number j Does the flight path of the drone contain i The flight point and number are The edges between the flight points; Number j The transfer equation of the flight point number passed by the UAV is: , In the formula, Indicates the number j The first drone r +1 number of the flight point you passed through, Indicates the number j The first drone r The flight point numbers passed by, among which, , for any set S, represents the number of elements of S, where Representing a collection The number of elements of ; Number j The time transfer equation of the drone passing through the flight point is: , In the formula, Indicates the number j The drone arrived at r +1 time for passing through the flight point, Indicates the number j The drone left r The time of passing the flight points, among which, , Indicates the number The flight point and number are The distance between the flight points, Indicates the number j The flight speed of the drone; Number j The time transfer equation of the UAV passing through the flight point is: 1) When hour, , 2) In other cases, , In the formula, Indicates the number j The drone left r +1 time for passing through the flight point, Indicates the number j The drone arrived at r +1 time for passing through the flight point, Indicates the number The number of smart airport pairs is The time required to charge and replace the battery of the drone type, Indicates the number The service time of the mission point is the time when the drone numbered arrives at the l The time of passing the flight point; Indicates the number The drone arrived at l The time of passing the flight point, ; Number j The remaining energy transfer equation for the UAV to reach the passing flight point is: 1) When hour, , 2) In other cases, , In the formula, Indicates the number j The drone arrived at r +1 remaining energy when passing through a flight point, of which: , Indicates the number j The drone arrived at r The remaining energy when passing the flight point, Indicates the number j The drone left r The time of passing the flight point, Indicates the number j The drone arrived at r The time of passing the flight point, ; Number j The serial numbers and route sequence of the drones that are charged and replaced at the smart airport are as follows: , In the formula, Indicates the number j The number and route sequence of drones that are charged and replaced at smart airports. Indicates the number a The first drone b The flight point numbers passed by, among which, a , b is an indicator variable, Indicates the number a The set of numbers of all the flight points that the drone passes through, Representing a collection The number of elements of ; For the number i The number of drone types serving the mission points: , In the formula, Indicates that the pair number is i The type number of the task point to provide service is k The number of drones.

[0011] Furthermore, the constraints are: , In the formula, Indicates the number j Does the flight path of the drone contain The flight point and number are The edges between the flight points; Number j The take-off / return point constraints of the drone: , In the formula, Indicates the number j The number of the first flight point that the drone passes through. Indicates the number j The first drone The numbers of the flight points passed through; The calculation number is j The number of flights of the drone between any two flight point sets: , In the formula, Indicates the number j The number of times the drone flies from flight point set A to flight point set B, Indicates the number j Does the flight path of the drone contain The flight point and number are The edges between the flight points; Number j The flight path validity constraints of the UAV are: , In the formula, Indicates the number j The drones are collected by flight points Fly to the flight point The number of times, among which, represents any set of flight points, express n+m A set of flight point numbers; Number j Energy consumption constraints of the UAV: , In the formula, Indicates the number j The drone arrived at r The remaining energy when passing the flight point, Indicates the number j The lower energy limit of the UAV; Number i The service drone type constraints of the mission point are: , In the formula, Indicates that the pair number is i The type number of the task point to provide service is k The number of drones, Indicates the number i How many task points do you need to be numbered? k Types of drones to be serviced; Number j The types of drones that can be charged and replaced at smart airports are as follows: , In the formula, Indicates the number The type number of the drone, Indicates the number The first drone r The number of the flight points passed by, Indicates the number The set of numbers of all the flight points that the drone passes through, Representing a collection The number of elements of Indicates the number j A numbered set of drone types that can be charged and replaced at smart airports; Numberj The smart airport can only charge and replace one drone at a time: , In the formula, Indicates the number a The drone arrived at b The time of passing the flight point, Indicates the number The drone arrived at The time of passing through the flight points, among which, a , , b and is an indicator variable, Indicates the number The type number of the drone, Indicates the number j The number of smart airport pairs is The time required to charge and replace the battery of the drone type; The calculation number is i Waiting time for task point: , In the formula, Indicates the number j The drone arrived at r The time for passing the flight point.

[0012] Furthermore, the objective function is: , In the formula, G represents the weighted sum of waiting time of all task points, n Indicates the number of drones, m Indicates the number of task points. Indicates the number i The importance weight of the task point, Indicates the number i The waiting time of the task point.

[0013] Furthermore, a genetic algorithm is used to solve the UAV scheduling model, including: Use 0-1 encoding to encode and decode drone scheduling decisions; Randomly generate a preset size K The initial population of For any code, if the scheduling scheme corresponding to the code is not feasible, let its fitness be - D ,in D is a sufficiently large positive number. If the scheduling scheme corresponding to the encoding is feasible, proceed to the next step; Calculate the objective function value G under the encoding corresponding scheduling scheme, and use as the fitness of the encoding; Use exponential sorting selection to select individuals in the current population for replication; The individuals generated by the selection-copy operation are randomly cross-paired to obtain the crossover probability; Perform mutation operation with mutation probability; Iterate in this way until the iteration reaches L The code with the highest fitness is output, and the optimal scheduling solution is obtained after decoding.

[0014] In a second aspect, the present invention further provides a heterogeneous UAV dispatching device that considers airport charging and battery replacement compatibility and collaborative operation, including: Mission scenario characterization module, used to characterize mission scenarios where heterogeneous multiple drones provide services for multiple mission points distributed in the mission area, multiple smart airports provide multiple charging and battery swapping for drones, different smart airports have restrictions on the types of drones that can be charged and swapped, and different mission points have requirements for drone types; The state transfer equation solving module is used to determine the decision variables according to the mission scenario, solve the UAV state transfer equation, and obtain the state of the UAV at each passing flight point; The model building module is used to build a UAV scheduling model based on the status of the UAV at each flight point, with the constraints of UAV flight consistency and energy consumption, service requirements of all mission points, and charging and swapping at smart airports, and with the goal of minimizing the weighted sum of waiting times at all mission points; The model solving module is used to solve the UAV scheduling model using a genetic algorithm to obtain the optimal UAV scheduling solution.

[0015] In a third aspect, the present invention further provides an electronic device, wherein the memory is used to store program codes, and the processor is used to execute the heterogeneous UAV scheduling method considering airport charging and battery replacement compatibility and collaborative operation provided in the first aspect according to the instructions in the program codes.

[0016] In a fourth aspect, the present invention further provides a computer storage medium, wherein the computer readable storage medium is used to store program code, and the program code is used to execute the heterogeneous UAV scheduling method provided in the first aspect that takes into account airport charging and battery replacement compatibility and collaborative operation.

[0017] The present invention provides a heterogeneous UAV scheduling method, device, electronic device and storage medium considering the compatibility and collaborative operation of airport charging and swapping. The method targets a task scenario in which heterogeneous multiple UAVs are used to provide services for multiple task points distributed in a task area, multiple smart airports provide multiple charging and swapping for UAVs, different smart airports have restrictions on the types of UAVs that can be charged and swapped, and different task points have requirements for the type of UAVs. According to the task scenario, the decision variables are determined, the UAV state transfer equation is solved, and the state of the UAV at each passing flight point is obtained. According to the state of the UAV at each passing flight point, with the constraints of satisfying the flight consistency and energy consumption of the UAV, the service requirements of all task points and the charging and swapping of the smart airport, and with the weighted sum of the waiting time of all task points minimized as the goal, a UAV scheduling model is constructed, and a genetic algorithm is used to solve the UAV scheduling model to obtain the optimal scheduling plan for the UAV.

[0018] The beneficial effect of the present invention is that in the traditional optimization problem of heterogeneous multiple UAVs providing services to multiple mission points, the type requirements of the mission points for collaborative UAVs are taken into account, and the type restrictions of smart airports on rechargeable and swappable UAVs are also taken into account. By incorporating the synchronous arrival of UAVs at mission points and the charging and swapping decisions of smart airports into traditional UAV task allocation and path planning problems, energy waste can be further reduced and the applicability of UAVs in various scenarios can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the heterogeneous UAV scheduling method introduced in the first embodiment of the present invention.

[0020] Figure 2 It is a schematic diagram of an implementation scenario of the heterogeneous UAV scheduling method introduced in the first embodiment of the present invention.

[0021] Figure 3 It is a schematic diagram of a task scenario of a heterogeneous UAV scheduling method introduced in the first embodiment of the present invention.

[0022] Figure 4 It is a schematic diagram of the optimal scheduling solution introduced in the first embodiment of the present invention.

[0023] Figure 5 It is a schematic diagram of the structure of a heterogeneous UAV scheduling device introduced in the second embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following further describes the structures involved in the present invention or the technical terms used in these descriptions. These descriptions are only used as examples to illustrate how the methods of the present invention are implemented, and cannot constitute any limitation to the present invention.

[0025] The present invention relates to a heterogeneous drone scheduling method, device, electronic device and storage medium considering airport charging and battery replacement compatibility and collaborative operation, wherein heterogeneous drones refer to that all drones used are not of the same type, but of different types. Collaborative operation refers to the use of multiple different types of drones to provide services to multiple mission points, wherein each mission point may require multiple types of drones to serve it at the same time, and different mission points may require different types of drones. Embodiment 1

[0026] This embodiment introduces a heterogeneous UAV scheduling method that considers charging and battery swapping compatibility and collaborative operation. Figure 1 and Figure 2 .

[0027] S110. Characterize the mission scenarios in which multiple heterogeneous drones provide services for multiple mission points distributed in the mission area, multiple smart airports provide multiple charging and battery replacement for drones, different smart airports have restrictions on the types of drones that can be charged and replaced, and different mission points have requirements on the types of drones.

[0028] In this embodiment, first, the mission scenario is determined, including heterogeneous multiple drones providing services for mission points distributed in the mission area, multiple smart airports providing multiple charging and battery replacement for drones, restrictions on the types of drones that can be charged and replaced at different smart airports, and drone type requirements at different mission points.

[0029] Among them, drone types include fixed-wing drones, multi-rotor drones, vertical take-off and landing drones, and professional sensor drones.

[0030] Smart airports provide charging and battery replacement services for drones, which means charging or replacing the batteries of drones. Whether it is charging or battery replacement, it can generally be performed multiple times within a certain period of time (such as within a day).

[0031] Mission scenarios such as traffic accident rescue: Multi-rotor drones: First arrive at the scene of the accident, transmit on-site images in real time, and help the emergency command center quickly understand the situation. Fixed-wing drones: Patrol traffic conditions in a larger area, provide road conditions information around the accident, and assist in planning the best driving routes for emergency vehicles. Professional sensor drones: Equipped with thermal imaging instruments, they search for injured people at the scene of the accident, especially at night or in low visibility. The accident scene and the surrounding area can be considered as the mission area, with the accident scene as one mission point and the surrounding area as another mission point.

[0032] Flood Disaster Emergency Management: Multi-rotor drones: fly low over the disaster area, take real-time photos of the disaster, and send back detailed images. Vertical take-off and landing drones: drop relief supplies, such as medicines and food, in remote or hard-to-reach areas, and monitor the environment at the same time. Professional sensor drones: equipped with water quality testing instruments, monitor pollutants in floods, and assess environmental impacts. The disaster area can be considered the mission area, and the disaster area, remote or hard-to-reach area, and flood area are mission points.

[0033] Safety management of large-scale public events: Fixed-wing drones: Conduct extensive patrols over the activity area to monitor crowd flow and overall safety conditions. The activity area can be considered the mission area.

[0034] The above-mentioned drone types and mission scenarios are only examples and are not limited to the above.

[0035] Secondly, characterize the task scenario, specifically: After all drones are numbered, they form a drone number set: , In the formula, N express n In this embodiment, n =3, the drone is Figure 3 The triangle in the figure indicates the number of the drone.

[0036] Mission points and smart airports are collectively referred to as the flight points of the drone, and all flight points are numbered to form a flight point number set: , In the formula, express n + m A set of flight point numbers, of which the first n The flight points are n A smart airport, m The flight points are m Task points, and numbered j The drone is numbered j After completing all tasks, the smart airport will be returned to the smart airport. m =17, the mission points are Figure 3 The circle in the figure shows the number of the task point. The smart airport is represented by Figure 3 The square in the figure indicates that the number of the smart airport is identified in the square.

[0037] Calculate the distance between flight points: , In the formula, Indicates the number The flight point and number are The distance between the flight points, where and Respectively indicate the number The flight point and number are The two-dimensional coordinates of the flight point.

[0038] All drone types are numbered to form a drone type number set: , In the formula, P express p In this embodiment, p =2.

[0039] Number j The type number of the drone is ,in, In this embodiment, w 1=1, w 2= w 3=2.

[0040] Number j The flight speed of the drone is In this embodiment, v 1=90km / h, v 2= v 3=70km / h.

[0041] Number j The energy consumption per unit time of the UAV is In this embodiment, all Both are 100W.

[0042] Number j The hovering energy consumption per unit time of the UAV is In this embodiment, all Both are 100W.

[0043] Number j The energy limit of the drone is In this embodiment, , .

[0044] Number j The lower limit of the energy of the UAV is In this embodiment, , .

[0045] Number i The service time of the task point is In this embodiment, all Both are 10 minutes.

[0046] Number i The importance weight of the task point is In this embodiment, ,other Both are 1.

[0047] Number i Mission point service drone vector ,in, , Indicates the number i How many task points do you need to be numbered? k In this embodiment, , , .

[0048] Number j The number set of drone types that can be charged and replaced at smart airports is ,in, In this embodiment, , .

[0049] Number j The number of smart airport pairs is k The time required for charging and replacing the battery of the drone type is , among which, when hour, ,when hour, In this embodiment, , .

[0050] S120, determining decision variables according to the mission scenario, solving the UAV state transfer equation, and obtaining the state of the UAV at each passing flight point.

[0051] The constraints and objective functions of the drone scheduling model are related to the drone state transfer equation, so the drone state transfer equation needs to be solved first. When the decision variables are given, it is equivalent to giving the flight path of each drone. Therefore, according to the flight order, the state of each drone at each passing flight point can be solved in turn, that is, the state transfer equation can be solved.

[0052] By solving the state transfer equation, we can know the state of each UAV at each flight point during the entire flight process under given decision variables.

[0053] The state transition equation is needed to know the state of each UAV at each flight point during the entire flight process, and these states are related to the constraints and objective functions of the UAV scheduling model.

[0054] The status of the UAV at each passing flight point includes the arrival and departure time of the UAV at each passing flight point, and the remaining energy of the UAV when arriving at each passing flight point.

[0055] Solving the drone state transfer equation is specifically: Number j The scheduling decision variables of the UAV are: , In the formula, Indicates the number j Does the flight path of the drone contain The flight point and number are The edge between the flight points, when =1, indicating that the number is j The flight path of the drone contains The flight point and number are The edge between the flight points, when =0, it means not included.

[0056] Number j The set of numbers of all the flight points passed by the drone is: , In the formula, Indicates the number j The set of numbers of all the flight points that the drone passes through, Indicates the number j Does the flight path of the drone contain i The flight point and number are The edges between the flight points.

[0057] Number j The transfer equation of the flight point number passed by the UAV is: , In the formula, Indicates the number j The first drone r +1 number of the flight point you passed through, Indicates the number j The first drone r The flight point numbers passed by, among which, , Indicates the numberj The set of numbers of all the flight points that the drone passes through, for any set S, represents the number of elements of S, where Representing a collection The number of elements.

[0058] Number j The time transfer equation of the drone passing through the flight point is: , In the formula, Indicates the number j The drone arrived at r +1 time for passing through the flight point, Indicates the number j The drone left r The time of passing through the flight points, among which, , Indicates the number The flight point and number are The distance between the flight points, Indicates the number j The flight speed of the drone, Indicates the number j The set of numbers of all the flight points that the drone has passed through.

[0059] Number j The time transfer equation of the UAV passing through the flight point is: 1) When hour, , 2) In other cases, , In the formula, Indicates the number j The drone left r +1 time for passing through the flight point, Indicates the number j The drone arrived at r +1 time for passing through the flight point, Indicates the number The number of smart airport pairs is The time required to charge and replace the battery of the drone type, Indicates the number The service time of the mission point is the time when the drone numbered arrives at the l The time of passing the flight point; Indicates the number The drone arrived at l The time of passing the flight point, .

[0060] Number j The remaining energy transfer equation for the UAV to reach the passing flight point is: 1) When hour, , 2) In other cases, , In the formula, Indicates the number j The drone arrived at r +1 remaining energy when passing through a flight point, of which: , Indicates the number j The drone arrived at r The remaining energy when passing the flight point, Indicates the number j The drone left r The time of passing the flight point, Indicates the number j The drone arrived at r The time of passing the flight point, .

[0061] Number j The serial numbers and route sequence of the drones that are charged and replaced at the smart airport are as follows: , In the formula, Indicates the number j The number and route sequence of drones that are charged and replaced at smart airports. Indicates the number a The first drone b The flight point numbers passed by, among which, a , b is an indicator variable, Indicates the number a The set of numbers of all the flight points that the drone passes through, Representing a collection The number of elements.

[0062] For the number i The number of drone types serving the mission points: , In the formula, Indicates that the pair number is i The type number of the task point to provide service is k The number of drones.

[0063] This example studies the flight scheduling of drones, and the decision variable is naturally the flight path of each drone. j Does the flight path contain The flight point and number are The edge between the flight points, that is In all task scenarios, decision variables are represented by However, in different mission scenarios, the number and type of drones will be different, and the location (i.e., 2D coordinates) and type of flight points will also be different. For ease of understanding, for example, if a scenario only contains drone 1, flight point 1, and flight point 2, then In fact, and These two variables.

[0064] S130. According to the status of the UAV at each flight point, with the constraints of meeting the flight consistency and energy consumption of the UAV, the service requirements of all mission points, and the charging and swapping of smart airports, and with the goal of minimizing the weighted sum of waiting times of all mission points, a UAV scheduling model is constructed.

[0065] The UAV state transfer equation is obtained through the above steps. The state of each UAV at each flight point during the entire flight process is known through the state transfer equation, and these states are related to the constraints and objective functions of the UAV scheduling model.

[0066] Flight consistency means that the flight path of the drone corresponding to the decision variable is actually feasible. For ease of understanding, for example, a drone will not fly from mission point 1 to mission point 2, then suddenly disappear at mission point 2 and appear at mission point 3, or fly from mission point 1 to mission point 2, then suddenly gain the ability to "split" at mission point 2 and fly to mission points 3 and 4 at the same time.

[0067] The constraints include the value constraints of the scheduling decision variables of the drone, the take-off / return point constraints of the drone, the flight path validity constraints of the drone, the energy consumption constraints of the drone, the service drone type constraints of the mission point, the type of drones that can be charged and replaced by the smart airport, and the constraint that the smart airport can only charge and replace one drone at a time. Specifically: Number j The value constraints of the UAV scheduling decision variables are: , In the formula, Indicates the number j Does the flight path of the drone contain The flight point and number are The edges between the flight points.

[0068] Number j The take-off / return point constraints of the drone: , In the formula, Indicates the number j The number of the first flight point that the drone passes through. Indicates the number j The first drone The number of the flight point passed by.

[0069] The calculation number is j The number of flights of the drone between any two flight point sets: , In the formula, Indicates the number j The number of times the drone flies from flight point set A to flight point set B, Indicates the number j Does the flight path of the drone contain The flight point and number are The edges between the flight points.

[0070] Number j The flight path validity constraints of the UAV are: , In the formula, Indicates the number j The drones are collected by flight points Fly to the flight point The number of times, among which, represents any set of flight points, express n+m A collection of flight point numbers.

[0071] Number j Energy consumption constraints of the UAV: , In the formula, Indicates the number j The drone arrived at r The remaining energy when passing the flight point, Indicates the number j The lower energy limit of the drone.

[0072] Number i The service drone type constraints of the mission point are: , In the formula, Indicates that the pair number is i The type number of the task point to provide service is k The number of drones, Indicates the number i How many task points do you need to have? k Types of drones to be serviced.

[0073] Number j The types of drones that can be charged and replaced at smart airports are as follows: , In the formula, Indicates the number The type number of the drone, Indicates the number The first drone r The number of the flight points passed by, Indicates the number The set of numbers of all the flight points that the drone passes through, Representing a collection The number of elements of Indicates the number j A numbered set of drone types that can be charged and replaced at smart airports.

[0074] Number j The smart airport can only charge and replace one drone at a time: , In the formula, Indicates the number a The drone arrived at b The time of passing the flight point, Indicates the number The drone arrived at The time of passing through the flight points, among which, a , , b and is an indicator variable, Indicates the number The type number of the drone, Indicates the number j The number of smart airport pairs is The time required to charge and replace the battery of different drone types.

[0075] The calculation number is i Waiting time for task point: , In the formula, Indicates the number j The drone arrived at r The time for passing the flight point.

[0076] After the constraints are determined, the objective function is established with the goal of minimizing the weighted sum of the waiting time of all task points, and the objective function is determined as: , In the formula, represents the weighted sum of waiting time of all task points, n Indicates the number of drones, m Indicates the number of task points. Indicates the number i The importance weight of the task point, Indicates the number i The waiting time of the task point.

[0077] By summarizing the constraints and objective functions, a heterogeneous UAV scheduling model is obtained with the goal of minimizing the weighted sum of waiting times of all task points.

[0078] The state of each flight point of each drone during the entire flight process is known through the state transfer equation, and these states are related to the constraints and objective functions of the drone scheduling model. For example, the state of "the arrival time of the drone at the flight point" is related to the state of "the number of j The smart airport can only charge and replace one drone at a time. i The waiting time of the task point is related to the objective function; j The remaining energy of the drone to reach the passing flight point is the same as the state of " j Therefore, only by solving the state transfer equation can we know whether the given decision variable violates the constraint and the corresponding objective function value.

[0079] For easier understanding, the relationship between the state transfer equation, constraints and objective function is further explained: the constraint and objective function formulas contain the state of the drone at each flight point as independent variables. For example, assuming the objective function is ,and b represents the state of the drone leaving time, then, to calculate y , you need to know b For example, suppose a constraint is ,in, d represents the state of the drone arrival time. Then, to determine whether this constraint is satisfied, we need to know dThe drone state is related to the constraints and objective function in this way. Furthermore, to find out the state of the drone at each flight point, we need to know how the state of the drone at one flight point is transferred from its state at the previous flight point, that is, we need to find out the state transfer equation.

[0080] It should be noted that the meaning of the same parameters involved in all the above formulas is consistent. For parameters whose meanings are not directly stated, they can be found in the formulas in the context.

[0081] S140. Use a genetic algorithm to solve the UAV scheduling model and obtain the optimal UAV scheduling solution.

[0082] In this embodiment, a drone scheduling model can be constructed according to the constraints and the objective function, and then a genetic algorithm is used to solve the drone scheduling model to obtain the optimal drone scheduling solution, which is specifically: S1401. Use 0-1 coding to encode and decode the drone scheduling decision.

[0083] S1402: Randomly generate an initial population with a preset size of 100.

[0084] S1403. For any code, if the scheduling scheme corresponding to the code is not feasible, set its fitness to - D ,in D is a sufficiently large positive number. If the scheduling scheme corresponding to the encoding is feasible, then proceed to S1404. In this embodiment, D =200.

[0085] S1404, calculate the objective function value G under the scheduling scheme corresponding to the encoding, and use as the fitness of the encoding.

[0086] S1405. Use the exponential sorting selection method to select individuals in the current population for replication.

[0087] S1406, the individuals generated by the selection-copy operation are randomly cross-paired, and the cross-over probability is , in this embodiment, =0.9.

[0088] S1407, based on mutation probability Perform a mutation operation. In this embodiment, =0.4.

[0089] S1408. The above S1403 to S1407 constitute one iteration, and the iteration is repeated until the 30th generation is reached. The code with the highest fitness at this time is output, and the optimal scheduling solution is obtained after decoding it.

[0090] In this embodiment, the minimum value of the objective function can be obtained as 2983.75, and the following can be obtained: Figure 4 The UAV scheduling scheme shown in FIG. Among them, the thin solid arrow represents the flight path of the UAV numbered 1, the dotted arrow represents the flight path of the UAV numbered 2, and the thick solid arrow represents the flight path of the UAV numbered 3. The arrow direction is the flight direction of the UAV.

[0091] Through the heterogeneous drone scheduling method, in the process of heterogeneous multi-drones providing services to multiple mission points, not only the type requirements of the mission points for collaborative drones are considered, but also the type restrictions of smart airports on rechargeable and swappable drones are considered. By incorporating the synchronous arrival of drones at mission points and the charging and swapping decisions of smart airports into traditional drone task allocation and path planning problems, energy waste can be further reduced and the applicability of drones in various scenarios can be improved.

[0092] Embodiment 2 This embodiment introduces a heterogeneous UAV scheduling device that considers the compatibility and collaborative operation of UAV charging and battery replacement. Figure 5 The heterogeneous UAV scheduling device implements the heterogeneous UAV scheduling method described in Example 1. The heterogeneous UAV scheduling device can be implemented in the form of hardware and / or software, and the heterogeneous UAV scheduling device can be configured in an electronic device. The heterogeneous UAV scheduling device includes: a mission scenario characterization module 210, a state transfer equation solving module 220, a model construction module 230, and a model solving module 240.

[0093] The mission scenario characterization module 210 is used to characterize mission scenarios in which heterogeneous multiple drones provide services for multiple mission points distributed in the mission area, multiple smart airports provide multiple charging and battery replacement for drones, different smart airports have restrictions on the types of drones that can be charged and replaced, and different mission points have drone type requirements.

[0094] The state transfer equation solving module 220 is used to determine the decision variables according to the mission scenario, solve the drone state transfer equation, and obtain the state of the drone at each passing flight point.

[0095] The model building module 230 is used to build a drone scheduling model based on the status of the drone at each flight point, with the constraints of drone flight consistency and energy consumption, service requirements of all mission points, and charging and swapping at smart airports, and with the goal of minimizing the weighted sum of waiting times at all mission points.

[0096] The model solving module 240 is used to solve the UAV scheduling model using a genetic algorithm to obtain an optimal UAV scheduling solution.

[0097] Through the heterogeneous drone scheduling device, the heterogeneous drone scheduling method considering airport charging and battery replacement compatibility and collaborative operation described in Example 1 can be implemented.

[0098] Embodiment 3 This embodiment also introduces an electronic device, which includes a memory and a processor, the memory is used to store program code, transfer the program code to the processor, and the processor is used to execute the steps of the heterogeneous drone scheduling method provided in any of the above embodiments according to the instructions in the program code.

[0099] Among them, the processor may include one or more processing cores, such as a 3-core processor, an 8-core processor, etc. The processor may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor may also include a main processor and a coprocessor, the main processor is a processor for processing data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning. The memory may include one or more storage media. The memory may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory is at least used to store the following program code, wherein, after the program code is loaded and executed by the processor, the relevant steps in the heterogeneous drone scheduling method disclosed in any of the aforementioned embodiments can be implemented. In addition, the resources stored in the memory may also include an operating system and data, etc., and the storage method may be temporary storage or permanent storage. The operating system may be Windows or other types of operating systems. The data may include but is not limited to the data involved in the above method.

[0100] Embodiment 4 This embodiment also introduces a computer-readable storage medium, on which program code is stored. When the program code is executed by a processor, the steps of the heterogeneous drone scheduling method provided in any of the above embodiments can be implemented.

[0101] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0102] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0103] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one unit. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0104] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A heterogeneous UAV scheduling method considering airport charging and battery swapping compatibility and collaborative operation, characterized in that: include: Characterize the mission scenarios where heterogeneous multiple drones provide services for multiple mission points distributed in the mission area, multiple smart airports provide multiple charging and battery replacement for drones, different smart airports have restrictions on the types of drones that can be charged and replaced, and different mission points have requirements for drone types; Determine the decision variables according to the mission scenario, solve the UAV state transition equation, and obtain the state of the UAV at each flight point; According to the status of the drone at each flight point, the drone scheduling model is constructed with the goal of minimizing the weighted waiting time of all mission points, meeting the flight consistency and energy consumption of the drone, the service requirements of all mission points, and the charging and swapping of smart airports as constraints; The genetic algorithm is used to solve the UAV scheduling model and obtain the optimal UAV scheduling solution.

2. The heterogeneous UAV scheduling method considering airport charging and battery replacement compatibility and collaborative operation according to claim 1 is characterized in that: The task scenario is specifically characterized as follows: After all drones are numbered, they form a drone number set: , In the formula, N express n A set of drone numbers; Mission points and smart airports are collectively referred to as the flight points of the drone, and all flight points are numbered to form a flight point number set: , In the formula, express n + m A set of flight point numbers, of which the first n The flight points are n A smart airport, m The flight points are m Task points, and numbered j The drone is numbered j Take off from the smart airport and return to the smart airport after completing all tasks; Calculate the distance between flight points: ， In the formula, Indicates the number The flight point and number are The distance between the flight points, where and Respectively indicate the number The flight point and number are The two-dimensional coordinates of the flight point; All drone types are numbered to form a drone type number set: ， In the formula, P express p A collection of drone type numbers; Number j The type number of the drone is ,in, ; Number j The flight speed of the drone is ; Number j The energy consumption per unit time of the UAV is ; Number j The hovering energy consumption per unit time of the UAV is ; Number j The energy limit of the drone is ; Number j The lower limit of the energy of the UAV is ; Number i The service time of the task point is ; Number i The importance weight of the task point is ; Number i Mission point service drone vector ,in, , Indicates the number i How many task points do you need to be numbered? k Types of drones to be serviced; Number j The number set of drone types that can be charged and replaced at smart airports is ,in, ; Number j The number of smart airport pairs is k The time required for charging and replacing the battery of the drone type is , among which, when hour, ,when hour, .

3. The heterogeneous UAV scheduling method considering airport charging and battery replacement compatibility and collaborative operation according to claim 1 is characterized in that: Number j The scheduling decision variables of the UAV are: ， In the formula, Indicates the number j Does the flight path of the drone contain The flight point and number are The edge between the flight points, when =1, indicating that the number is j The flight path of the drone contains The flight point and number are The edge between the flight points, when =0, it means not included.

4. The heterogeneous UAV scheduling method considering airport charging and battery replacement compatibility and collaborative operation according to claim 1 is characterized in that: The specific state transfer equation of the drone is: Number j The set of numbers of all the flight points passed by the drone is: ， In the formula, Indicates the number j The set of numbers of all the flight points that the drone passes through, Indicates the number j Does the flight path of the drone contain i The flight point and number are The edges between the flight points; Number j The transfer equation of the flight point number passed by the UAV is: ， In the formula, Indicates the number j The first drone r +1 number of the flight point you passed through, Indicates the number j The first drone r The flight point numbers passed by, among which, , for any set S, represents the number of elements of S, where Representing a collection The number of elements of ; Number j The time transfer equation of the drone passing through the flight point is: ， In the formula, Indicates the number j The drone arrived at r +1 time for passing through the flight point, Indicates the number j The drone left r The time of passing through the flight points, among which, , Indicates the number The flight point and number are The distance between the flight points, Indicates the number j The flight speed of the drone; Number j The time transfer equation of the UAV passing through the flight point is: 1) When hour, , 2) In other cases, , In the formula, Indicates the number j The drone left r +1 time for passing through the flight point, Indicates the number j The drone arrived at r +1 time for passing through the flight point, Indicates the number The number of smart airport pairs is The time required to charge and replace the battery of the drone type, Indicates the number The service time of the mission point is the time when the drone numbered arrives at the l The time of passing the flight point; Indicates the number The drone arrived at l The time of passing the flight point, ; Number j The remaining energy transfer equation for the UAV to reach the passing flight point is: 1) When hour, , 2) In other cases, , In the formula, Indicates the number j The drone arrived at r +1 remaining energy when passing through a flight point, of which: , Indicates the number j The drone arrived at r The remaining energy when passing the flight point, Indicates the number j The drone left r The time of passing the flight point, Indicates the number j The drone arrived at r The time of passing the flight point, ; Number j The serial numbers and route sequence of the drones that are charged and replaced at the smart airport are as follows: ， In the formula, Indicates the number j The number and route sequence of drones that are charged and replaced at smart airports. Indicates the number a The first drone b The flight point numbers passed by, among which, a , b is an indicator variable, Indicates the number a The set of numbers of all the flight points that the drone passes through, Representing a collection The number of elements of ; For the number i The number of drone types serving the mission points: ， In the formula, Indicates that the pair number is i The type number of the task point to provide service is k The number of drones.

5. The heterogeneous UAV scheduling method considering airport charging and battery replacement compatibility and collaborative operation according to claim 1 is characterized in that: The constraints are: Number j The value constraints of the UAV scheduling decision variables are: ， In the formula, Indicates the number j Does the flight path of the drone contain The flight point and number are The edges between the flight points; Number j The take-off / return point constraints of the drone: ， In the formula, Indicates the number j The number of the first flight point that the drone passes through. Indicates the number j The first drone The number of the flight point passed by; The calculation number is j The number of flights of the drone between any two flight point sets: ， In the formula, Indicates the number j The number of times the drone flies from flight point set A to flight point set B, Indicates the number j Does the flight path of the drone contain The flight point and number are The edges between the flight points; Number j The flight path validity constraints of the UAV are: ， In the formula, Indicates the number j The drones are collected by flight points Fly to the flight point The number of times, among which, represents any set of flight points, express n+m A set of flight point numbers; Number j Energy consumption constraints of the UAV: ， In the formula, Indicates the number j The drone arrived at r The remaining energy when passing the flight point, Indicates the number j The lower energy limit of the UAV; Number i The service drone type constraints of the mission point are: ， In the formula, Indicates that the pair number is i The type number of the task point to provide service is k The number of drones, Indicates the number i How many task points do you need to be numbered? k Types of drones to be serviced; Number j The types of drones that can be charged and replaced at smart airports are as follows: ， In the formula, Indicates the number The type number of the drone, Indicates the number The first drone r The number of the flight points passed by, Indicates the number The set of numbers of all the flight points that the drone passes through, Representing a collection The number of elements of Indicates the number j A numbered set of drone types that can be charged and replaced at smart airports; Number j The smart airport can only charge and replace one drone at a time: ， In the formula, Indicates the number a The drone arrived at b The time of passing the flight point, Indicates the number The drone arrived at The time of passing through the flight points, among which, a , , b and is an indicator variable, Indicates the number The type number of the drone, Indicates the number j The number of smart airport pairs is The time required to charge and replace the battery of the drone type; The calculation number is i Waiting time for task point: ， In the formula, Indicates the number j The drone arrived at r The time for passing the flight point.

6. The heterogeneous UAV scheduling method considering airport charging and battery replacement compatibility and collaborative operation according to claim 1 is characterized in that: The objective function is: ， In the formula, G represents the weighted sum of waiting time of all task points, n Indicates the number of drones, m Indicates the number of task points. Indicates the number i The importance weight of the task point, Indicates the number i The waiting time of the task point.

7. The heterogeneous UAV scheduling method considering airport charging and battery replacement compatibility and collaborative operation according to claim 1 is characterized in that: Genetic algorithm is used to solve the UAV scheduling model, including: Use 0-1 encoding to encode and decode drone scheduling decisions; Randomly generate a preset size K The initial population of For any code, if the scheduling scheme corresponding to the code is not feasible, let its fitness be - D ,in D is a sufficiently large positive number. If the scheduling scheme corresponding to the encoding is feasible, proceed to the next step; Calculate the objective function value G under the encoding corresponding scheduling scheme, and use as the fitness of the encoding; Use exponential sorting selection to select individuals in the current population for replication; The individuals generated by the selection-copy operation are randomly cross-paired to obtain the crossover probability; Perform mutation operation with mutation probability; Iterate in this way until the iteration reaches L The code with the highest fitness is output, and the optimal scheduling solution is obtained after decoding.

8. A device, characterized in that: The device is used to implement the heterogeneous UAV scheduling method considering airport charging and battery replacement compatibility and collaborative operation as described in any one of claims 1 to 7, comprising: Mission scenario characterization module, used to characterize mission scenarios where heterogeneous multiple drones provide services for multiple mission points distributed in the mission area, multiple smart airports provide multiple charging and battery swapping for drones, different smart airports have restrictions on the types of drones that can be charged and swapped, and different mission points have requirements for drone types; The state transfer equation solving module is used to determine the decision variables according to the mission scenario, solve the UAV state transfer equation, and obtain the state of the UAV at each passing flight point; The model building module is used to build a UAV scheduling model based on the status of the UAV at each flight point, with the constraints of UAV flight consistency and energy consumption, service requirements of all mission points, and charging and swapping at smart airports, and with the goal of minimizing the weighted sum of waiting times at all mission points; The model solving module is used to solve the UAV scheduling model using a genetic algorithm to obtain the optimal UAV scheduling solution.

9. An electronic device, characterized in that: It includes a processor and a memory, the memory is used to store program code, and the processor is used to execute the heterogeneous UAV scheduling method considering airport charging and battery replacement compatibility and collaborative operation as described in any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the heterogeneous drone scheduling method considering airport charging and battery replacement compatibility and collaborative operation as described in any one of claims 1-7.

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