A method, device and charging network for parallel cooperative charging of unmanned aerial vehicles

Abstract

The present invention belongs to the technical field of unmanned aerial vehicle charging scheduling, and specifically discloses a method, device and charging network for parallel cooperative charging scheduling of unmanned aerial vehicles. The method includes: S1 defining a charging station set, a unmanned aerial vehicle set and a target location set; S2 defining a parallel cooperative charging scheduling pricing model; S3 formalizing the unmanned aerial vehicle parallel cooperative charging scheduling problem; S4 calling a unmanned aerial vehicle parallel cooperative charging scheduling algorithm with cost optimization benefits to obtain a charging allocation plan for the unmanned aerial vehicle. The present invention provides a method, device and charging network for parallel cooperative charging scheduling of unmanned aerial vehicles, which can determine the parallel cooperative charging scheduling plan of unmanned aerial vehicles on the premise of meeting the charging needs of more unmanned aerial vehicles, minimize the total cost, and provide technical support for the design of commercial unmanned aerial vehicle charging systems.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle charging scheduling, and specifically discloses a method, a device and a charging network for unmanned aerial vehicle parallel cooperative charging scheduling. Background Art

[0002] Drones have attracted extensive attention from academia and industry because of their strong mobility, easy deployment, and ability to quickly and flexibly provide services to a variety of users. In particular, in some special cases, such as wilderness, when ground infrastructure cannot be deployed, drones can serve as an important alternative. So far, drones have been widely used in disaster monitoring, sensor perception, and mobile edge computing.

[0003] However, due to the limited battery capacity of drones, the energy replenishment problem of drones cannot be ignored. As a wireless charging technology with long-distance and one-to-many characteristics, RF wireless charging can also be used to charge high-power devices such as shared bicycles in recent research results. In addition, based on the characteristics of RF charging, multiple rechargeable devices within the public charging time can share the charging cost together, reducing individual costs.

[0004] Although there have been studies on cooperative charging scheduling, there are still deficiencies in this area. For example, in scenarios such as agriculture and logistics, a large number of drones are needed to complete tasks in large-scale scenarios. However, when drones are charging, they need to occupy a certain amount of space. Therefore, there may not be enough space to meet the charging needs of all drones at the same time. Summary of the invention

[0005] The technical problem to be solved by the present invention is: to determine a charging allocation plan for each drone to minimize the total cost of all charging groups so that each drone is assigned to a charging facility and its charging needs are met.

[0006] In order to solve the above problems, the present invention discloses a method, device and charging network for parallel cooperative charging of unmanned aerial vehicles.

[0007] The technical solution of the present invention is as follows:

[0008] A method for parallel cooperative charging scheduling of unmanned aerial vehicles comprises the following steps:

[0009] S1 defines a set of charging stations, a set of drones, and a set of target locations;

[0010] S2 defines the parallel cooperative charging scheduling pricing model;

[0011] S3 formalizes the UAV parallel cooperative charging scheduling problem;

[0012] S4 calls the UAV parallel cooperative charging scheduling algorithm with cost optimization benefits to obtain the charging distribution plan for the UAVs.

[0013] As the UAV parallel cooperative charging scheduling method of the present invention, wherein: in S1, the charging station set is M = *s1,s2,…,s m}, where m is the number of charging stations, belonging to different charging providers; in the RF wireless charging mode, the received power will be different due to the different locations of the charging facilities. Therefore, for each charging station s j ∈M, there is a set of charging facilities The corresponding set of facility receiving powers is where q j is the number of charging facilities in the charging station; each charging facility can allow multiple drones to queue up for charging, and a charging station can form multiple parallel charging queues; the drone set is N = *o1,o2,…,o n +, the target position set is L = *l1,l2,…,l n +, where n is the number of drones, and each drone o i ∈N and a target position l i ∈L corresponds to the need to reach the target location after charging, and there is a charging demand E i , remaining power Unit moving energy consumption i , energy limit Here E i It refers to the energy added to the remaining power after reaching the target location, rather than the energy actually added by the charger; the charging station, drone and target location are all distributed on a two-dimensional plane. Considering that in most scenarios, the moving distance is much greater than the distance between facilities in the charging station, the distance in the charging station is ignored. i Location to charging stations j The distance is ||o i s j ||, from charging station s j To the target location i The distance is ||s j l i ||; Scenario diagram as follows Figure 1 As shown, the schematic diagram of parallel charging in the charging station is as follows Figure 2 shown.

[0014] As the UAV parallel cooperative charging scheduling method of the present invention, wherein: in S2, the parallel cooperative charging scheduling pricing model includes:

[0015] Charging Stations j Need to give drone oi The actual energy replenishment for charging is calculated as:

[0016] E i (s j )=E i +b i (||o i s j ||+||s j l i ||) (1)

[0017] Let G j For j The charging group, that is, the charging station s j Charging drone collection; use To represent G j The charging arrangement of the drone, including k=1,2,...,q j , which is provided by the charging facilities A collection of drones being charged; a charging facility The charging time is:

[0018]

[0019] For charging pack G j With charging arrangement φ j , the charging time of the charging group is the maximum charging time of all charging facilities in the charging arrangement:

[0020]

[0021] When the charging arrangement is φ j When charging group G j The cost is defined as:

[0022]

[0023] Among them, A j For charging stations j The starting price, a j For charging stations j The recharge unit price is T j For charging stations j The charging time threshold, according to the actual starting charging price rules, is:

[0024] A j ≥a j T j (5)

[0025] As the UAV parallel cooperative charging scheduling method of the present invention, wherein: in S3, the formalized UAV parallel cooperative charging scheduling problem includes:

[0026] To simplify the expression, use 0-1 variables Indicates whether the drone o i Belongs to the collection If it belongs to otherwise

[0027]

[0028]

[0029]

[0030] Among them, formula (6) aims to minimize the total cost, constraint (6-1) ensures that all drones are charged and can only get charging services from one charging facility at a time, and constraint (6-2) ensures that is a 0-1 variable;

[0031] To ensure the existence of a feasible solution to the problem, two conditions need to be met; first, the remaining energy of any drone should be sufficient to reach the farthest charging station, that is,

[0032]

[0033] Second, the energy requirements of any drone can be met after reaching the target location, namely:

[0034]

[0035] If the above two conditions cannot be met, a simple preprocessing can be performed before solving the problem: remove the drones that do not meet constraints (7) and (8) from the drone set N.

[0036] As the UAV parallel cooperative charging scheduling method of the present invention, wherein: in S4, the UAV parallel cooperative charging scheduling algorithm with cost optimization benefit is called to obtain the charging allocation plan of the UAV, such as the process Figure 3 As shown, there are the following specific steps:

[0037] A1: Input parameters: charging station set M, drone set N, target location set L, each drone o i Charging demand E i , unit moving energy consumption b i , each charging station j The recharge price of j , starting price A j , time threshold T j , Charging facility set F j , charging facility power set Pj ;

[0038] A2: Initialize the set of drones of all charging groups and charging facilities, that is, for all s j ∈M, let For all make

[0039] A3: Define the uncovered drone set N′ and initialize N′=N;

[0040] A4: If Execute steps A5 to A7, otherwise execute step A8;

[0041] A5: For each s j ∈M, find the charging group G′ that minimizes the average marginal cost j And corresponding charging arrangement φ′ j ;

[0042] A6: Find the charging station with the lowest average marginal cost among all charging stations, that is, where s j′ For any charging station in M, G′ j′ For j′ Extended charging pack, G j′ For j′ Charging group before expansion, φ′ j′ For j′ Extended charging arrangement, φ j′ For j′ charging arrangements prior to expansion;

[0043] A7: Update G j =G′ j ,φ j =φ′ j , G′ j The UAV is deleted from N′, that is, N′=N′\G′ j , return to step A4;

[0044] A8: Return all (G j ,φ j ).

[0045] Furthermore, in step A5, for each s j ∈M, find the charging group G′ that minimizes the average marginal cost j And corresponding charging arrangement φ′ j The specific process is as follows:

[0046] A5.1: Define the number of drones w added, and the charging group G after adding w drones j(w), and the temporary uncovered drone set N * , initialize w = 0, G j (w) = G j , N * =N′;

[0047] A5.2: If w<|N′|, execute steps A5.3 to A5.7, otherwise execute step A5.8;

[0048] A5.3: Take out N * Medium E i (s j )The smallest drone, Among them i′ N * Any drone in the game;

[0049] A5.4: From N * Delete o i , that is, N * =N * \*o i +;

[0050] A5.5: Let w = w + 1, G j (w) = G j (w-1)∪*o i +;

[0051] A5.6: G j (w) and P j As input, call the existing approximate algorithm for the unified parallel machine scheduling problem, find the approximate optimal parallel scheduling result, and record it as φ j (w), return to step A5.2;

[0052] A5.7: Find the case where the average marginal cost is minimized when w>0 and return it as the result, that is, like Figure 4 shown.

[0053] Furthermore, the present invention discloses a UAV parallel charging control device, comprising:

[0054] 1) one or more processors;

[0055] 2) a storage device for storing one or more programs;

[0056] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-mentioned methods for parallel cooperative charging scheduling of unmanned aerial vehicles.

[0057] Furthermore, the present invention also discloses a UAV parallel charging network, which includes the above-mentioned UAV parallel charging control device.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. In the special case where there is only one charging station, call any existing approximation algorithm for the unified parallel machine scheduling problem to execute A5.6. If the approximation degree of the called approximation algorithm is γ, then the approximation degree of the cost-optimization-effective UAV parallel cooperative charging scheduling algorithm is also γ.

[0060] The problem in the special case of only one charging station can be stated as:

[0061] (P1):min c(φ j )(9)

[0062]

[0063]

[0064] The problem in this special case is referred to as P1. represents the optimal solution of P1, and the corresponding charging time and charging cost are and Use φ j The solutions obtained by the approximate algorithm for the unified parallel machine scheduling problem are T(v j ) and c(φ j ). Since the approximation degree of the approximation algorithm for the unified parallel machine scheduling problem is γ, we have:

[0065]

[0066] Further, consider the following three cases:

[0067] According to formula (4), we have

[0068]

[0069] have

[0070]

[0071] The first inequality is based on formula (5), and the second inequality is based on

[0072] have

[0073]

[0074] Among them, the first inequality is based on formula (10), and the second inequality is based on formula (5).

[0075] In summary, the beneficial effects are established.

[0076] 2. Call any existing approximation algorithm for the unified parallel machine scheduling problem to execute A5.6. If the approximation degree of the called approximation algorithm is γ, then the approximation degree of the cost-optimization-effective UAV parallel cooperative charging scheduling algorithm is γ(lnn+1).

[0077] For any charging group G j , we can use the approximate algorithm for the unified parallel machine scheduling problem to obtain the corresponding charging schedule φ j ,use It can minimize G j The optimal charging schedule for charging time. Use function and c * (G j ) represent φ j and The charging cost is, According to the conclusion of beneficial effect 1, there are:

[0078]

[0079] Consider the following two questions:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] Obviously, the total cost of the optimal solution of P3 is equivalent to the total cost of the optimal solution of the original problem. If the optimal total cost is defined as OPT, then:

[0087]

[0088] in, Charging stations for P3 optimal solution j charging pack.

[0089] The total cost of the optimal solution for P2 is defined as According to formula (14), we have:

[0090]

[0091] because is the total cost of the optimal solution for P2, which is:

[0092]

[0093] Next, it is proved that the cost-optimized UAV parallel cooperative charging scheduling algorithm has an approximation of (lnn+1) for P2. The UAVs are numbered in the order covered by the cost-optimized UAV parallel cooperative charging scheduling algorithm, and are set as o1, o2, o3, ..., o n Assume that k ,k=1,2,…,n is s j Extended charging group G′ j In G j The basis of coverage. k The cost-effectiveness is:

[0094]

[0095] Consider covering o k In the iteration of , the optimal solution charging group covers the remaining drones in N′, and the cost is at most Therefore, among all the charging groups in the optimal solution, there must be one with the highest cost-effectiveness. A charging group, where |N′|≥n-k+1. Since o k is covered with the least cost-effectiveness, then

[0096]

[0097] Since the total cost is the sum of the cost-effectiveness of each coverage drone, we have

[0098]

[0099] Combining formula (19) and formula (22), we have BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 This is a schematic diagram of the structure of the UAV parallel cooperative charging scheduling scenario of the present invention;

[0101] Figure 2 A schematic diagram of parallel charging in a charging station of the present invention;

[0102] Figure 3 A flowchart of a cost-optimized UAV parallel cooperative charging scheduling algorithm in the present invention;

[0103] Figure 4 To find the charging group G′ that makes the average marginal cost approximately minimum in the present invention j And corresponding charging arrangement φ′ j ;

[0104] Figure 5 is a coordinate schematic diagram of the second embodiment of the present invention;

[0105] Figure 6 It is a schematic diagram of the scheduling result in the second embodiment of the present invention;

[0106] Figure 7 This is a data diagram of the total cost of changing the number of drones in the third embodiment of the present invention;

[0107] Figure 8 A total cost data diagram of changing the number of charging stations in the third embodiment of the present invention;

[0108] Fig. 9 A total cost data diagram for changing the number of charging facilities at each charging station in the third embodiment of the present invention;

[0109] Fig.10 This is a data diagram of the total cost of changing the charging power in the third embodiment of the present invention;

[0110] Fig.11 A total cost data diagram of changing energy demand in a third embodiment of the present invention;

[0111] Fig.12 A data diagram showing the total cost of changing the unit moving energy consumption in the third embodiment of the present invention;

[0112] Fig.13 This is a data diagram of the total cost of changing the recharge unit price in the third embodiment of the present invention. DETAILED DESCRIPTION

[0113] It should be noted that the following embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0114] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0115] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0116] Example 1

[0117] The method for parallel cooperative charging scheduling of unmanned aerial vehicles described in the present invention specifically comprises the following steps:

[0118] S1: Define the charging station set, drone set, and target location set;

[0119] The charging station set is M = *s1,s2,…,s m}, where m is the number of charging stations, belonging to different charging providers; in the RF wireless charging mode, the received power will be different due to the different locations of the charging facilities. Therefore, for each charging station s j ∈M, there is a set of charging facilities The corresponding set of facility receiving powers is where q j is the number of charging facilities in the charging station; each charging facility can allow multiple drones to queue up for charging, and a charging station can form multiple parallel charging queues; the drone set is N = *o1,o2,…,o n +, the target position set is L = *l1,l2,…,l n +, where n is the number of drones, and each drone o i ∈N and a target position l i ∈L corresponds to the need to reach the target location after charging, and there is a charging demand E i , remaining power Unit moving energy consumption i , energy limit Here E i It refers to the energy added to the remaining power after reaching the target location, rather than the energy actually added by the charger; the charging station, drone and target location are all distributed on a two-dimensional plane. Considering that in most scenarios, the moving distance is much greater than the distance between facilities in the charging station, the distance in the charging station is ignored. i Location to charging stations j The distance is ||o i s j ||, from charging station s j To the target location i The distance is ||s j l i||; Scenario diagram as follows Figure 1 As shown, the schematic diagram of parallel charging in the charging station is as follows Figure 2 shown.

[0120] S2: Define the parallel cooperative charging scheduling pricing model;

[0121] Charging Stations j Need to give drone o i The actual energy replenishment for charging is calculated as:

[0122] E i (s j )=E i +b i (||o i s j ||+||s j l i ||) (1)

[0123] Let G j For j The charging group, that is, the charging station s j Charging drone collection; use To represent G j The charging arrangement of the drone, including k=1,2,...,q j , which is provided by the charging facilities A collection of drones being charged; a charging facility The charging time is:

[0124]

[0125] For charging pack G j With charging arrangement φ j , the charging time of the charging group is the maximum charging time of all charging facilities in the charging arrangement:

[0126]

[0127] When the charging arrangement is φ j When charging group G j The cost is defined as:

[0128]

[0129] Among them, A j For charging stations j The starting price, a j For charging stations j The recharge unit price is T j For charging stations j The charging time threshold, according to the actual starting charging price rules, is:

[0130] A j ≥a j T j (5)

[0131] S3: Formalize the UAV parallel cooperative charging scheduling problem;

[0132] To simplify the expression, use 0-1 variables Indicates whether the drone o i Belongs to the collection If it belongs to otherwise

[0133]

[0134]

[0135]

[0136] Among them, formula (6) aims to minimize the total cost, constraint (6-1) ensures that all drones are charged and can only get charging services from one charging facility at a time, and constraint (6-2) ensures that is a 0-1 variable;

[0137] To ensure the existence of a feasible solution to the problem, two conditions need to be met; first, the remaining energy of any drone should be sufficient to reach the farthest charging station, that is,

[0138]

[0139] Second, the energy requirements of any drone can be met after reaching the target location, namely:

[0140]

[0141] If the above two conditions cannot be met, a simple preprocessing can be performed before solving the problem: remove the drones that do not meet constraints (7) and (8) from the drone set N.

[0142] S4: calling the UAV parallel cooperative charging scheduling algorithm with cost optimization benefits to obtain the charging allocation plan of the UAVs;

[0143] A1: Input parameters: charging station set M, drone set N, target location set L, each drone o i Charging demand E i , unit moving energy consumption b i , each charging station j The recharge price of j , starting price Aj , time threshold T j , Charging facility set F j , charging facility power set P j ;

[0144] A2: Initialize the set of drones of all charging groups and charging facilities, that is, for all s j ∈M, let For all make

[0145] A3: Define the uncovered drone set N′ and initialize N′=N;

[0146] A4: If Execute steps A5 to A7, otherwise execute step A8;

[0147] A5: For each s j ∈M, find the charging group G′ that minimizes the average marginal cost j And corresponding charging arrangement φ′ j ;

[0148] A6: Find the charging station with the lowest average marginal cost among all charging stations, that is, where s j′ For any charging station in M, G′ j′ For j′ Extended charging pack, G j′ For j′ Charging group before expansion, φ′ j′ For j′ Extended charging arrangement, φ j′ For j′ charging arrangements prior to expansion;

[0149] A7: Update G j =G′ j ,φ j =φ′ j , G′ j The UAV is deleted from N′, that is, N′=N′\G′ j , return to step A4;

[0150] A8: Return all (G j ,φ j ).

[0151] Furthermore, in step A5, for each s j ∈M, find the charging group G′ that minimizes the average marginal cost j And corresponding charging arrangement φ′ j The specific process is as follows:

[0152] A5.1: Define the number of drones w added, and the charging group G after adding w drones j (w), and the temporary uncovered drone set N * , initialize w = 0, G j (w) = G j , N * =N′;

[0153] A5.2: If w<|N′|, execute steps A5.3 to A5.7, otherwise execute step A5.8;

[0154] A5.3: Take out N * Medium E i (s j )The smallest drone, Among them i′ N * Any drone in the game;

[0155] A5.4: From N * Delete o i , that is, N * =N * \*o i +;

[0156] A5.5: Let w = w + 1, G j (w) = G j (w-1)∪*o i +;

[0157] A5.6: G j (w) and P j As input, call the existing approximate algorithm for the unified parallel machine scheduling problem, find the approximate optimal parallel scheduling result, and record it as φ j (w), return to step A5.2;

[0158] A5.7: Find the case where the average marginal cost is minimized when w>0 and return it as the result, that is,

[0159] Example 2

[0160] A method for parallel cooperative charging scheduling of unmanned aerial vehicles according to the present invention comprises the following steps:

[0161] In this embodiment, there is a charging station set M=*s1,s2} in S1 and S2, and a drone set

[0162] N = *o1,o2,o3,o4,o5,o6,o7,o8,o9+, target location set L = *l1,l2,l3,l4,l5,l6,l7,l8,l9+, all distributed on a 2km*2km two-dimensional plane. Table 1 shows the relevant parameters of the drone and the corresponding target location, and Table 2 shows the relevant parameters of the charging station. Figure 5 A schematic diagram of coordinates on a two-dimensional plane.

[0163] Table 1. Related parameters of UAV and corresponding target positions

[0164]

[0165] Table 2 Relevant parameters of charging station

[0166]

[0167] This embodiment meets the preprocessing constraint requirements in S3.

[0168] In this embodiment S4, the specific process of the UAV parallel cooperative charging scheduling algorithm with cost optimization benefits is as follows:

[0169] A1: Input parameters: charging station set M = *s1,s2}, drone set

[0170] N = *o1,o2,o3,o4,o5,o6,o7,o8,o9+, target position set L = *l1,l2,l3,l4,l5,l6,l7,l8,l9+, and other parameters;

[0171] A2: Initialize the set of drones of all charging groups and charging facilities, that is, for all s j ∈M, let For all make

[0172] A3: Initialize N′=*o1,o2,o3,o4,o5,o6,o7,o8,o9+;

[0173] A4: If Execute steps A5 to A7, otherwise execute step A8;

[0174] A5: For each s j ∈M, find the charging group G′ that minimizes the average marginal cost j And corresponding charging arrangement φ′ j ; Take s1 as an example, enter A5.1;

[0175] A5.1: Initialize w = 0, N *=*o1,o2,o3,o4,o5,o6,o7,o8,o9+;

[0176] A5.2: If w<|N′|, execute steps A5.3 to A5.7, otherwise execute step A5.8; the condition is satisfied here;

[0177] A5.3: Take out N * Medium E i (s j ) The smallest drone is o8 at this time;

[0178] A5.4: From N * Delete o i , that is, N * =*o1,o2,o3,o4,o5,o6,o7,o9+;

[0179] A5.5: w = 1, G j (1) = *o8+;

[0180] A5.6: Take G1(1) and P1 as input, call the existing approximate algorithm for the unified parallel machine scheduling problem, find the approximate optimal parallel scheduling result, and record it as φ1(1); here, the existing approximate algorithm for the unified parallel machine scheduling problem with an approximation degree of 2(1+ε) is used, where ε is a search accuracy greater than 0. In this embodiment, ε=4 is taken, and then it can be obtained Return to step A5.2 to obtain the remaining charging arrangements, which are φ1(2) = **o8+, *o5++, φ1(3) = **o8+, *o3,o5++, φ1(4) = **o3,o5+, *o7,o8++, φ1(5) = **o3,o7+, *o5,o8,o9++, φ1(6) = **o7,o9+, *o3,o5,o6,o8++, φ1(7) =

[0181] **o4,o6+,*o3,o5,o7,o8,o9++,φ1(8)=**o2,o4+,*o3,o5,o6,o7,o8,o9++,φ1(9)=

[0182] **o1,o2+,*o3,o4,o5,o6,o7,o8,o9++;

[0183] A5.7: Find the case where the average marginal cost is minimized when w>0 and return it as the result, that is, at this time

[0184] G′1=G1(4)=*o3,o5,o7,o8+, φ′1=φ1(4)=**o3,o5+,*o7,o8++;

[0185] Perform the same operation on s2, and we get G′2=*o3,o4,o6,o8+, φ′2=**o3,o6+,*o4,o8++;

[0186] A6: Find the charging station with the lowest average marginal cost among all charging stations, that is, at this time

[0187] s1 is selected;

[0188] A7: Update G1=*o3,o5,o7,o8+, φ1=**o3,o5+,*o7,o8++, N′=*o1,o2,o4,o5,o9+,

[0189] Return to step A4;

[0190] Repeat A5 to A7 until

[0191] A8: Return all (G j ,φ j ), the final result is G1=*o2,o3,o5,o7,o8+,

[0192] φ1=**o3,o7+,*o2,o5,o8++,G2=*o1,o4,o6,o9+,φ2=**o6,o9+,*o1,o4++,the total cost is 137.1536,the scheduling result diagram is as follows Figure 6 shown.

[0193] Example 3

[0194] In order to verify and illustrate the technical effects used in this method, Example 3 involves three solutions for comparative testing with the method of the present invention:

[0195] (1) Greedy selection algorithm based on average cost: The overall steps are similar to those of the present invention, but there are two differences. First, for each s j ∈M, based on N and P j , a charging schedule is obtained using heuristic greedy scheduling, in which each drone is assigned to the charging facility with the smallest charging time, which is different from step A5 in the present invention. Secondly, the charging station with the lowest average cost (ratio of the total cost of the charging group to the size of the charging group) is selected, which is different from step A6 in the present invention.

[0196] (2) Improved greedy selection algorithm based on average cost: The overall steps are similar to those of the present invention. For each s j ∈M, based on N and P j, using an approximate algorithm for the unified parallel machine scheduling problem to obtain the charging schedule, which is different from step A5 in the present invention. Selecting the charging station with the lowest average cost, which is different from step A6 in the present invention.

[0197] (3) Greedy selection algorithm based on average marginal cost: The overall steps are similar to those of the present invention. j (w) and P j As input, a heuristic greedy scheduling is used to obtain a charging schedule, in which each drone is assigned to the charging facility with the smallest charging time, which is different from step A5.6 in the present invention.

[0198] Parameter setting: The charging stations, drones and target locations are randomly distributed in a 2km*2km square two-dimensional plane. The default parameters are set as follows: the number of charging stations m = 5, the number of charging facilities in the charging station q j ∈,4,6-, charging facility power The number of drones n = 40, the charging demand of drones E i ∈,50,60-Wh, unit moving energy consumption b i ∈,220,230-J / m, recharge price

[0199] a j ∈,0.0015,0.002- / s, starting price A j ∈,40,50-,time threshold T j ∈, 16000, 18000-s, using the existing unified parallel machine scheduling problem approximation algorithm with an approximation degree of 2(1+ε), where ε is a search precision greater than 0, and here, the search precision ε=4. Because only scenarios that must have a solution after preprocessing are considered, the other parameters do not need to be involved. Next, the values ​​of key parameters will be changed to explore their impact on the algorithm. Each measurement value is the average of more than 100 random topologies.

[0200] like Figure 7 As shown in the figure, the total cost of all algorithms increases with the number of drones. The drone parallel cooperative charging scheduling algorithm with cost optimization benefits always outputs the lowest total cost. Specifically, the drone parallel cooperative charging scheduling algorithm with cost optimization benefits reduces the total cost by 58.17%, 55.74% and 22.16% on average compared with the other three comparison algorithms.

[0201] like Figure 8As shown in the figure, the total cost of all algorithms decreases with the increase of the number of charging stations. This is because with more charging stations, drones can move to closer or cheaper charging stations. The cost-optimized drone parallel cooperative charging scheduling algorithm reduces the total cost by 58.61%, 55.72%, and 29.02% on average compared with the other three comparison algorithms.

[0202] like Fig. 9 As shown in the figure, as the number of charging facilities in each charging station increases, the total cost of all algorithms decreases. This is because as the number of charging facilities increases, the number of charging queues increases, resulting in a reduction in the maximum charging time. The cost-optimized UAV parallel cooperative charging scheduling algorithm reduces the total cost by an average of 52.45%, 49.22%, and 23.14% respectively compared with the other three comparison algorithms.

[0203] like Fig.10 As shown in the figure, as the charging power increases, the total cost of all algorithms decreases. This is because the higher the charging power, the shorter the charging time for charging the same amount of energy. The cost-optimized UAV parallel cooperative charging scheduling algorithm reduces the total cost by 58.38%, 55.18% and 27.83% on average compared with the other three comparison algorithms.

[0204] like Fig.11 As shown in the figure, as the energy demand increases, the total cost of all algorithms increases accordingly. The UAV parallel cooperative charging scheduling algorithm with cost optimization benefits reduces the total cost by 58.95%, 55.88% and 29.74% on average compared with the other three comparison algorithms.

[0205] like Fig.12 As shown in the figure, with the increase of unit moving energy consumption, the total cost of all algorithms is increasing. The UAV parallel cooperative charging scheduling algorithm with cost optimization benefit reduces the total cost by 59.81%, 56.60% and 31.07% respectively on average compared with the other three comparison algorithms.

[0206] like Fig.13 As shown in the figure, with the increase of the recharge unit price, the total cost of all algorithms increases accordingly. The UAV parallel cooperative charging scheduling algorithm with cost optimization benefits reduces the total cost by 54.60%, 52.61% and 23.10% respectively on average compared with the other three comparison algorithms. In addition, from a j ∈,0.0008,0.001- / s to a j∈, 0.0018, 0.002- / s, the total costs of the three comparison algorithms increased by 102.12%, 87.69% and 94.36% respectively, while the growth rate of the UAV parallel cooperative charging scheduling algorithm with cost optimization benefit was only 44.50%. This shows that the algorithm in the present invention has a good resistance to market price fluctuations. Even if the recharge unit price rises sharply, the UAV parallel cooperative charging scheduling algorithm with cost optimization benefit can also suppress the increase in total cost as much as possible.

[0207] From the above embodiments, it can be seen that the technical solution of the present invention achieves the following technical effects: a charging allocation plan is determined for each drone to minimize the total cost of all charging groups, so that each drone is assigned to a charging facility and its charging needs are met.

[0208] The above are only a few preferred embodiments of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for parallel cooperative charging scheduling of unmanned aerial vehicles, characterized in that: The following steps are involved: S1 defines a set of charging stations, a set of drones, and a set of target locations; S2 defines the parallel cooperative charging scheduling pricing model; S3 formalizes the UAV parallel cooperative charging scheduling problem; S4 calls the UAV parallel cooperative charging scheduling algorithm with cost optimization benefits to obtain the charging allocation plan for the UAVs; The S1 charging station set, drone set and target location set include: Charging station set M = {s1, s2, ..., s m }, where m is the number of charging stations, and each charging station s j ∈M, there is a set of charging facilities The corresponding set of facility receiving powers is where q j is the number of charging facilities in the charging station; each charging facility allows multiple drones to queue up for charging, and a charging station forms multiple parallel charging queues; drone collection N={o1,o2,…,o n }, target location set L = {l1,l2,…,l n }, where n is the number of drones, and each drone o i ∈N and a target position l i ∈L corresponds to the need to reach the target location after charging, and there is a charging demand E i , remaining power Unit moving energy consumption i , energy limit From drone o i Location to charging stations j The distance is ||o i s j ||, from charging station s j To the target location i The distance is ||s j l i ||; The S2 parallel cooperative charging scheduling pricing model includes: Charging Stations j Need to give drone o i The actual energy replenishment for charging is calculated as: E i (s j )=E i +b i (||o i s j ||+||s j l i ||)(1) Let G j For j The charging group, that is, the charging station s j Charging drone collection; use To represent G j The charging arrangement of the drone, including k=1,2,...,q j , which is provided by the charging facilities A collection of drones being charged; a charging facility The charging time is: For charging pack G j With charging arrangement φ j , the charging time of the charging group is the maximum charging time of all charging facilities in the charging arrangement: When the charging arrangement is φ j When charging group G j The cost is defined as: Among them, A j For charging stations j The starting price, a j For charging stations j The recharge unit price is T j For charging stations j The charging time threshold, according to the actual starting charging price rules, is: A j ≥a j T j (5); The S3 formalizes the UAV parallel cooperative charging scheduling problem, including: Using 0-1 variables Indicates whether the drone o i Belongs to the collection If it belongs to otherwise Among them, formula (6) aims to minimize the total cost, constraint (6-1) ensures that all drones are charged and can only get charging services from one charging facility at a time, and constraint (6-2) ensures that is a 0-1 variable; In order to ensure the existence of a feasible solution to the problem, the following conditions need to be met: Condition 1: The remaining energy of any drone should be enough to reach the farthest charging station, i.e. Condition 2: The energy requirements of any drone can be met after reaching the target location, that is: If conditions 1 and 2 cannot be met, preprocessing is performed before solving the problem, and the preprocessing includes: removing drones that do not meet constraints (7) and (8) from the drone set N; The S4 calls a UAV parallel cooperative charging scheduling algorithm with cost optimization benefits to obtain a charging allocation plan for the UAVs, including: A1: Input parameters: charging station set M, drone set N, target location set L, each drone o i Charging demand E i , unit moving energy consumption b i , each charging station j The recharge price of j , starting price A j , time threshold T j , Charging facility set F j , charging facility power set P j ; A2: Initialize the set of drones of all charging groups and charging facilities, that is, for all s j ∈M, let For all make A3: Define the uncovered drone set N′ and initialize N′=N; A4: If Execute steps A5 to A7, otherwise execute step A8; A5: For each s j ∈M, find the charging group G′ that minimizes the average marginal cost j And corresponding charging arrangement φ′ j ; A6: Find the charging station with the lowest average marginal cost among all charging stations, that is, where s j′ For any charging station in M, G′ j′ For j′ Extended charging pack, G j′ For j′ Charging group before expansion, φ′ j′ For j′ Extended charging arrangement, φ j′ For j′ charging arrangements prior to expansion; A7: Update G j =G′ j ,φ j =φ′ j , G′ j The UAV is deleted from N′, that is, N′=N′\G′ j , return to step A4; A8: Return all (G j ,φ j ); For each s j ∈M, find the charging group G′ that minimizes the average marginal cost j And corresponding charging arrangement φ′ j ,include: A5.1: Define the number of drones w added, and the charging group G after adding w drones j (w), and the temporary uncovered drone set N * , initialize w = 0, G j (w) = G j , N * =N′; A5.2: If w<|N′|, execute steps A5.3 to A5.6, otherwise execute step A5.7; A5.3: Take out N * Medium E i (s j )The smallest drone, Among them i′ N * Any drone in the game; A5.4: From N * Delete o i , that is, N * =N * \{o i }; A5.5: Let w = w + 1, G j (w) = G j (w - 1) ∪ {o i}; A5.6: G j (w) and P j As input, call the existing approximate algorithm for the unified parallel machine scheduling problem, find the approximate optimal parallel scheduling result, and record it as φ j (w), return to step A5.2; A5.7: Find the case where the average marginal cost is minimized when w>0 and return it as the result, that is, 2. A UAV parallel charging control device, characterized in that: include: 1) one or more processors; 2) a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in claim 1.

3. A drone parallel charging network, characterized in that: Contains the drone parallel charging control device as described in claim 2.

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