An integrated optimization system for the coverage area of ​​UAV take-off and landing points

Abstract

The present invention relates to the field of drone technology, and in particular to a drone take-off and landing point coverage area integration and optimization system, comprising a processor and a memory storing a computer program, a target traffic cell set, a first take-off and landing point weight set, a to-be-merged traffic cell set, a second take-off and landing point weight set, a third traffic flow set, and a first geographic distance set. When the computer program is executed by the processor, a first merged traffic cell and candidate traffic cells are screened out using a traffic flow threshold and a first geographic distance threshold. The first matching degree between the first merged traffic cell and the candidate traffic cell is measured in combination with the third traffic flow and the first geographic distance to determine the merging object of the first merged traffic cell, thereby improving the rationality of regional merging. By redividing the area covered by drone take-off and landing facilities, the distribution of logistics delivery tasks in each area is clarified, thereby improving the efficiency and rationality of drone logistics distribution.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to an integrated optimization system for UAV take-off and landing point coverage areas. Background Art

[0002] Currently, urban ground transportation in China faces significant pressure, with congestion and frequent accidents. However, drones suitable for urban logistics weigh only approximately 15kg and can reach speeds of up to 40km / h. Their routes do not conflict with ground traffic, and they can autonomously complete their deliveries while being remotely monitored by professionals. These drones are suitable for delivering express and food delivery services within urban areas. Based on urban takeoff and landing facilities, drones can be used to deliver logistics within their coverage areas, alleviating some of the pressure on ground transportation, optimizing the work structure of logistics delivery personnel, and meeting the high-value, small-volume, time-sensitive logistics needs of urban residents for express delivery, food delivery, pharmaceuticals, and other high-quality goods. Properly integrating the areas covered by each drone's takeoff and landing points is crucial for improving drone efficiency.

[0003] Existing technologies typically combine two or more small, closely spaced traffic zones into a larger area based on their area and the distance between them. However, this approach struggles to accurately measure the relationships between traffic zones, making it difficult to clearly define the distribution of logistics delivery tasks within each zone. This can lead to wasted drone resources and reduced operational efficiency.

[0004] Therefore, how to improve the integration rationality of the coverage area of ​​UAV take-off and landing points, and thus improve the operating efficiency of UAVs, has become an urgent problem to be solved. Summary of the Invention

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is a system for integrating and optimizing the coverage area of ​​UAV take-off and landing points. The system comprises a processor and a memory storing a computer program. The memory also stores a set of target traffic cells QY corresponding to the target UAV take-off and landing points = {QY1, QY2, ..., QY ψ ,……,QY Щ}、The weight set Q of the first take-off and landing point 1 ={Q 1 1. Q 1 2, ..., Q 1 ψ ,……,Q 1 Щ}、N-Щ non-target UAV take-off and landing points corresponding to the set of traffic cells to be merged JT={JT1,JT2,……,JT ω ,……,JT N-Щ}, the second take-off and landing point weight set Q2 ={Q 2 1. Q 2 2, ..., Q 2 ω ,……,Q 2 N-Щ}, the third traffic flow set CX = {CX1, CX2, ..., CX ω ,……,CX N-Щ} and the first geographical distance set DL = {DL1, DL2, ..., DL ω ,……,DL N-Щ}, where QY ψ It refers to the target traffic area corresponding to the ψth target UAV take-off and landing point, Q 1 ψ Refers to QY ψ The corresponding first take-off and landing point weight, JT ω It refers to the traffic area to be merged corresponding to the ωth non-target UAV take-off and landing point, Q 2 ω JT ω The corresponding second take-off and landing point weight, CX ω ={CX ω1 , CX ω2 ,……,CX ωψ ,……,CX ωЩ}, CX ωψ It refers to the third traffic flow between the ωth traffic area to be merged and the ψth target traffic area, DL ω ={DL ω1 , DL ω2 ,……,DL ωψ ,……,DL ωЩ}, DL ωψ It refers to the first geographical distance between the ωth traffic cell to be merged and the ψth target traffic cell, ψ=1, 2, ..., Щ, Щ refers to the total number of target UAV take-off and landing points, ω=1, 2, ..., N-Щ, N-Щ refers to the total number of non-target UAV take-off and landing points. When the computer program is executed by the processor, the following steps are implemented:

[0006] S101, according to Q 1 , Q 2 , CX and DL, obtain the traffic flow threshold CX 0 and the first geographic distance threshold DL 0 , among which, CX 0 and DL 0 Meet the following conditions:

[0007] CX 0 =(Σ ω＝1 N-Щ Σψ＝1 Щ ((Q 1 ψ +Q 2 ω )×CX ωψ / 2)) / (Щ×(N-Щ)).

[0008] DL 0 =(Σ ω＝1 N-Щ Σ ψ＝1 Щ ((Q 1 ψ +Q 2 ω )×DL ωψ / 2)) / (Щ×(N-Щ)).

[0009] S102, if CX ωψ >CX 0 And DL ωψ <DL 0 , the ωth traffic cell to be merged is taken as the first merged traffic cell, and the ψth target traffic cell is determined as the candidate traffic cell corresponding to the current first merged traffic cell.

[0010] S103 , traverse ψ=1, 2, ..., Щ, and obtain Γ(ω) candidate traffic cells of the current first merged traffic cell.

[0011] S104, according to CX ω and DL ω , get the first matching degree set PP 1 ω ={PP 1 ω1 , PP 1 ω2 ,……,PP 1 ωЧ ,……,PP 1 ωΓ(ω)}, where Ч=1, 2, ..., Γ(ω), Γ(ω) refers to the total number of candidate traffic cells of the current first merged traffic cell, the first matching degree PP between the current first merged traffic cell and the Чth candidate traffic cell 1 ωЧ Meet the following conditions:

[0012] PP 1 ωЧ =CX 1 ωЧ ×e^(-DL 1 ωЧ ), where CX 1ωЧ Refers to the third traffic flow between the current first merged traffic area and the Чth candidate traffic area, DL 1 ωЧ It refers to the first geographical distance between the current first merged traffic cell and the Чth candidate traffic cell, e refers to the natural constant, CX 1 ωЧ ∈CX ω , DL 1 ωЧ ∈DL ω .

[0013] S105, merge the current first merged traffic zone into max(PP 1 ω ) corresponds to the target traffic cell corresponding to the candidate traffic cell, where max() refers to the maximum value function.

[0014] S106 , traversing ω=1, 2, ..., N-Щ, and determining the target traffic cell merged with the first merged traffic cell as the first reference coverage area of ​​the UAV.

[0015] The present invention has at least the following beneficial effects: 1 , Q 2 , CX and DL, obtain the traffic flow threshold CX 0 and the first geographic distance threshold DL 0 , if CX ωψ >CX 0 And DL ωψ <DL 0 , then the ωth traffic cell to be merged is taken as the first merged traffic cell, and the ψth target traffic cell is determined as the candidate traffic cell corresponding to the current first merged traffic cell. Traverse ψ=1,2,……,Щ, and obtain Γ(ω) candidate traffic cells of the current first merged traffic cell. According to CX ω and DL ω , get the first matching degree set PP 1 ω ={PP 1 ω1 , PP 1 ω2 ,……,PP 1 ωЧ ,……,PP 1 ωΓ(ω)}, merge the current first merged traffic zone into max(PP 1 ω) in the target traffic cell corresponding to the candidate traffic cell, traverse ω = 1, 2, ..., N-Щ, and determine the target traffic cell merged with the first merged traffic cell as the first reference coverage area of ​​the drone. It can be seen that through the traffic flow threshold CX 0 and the first geographic distance threshold DL 0 The first merged traffic cell to be merged into the target traffic cell is screened out, and the candidate traffic cell corresponding to the first merged traffic cell is determined. The third traffic flow and the first geographical distance between the first merged traffic cell and the candidate traffic cell are further combined to measure the first matching degree between the first merged traffic cell and the candidate traffic cell to screen the merging object of the first merged traffic cell, thereby improving the rationality of the merging of the first merged traffic cell, and thus re-dividing the area covered by each drone take-off and landing facility to clarify the distribution of logistics delivery tasks in each area, thereby improving the efficiency and rationality of drone logistics distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of a computer program for executing a system for integrating and optimizing the coverage area of ​​a UAV take-off and landing point provided in the first embodiment of the present invention;

[0018] Figure 2 A flowchart of a computer program for executing a system for selecting a take-off and landing point for a UAV based on the priority of candidate take-off and landing points provided in the second embodiment of the present invention;

[0019] Figure 3 A flowchart of a method for determining the priority of candidate take-off and landing points for unmanned aerial vehicles based on a complex network model provided in Example 3 of the present invention;

[0020] Figure 4 A flowchart of a computer program for executing a complex network model construction system for selecting a take-off and landing point for a UAV provided in the fourth embodiment of the present invention;

[0021] Figure 5 This is a flowchart of a method for generating an initial site selection for a UAV take-off and landing point based on multiple factors provided in Example 5 of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It is understood that, where appropriate, the above-mentioned terms used to distinguish similar objects can be interchanged so that the present invention can also implement other embodiments other than the above-mentioned illustrated embodiments or described embodiments. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] Example 1

[0025] This embodiment provides a system for integrating and optimizing the coverage area of ​​drone take-off and landing points. Figure 1 As shown, the UAV take-off and landing point coverage area integration optimization system includes a processor and a memory storing a computer program, and the memory also stores a target traffic cell set QY corresponding to the target UAV take-off and landing points = {QY1, QY2, ..., QY ψ ,……,QY Щ}、The weight set Q of the first take-off and landing point 1 ={Q 1 1. Q 1 2, ..., Q 1 ψ ,……,Q 1 Щ}、N-Щ non-target UAV take-off and landing points corresponding to the set of traffic cells to be merged JT={JT1,JT2,……,JT ω ,……,JT N-Щ}, the second take-off and landing point weight set Q 2 ={Q 2 1. Q 2 2, ..., Q 2 ω ,……,Q 2 N-Щ}, the third traffic flow set CX = {CX1, CX2, ..., CX ω ,……,CX N-Щ} and the first geographical distance set DL = {DL1, DL2, ..., DL ω ,……,DL N-Щ}, where QY ψ It refers to the target traffic area corresponding to the ψth target UAV take-off and landing point, Q 1 ψ Refers to QY ψ The corresponding first take-off and landing point weight, JT ω It refers to the traffic area to be merged corresponding to the ωth non-target UAV take-off and landing point, Q 2 ω JT ω The corresponding second take-off and landing point weight, CX ω ={CX ω1 , CX ω2 ,……,CX ωψ ,……,CX ωЩ}, CX ωψ It refers to the third traffic flow between the ωth traffic area to be merged and the ψth target traffic area, DL ω ={DL ω1 , DL ω2 ,……,DL ωψ ,……,DL ωЩ}, DL ωψ It refers to the first geographical distance between the ωth traffic cell to be merged and the ψth target traffic cell, ψ = 1, 2, ..., Щ, Щ refers to the total number of target UAV take-off and landing points, ω = 1, 2, ..., N-Щ, N-Щ refers to the total number of non-target UAV take-off and landing points.

[0026] Traffic zones are defined as geographic units divided into a study area based on traffic analysis and traffic demand forecasting models. They are the smallest spatial units for analyzing the travel and distribution of residents and vehicles. In this embodiment, initial drone landing and takeoff points can be designated as locations for drone landing and takeoff facilities within the geographic area where drone landing and takeoff facilities are located. Each initial drone landing and takeoff point covers drone logistics delivery tasks within the corresponding traffic zone.

[0027] Based on relevant data such as the building conditions, population conditions, traffic flow conditions, distances between the initial drone take-off and landing points, and connection relationships near each initial drone take-off and landing point, the initial drone take-off and landing points can be divided into target drone take-off and landing points and non-target drone take-off and landing points. Among them, the target drone take-off and landing point may refer to a screened take-off and landing point with a higher priority for setting up drone take-off and landing facilities, and the non-target drone take-off and landing point may refer to a take-off and landing point with a lower priority for setting up drone take-off and landing facilities.

[0028] This embodiment directly obtains the target drone take-off and landing points and non-target drone take-off and landing points, and uses the traffic area covered by the target drone take-off and landing point as the target traffic area. By setting up drone take-off and landing facilities at the target drone take-off and landing point, drones can be used to undertake logistics delivery tasks within the target traffic area. However, since the traffic areas corresponding to non-target drone take-off and landing points do not have drone take-off and landing facilities, it is unclear which drones can undertake logistics delivery tasks within the traffic areas corresponding to non-target drone take-off and landing points.

[0029] Therefore, this embodiment takes the traffic cells corresponding to the non-target UAV take-off and landing points as the traffic cells to be merged. Based on the relevant data such as the traffic flow and distance between the target traffic cells and the traffic cells corresponding to the non-target UAV take-off and landing points, the traffic cells corresponding to the non-target take-off and landing points are merged into the target traffic cells, and the scope of the traffic cells covered by each UAV take-off and landing facilities is re-divided to clarify the distribution of logistics delivery tasks in each traffic cell and improve the efficiency and rationality of UAV logistics distribution.

[0030] Among them, the first take-off and landing point weight can represent the possibility of setting up drone take-off and landing facilities at the corresponding target drone take-off and landing point, the second take-off and landing point weight can represent the possibility of setting up drone take-off and landing facilities at the corresponding non-target drone take-off and landing point, the third traffic flow can refer to the traffic flow between the traffic area to be merged and the target traffic area within a preset time period, and the first geographical distance can refer to the distance between the regional center point of the traffic area to be merged and the regional center point of the target traffic area.

[0031] When the computer program is executed by a processor, the following steps are performed:

[0032] S101, according to Q 1 , Q 2 , CX and DL, obtain the traffic flow threshold CX 0 and the first geographic distance threshold DL 0 , among which, CX 0 and DL 0 Meet the following conditions:

[0033] CX 0 =(Σ ω＝1 N-Щ Σ ψ＝1 Щ ((Q 1 ψ +Q 2 ω )×CX ωψ / 2)) / (Щ×(N-Щ)).

[0034] DL 0 =(Σ ω＝1N-Щ Σ ψ＝1 Щ ((Q 1 ψ +Q 2 ω )×DL ωψ / 2)) / (Щ×(N-Щ)).

[0035] Among them, the weighted average of the third traffic flow between the traffic area to be merged and the target traffic area is determined as the traffic flow threshold CX by combining the weight of the first take-off and landing point and the weight of the second take-off and landing point. 0 The weighted average of the first geographical distances between the traffic cell to be merged and the target traffic cell is determined as the first geographical distance threshold DL 0 , as the data basis for merging the traffic area to be merged and the target traffic area.

[0036] In a specific embodiment, the memory also stores a preset prediction model, an initial drone take-off and landing point set A={A1, A2, ..., A n ,……,A N}, Initial UAV take-off and landing point code set A 1 ={A 1 1, A 1 2, ..., A 1 n ,……,A 1 N}、Predicted flight route set B={B1,B2,……,B n ,……,B N}、The take-off and landing point weight set Q={Q1,Q2,……,Q n ,……,Q N}、Route weight set P={P1,P2,……,P n ,……,P N}、Node betweenness set JS={JS1,JS2,……,JS n ,……,JS N}、Preset key take-off and landing point code set GJ={GJ1,GJ2,……,GJ μ ,……,GJ M} and preset reference vector YS=(1,1,……,1,0,0,……,0), where A n Refers to the nth initial drone take-off and landing point, A 1 n Refers to A n The corresponding initial take-off and landing point code, B n ={B n1 , B n2 ,……,Bni ,……,B nI(n)}, B ni Refers to A n The corresponding i-th predicted flight route, Q n Refers to A n The corresponding take-off and landing point weight, P n ={P n1 , P n2 ,……,P ni ,……,P nI(n)}, P ni Refers to B ni The corresponding route weight, JS n Refers to A n The corresponding node betweenness, GJ μ Refers to the key take-off and landing point code corresponding to the preset μth key take-off and landing point, n = 1, 2, ..., N, N refers to the total number of initial UAV take-off and landing points, i = 1, 2, ..., I(n), I(n) refers to the total number of predicted flight routes corresponding to the nth initial UAV take-off and landing point, μ = 1, 2, ..., M, M refers to the total number of preset key take-off and landing points, the first Щ elements in YS are value 1, and the last N-Щ elements are value 0. The Щ target UAV take-off and landing points are obtained by the following steps:

[0037] S100: Input A, B, Q, P and JS into the preset prediction model to obtain the prediction priority ranking vector PX=(PX1, PX2, ..., PX n ,……,PX N ), where PX n It refers to the initial UAV take-off and landing point with the predicted priority of n.

[0038] There are flight routes suitable for drones transporting cargo between the initial drone take-off and landing points. The locations and flight paths of the initial drone take-off and landing points can be determined by the implementer based on actual circumstances. The initial drone take-off and landing point codes can be pre-set by the implementer based on actual circumstances to identify the initial drone take-off and landing points, such as 1, 2, 3, etc.

[0039] The take-off and landing point weight can represent the possibility of setting up drone take-off and landing facilities at the corresponding initial drone take-off and landing point, the route weight can represent the possibility of the flight route as the flight path of the drone, and the node betweenness can represent the influence of the corresponding initial drone take-off and landing point relative to other initial drone take-off and landing points.

[0040] Key take-off and landing points can be the initial drone take-off and landing points provided by the implementer, where drone take-off and landing facilities must be installed. These points can be determined by professionals based on the actual conditions of each initial drone take-off and landing point and based on manual experience. This serves as a prerequisite for selecting drone take-off and landing points. This means that the predicted priorities of the M key take-off and landing points in the final predicted priority ranking vector are in the top 10. Preset key take-off and landing point codes can be pre-set by the implementer based on actual conditions to identify key take-off and landing points, such as -1, -2, -3, etc.

[0041] The preset prediction model is used to extract and map the input unordered initial UAV take-off and landing points, flight routes, node betweenness, take-off and landing point weights, and route weight data, and output the predicted priority ranking vector of the ordered initial UAV take-off and landing points.

[0042] The preset reference vector YS = (1, 1, ..., 1, 0, 0, ..., 0), the first N elements in YS are the value 1, and the last N-N elements are the value 0, which is used to process the prediction priority sorting vector, and extract the first N elements in the prediction priority sorting vector as the evaluation of the degree of match between the sorting result of the prediction priority sorting vector and the premise condition.

[0043] The higher the priority ranking, the higher the possibility that the corresponding initial UAV take-off and landing point will be used as the target UAV take-off and landing point for setting up UAV take-off and landing facilities.

[0044] S200, perform vector multiplication on PX and YS to obtain the take-off and landing point reference vector CK 1 =(PX1, PX2, ..., PX σ ,……,PX Щ ,0,0,……,0), where PX σ Refers to the initial UAV take-off and landing point with the predicted priority of σ, PX Щ It refers to the initial UAV take-off and landing point with the predicted priority of Щ, σ=1, 2, …, Щ.

[0045] Among them, based on the task of screening out the target UAV take-off and landing points, vector multiplication of PX and YS is performed to obtain the take-off and landing point reference vector CK 1 =(PX1, PX2, ..., PX σ ,……,PX Щ , 0, 0, ..., 0), in order to extract the initial UAV take-off and landing points with the top prediction priority as the basis for measuring the sorting effect of the prediction priority sorting vector PX.

[0046] S300, according to CK 1 、A 1 and GJ, get CK1 The corresponding first take-off and landing point coding reference vector BM 1 =(BM1, BM2, ..., BM σ ,……,BM Щ ,0,0,……,0), where BM σ Refers to PX σ The corresponding first take-off and landing point code, BM Щ Refers to PX Щ The corresponding first take-off and landing point code.

[0047] Among them, according to the type of the extracted top Щ initial UAV take-off and landing points, that is, key take-off and landing points or non-key take-off and landing points, the first take-off and landing point code corresponding to each initial UAV take-off and landing point in the top Щ initial UAV take-off and landing points is obtained, so as to judge the degree of matching between the top Щ initial UAV take-off and landing points and the prerequisites through the coding, and provide a data basis for measuring the sorting effect of the predicted priority sorting vector PX.

[0048] In a specific implementation, the key take-off and landing point code is less than 0, and the initial take-off and landing point code is greater than 0.

[0049] As mentioned above, the first Щ initial UAV take-off and landing points are converted into corresponding take-off and landing point codes, which provides a data basis for judging the matching degree between the first Щ initial UAV take-off and landing points and the prerequisites through coding, and then measuring the sorting effect of the prediction priority sorting vector PX.

[0050] S400, transposed vector of GJ and BM 1 Multiply to obtain the second take-off and landing point coding reference vector BM 2 .

[0051] Among them, the matrix dimension of GJ is 1×M, and the transposed vector of GJ (GJ) T The dimension is M×1, BM 1 The matrix dimension is 1×N, then (GJ) T ×BM 1 The second take-off and landing point coding reference vector BM obtained 2 The matrix dimension is M×N.

[0052] Among them, (GJ) T The codes of each key take-off and landing point are less than 0, BM 1 The first take-off and landing point code of the corresponding key take-off and landing point is less than 0, BM 1 If the first take-off and landing point code of the corresponding non-critical take-off and landing point is greater than 0, then BM 1 The first take-off and landing point code of the corresponding key take-off and landing point is (GJ) T The product of the codes of each key take-off and landing point is greater than 0, BM 1The corresponding non-critical take-off and landing points in (GJ) T The product of the key take-off and landing point codes in is less than 0, so it can be achieved through BM 2 The number of elements greater than 0 in , reverse BM 1 The number of corresponding key take-off and landing points in , which further represents the matching degree between the corresponding prediction priority ranking vector PX and the prerequisite.

[0053] For example, BM 2 The number of elements greater than 0 in is M×Amount, then we can determine that BM 1 The corresponding number of key take-off and landing points is Amount.

[0054] S500, for BM 2 Perform binarization processing to obtain the third take-off and landing point coding reference vector BM 3 .

[0055] Among them, BM 2 Perform binary processing, set the elements greater than 0 to 1, and the elements less than or equal to 0 to 0, then BM 3 The data scale is normalized to avoid the influence of the value of each element on the prediction loss, thereby improving the accuracy of the prediction loss and further improving the accuracy of the judgment of the matching degree between the prediction priority sorting vector PX and the prerequisite.

[0056] S600, according to BM 3 And the preset encoding threshold YZ, the prediction loss Loss = ρ × (YZ-SUM (BM 3 )), where ρ is the preset loss coefficient, SUM(BM 3 ) means BM 3 The sum of all elements in .

[0057] Among them, the premise of this embodiment is that the prediction priorities corresponding to the M key take-off and landing points in the final prediction priority sorting vector belong to the top Щ position, SUM(BM 3 ) can characterize BM 3 The number of elements 1 in BM 1 The number of key take-off and landing points corresponding to , that is, the number of key take-off and landing points in the prediction priority sorting vector PX, and the more key take-off and landing points in the prediction priority sorting vector PX, the higher the degree of matching between the prediction priority sorting vector PX and the prerequisite.

[0058] SUM(BM 3 ) has a value range of [0, M 2 ], therefore, based on the prediction loss Loss = ρ × (YZ-SUM (BM 3)) is used to characterize the matching degree between the prediction priority ranking vector PX and the premise conditions, which serves as the basis for updating the parameters of the prediction model to improve the prediction accuracy of the prediction model.

[0059] In one embodiment, YZ=M 2 .

[0060] As mentioned above, increasing the prediction loss to characterize the degree of matching between the prediction priority ranking vector PX and the precondition provides a data basis for updating the parameters of the prediction model to improve the prediction accuracy of the prediction model.

[0061] S700: Update the parameters of the preset prediction model according to the Loss until the Loss converges and obtain the target prediction model.

[0062] S800: Input A, B, Q, P and JS into the target prediction model to obtain the target priority ranking vector.

[0063] S900: Determine the first Щ initial UAV take-off and landing points in the target priority sorting vector as target UAV take-off and landing points.

[0064] Among them, the target priority sorting vector obtained when Loss = 0 indicates that the number of key take-off and landing points in the predicted priority sorting vector PX is M, which means that the target priority sorting vector matches the prerequisite. Therefore, the first Щ initial drone take-off and landing points in the target priority sorting vector are determined as the target drone take-off and landing points, which serve as the location for setting up drone take-off and landing facilities to provide support for drone delivery logistics and other tasks.

[0065] In a specific embodiment, the memory also stores a take-off and landing point location importance set H={H1, H2, ..., H n ,……,H N}, the first traffic flow set C = {C1, C2, ..., C n ,……,C N} and the first building interference level set G = {G1, G2, ..., G n ,……,G N}, where H n Refers to A n The importance of the corresponding take-off and landing point position, C n Refers to A n The corresponding first traffic flow, G n Refers to A n The corresponding first building interference level and the take-off and landing point weight set Q are obtained by the following steps:

[0066] S1, according to H, C and G, obtain the take-off and landing point weight set Q = {Q1, Q2, ..., Q n,……,Q N}, where A n The corresponding take-off and landing point weight Q n Meet the following conditions:

[0067] Q n =α1×H n +α2×C n +α3×e^(-G n ), wherein α1 refers to the preset first position weight, α2 refers to the preset first flow weight, and α3 refers to the preset building interference weight.

[0068] Among them, the importance of the take-off and landing point location can be used to characterize the importance of the location of the corresponding initial UAV take-off and landing point in the UAV transportation mission. The first traffic flow can refer to the traffic flow within a certain range corresponding to the initial UAV take-off and landing point, which can be used to characterize the demand degree of the initial UAV take-off and landing point for UAV transportation of goods. The first building interference degree can be used to characterize the degree of interference of buildings near the initial UAV take-off and landing point on the take-off and landing of the UAV when the UAV takes off and lands at the corresponding initial UAV take-off and landing point.

[0069] Among them, the greater the importance of the take-off and landing point location corresponding to the initial drone take-off and landing point, the greater the first traffic flow, and the smaller the first building interference level, the higher the possibility of setting up drone take-off and landing facilities at the initial drone take-off and landing point.

[0070] Therefore, the importance of the take-off and landing point location and the first traffic flow are positively correlated with the take-off and landing point weight, and the first building interference degree is negatively correlated with the take-off and landing point weight. Based on H, C and G, the take-off and landing point weight set Q can be obtained as the basis for constructing a complex network model and selecting the location of the drone take-off and landing point.

[0071] The specific values ​​of α1, α2 and α3 can be set by the implementer according to actual conditions.

[0072] In the above, the influence of the importance of the take-off and landing point location, the first traffic flow and the first building interference level on the possibility of setting up drone take-off and landing facilities at the initial drone take-off and landing point is measured, and the take-off and landing point weight corresponding to each initial drone take-off and landing point is obtained, which improves the accuracy of the take-off and landing point weight, thereby improving the accuracy of the construction of the complex network model, the rationality of the site selection of the drone take-off and landing point, and the rationality of the division of traffic zones.

[0073] In a specific embodiment, the first take-off and landing point weight set Q 1 Obtain it through the following steps:

[0074] Extract the take-off and landing point weights corresponding to the first target drone take-off and landing points from Q, and obtain the first take-off and landing point weight set Q 1 ={Q1 1. Q 1 2, ..., Q 1 ψ ,……,Q 1 Щ}.

[0075] In a specific embodiment, the second take-off and landing point weight set Q 2 Obtain it through the following steps:

[0076] Extract the take-off and landing point weights corresponding to N-Щ non-target UAV take-off and landing points from Q, and obtain the first take-off and landing point weight set Q 1 ={Q 1 1. Q 1 2, ..., Q 1 ψ ,……,Q 1 Щ}.

[0077] In a specific embodiment, the memory further stores a route abnormality degree set D={D1, D2, ..., D n ,……,D N}、Route length set E={E1,E2,……,E n ,……,E N}, D n ={D n1 , D n2 ,……,D ni ,……,D nI(n)}, D ni Refers to B ni The corresponding route abnormality degree, E n ={E n1 , E n2 ,……,E ni ,……,E nI(n)}, E ni Refers to B ni The corresponding route length and route weight set P are obtained through the following steps:

[0078] S2, according to C, obtain the second traffic flow set F corresponding to B = {F1, F2, ..., F n ,……,F N}, where B n The corresponding second traffic flow list F n ={F n1 , F n2 ,……,F ni ,……,F nI(n)}, F ni Equal to B niThe average value of the first traffic flow of the two corresponding initial drone take-off and landing points.

[0079] Among them, the degree of route abnormality can be used to characterize the degree of damage to buildings, human life and property when a drone encounters a flight abnormality in the corresponding flight route.

[0080] The average value of the first traffic flow of the two initial drone take-off and landing points corresponding to each flight route can be used as the second traffic flow of the corresponding flight route to characterize the degree of protection of the corresponding flight route for drone transportation of goods, and serve as the basis for obtaining the route weight corresponding to the flight route.

[0081] S3, according to B and H, obtain the route position importance set K corresponding to B = {K1, K2, ..., K n ,……,K N}, where B n Corresponding route location importance list K n ={K n1 , K n2 ,……,K ni ,……,K nI(n)}, K ni Equal to B ni The average importance of the take-off and landing point locations of the two corresponding initial UAV take-off and landing points.

[0082] Among them, the average value of the take-off and landing point position importance of the two initial UAV take-off and landing points corresponding to each flight route can represent the route position importance of the corresponding flight route, which serves as the basis for obtaining the route weight corresponding to the flight route.

[0083] S4, according to B, D, E, F and K, obtain the route weight set P corresponding to B = {P1, P2, ..., P n ,……,P N}, where P n ={P n1 , P n2 ,……,P ni ,……,P nI(n)}, B ni The corresponding route weight P ni Meet the following conditions:

[0084] P ni =β1×e^(-D ni )+β2×E ni / ((∑ n＝1 N (Σ i＝1 I(n) E ni )) / (Σ n＝1N I(n)))+β3×F ni +β4×K ni , where β

[0085] 1 refers to the preset abnormality weight, β2 refers to the preset route length weight, β3 refers to the preset second traffic weight, and β4 refers to the preset second position weight.

[0086] Among them, the smaller the route abnormality corresponding to the flight route, the longer the route length, the more the second traffic flow, and the higher the importance of the route position, the higher the possibility that the flight route is the flight path of the drone.

[0087] Therefore, the route weight corresponding to the flight route is negatively correlated with the degree of route abnormality, and positively correlated with the route length, the second traffic flow, and the importance of the route location. Based on B, D, E, F, and K, the route weight set P corresponding to B can be obtained as the basis for constructing a complex network model and selecting the location of the UAV take-off and landing points.

[0088] The specific values ​​of β1, β2, β3 and β4 can be set by the implementer according to actual conditions.

[0089] In the above, the influence of the route abnormality degree, route length, second traffic flow and route location importance on the possibility of the flight route as the flight path of the UAV is measured, and the route weight corresponding to each flight route is obtained, which improves the accuracy of the route weight, thereby improving the accuracy of the construction of the complex network model, the rationality of the site selection of the UAV take-off and landing points, and the rationality of the division of traffic zones.

[0090] In a specific implementation, the node betweenness set JS is obtained by the following steps:

[0091] S5, based on A, B, Q and P, takes the initial UAV take-off and landing points as nodes, the flight routes as edges, the take-off and landing point weights corresponding to the initial UAV take-off and landing points as the node values ​​of the corresponding nodes, and the route weights corresponding to the flight routes as the edge values ​​of the corresponding edges, to obtain the complex network model T, where the complex network model T is used to screen the target UAV take-off and landing points from A.

[0092] S61: Based on T and E, obtain the shortest path between any two nodes in T.

[0093] S62, the number of shortest paths passing through the nodes corresponding to the nth initial UAV take-off and landing point is determined as the node betweenness JS corresponding to the nth initial UAV take-off and landing point n .

[0094] S63, traverse n=1, 2, ..., N, and obtain the node betweenness set JS={JS1, JS2, ..., JS n ,……,JS N}.

[0095] As mentioned above, the more shortest paths passing through the nodes corresponding to the nth initial UAV take-off and landing point, the greater the influence of the nth initial UAV take-off and landing point, and the higher the possibility of setting up UAV take-off and landing facilities at the initial UAV take-off and landing point.

[0096] S102, if CX ωψ >CX 0 And DL ωψ <DL 0 , the ωth traffic cell to be merged is taken as the first merged traffic cell, and the ψth target traffic cell is determined as the candidate traffic cell corresponding to the current first merged traffic cell.

[0097] Among them, if CX ωψ >CX 0 And DL ωψ <DL 0 , indicating that the logistics exchanges between the ωth traffic cell to be merged and the ψth target traffic cell are frequent, and the distance between the two traffic cells is short, and the connection between the ωth traffic cell to be merged and the ψth target traffic cell is close. If the ωth traffic cell to be merged is merged into the ψth target traffic cell, the drones corresponding to the target drone take-off and landing points in the ψth target traffic cell can jointly undertake the logistics delivery task between the ωth traffic cell to be merged and the ψth target traffic cell.

[0098] As described above, according to the third traffic flow and the traffic flow threshold CX 0 The comparison result of the first geographic distance and the first geographic distance threshold DL 0 Based on the comparison results, the first merged traffic cell that can be merged into the target traffic cell is screened out from the N-Щ traffic cells to be merged, and the candidate traffic cells corresponding to the first merged traffic cell are determined, providing a merging basis for the merging of traffic cells.

[0099] S103 , traverse ψ=1, 2, ..., Щ, and obtain Γ(ω) candidate traffic cells of the current first merged traffic cell.

[0100] S104, according to CX ω and DL ω , get the first matching degree set PP 1 ω ={PP 1 ω1 , PP 1ω2 ,……,PP 1 ωЧ ,……,PP 1 ωΓ(ω)}, where Ч=1, 2, ..., Γ(ω), Γ(ω) refers to the total number of candidate traffic cells of the current first merged traffic cell, the first matching degree PP between the current first merged traffic cell and the Чth candidate traffic cell 1 ωЧ Meet the following conditions:

[0101] PP 1 ωЧ =CX 1 ωЧ ×e^(-DL 1 ωЧ ), where CX 1 ωЧ Refers to the third traffic flow between the current first merged traffic area and the Чth candidate traffic area, DL 1 ωЧ It refers to the first geographical distance between the current first merged traffic cell and the Чth candidate traffic cell, e refers to the natural constant, CX 1 ωЧ ∈CX ω , DL 1 ωЧ ∈DL ω .

[0102] Among them, the greater the third traffic flow and the smaller the first geographical distance between the current first merged traffic cell and the candidate traffic cell, the more frequent the logistics between the current first merged traffic cell and the candidate traffic cell and the closer the connection, which in turn indicates that the first matching degree between the current first merged traffic cell and the candidate traffic cell is higher.

[0103] Therefore, the third traffic flow between the current first merged traffic cell and the candidate traffic cell is positively correlated with the first matching degree, and the first geographical distance between the current first merged traffic cell and the candidate traffic cell is negatively correlated with the first matching degree.

[0104] In the above, the first matching degree between the first merged traffic cell and the candidate traffic cell is measured in combination with the third traffic flow and the first geographical distance between the first merged traffic cell and the candidate traffic cell to screen the merging objects of the first merged traffic cell, thereby improving the merging rationality of the first merged traffic cell.

[0105] S105, merge the current first merged traffic zone into max(PP 1 ω) corresponds to the target traffic cell corresponding to the candidate traffic cell, where max() refers to the maximum value function.

[0106] S106 , traversing ω=1, 2, ..., N-Щ, and determining the target traffic cell merged with the first merged traffic cell as the first reference coverage area of ​​the UAV.

[0107] Among them, traverse ω=1, 2,..., N-Щ, screen out all the first merged traffic cells, and screen out the merging objects of each first merged traffic cell, complete the merging of all the first merged traffic cells, and determine each target traffic cell merged with the first merged traffic cell as the first reference coverage area of ​​the drone. By setting the drone take-off and landing settings within the first reference coverage area, all drone logistics delivery tasks in the first reference coverage area are undertaken, thereby increasing the delivery area of ​​the drone and improving the efficiency of logistics delivery.

[0108] In a specific embodiment, the UAV take-off and landing point coverage area integration optimization system further includes a first area set MJ 1 ={MJ 1 1. MJ 1 2, ..., MJ 1 ψ ,……,MJ 1 Щ} and the second region area set MJ 2 ={MJ 2 1. MJ 2 2, ..., MJ 2 ω ,……,MJ 2 N-Щ}, among which MJ 1 ψ Refers to QY ψ The corresponding first region area, MJ 2 ω JT ω The corresponding second region area, when the computer program is executed by the processor, further implements the following steps:

[0109] S107, according to MJ 1 and MJ 2 The area of ​​each first reference coverage area and the sum of the areas of all first merged traffic cells merged into each first reference coverage area are determined as the third area of ​​each first reference coverage area.

[0110] S108: Determine the unmerged traffic cells among the N-Щ traffic cells to be merged as the second merged traffic cells, and determine the unmerged target traffic cells among the Щ target traffic cells as the optional traffic cells.

[0111] S109 : Acquire, according to the DL, a second geographical distance between each second merged traffic cell and each first reference coverage area, and a third geographical distance between each second merged traffic cell and each optional traffic cell.

[0112] S1010. For any second merged traffic cell, obtain a second matching degree between the current second merged traffic cell and each first reference coverage area based on a second geographical distance between the current second merged traffic cell and each first reference coverage area and a third area of ​​each first reference coverage area.

[0113] S1011 : Acquire a third matching degree between the current second merged transportation cell and each optional transportation cell according to a third geographical distance between the current second merged transportation cell and each optional transportation cell and a first area of ​​each optional transportation cell.

[0114] S1012: Determine the first reference coverage area or optional traffic cell corresponding to the maximum value among all the second matching degrees and all the third matching degrees as the target merged cell corresponding to the current second merged traffic cell.

[0115] S1013: Merge the current second merged traffic cell into the target merged cell.

[0116] S1014: traverse all second merged traffic cells, and determine the target merged cell merged with the second merged traffic cell as the second reference coverage area of ​​the UAV.

[0117] The area of ​​the first region refers to the area of ​​the corresponding target traffic zone, and the area of ​​the second region refers to the area of ​​the corresponding traffic zone to be merged.

[0118] On the basis of merging the first merged traffic cell into the corresponding target traffic cell and obtaining the corresponding first reference coverage area, the unmerged traffic cells to be merged are merged into the first reference coverage area or the unmerged target traffic cell according to the distance, area and other relevant data between the remaining unmerged traffic cells to be merged and the first reference coverage area and the unmerged target traffic cell, so as to complete the merging of various traffic cells in the entire geographical area, thereby re-dividing the area covered by each drone take-off and landing facility to clarify the distribution of logistics delivery tasks in each area and improve the efficiency and rationality of drone logistics distribution.

[0119] Specifically, the second geographical distance between the second merged traffic cell and the first reference coverage area may refer to the distance between the center point of the second merged traffic cell and the center point of the first reference coverage area, and the third geographical distance between the second merged traffic cell and the optional traffic cell may refer to the distance between the center point of the second merged traffic cell and the center point of the optional traffic cell.

[0120] A second geographical distance between the second merged traffic cell and the first reference coverage area is positively correlated with a second degree of matching between the second merged traffic cell and the first reference coverage area. A third geographical distance between the second merged traffic cell and the optional traffic cell is positively correlated with a third degree of matching between the second merged traffic cell and the optional traffic cell.

[0121] In order to balance the logistics distribution task intensity and logistics distribution efficiency of the drones corresponding to each drone take-off and landing facility, this embodiment prefers to merge the second merged traffic cell into the first reference coverage area or optional traffic cell with a smaller area to balance the area size of each merged area.

[0122] Therefore, the second matching degree between the second merged traffic cell and the first reference coverage area is positively correlated with the third area corresponding to the first reference coverage area. The third matching degree between the second merged traffic cell and the optional traffic cell is positively correlated with the first area corresponding to the optional traffic cell.

[0123] Combined with the second geographical distance and the third area, the second matching degree between the second merged traffic cell and each first reference coverage area is obtained. Combined with the third geographical distance and the first area, the third matching degree between the current second merged traffic cell and each optional traffic cell is obtained. Then, based on the sizes of all the second matching degrees and the third matching degrees, the merging object of the second merged traffic cell can be determined, and the target merged cell after merging with the second merged traffic cell can be determined as the second reference coverage area of ​​the drone. By setting up drone take-off and landing facilities in the second reference coverage area, all drone logistics delivery tasks in the second reference coverage area can be undertaken, thereby increasing the delivery area of ​​the drone and improving the efficiency of logistics delivery.

[0124] As described above, on the basis of merging the first merged traffic cell into the corresponding target traffic cell and obtaining the corresponding first reference coverage area, the unmerged traffic cells to be merged are merged into the first reference coverage area or the unmerged target traffic cell according to the distance, area and other relevant data between the remaining unmerged traffic cells to be merged and the first reference coverage area and the unmerged target traffic cell, thereby completing the merger of various traffic cells in the entire geographical area, thereby re-dividing the area covered by each drone take-off and landing facility, so as to clarify the distribution of logistics delivery tasks in each area and improve the efficiency and rationality of drone logistics distribution.

[0125] According to Q 1 , Q 2 , CX and DL, obtain the traffic flow threshold CX 0 and the first geographic distance threshold DL 0 , if CX ωψ >CX 0 And DL ωψ <DL 0 , then the ωth traffic cell to be merged is taken as the first merged traffic cell, and the ψth target traffic cell is determined as the candidate traffic cell corresponding to the current first merged traffic cell. Traverse ψ=1,2,……,Щ, and obtain Γ(ω) candidate traffic cells of the current first merged traffic cell. According to CX ω and DL ω , get the first matching degree set PP 1 ω ={PP 1 ω1 , PP 1 ω2 ,……,PP 1 ωЧ ,……,PP 1 ωΓ(ω)}, merge the current first merged traffic zone into max(PP 1 ω ) in the target traffic cell corresponding to the candidate traffic cell, traverse ω = 1, 2, ..., N-Щ, and determine the target traffic cell merged with the first merged traffic cell as the first reference coverage area of ​​the drone. It can be seen that through the traffic flow threshold CX 0 and the first geographic distance threshold DL 0The first merged traffic cell to be merged into the target traffic cell is screened out, and the candidate traffic cell corresponding to the first merged traffic cell is determined. The third traffic flow and the first geographical distance between the first merged traffic cell and the candidate traffic cell are further combined to measure the first matching degree between the first merged traffic cell and the candidate traffic cell to screen the merging object of the first merged traffic cell, thereby improving the rationality of the merging of the first merged traffic cell, and thus re-dividing the area covered by each drone take-off and landing facility to clarify the distribution of logistics delivery tasks in each area, thereby improving the efficiency and rationality of drone logistics distribution.

[0126] Example 2

[0127] This embodiment 2 provides a drone take-off and landing point selection system based on the priority of candidate take-off and landing points, such as Figure 2 As shown, the drone take-off and landing point selection system based on the take-off and landing point candidate priority includes a processor and a memory storing a computer program, and the memory also stores a preset prediction model, an initial drone take-off and landing point set A = {A1, A2, ..., A n ,……,A N}, Initial UAV take-off and landing point code set A 1 ={A 1 1, A 1 2, ..., A 1 n ,……,A 1 N}、Predicted flight route set B={B1,B2,……,B n ,……,B N}、The take-off and landing point weight set Q={Q1,Q2,……,Q n ,……,Q N}、Route weight set P={P1,P2,……,P n ,……,P N}、Node betweenness set JS={JS1,JS2,……,JS n ,……,JS N}、Preset key take-off and landing point code set GJ={GJ1,GJ2,……,GJ μ ,……,GJ M} and preset reference vector YS=(1,1,……,1,0,0,……,0), where A n Refers to the nth initial drone take-off and landing point, A 1 n Refers to A n The corresponding initial take-off and landing point code, B n ={B n1 , B n2 ,……,B ni,……,B nI(n)}, B ni Refers to A n The corresponding i-th predicted flight route, Q n Refers to A n The corresponding take-off and landing point weight, P n ={P n1 , P n2 ,……,P ni ,……,P nI(n)}, P ni Refers to B ni The corresponding route weight, JS n Refers to A n The corresponding node betweenness, GJ μ It refers to the key take-off and landing point code corresponding to the preset μth key take-off and landing point, n = 1, 2, ..., N, N refers to the total number of initial UAV take-off and landing points, i = 1, 2, ..., I(n), I(n) refers to the total number of predicted flight routes corresponding to the nth initial UAV take-off and landing point, μ = 1, 2, ..., M, M refers to the total number of preset key take-off and landing points, the first Щ elements in YS are value 1, and the last N-Щ elements are value 0, Щ refers to the preset number of target UAV take-off and landing points.

[0128] When the computer program is executed by a processor, the following steps are performed:

[0129] S100: Input A, B, Q, P and JS into the preset prediction model to obtain the prediction priority ranking vector PX=(PX1, PX2, ..., PX n ,……,PX N ), where PX n It refers to the initial UAV take-off and landing point with the predicted priority of n.

[0130] S200, perform vector multiplication on PX and YS to obtain the take-off and landing point reference vector CK 1 =(PX1, PX2, ..., PX σ ,……,PX Щ ,0,0,……,0), where PX σ Refers to the initial UAV take-off and landing point with the predicted priority of σ, PX Щ It refers to the initial UAV take-off and landing point with the predicted priority of Щ, σ=1, 2, …, Щ.

[0131] S300, according to CK 1 、A 1 and GJ, get CK 1 The corresponding first take-off and landing point coding reference vector BM 1 =(BM1, BM2, ..., BM σ,……,BM Щ ,0,0,……,0), where BM σ Refers to PX σ The corresponding first take-off and landing point code, BM Щ Refers to PX Щ The corresponding first take-off and landing point code.

[0132] In a specific embodiment, S300 further includes the following steps:

[0133] S310, if PX σ If it is a key take-off and landing point, PX σ The corresponding key take-off and landing point code is determined to be PX σ The corresponding first take-off and landing point code.

[0134] S320, if PX σ If it is not a key take-off and landing point, PX σ The corresponding initial take-off and landing point code is determined to be PX σ The corresponding first take-off and landing point code.

[0135] S330, traverse PX1, PX2, ..., PX σ ,……,PX Щ , get CK 1 The corresponding first take-off and landing point coding reference vector BM 1 =(BM1, BM2, ..., BM σ ,……,BM Щ ,0,0,……,0).

[0136] Among them, according to the type of the extracted top Щ initial UAV take-off and landing points, that is, key take-off and landing points or non-key take-off and landing points, the first take-off and landing point code corresponding to each initial UAV take-off and landing point in the top Щ initial UAV take-off and landing points is obtained, so as to judge the degree of matching between the top Щ initial UAV take-off and landing points and the prerequisites through the coding, and provide a data basis for measuring the sorting effect of the predicted priority sorting vector PX.

[0137] In a specific implementation, the key take-off and landing point code is less than 0, and the initial take-off and landing point code is greater than 0.

[0138] As mentioned above, the first Щ initial UAV take-off and landing points are converted into corresponding take-off and landing point codes, which provides a data basis for judging the matching degree between the first Щ initial UAV take-off and landing points and the prerequisites through coding, and then measuring the sorting effect of the prediction priority sorting vector PX.

[0139] S400, transposed vector of GJ and BM 1 Multiply to obtain the second take-off and landing point coding reference vector BM 2 .

[0140] S500, for BM 2 Perform binarization processing to obtain the third take-off and landing point coding reference vector BM 3 .

[0141] In a specific implementation, S500 further includes the following steps:

[0142] S510, if BM 2 If the element in the μth row and σth column is greater than 0, then BM 3 The element in the μth row and the σth column is set to 1.

[0143] S520, if BM 2 If the element in the μth row and σth column is less than 0, then BM 3 The elements in the μth row and the σth column are set to 0.

[0144] S530, BM 3 The elements in columns Щ+1 to N are set to 0.

[0145] S530, traverse μ=1, 2, ..., M and σ=1, 2, ..., Щ, and obtain the third take-off and landing point coding reference vector BM 3 .

[0146] Among them, BM 2 Perform binary processing, set the elements greater than 0 to 1, and the elements less than or equal to 0 to 0, then BM 3 The data scale is normalized to avoid the influence of the value of each element on the prediction loss, thereby improving the accuracy of the prediction loss and further improving the accuracy of the judgment of the matching degree between the prediction priority sorting vector PX and the prerequisite.

[0147] S600, according to BM 3 And the preset encoding threshold YZ, the prediction loss Loss = ρ × (YZ-SUM (BM 3 )), where ρ is the preset loss coefficient, SUM(BM 3 ) means BM 3 The sum of all elements in .

[0148] In one embodiment, YZ=M 2 .

[0149] In one embodiment, p=1000.

[0150] The specific value of ρ can be set by the implementer according to the actual situation, for example, ρ = 100, 1000, 10000, etc., to amplify the prediction loss, improve the training effect of the prediction model, and improve the prediction accuracy of the prediction model.

[0151] As mentioned above, increasing the prediction loss to characterize the degree of matching between the prediction priority ranking vector PX and the precondition provides a data basis for updating the parameters of the prediction model to improve the prediction accuracy of the prediction model.

[0152] S700: Update the parameters of the preset prediction model according to Loss until Loss=0, and obtain the target prediction model.

[0153] S800: Input A, B, Q, P and JS into the target prediction model to obtain the target priority ranking vector.

[0154] S900: Determine the first Щ initial UAV take-off and landing points in the target priority sorting vector as target UAV take-off and landing points.

[0155] As mentioned above, A, B, Q, P and JS are input into the preset prediction model to obtain the prediction priority ranking vector PX=(PX1, PX2, ..., PX n ,……,PX N ), perform vector multiplication on PX and YS to obtain the take-off and landing point reference vector CK 1 =(PX1, PX2, ..., PX σ ,……,PX Щ ,0,0,……,0), according to CK 1 、A 1 and GJ, get CK 1 The corresponding first take-off and landing point coding reference vector BM 1 =(BM1, BM2, ..., BM σ ,……,BM Щ ,0,0,……,0), the transposed vector of GJ and BM 1 Multiply to obtain the second take-off and landing point coding reference vector BM 2 , for BM 2 Perform binarization processing to obtain the third take-off and landing point coding reference vector BM 3 , according to BM 3 And the preset encoding threshold YZ, the prediction loss Loss = ρ × (YZ-SUM (BM 3 )), update the parameters of the preset prediction model according to Loss until Loss = 0, obtain the target prediction model, input A, B, Q, P and JS into the target prediction model, obtain the target priority sorting vector, and determine the first Щ initial UAV take-off and landing points in the target priority sorting vector as the target UAV take-off and landing points. It can be seen that through BM 3 The number of elements 1 in the , reverse BM 2 The number of elements greater than 0 in , and then reverse BM1 The number of key take-off and landing points corresponding to the preset coding thresholds YZ and BM 3 The prediction loss is constructed based on the number of elements 1 in the matrix, and the parameters of the prediction model are updated accordingly, which improves the matching degree between the target priority sorting vector and the prerequisites, thereby improving the rationality of the location selection of the target UAV take-off and landing points.

[0156] Example 3

[0157] This embodiment 3 provides a method for determining the priority of candidate take-off and landing points of drones based on a complex network model. Figure 3 As shown in FIG, the method for determining the priority of candidate take-off and landing points of UAVs based on a complex network model includes:

[0158] S1000, obtain the initial drone take-off and landing point set A = {A1, A2, ..., A n ,……,A N}、Predicted flight route set B={B1,B2,……,B n ,……,B N}、The take-off and landing point weight set Q={Q1,Q2,……,Q n ,……,Q N}、Route weight set P={P1,P2,……,P n ,……,P N}、Node betweenness set JS={JS1,JS2,……,JS n ,……,JS N}、Clustering coefficient set JL={JL1,JL2,……,JL n ,……,JL N}、Route abnormality degree set D={D1,D2,……,D n ,……,D N}、Route length set E={E1,E2,……,E n ,……,E N} and the second traffic flow set F={F1,F2,……,F n ,……,F N}, where A n Refers to the nth initial drone take-off and landing point, B n ={B n1 , B n2 ,……,B ni ,……,B nI(n)}, B ni Refers to A n The corresponding i-th predicted flight route, Q n Refers to A n The corresponding take-off and landing point weight, P n ={Pn1 , P n2 ,……,P ni ,……,P nI(n)}, P ni Refers to B ni The corresponding route weight, JS n Refers to A n The corresponding node betweenness, JL n Refers to A n The corresponding clustering coefficient, D n ={D n1 , D n2 ,……,D ni ,……,D nI(n)}, D ni Refers to B ni The corresponding route abnormality degree, E n ={E n1 , E n2 ,……,E ni ,……,E nI(n)}, E ni Refers to B ni The corresponding route length, F n ={F n1 , F n2 ,……,F ni ,……,F nI(n)}, F ni Refers to B ni The corresponding second traffic flow, n = 1, 2, ..., N, N refers to the total number of initial drone take-off and landing points, i = 1, 2, ..., I(n), I(n) refers to the total number of predicted flight routes corresponding to the nth initial drone take-off and landing point.

[0159] The take-off and landing point weight represents the likelihood of installing drone take-off and landing facilities at the corresponding initial drone take-off and landing point, the route weight represents the likelihood of the predicted flight route being used as the drone's flight path, and the node betweenness represents the influence of the corresponding initial drone take-off and landing point relative to other initial drone take-off and landing points. The clustering coefficient represents the degree of clustering between the corresponding initial drone take-off and landing point and other initial drone take-off and landing points.

[0160] S2000: According to A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, the candidate priority set HX = {HX1, HX2, ..., HX n ,……,HX N}, where QZ1 refers to the preset weight of the take-off and landing point weight, QZ2 refers to the preset weight of the route weight, QZ3 refers to the preset weight of the number of routes, QZ4 refers to the preset weight of the node betweenness, QZ5 refers to the preset weight of the clustering coefficient, and A n Corresponding candidate priority HX n Meet the following conditions:

[0161] HX n =QZ1×Q n +QZ2×∑ i＝1 I(n) (P ni / I(n))+QZ3×I(n)+QZ4×JS n +QZ5×JL n .

[0162] Among them, the greater the take-off and landing point weight corresponding to the initial UAV take-off and landing point, the greater the average route weight corresponding to the predicted flight route connected by the initial UAV take-off and landing point, the more routes of the predicted flight route connected by the initial UAV take-off and landing point, and the greater the node betweenness and clustering coefficient corresponding to the initial UAV take-off and landing point, the higher the possibility of setting up UAV take-off and landing facilities at the initial UAV take-off and landing point.

[0163] Therefore, combining Q, P, JS, JL and the corresponding preset initial weights, the candidate priority corresponding to each initial UAV take-off and landing point is obtained, which indicates that the corresponding initial UAV take-off and landing point is determined as the target UAV take-off and landing point to set the priority of the UAV take-off and landing facilities.

[0164] The specific values ​​of QZ1, QZ2, QZ3, QZ4 and QZ5 can be set by the implementer according to actual conditions.

[0165] As mentioned above, the take-off and landing point weights, average route weights, predicted number of flight routes, node betweenness and clustering coefficient corresponding to the initial UAV take-off and landing points, as well as the preset initial weights are comprehensively considered to measure the candidate priority of each initial UAV take-off and landing point to be determined as the target UAV take-off and landing point, thereby improving the accuracy of the candidate priority and thus improving the accuracy of the target UAV screening.

[0166] S3000, based on HX, selects M candidate UAV take-off and landing points from N initial UAV take-off and landing points.

[0167] In a specific implementation, S3000 further includes the following steps:

[0168] S3100: Determine the initial UAV take-off and landing points corresponding to the largest M candidate priorities in HX as candidate UAV take-off and landing points.

[0169] S4000: Based on D, E and F, obtain the candidate take-off and landing point prediction score set FZ = {FZ1, FZ2, ..., FZ n ,……,FZ N}, where A n Corresponding candidate take-off and landing point prediction score FZ n Meet the following conditions:

[0170] FZ n =β1×(Σ i＝1 I(n) e^(-D ni ))+β2×(∑ n＝1 N (Σ i＝1 I(n) E ni ))+β3×(∑ n＝1 N (Σ i＝1 I(n) F ni )), where β1

[0171] refers to the preset abnormality weight, β2 refers to the preset route length weight, and β3 refers to the preset second traffic weight.

[0172] Among them, the smaller the route abnormality degree of all predicted flight routes corresponding to the candidate UAV take-off and landing point, the longer the route length, and the larger the second traffic flow, which means that the candidate UAV take-off and landing point has a lower demand for UAV take-off and landing facilities, and the higher the possibility of setting up UAV take-off and landing facilities at the candidate UAV take-off and landing point. Correspondingly, the larger the candidate take-off and landing point prediction score corresponding to the candidate UAV take-off and landing point.

[0173] As described above, based on D, E, and F, the possibility of setting up drone take-off and landing facilities at the candidate drone take-off and landing points is measured, and then the candidate take-off and landing point prediction score set FZ is obtained, which improves the accuracy of the candidate take-off and landing point prediction scores.

[0174] S5000: Based on FZ, obtain the screening scores FS of M candidate drone take-off and landing points, where FS meets the following conditions:

[0175] FS=(Σ n＝1 N (FZ n -FZ0) 2 / N, where the predicted mean value of candidate take-off and landing points FZ0 = Σ n＝1 N (FZ n ) / N.

[0176] In order to ensure that the delivery efficiency and delivery intensity of drones at each take-off and landing point are as balanced as possible, this embodiment hopes that the candidate take-off and landing point prediction scores between the selected target drone take-off and landing points are as consistent as possible.

[0177] In the above, by measuring the differences between the candidate take-off and landing point prediction scores of M candidate drone take-off and landing points, the screening score FS of the M candidate drone take-off and landing points is obtained to characterize the screening accuracy of the M candidate drone take-off and landing points. This is used as the basis for updating the initial weight set to re-screen M take-off and landing points as the target drone take-off and landing points, providing setting locations for drone take-off and landing facilities, thereby achieving the effect of balancing the delivery efficiency and delivery intensity of the drones corresponding to each target drone take-off and landing point, thereby improving the overall work efficiency of the drones.

[0178] S6000, if FS>FS 0 , then update QZ, and use the updated QZ to replace the preset initial weight set, and repeat step S2000 until FS≤FS 0 , get FS≤FS 0 The candidate priority set HX when FS≤FS 0 The candidate priority set HX is used to filter the target UAV take-off and landing points from the N initial UAV take-off and landing points, FS 0 Refers to the preset screening score threshold.

[0179] Among them, the preset screening score threshold FS 0 The specific value of can be set by the implementer according to the actual situation.

[0180] The candidate priority corresponding to each initial UAV take-off and landing point is obtained by comprehensively considering the take-off and landing point weight, average route weight, predicted number of flight routes, node betweenness and clustering coefficient corresponding to the initial UAV take-off and landing point, as well as the preset initial weight. Therefore, the accuracy of the preset initial weight affects the accuracy of the candidate priority, and thus affects the accuracy of the screening of candidate UAV take-off and landing points.

[0181] The larger the screening score FS of the M candidate drone take-off and landing points, the greater the difference in delivery efficiency and delivery intensity between the M candidate drone take-off and landing points. Therefore, when FS>FS 0 Update QZ and use the updated QZ to replace the preset initial weight set, repeat step S2000 to obtain new M candidate drone take-off and landing points until FS≤FS 0 , get FS≤FS 0The candidate priority set HX at this time is used to screen the target UAV take-off and landing points from the N initial UAV take-off and landing points, thereby improving the consistency of the delivery efficiency and delivery intensity between the target UAV take-off and landing points and improving the accuracy of the screening of UAV take-off and landing points.

[0182] As described above, based on A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, the candidate priority set HX = {HX1, HX2, ..., HX n ,……,HX N}, according to HX, M candidate drone take-off and landing points are selected from N initial drone take-off and landing points, and according to D, E and F, the candidate take-off and landing point prediction score set FZ = {FZ1, FZ2, ..., FZ n ,……,FZ N}, according to FZ, obtain the screening score FS of M candidate drone take-off and landing points, if FS>FS 0 , then update QZ, and use the updated QZ to replace the preset initial weight set, and repeat the above steps until FS≤FS 0 , FS≤FS 0 The M candidate UAV take-off and landing points at the time are determined as the target UAV take-off and landing points. It can be seen that by measuring the differences between the candidate take-off and landing point prediction scores among the M candidate UAV take-off and landing points, the screening scores FS of the M candidate UAV take-off and landing points are obtained to characterize the screening accuracy of the M candidate UAV take-off and landing points. This is used as the basis for updating the initial weight set and the candidate priority set to screen out the target UAV take-off and landing points and provide a setting location for the UAV take-off and landing facilities, thereby achieving the effect of balancing the delivery efficiency and delivery intensity of the UAVs corresponding to each target UAV take-off and landing point, thereby improving the overall work efficiency of the UAVs.

[0183] Example 4

[0184] This fourth embodiment provides a complex network model construction system for selecting the location of drone take-off and landing points. Figure 4 As shown, the complex network model construction system for selecting the take-off and landing points of UAVs includes a processor and a memory storing a computer program, and the memory also stores an initial set of UAV take-off and landing points A = {A1, A2, ..., A n ,……,A N}、Predicted flight route set B={B1,B2,……,B n ,……,B N}、The importance set of take-off and landing point locations H={H1,H2,……,H n ,……,H N}, the first traffic flow set C = {C1, C2, ..., Cn ,……,C N}, the first building interference level set G = {G1, G2, ..., G n ,……,G N}、Route abnormality degree set D={D1,D2,……,D n ,……,D N}、Route length set E={E1,E2,……,E n ,……,E N}, where A n Refers to the nth initial drone take-off and landing point, B n ={B n1 , B n2 ,……,B ni ,……,B nI(n)}, B ni Refers to A n The corresponding i-th predicted flight route, H n Refers to A n The importance of the corresponding take-off and landing point position, C n Refers to A n The corresponding first traffic flow, G n Refers to A n The corresponding first building interference level, D n ={D n1 , D n2 ,……,D ni ,……,D nI(n)}, D ni Refers to B ni The corresponding route abnormality degree is used to characterize the degree of damage to the outside world when the UAV encounters a flight abnormality on the corresponding predicted flight route. n ={E n1 , E n2 ,……,E ni ,……,E nI(n)}, E ni Refers to B ni The corresponding route length, n = 1, 2, ..., N, N refers to the total number of initial drone take-off and landing points, i = 1, 2, ..., I(n), I(n) refers to the total number of predicted flight routes corresponding to the nth initial drone take-off and landing point.

[0185] When the computer program is executed by a processor, the following steps are performed:

[0186] S1, according to H, C and G, obtain the take-off and landing point weight set Q = {Q1, Q2, ..., Q n ,……,Q N}, where A n The corresponding take-off and landing point weight Qn Meet the following conditions:

[0187] Q n =α1×H n +α2×C n +α3×e^(-G n ), wherein α1 refers to the preset first position weight, α2 refers to the preset first flow weight, and α3 refers to the preset building interference weight.

[0188] The specific values ​​of α1, α2 and α3 can be set by the implementer according to actual conditions.

[0189] In the above, the influence of the importance of the take-off and landing point location, the first traffic flow and the first building interference level on the possibility of setting up drone take-off and landing facilities at the initial drone take-off and landing point is measured, and the take-off and landing point weight corresponding to each initial drone take-off and landing point is obtained, which improves the accuracy of the take-off and landing point weight, thereby improving the accuracy of the construction of the complex network model and the rationality of the site selection of the drone take-off and landing point.

[0190] S2, according to C, obtain the second traffic flow set F corresponding to B = {F1, F2, ..., F n ,……,F N}, where B n The corresponding second traffic flow list F n ={F n1 , F n2 ,……,F ni ,……,F nI(n)}, F ni Equal to B ni The average value of the first traffic flow of the two corresponding initial drone take-off and landing points.

[0191] S3, according to B and H, obtain the route position importance set K corresponding to B = {K1, K2, ..., K n ,……,K N}, where B n Corresponding route location importance list K n ={K n1 , K n2 ,……,K ni ,……,K nI(n)}, K ni Equal to B ni The average importance of the take-off and landing point locations of the two corresponding initial UAV take-off and landing points.

[0192] S4, according to B, D, E, F and K, obtain the route weight set P corresponding to B = {P1, P2, ..., P n ,……,P N}, where P n ={P n1 , P n2 ,……,P ni ,……,P nI(n)}, B ni The corresponding route weight P ni Meet the following conditions:

[0193] P ni =β1×e^(-D ni )+β2×E ni / ((∑ n＝1 N (Σ i＝1 I(n) E ni )) / (Σ n＝1 N I(n)))+β3×F ni +β4×K ni , where β

[0194] 1 refers to the preset abnormality weight, β2 refers to the preset route length weight, β3 refers to the preset second traffic weight, and β4 refers to the preset second position weight.

[0195] In the above, the influence of the route abnormality degree, route length, second traffic flow and route location importance on the possibility of the predicted flight route as the flight path of the UAV is measured, and the route weight corresponding to each predicted flight route is obtained, which improves the accuracy of the route weight, thereby improving the accuracy of the construction of the complex network model and the rationality of the site selection of the UAV take-off and landing points.

[0196] S5, based on A, B, Q and P, takes the initial UAV take-off and landing points as nodes, the predicted flight routes as edges, the take-off and landing point weights corresponding to the initial UAV take-off and landing points as the node values ​​of the corresponding nodes, and the route weights corresponding to the predicted flight routes as the edge values ​​of the corresponding edges, to obtain the complex network model T, where the complex network model T is used to screen the target UAV take-off and landing points from A.

[0197] Among them, the complex network model T is used to screen the target drone take-off and landing points from A. The target drone take-off and landing points can be used as the location for setting up drone take-off and landing facilities to provide support for drone delivery logistics and other tasks.

[0198] As mentioned above, based on H, C and G, we can get the take-off and landing point weight set Q = {Q1, Q2, ..., Q n ,……,Q N}, weigh the influence of the importance of the take-off and landing point location, the first traffic flow and the first building interference level on the possibility of setting up drone take-off and landing facilities at the initial drone take-off and landing point, obtain the take-off and landing point weight corresponding to each initial drone take-off and landing point, improve the accuracy of the take-off and landing point weight, and obtain the second traffic flow set F corresponding to B according to C = {F1, F2, ..., F n ,……,F N}, to characterize the degree of protection of the corresponding predicted flight route for the drone's cargo transportation. According to B and H, the importance set of the route position corresponding to B is obtained, K = {K1, K2, ..., K n ,……,K N}, according to B, D, E, F and K, obtain the route weight set P corresponding to B = {P1, P2, ..., P n ,……,P N}, measure the impact of the route abnormality, route length, second traffic flow and route location importance on the possibility of the predicted flight route as the flight path of the UAV, obtain the route weight corresponding to each predicted flight route, and improve the accuracy of the route weight. According to A, B, Q and P, the initial UAV take-off and landing point is used as the node, the predicted flight route is used as the edge, the take-off and landing point weight corresponding to the initial UAV take-off and landing point is used as the node value of the corresponding node, and the route weight corresponding to the predicted flight route is used as the edge value of the corresponding edge. The complex network model T is obtained and used to screen the target UAV take-off and landing point from A, which improves the construction accuracy of the complex network model T, and then improves the rationality of the screening of the target UAV take-off and landing point, that is, improves the rationality of the site selection of the UAV take-off and landing point.

[0199] Example 5

[0200] This embodiment 5 provides a method for generating an initial location for a drone take-off and landing point based on multiple factors, such as Figure 5 As shown in FIG, the method for generating the initial site selection of the take-off and landing points of the UAV based on multiple factors includes the following steps:

[0201] S10, obtain Θ original drone take-off and landing points, several predicted flight routes corresponding to each original drone take-off and landing point, and the route length, second traffic flow and route abnormality degree corresponding to each predicted flight route, where the second traffic flow refers to the average of the first traffic flow corresponding to the two original drone take-off and landing points corresponding to the predicted flight route. The route abnormality degree is used to characterize the degree of damage to the outside world when a drone encounters a flight abnormality on the corresponding predicted flight route. Θ is an integer greater than 0.

[0202] In a specific embodiment, S10 further includes the following steps:

[0203] S11, obtain map data corresponding to Θ original drone take-off and landing points and the first traffic flow corresponding to each original drone take-off and landing point.

[0204] S12, input the map data corresponding to Θ original drone take-off and landing points and the first traffic flow corresponding to Θ original drone take-off and landing points into the preset drone route setting model, and obtain several predicted flight routes corresponding to each original drone take-off and landing point.

[0205] Among them, implementers can use path planning algorithms, such as A* search algorithm, Dijkstra algorithm, genetic algorithm, etc., on the basis of determining the original UAV take-off and landing points, according to the map data and the first traffic flow corresponding to the original UAV take-off and landing points, quickly generate UAV routes between the original UAV take-off and landing points, as predicted flight routes for the site selection task of UAV take-off and landing points.

[0206] Among them, map data may include factors such as terrain data, obstacle data, and weather condition data.

[0207] S20, based on the number of predicted flight routes corresponding to each original UAV take-off and landing point, the route length corresponding to each predicted flight route and the second traffic flow, obtain the number of predicted flight routes, the total route length and the total traffic volume corresponding to each original UAV take-off and landing point.

[0208] Among them, for any original drone take-off and landing point, the sum of the route lengths of all predicted flight routes corresponding to the current original drone take-off and landing point is determined as the total route length corresponding to the current original drone take-off and landing point, and the sum of the second traffic flow of all predicted flight routes corresponding to the current original drone take-off and landing point is determined as the total traffic volume corresponding to the current original drone take-off and landing point, which serves as the basis for screening drone take-off and landing points.

[0209] S30, based on the predicted number of flight routes, total route length, total traffic volume, preset route number threshold, preset route length threshold, and preset traffic threshold corresponding to each original drone take-off and landing point, select ζ take-off and landing points to be merged from Θ original drone take-off and landing points, where 0<ζ<Θ.

[0210] The specific values ​​of the preset route quantity threshold, the preset route length threshold, and the preset traffic flow threshold can be set by the implementer according to actual conditions.

[0211] In a specific embodiment, S30 further includes the following steps:

[0212] S31, for any original UAV take-off and landing point, if the number of predicted flight routes corresponding to the current original UAV take-off and landing point is less than the preset route number threshold, the total route length is less than the preset route length threshold, and the total traffic volume is less than the preset flow threshold, then the current original UAV take-off and landing point is determined as the take-off and landing point to be merged.

[0213] S32, traverse Θ original drone take-off and landing points, and obtain ζ take-off and landing points to be merged.

[0214] Among them, the more predicted flight routes corresponding to the current original drone take-off and landing point, the longer the total length of the routes, and the greater the total traffic volume, it means that the original drone take-off and landing point has a lower demand for drone take-off and landing facilities, and the higher the possibility of setting up drone take-off and landing facilities at the original drone take-off and landing point. Conversely, it means that the demand for drone take-off and landing facilities is higher, and the possibility of setting up drone take-off and landing facilities at the original drone take-off and landing point is lower.

[0215] Therefore, for the original drone take-off and landing points with low demand and possibility for setting up drone take-off and landing facilities, they can be used as take-off and landing points to be merged and merged into the take-off and landing points connected to them with high demand and possibility for setting up drone take-off and landing facilities, thereby improving the correlation between the take-off and landing points, and ensuring that the possibility of setting up drone take-off and landing facilities at the merged take-off and landing points is high, and improving the coverage and rationality of the drone take-off and landing facilities by merging the take-off and landing points.

[0216] S40: According to all predicted flight routes corresponding to each take-off and landing point to be merged, obtain n original UAV take-off and landing points connected to each take-off and landing point to be merged, where n is an integer greater than 0.

[0217] S50, for any take-off and landing point to be merged, any original UAV take-off and landing point connected to the current take-off and landing point to be merged is used as the intermediate take-off and landing point, and the route length between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points and the intermediate take-off and landing point, as well as the route length between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, the route length priority corresponding to the intermediate take-off and landing point is obtained.

[0218] In a specific embodiment, S50 further includes the following steps:

[0219] S51: Determine the average of the route lengths between the current take-off and landing point to be merged and the other n-1 original UAV take-off and landing points as the first route length.

[0220] S52: Determine the average of the route lengths between the middle take-off and landing point and the other n-1 original UAV take-off and landing points as the second route length.

[0221] S53: Determine the sum of the route length between the current take-off and landing point to be merged and the intermediate take-off and landing point, and the second route length, as the third route length.

[0222] S54: Determine the ratio of the first route length to the third route length as the route length priority corresponding to the intermediate take-off and landing point.

[0223] Among them, the first route length can represent the average distance from the current take-off and landing point to the other η-1 original drone take-off and landing points, the second route length can represent the average distance from the current intermediate take-off and landing point to the other η-1 original drone take-off and landing points, and the third route length can represent the average distance from the intermediate take-off and landing point as a transit point, and then to the other η-1 original drone take-off and landing points. If the third route length is less than the corresponding first route length, it means that compared with the current take-off and landing point to be merged directly to the other η-1 original drone take-off and landing points, the drone's flight distance is shorter when the intermediate take-off and landing point is used as a transit point from the current take-off and landing point to the other η-1 original drone take-off and landing points. This can indicate that the intermediate take-off and landing point has a higher priority than the current take-off and landing point to be merged, and after the take-off and landing point to be merged is merged into the intermediate take-off and landing point, the impact on the drone flight demand from the current take-off and landing point to the other η-1 original drone take-off and landing points is small.

[0224] Correspondingly, the larger the ratio of the first route length to the third route length is, the higher the priority of the route length corresponding to the intermediate take-off and landing point is.

[0225] In the above, based on the route lengths between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points and the intermediate take-off and landing point, as well as the route lengths between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, the route length priority corresponding to the intermediate take-off and landing point is obtained to characterize the priority of the intermediate take-off and landing point compared to the current take-off and landing point to be merged, and the degree of influence on the UAV flight demand from the current take-off and landing point to be merged to the other η-1 original UAV take-off and landing points after the take-off and landing point to be merged is merged into the intermediate take-off and landing point. This serves as the basis for merging the take-off and landing points to be merged, thereby improving the rationality of the take-off and landing point merging.

[0226] S60, based on the route abnormality degree of the predicted flight route between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points and the intermediate take-off and landing point, and the route abnormality degree of the predicted flight route between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, obtain the route abnormality degree priority corresponding to the intermediate take-off and landing point.

[0227] In a specific embodiment, S60 further includes the following steps:

[0228] S61: Determine the average of the route anomaly levels between the current take-off and landing point to be merged and the other n-1 original UAV take-off and landing points as the first route anomaly level.

[0229] S62: Determine the average of the route abnormality levels between the middle take-off and landing point and the other n-1 original UAV take-off and landing points as the second route abnormality level.

[0230] S63: Determine the sum of the route abnormality degree between the current take-off and landing point to be merged and the intermediate take-off and landing point and the second route abnormality degree as a third route abnormality degree.

[0231] S64: Determine the ratio of the first route abnormality level to the third route abnormality level as the route abnormality level priority corresponding to the intermediate take-off and landing point.

[0232] Among them, the first route abnormality degree can represent the average abnormality degree of the drone directly from the current take-off and landing point to the other η-1 original drone take-off and landing points, the second route abnormality degree can represent the average abnormality degree of the drone directly from the current intermediate take-off and landing point to the other η-1 original drone take-off and landing points, and the third route abnormality degree can represent the average abnormality degree of the drone using the intermediate take-off and landing point as a transit point, from the current take-off and landing point to the intermediate take-off and landing point and then to the other η-1 original drone take-off and landing points. If the third route abnormality degree is less than the corresponding first route abnormality degree, it means that compared with the drone directly from the current take-off and landing point to the other η-1 original drone take-off and landing points, the drone's flight abnormality degree is lower when the intermediate take-off and landing point is used as a transit point from the current take-off and landing point to the other η-1 original drone take-off and landing points. This can indicate that the intermediate take-off and landing point has a higher priority than the current take-off and landing point to be merged, and after the take-off and landing point to be merged is merged into the intermediate take-off and landing point, the impact on the safety of the drone is smaller.

[0233] Correspondingly, the greater the ratio of the first route abnormality level to the third route abnormality level, the higher the priority of the route abnormality level corresponding to the intermediate take-off and landing point.

[0234] In the above, based on the route anomaly degree between the current take-off and landing point to be merged and the other η-1 original drone take-off and landing points and the intermediate take-off and landing point, as well as the route anomaly degree between the intermediate take-off and landing point and the other η-1 original drone take-off and landing points, the route anomaly degree priority corresponding to the intermediate take-off and landing point is obtained to characterize the priority of the intermediate take-off and landing point compared to the current take-off and landing point to be merged, and the impact on the safety of the drone after merging the take-off and landing point to be merged into the intermediate take-off and landing point. This serves as the basis for merging the take-off and landing points to be merged, thereby improving the rationality of the take-off and landing point merging.

[0235] S70: Obtain the merge priority corresponding to the current intermediate take-off and landing point based on the route length priority and the route abnormality priority.

[0236] In a specific embodiment, S70 further includes the following steps:

[0237] S71, obtaining a preset route length weight and a preset abnormality degree weight.

[0238] S72: Obtain the merge priority corresponding to the current intermediate take-off and landing point according to the route length priority, the preset route length weight, the route anomaly priority, and the preset anomaly weight.

[0239] Among them, the first product of the route length priority and the preset route length weight, as well as the second product of the route anomaly degree priority and the preset anomaly degree weight, are calculated. The sum of the first product and the second product is determined as the merging priority corresponding to the current intermediate take-off and landing point, representing the priority of merging the current take-off and landing point to be merged into the current intermediate take-off and landing point.

[0240] The specific values ​​of the preset route length weight and the preset abnormality degree weight can be set by the implementer according to actual conditions.

[0241] The above-mentioned route length priority, the preset route length weight, the route anomaly priority, and the preset anomaly weight are combined to obtain the merging priority corresponding to the current intermediate take-off and landing point. This represents the priority of merging the current take-off and landing point to be merged into the current intermediate take-off and landing point. This serves as the basis for merging the current take-off and landing point to be merged, thereby improving the rationality of take-off and landing point merging.

[0242] S80: Traverse the n original UAV take-off and landing points connected to the current take-off and landing point to be merged, and obtain n merging priorities.

[0243] S90: Merge the current take-off and landing point to be merged with the original UAV take-off and landing point corresponding to the highest merging priority, and determine the merged take-off and landing point as the initial UAV take-off and landing point.

[0244] Among them, the take-off and landing points to be merged, which have low demand for drone take-off and landing facilities and low possibility of setting up drone take-off and landing facilities, are merged into other take-off and landing points with high demand for drone take-off and landing facilities and high possibility of setting up drone take-off and landing facilities. The initial drone take-off and landing points are obtained as the setting locations of drone take-off and landing facilities, thereby generating a batch of initial points that can be used for drone take-off and landing site selection, so that the setting locations of drone take-off and landing facilities match the take-off and landing point demand, thereby improving the relative coverage and setting rationality of drone take-off and landing facilities.

[0245] In the above, according to the obtained predicted number of flight routes, total route length and total traffic volume corresponding to each original UAV take-off and landing point, combined with the preset route number threshold, the preset route length threshold and the preset traffic threshold, z take-off and landing points to be merged are screened out from Θ original UAV take-off and landing points, and according to all predicted flight routes corresponding to each take-off and landing point to be merged, n original UAV take-off and landing points connected to each take-off and landing point to be merged are obtained. For any take-off and landing point to be merged, any original UAV take-off and landing point connected to the current take-off and landing point to be merged is used as the intermediate take-off and landing point. According to the route length and route anomaly degree between the current take-off and landing point to be merged and the other n-1 original UAV take-off and landing points and the intermediate take-off and landing point, as well as the route length and route anomaly degree between the intermediate take-off and landing point and the other n-1 original UAV take-off and landing points, the route length priority and route anomaly degree corresponding to the intermediate take-off and landing point are obtained. According to the route length priority and the route abnormality priority, the merging priority corresponding to the current intermediate take-off and landing point is obtained, the current take-off and landing point to be merged is merged with the original drone take-off and landing point corresponding to the largest merging priority, and the merged take-off and landing point is determined as the initial drone take-off and landing point. It can be seen that the take-off and landing points to be merged, which have a low possibility of setting up drone take-off and landing facilities and whose predicted number of flight routes is less than the preset route number threshold, the total route length is less than the preset route length threshold, and the total traffic volume is less than the preset flow threshold, are merged into other take-off and landing points with a higher possibility of setting up drone take-off and landing facilities, thereby generating a batch of initial points that can be used for drone take-off and landing site selection, so that when drone take-off and landing facilities are set at the initial drone take-off and landing points, they can match the take-off and landing point requirements, thereby improving the relative coverage and setting rationality of drone take-off and landing facilities.

[0246] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A system for integrating and optimizing the coverage area of ​​drone take-off and landing points, characterized in that: The UAV take-off and landing point coverage area integration optimization system includes a processor and a memory storing a computer program, and the memory also stores a set of target traffic cells corresponding to the target UAV take-off and landing points. The first drop point weight set The set of traffic cells to be merged corresponding to the take-off and landing points of non-target UAVs The second take-off and landing point weight set The third traffic flow set and the first geographic distance set Among them, QY ψ It refers to the target traffic area corresponding to the ψth target UAV take-off and landing point, Q 1 ψ Refers to QY ψ The corresponding first take-off and landing point weight, JT ω It refers to the traffic area to be merged corresponding to the ωth non-target UAV take-off and landing point, Q 2 ω JT ω The corresponding second take-off and landing point weight, CX ω ={CX ω1 , CX ωψ It refers to the third traffic flow between the ωth traffic area to be merged and the ψth target traffic area, DL ωψ It refers to the first geographical distance between the ωth traffic cell to be merged and the ψth target traffic cell, ψ=1, 2, ..., Refers to the total number of target drone take-off and landing points, Refers to the total number of non-target UAV take-off and landing points. When the computer program is executed by the processor, the following steps are implemented: S101, according to Q 1 , Q 2 , CX and DL, obtain the traffic flow threshold CX 0 and the first geographic distance threshold DL 0 , among which, CX 0 and DL 0 Meet the following conditions: S102, if CX ωψ >CX 0 And DL ωψ <DL 0 , then the ωth traffic cell to be merged is taken as the first merged traffic cell, and the ψth target traffic cell is determined as the candidate traffic cell corresponding to the current first merged traffic cell; S103, traverse Obtain Γ(ω) candidate traffic cells of the current first merged traffic cell; S104, according to CX ω and DL ω , get the first matching degree set in, Γ(ω) refers to the total number of candidate traffic cells of the current first merged traffic cell, the first matching degree between the current first merged traffic cell and the Чth candidate traffic cell Meet the following conditions: in, Refers to the current first merged traffic zone and the The third traffic flow between candidate traffic zones, Refers to the current first merged traffic zone and the The first geographical distance between candidate traffic zones, e is a natural constant, S105, merge the current first merged traffic zone into max(PP 1 ω ) in the target traffic cell corresponding to the candidate traffic cell, wherein max() refers to the maximum value function; S106 , traversing ω=1, 2, ..., N-Щ, and determining the target traffic cell merged with the first merged traffic cell as the first reference coverage area of ​​the UAV.

2. The integrated optimization system for the coverage area of ​​UAV take-off and landing points according to claim 1 is characterized in that: The UAV take-off and landing point coverage area integration optimization system also includes a first area set and the second region area set Among them, MJ 1 ψ Refers to QY ψ The corresponding first region area, MJ 2 ω JT ω The corresponding second region area, when the computer program is executed by a processor, further implements the following steps: S107, according to MJ 1 and MJ 2 , determining the area of ​​each first reference coverage area and the sum of the areas of all first merged traffic cells merged from each first reference coverage area as the third area of ​​each first reference coverage area; S108, determining the unmerged traffic cells among the N-Щ traffic cells to be merged as the second merged traffic cells, and determining the unmerged target traffic cells among the Щ target traffic cells as the optional traffic cells; S109 , acquiring, according to the DL, a second geographical distance between each second merged traffic cell and each first reference coverage area, and a third geographical distance between each second merged traffic cell and each optional traffic cell; S1010: For any second merged traffic cell, obtain a second matching degree between the current second merged traffic cell and each first reference coverage area based on a second geographical distance between the current second merged traffic cell and each first reference coverage area and a third area of ​​each first reference coverage area; S1011, obtaining a third matching degree between the current second merged transportation cell and each optional transportation cell based on a third geographical distance between the current second merged transportation cell and each optional transportation cell and a first area of ​​each optional transportation cell; S1012: Determine the first reference coverage area or optional traffic cell corresponding to the maximum value among all the second matching degrees and all the third matching degrees as the target merged cell corresponding to the current second merged traffic cell; S1013, merging the current second merged traffic cell into the target merged cell; S1014: traverse all second merged traffic cells, and determine the target merged cell merged with the second merged traffic cell as the second reference coverage area of ​​the UAV.

3. The integrated optimization system for UAV take-off and landing point coverage areas according to claim 1 is characterized in that: The memory also stores a preset prediction model, an initial drone take-off and landing point set A={A1, A2, ..., A n ,……,A N }, Initial UAV take-off and landing point code set A 1 ={A 1 1, A 1 2, ..., A 1 n ,……,A 1 N }、Predicted flight route set B={B1,B2,……,B n ,……,B N }、The take-off and landing point weight set Q={Q1,Q2,……,Q n ,……,Q N }、Route weight set P={P1,P2,……,P n ,……,P N }、Node betweenness set JS={JS1,JS2,……,JS n ,……,JS N }、Preset key take-off and landing point code set GJ={GJ1,GJ2,……,GJ μ ,……,GJ M } and preset reference vector YS=(1,1,……,1,0,0,……,0), where A n Refers to the nth initial drone take-off and landing point, A 1 n Refers to A n The corresponding initial take-off and landing point code, B n ={B n1 , B n2 ,……,B ni ,……,B nI(n) }, B ni Refers to A n The corresponding i-th predicted flight route, Q n Refers to A n The corresponding take-off and landing point weight, P n ={P n1 , P n2 ,……,P ni ,……,P nI(n) }, P ni Refers to B ni The corresponding route weight, JS n Refers to A n The corresponding node betweenness, GJ μ Refers to the key take-off and landing point code corresponding to the preset μth key take-off and landing point, n = 1, 2, ..., N, N refers to the total number of initial UAV take-off and landing points, i = 1, 2, ..., I(n), I(n) refers to the total number of predicted flight routes corresponding to the nth initial UAV take-off and landing point, μ = 1, 2, ..., M, M refers to the total number of preset key take-off and landing points, the first Щ elements in YS are value 1, and the last N-Щ elements are value 0. The Щ target UAV take-off and landing points are obtained by the following steps: S100, input A, B, Q, P and JS into the preset prediction model, and obtain the prediction priority ranking vector PX=(PX1, PX2, ..., PX n ,……,PX N ), where PX n It refers to the initial UAV take-off and landing point with the predicted priority of nth; S200, perform vector multiplication on PX and YS to obtain the take-off and landing point reference vector CK 1 =(PX1, PX2, ..., PX σ ,……,PX Щ ,0,0,……,0), where PX σ Refers to the initial UAV take-off and landing point with the predicted priority of σ, Refers to the initial drone take-off and landing point with the predicted priority being No. S300, according to CK 1 、A 1 and GJ, get CK 1 The corresponding first take-off and landing point coding reference vector Among them, BM σ Refers to PX σ The corresponding first take-off and landing point code, means The corresponding first take-off and landing point code; S400, transposed vector of GJ and BM 1 Multiply to obtain the second take-off and landing point coding reference vector BM 2 ; S500, for BM 2 Perform binarization processing to obtain the third take-off and landing point coding reference vector BM 3 ; S600, according to BM 3 And the preset encoding threshold YZ, the prediction loss Loss = ρ × (YZ-SUM (BM 3 )), where ρ is the preset loss coefficient, SUM(BM 3 ) means BM 3 The sum of all elements in ; S700, updating the parameters of the preset prediction model according to the Loss until the Loss converges, and obtaining the target prediction model; S800, inputting A, B, Q, P and JS into the target prediction model to obtain a target priority ranking vector; S900: Determine the first 10 initial UAV take-off and landing points in the target priority sorting vector as target UAV take-off and landing points.

4. The integrated optimization system for the coverage area of ​​UAV take-off and landing points according to claim 3 is characterized in that: The memory also stores a take-off and landing point location importance set H={H1, H2, ..., H n ,……,H N }, the first traffic flow set C = {C1, C2, ..., C n ,……,C N } and the first building interference level set G = {G1, G2, ..., G n ,……,G N }, where H n Refers to A n The importance of the corresponding take-off and landing point position, C n Refers to A n The corresponding first traffic flow, G n Refers to A n The corresponding first building interference level and the take-off and landing point weight set Q are obtained by the following steps: S1, according to H, C and G, obtain the take-off and landing point weight set Q = {Q1, Q2, ..., Q n ,……,Q N }, where A n The corresponding take-off and landing point weight Q n Meet the following conditions: Q n =α1×H n +α2×C n +α3×e^(-G n ), wherein α1 refers to the preset first position weight, α2 refers to the preset first flow weight, and α3 refers to the preset building interference weight.

5. The integrated optimization system for the coverage area of ​​UAV take-off and landing points according to claim 4 is characterized in that: The first drop point weight set Q 1 Obtain it through the following steps: Extract the take-off and landing point weights corresponding to the first target drone take-off and landing points from Q, and obtain the first take-off and landing point weight set Q 1 ={Q 1 1. Q 1 2, ..., Q 1 ψ ,……,Q 1 Щ }.

6. The integrated optimization system for UAV take-off and landing point coverage areas according to claim 4 is characterized in that: The second take-off and landing point weight set Q 2 Obtain it through the following steps: Extract the take-off and landing point weights corresponding to N-Щ non-target UAV take-off and landing points from Q, and obtain the first take-off and landing point weight set Q 1 ={Q 1 1. Q 1 2, ..., Q 1 ψ ,……,Q 1 Щ }.

7. The integrated optimization system for UAV take-off and landing point coverage areas according to claim 3 is characterized in that: The memory also stores a set of route anomaly levels D={D1, D2, ..., D n ,……,D N }、Route length set E={E1,E2,……,E n ,……,E N }, D n ={D n1 , D n2 ,……,D ni ,……,D nI(n) }, D ni Refers to B ni The corresponding route abnormality degree, E n ={E n1 , E n2 ,……,E ni ,……,E nI(n) }, E ni Refers to B ni The corresponding route length and route weight set P are obtained through the following steps: S2, according to C, obtain the second traffic flow set F corresponding to B = {F1, F2, ..., F n ,……,F N }, where B n The corresponding second traffic flow list F n ={F n1 , F n2 ,……,F ni ,……,F nI(n) }, F ni Equal to B ni The average value of the first traffic flow of the two corresponding initial drone take-off and landing points; S3, according to B and H, obtain the route position importance set K corresponding to B = {K1, K2, ..., K n ,……,K N }, where B n Corresponding route location importance list K n ={K n1 , K n2 ,……,K ni ,……,K nI(n) }, K ni Equal to B ni The average importance of the take-off and landing points of the two corresponding initial UAV take-off and landing points; S4, according to B, D, E, F and K, obtain the route weight set P corresponding to B = {P1, P2, ..., P n ,……,P N }, where P n ={P n1 , P n2 ,……,P ni ,……,P nI(n) }, B ni The corresponding route weight P ni Meet the following conditions: P ni =β1×e^(-D ni )+β2×E ni / ((∑ n＝1 N (S i＝1 I(n) E ni )) / (S n＝1 N I(n)))+β3×F ni +β4×K ni , among them, β1 refers to the preset abnormality weight, β2 refers to the preset route length weight, β3 refers to the preset second traffic weight, and β4 refers to the preset second position weight.

8. The integrated optimization system for the coverage area of ​​UAV take-off and landing points according to claim 7 is characterized in that: The node betweenness set JS is obtained through the following steps: S5: Based on A, B, Q, and P, the initial UAV take-off and landing points are used as nodes, the predicted flight routes are used as edges, the take-off and landing point weights corresponding to the initial UAV take-off and landing points are used as the node values ​​of the corresponding nodes, and the route weights corresponding to the predicted flight routes are used as the edge values ​​of the corresponding edges to obtain a complex network model T. The complex network model T is used to filter the target UAV take-off and landing points from A. S61, based on T and E, obtain the shortest path between any two nodes in T; S62, the number of shortest paths passing through the nodes corresponding to the nth initial UAV take-off and landing point is determined as the node betweenness JS corresponding to the nth initial UAV take-off and landing point n ; S63, traverse n=1, 2, ..., N, and obtain the node betweenness set JS={JS1, JS2, ..., JS n ,……,JS N }.

Images