Unmanned aerial vehicle take-off and landing point site selection system based on take-off and landing point candidate priorities

Abstract

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle take-off and landing point site selection system based on take-off and landing point candidate priorities. Comprising a processor and a memory in which a preset prediction model, an initial unmanned aerial vehicle take-off and landing point set, an initial unmanned aerial vehicle take-off and landing point code set, a predicted flight route set, a take-off and landing point weight set, a route weight set, a node betweenness set, a preset key take-off and landing point code set and a preset reference vector are stored, the number of elements larger than 0 in the second take-off and landing point coding reference vector is reversely deduced through the number of elements 1 in the third take-off and landing point coding reference vector, and then the number of corresponding key take-off and landing points in the first take-off and landing point coding reference vector is reversely deduced; the prediction loss is constructed according to the preset coding threshold and the number of elements 1 in the third take-off and landing point coding reference vector, the parameters of the prediction model are updated, and the matching degree of the target priority ranking vector and the preset precondition is improved, so that the site selection rationality of the target unmanned aerial vehicle take-off and landing point is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a system for selecting a take-off and landing point for an unmanned aerial vehicle based on the priority of candidate take-off and landing points. Background Art

[0002] The concept of UAM (Urban Air Mobility) has attracted the attention of academia, industry and government since it was proposed. Its transportation method of using eVTOL (Electric Vertical Takeoff and Landing) and unmanned aircraft can not only meet the transportation needs of various distances within the city, but also improve the level of transportation safety and efficiency.

[0003] Based on the take-off and landing facilities set up in the city, drones can alleviate the pressure on ground traffic to a certain extent, optimize the work structure of logistics delivery personnel, and meet the high-value, small-volume, and time-sensitive logistics needs of urban residents for express delivery, takeout, and medicine. Therefore, with the widespread application of drones, the reasonable location of drone take-off and landing points has become crucial.

[0004] As a collection of new technologies, drone technologies are developing rapidly, but there is still a gap in the research on automated airport facilities in urban areas that can widely adapt to drone pilot aviation systems. When selecting the location of drone take-off and landing points, existing technologies usually rely on professionals to analyze the local geographical coordinates, altitude, topography, airspace conditions and other geographical environments to determine the location of drone take-off and landing points, which requires a lot of time and manpower investment. It is not only difficult to meet the needs of rapid site selection, but also the scientificity and rationality of the site selection results are poor, and the reliability, stability and safety of drones cannot be guaranteed.

[0005] Therefore, the planning and layout of drone take-off and landing facilities is very important to meet the needs of urban logistics. How to improve the rationality of the site selection of drone take-off and landing points has become an urgent problem to be solved. Summary of the invention

[0006] In view of the above technical problems, the technical solution adopted by the present invention is a UAV take-off and landing point site selection system based on the take-off and landing point candidate priority, including a processor and a memory storing a computer program, the memory also stores a preset prediction model, an initial UAV 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 1n , ..., 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 A n The corresponding initial take-off and landing point code, B n = {B n1 , B n2 , ..., B ni , ..., B nI(n)}, B ni A n The corresponding i-th predicted flight route, Q n 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 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, the last N-Щ elements are value 0, Щ refers to the preset number of target UAV take-off and landing points, when the computer program is executed by the processor, the following steps are implemented:

[0007] S100, input A, B, Q, P and JS into a preset prediction model, and obtain a 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.

[0008] 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 It refers to the initial UAV take-off and landing point with the prediction priority of σth, PX Щ It refers to the initial UAV take-off and landing point with the predicted priority of the Щth position, σ=1, 2, …, Щ.

[0009] 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 PX σ The corresponding first take-off and landing point code, BM Щ PX Щ The corresponding first take-off and landing point code.

[0010] S400, transposed vector of GJ and BM 1 Multiply them to obtain the second take-off and landing point coding reference vector BM 2 .

[0011] S500, for BM 2 Perform binarization processing to obtain the third take-off and landing point coding reference vector BM 3 .

[0012] 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 .

[0013] S700, update the parameters of the preset prediction model according to Loss until Loss=0, and obtain the target prediction model.

[0014] S800, input A, B, Q, P and JS into the target prediction model to obtain the target priority ranking vector.

[0015] S900: Determine the first initial UAV take-off and landing points in the target priority sorting vector as the target UAV take-off and landing points.

[0016] The present invention has at least the following beneficial effects: A, B, Q, P and JS are input into a preset prediction model to obtain a prediction priority sorting 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 them 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 infer BM 1 The number of key take-off and landing points corresponding to the preset coding thresholds YZ and BM 3The number of elements 1 in the formula is used to construct the prediction loss, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] Figure 1 A flowchart of an execution computer program of a UAV take-off and landing point site selection system based on the take-off and landing point candidate priorities provided in Embodiment 1 of the present invention.

[0019] Figure 2 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 Embodiment 2 of the present invention.

[0020] Figure 3 A flowchart of an execution computer program of a complex network model building system for selecting the site of a UAV take-off and landing point provided in Example 3 of the present invention.

[0021] Figure 4 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 4 of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification 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] Embodiment 1

[0025] This embodiment 1 provides a drone take-off and landing point site selection system based on the take-off and landing point candidate priority, such as Figure 1 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 A n The corresponding initial take-off and landing point code, Bn = {B n1 , B n2 , ..., B ni , ..., B nI(n)}, B ni A n The corresponding i-th predicted flight route, Q n 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 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 1, and the last N-Щ elements are 0, Щ refers to the preset number of target UAV take-off and landing points.

[0026] Among them, the initial UAV take-off and landing point may refer to a take-off and landing point that can be used as the layout location of the UAV take-off and landing facilities in a geographical area where the UAV take-off and landing facilities need to be sited. There are predicted flight routes suitable for UAV flights to transport goods between the initial UAV take-off and landing points. The location of the initial UAV take-off and landing point and the trajectory of the predicted flight route can be determined by the implementer based on actual conditions.

[0027] The initial UAV take-off and landing point code may be a code pre-set by the implementer according to actual conditions, and is used to identify the initial UAV take-off and landing point, such as 1, 2, 3...

[0028] 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 predicted 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.

[0029] The key take-off and landing points can be the take-off and landing points where the drone take-off and landing facilities must be set among the initial drone take-off and landing points provided by the implementer. They can be determined by professionals based on the actual conditions of each initial drone take-off and landing point based on manual experience, as a prerequisite for selecting the drone take-off and landing point, that is, the prediction priorities corresponding to the M key take-off and landing points in the final prediction priority sorting vector belong to the top position. The preset key take-off and landing point code can be a code pre-set by the implementer according to the actual situation, used to identify the key take-off and landing point, such as -1, -2, -3...

[0030] The preset prediction model is used to extract and map the features of the input unordered initial UAV take-off and landing points, predicted 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.

[0031] 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 matching degree between the sorting result of the prediction priority sorting vector and the premise condition.

[0032] When the computer program is executed by a processor, the following steps are performed:

[0033] S100, input A, B, Q, P and JS into a preset prediction model, and obtain a 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.

[0034] Among them, 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.

[0035] In a specific implementation manner, 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 A n The corresponding take-off and landing point location importance, C n An The corresponding first traffic flow, G n A n The corresponding first building interference degree and the take-off and landing point weight set Q are obtained by the following steps:

[0036] S1, based on 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:

[0037] 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, α3 refers to the preset building interference weight, and e refers to a natural constant.

[0038] 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 interference degree of the 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.

[0039] Among them, the greater the importance of the take-off and landing point corresponding to the initial UAV take-off and landing point, the greater the first traffic flow, and the smaller the first building interference, the higher the possibility of setting up UAV take-off and landing facilities at the initial UAV take-off and landing point.

[0040] 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.

[0041] The specific values ​​of α1, α2 and α3 can be set by the implementer according to actual conditions.

[0042] In the above, the importance of the take-off and landing point location, the influence of 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 are measured, and the take-off and landing point weights corresponding to each initial drone take-off and landing point are obtained, which improves the accuracy of the take-off and landing point weights, 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 points.

[0043] In a specific implementation manner, 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:

[0044] 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 at the corresponding two initial drone take-off and landing points.

[0045] S3, based on B and H, obtain the route location importance set K corresponding to B = {K1, K2, ..., K n , ..., K N}, where B n The corresponding route location importance list K n ={K n1 , K n2 , ..., K ni , ..., K nI(n)}, K ni Equal to B ni The average value of the importance of the take-off and landing point locations of the two corresponding initial UAV take-off and landing points.

[0046] S4, based on 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:

[0047] 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

[0048] 1 refers to the preset abnormal 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.

[0049] Among them, the degree of route abnormality can be used to characterize the degree of damage to buildings, human lives and properties when a UAV encounters a flight abnormality in the corresponding predicted flight route.

[0050] The average value of the first traffic flow of the two initial drone take-off and landing points corresponding to each predicted flight route can be used as the second traffic flow of the corresponding predicted flight route to characterize the degree of security of the corresponding predicted flight route for drone transportation of goods, which serves as the basis for obtaining the route weight corresponding to the predicted flight route.

[0051] 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 predicted flight route can represent the route position importance of the corresponding predicted flight route, which serves as the basis for obtaining the route weight corresponding to the predicted flight route.

[0052] The smaller the route abnormality corresponding to the predicted 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 predicted flight route is the flight path of the drone.

[0053] Therefore, the route weight corresponding to the predicted 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 drone take-off and landing point.

[0054] The specific values ​​of β1, β2, β3 and β4 can be set by the implementer according to the actual situation.

[0055] As mentioned above, the influence 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 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.

[0056] In a specific implementation, the node betweenness set JS is obtained by the following steps:

[0057] S5, 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 to obtain the complex network model T, wherein the complex network model T is used to screen the target UAV take-off and landing point from A.

[0058] S61, according to T and E, obtain the shortest path between any two nodes in T.

[0059] 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 .

[0060] S63, traverse n = 1, 2, ..., N, and obtain the node betweenness set JS = {JS1, JS2, ..., JS n , ..., JS N}.

[0061] In a specific implementation manner, the memory further stores a clustering coefficient set JL={JL1, JL2, . . . , JL n , ..., JL N}, JL n An The corresponding clustering coefficient, M preset key take-off and landing points are obtained through the following steps:

[0062] S2000: According to A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, a 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:

[0063] HX n =QZ1×Q n +QZ2×∑ i＝1 I(n) (P ni / I(n))+QZ3×I(n)+QZ4×JS n +QZ5×JL n .

[0064] S3000, based on HX, selects M candidate UAV take-off and landing points from N initial UAV take-off and landing points.

[0065] 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:

[0066] 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

[0067] It refers to the preset abnormal weight, β2 refers to the preset route length weight, and β3 refers to the preset second traffic weight.

[0068] S5000: According to FZ, the screening scores FS of M candidate drone take-off and landing points are obtained, where FS meets the following conditions:

[0069] FS=(Σ n＝1 N (FZ n -FZ0) 2 / N, where the candidate take-off and landing point prediction mean FZ0 ​​= Σ n＝1 N (FZ n ) / N.

[0070] S7000, 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 , FS≤FS 0 The M candidate UAV take-off and landing points at the time are determined as M key take-off and landing points, FS 0 Refers to the preset screening score threshold.

[0071] As mentioned above, the more the shortest paths passing through the nodes corresponding to the nth initial UAV take-off and landing point are, the greater the influence of the nth initial UAV take-off and landing point is, and the higher the possibility of setting up UAV take-off and landing facilities at the initial UAV take-off and landing point is.

[0072] 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 It refers to the initial UAV take-off and landing point with the prediction priority of σth, PX Щ It refers to the initial UAV take-off and landing point with the predicted priority of the Щth position, σ=1, 2, …, Щ.

[0073] Among them, based on the task of screening out the target UAV take-off and landing points, PX and YS are vector-multiplied 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.

[0074] 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 PX σ The corresponding first take-off and landing point code, BM Щ PX Щ The corresponding first take-off and landing point code.

[0075] In a specific implementation, S300 further includes the following steps:

[0076] 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 as PX σ The corresponding first take-off and landing point code.

[0077] 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 as PX σ The corresponding first take-off and landing point code.

[0078] 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).

[0079] Among them, according to the type of the extracted top 10 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 10 initial UAV take-off and landing points is obtained, so as to judge the matching degree between the top 10 initial UAV take-off and landing points and the prerequisites through coding, so as to provide a data basis for measuring the sorting effect of the prediction priority sorting vector PX.

[0080] 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.

[0081] As mentioned above, the first 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 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.

[0082] S400, transposed vector of GJ and BM 1 Multiply them to obtain the second take-off and landing point coding reference vector BM 2 .

[0083] 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 2 The matrix dimension is M×N.

[0084] Among them, (GJ) T The codes of each key take-off and landing point are less than 0, BM 1 The first landing point code of the corresponding key landing point is less than 0, BM 1 If the first landing point code of the corresponding non-critical 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 key take-off and landing point codes in is greater than 0, BM 1 The 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 characterizes the matching degree between the corresponding prediction priority sorting vector PX and the prerequisite.

[0085] For example, BM 2 The number of elements greater than 0 in is M×Amount, so we can determine that BM 1 The corresponding number of key take-off and landing points is Amount.

[0086] S500, for BM 2 Perform binarization processing to obtain the third take-off and landing point coding reference vector BM 3 .

[0087] In a specific implementation, S500 further includes the following steps:

[0088] 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.

[0089] S520, if BM 2 If the element in the μth row and σth column is less than 0, then BM 3The elements in the μth row and the σth column are set to 0.

[0090] S530, BM 3 The elements in columns Щ+1 to N are set to 0.

[0091] 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 .

[0092] Among them, BM 2 Binarization is performed, and elements greater than 0 are set to 1, and elements less than or equal to 0 are set to 0. 3 The data scale is normalized to avoid the influence of the value of each element on the prediction loss, improve the accuracy of the prediction loss, and further improve the accuracy of the judgment of the matching degree between the prediction priority sorting vector PX and the prerequisite.

[0093] 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 .

[0094] 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 position, SUM(BM 3 ) can characterize BM 3 The number of elements 1 in the 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 the number of 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.

[0095] SUM(BM 3 ) ranges from [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.

[0096] In one embodiment, YZ=M 2 .

[0097] In a specific embodiment, p=1000.

[0098] The specific value of ρ can be set by the implementer according to the actual situation, for example, ρ=100, 1000, 10000 and other values, so as to amplify the prediction loss, improve the training effect of the prediction model, and improve the prediction accuracy of the prediction model.

[0099] As mentioned above, the prediction loss is increased to characterize the matching degree between the prediction priority ranking vector PX and the premise, which provides a data basis for updating the parameters of the prediction model to improve the prediction accuracy of the prediction model.

[0100] S700, update the parameters of the preset prediction model according to Loss until Loss=0, and obtain the target prediction model.

[0101] S800, input A, B, Q, P and JS into the target prediction model to obtain the target priority ranking vector.

[0102] S900: Determine the first initial UAV take-off and landing points in the target priority sorting vector as the target UAV take-off and landing points.

[0103] 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 UAV take-off and landing points in the target priority sorting vector are determined as the target UAV take-off and landing points, which serve as the setting location of the UAV take-off and landing facilities to provide support for UAV delivery logistics and other tasks.

[0104] 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 them to obtain the second take-off and landing point coding reference vector BM 2 , for BM 2Perform 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 infer BM 1 The number of key take-off and landing points corresponding to the preset coding thresholds YZ and BM 3 The number of elements 1 in the is used to construct the prediction loss, 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 accuracy of the target UAV take-off and landing points.

[0105] Embodiment 2

[0106] This embodiment 2 provides a method for determining the priority of candidate take-off and landing points of unmanned aerial vehicles based on a complex network model. Figure 2 As shown, the method for determining the priority of candidate take-off and landing points of unmanned aerial vehicles based on a complex network model includes:

[0107] 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 ,……,EN} 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 A n The corresponding i-th predicted flight route, Q n 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 A n The corresponding node betweenness, JL n 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.

[0108] Among them, 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 predicted 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. The clustering coefficient can represent the degree of clustering between the corresponding initial drone take-off and landing point and other initial drone take-off and landing points.

[0109] S2000: According to A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, a 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:

[0110] HX n =QZ1×Q n +QZ2×∑ i＝1 I(n) (P ni / I(n))+QZ3×I(n)+QZ4×JS n +QZ5×JL n .

[0111] 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 to the initial UAV take-off and landing point, the more routes of the predicted flight route connected to 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.

[0112] Therefore, in combination with 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.

[0113] Among them, the specific values ​​of QZ1, QZ2, QZ3, QZ4 and QZ5 can be set by the implementer according to actual conditions.

[0114] 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, and 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, which improves the accuracy of the candidate priority and thus improves the accuracy of the screening of target UAVs.

[0115] S3000, based on HX, selects M candidate UAV take-off and landing points from N initial UAV take-off and landing points.

[0116] In a specific implementation, S3000 further includes the following steps:

[0117] 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.

[0118] 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:

[0119] 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

[0120] It refers to the preset abnormal weight, β2 refers to the preset route length weight, and β3 refers to the preset second traffic weight.

[0121] Among them, the smaller the route abnormality degree of all predicted flight routes corresponding to the candidate UAV take-off and landing points, the longer the route length, and the larger the second traffic flow, it means that the candidate UAV take-off and landing points have 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 points. Correspondingly, the larger the candidate take-off and landing point prediction score corresponding to the candidate UAV take-off and landing points.

[0122] As described above, based on D, E and F, the possibility of setting up UAV take-off and landing facilities at the candidate UAV 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.

[0123] S5000: According to FZ, the screening scores FS of M candidate drone take-off and landing points are obtained, where FS meets the following conditions:

[0124] FS=(Σ n＝1 N (FZ n -FZ0) 2 / N, where the candidate take-off and landing point prediction mean FZ0 ​​= Σ n＝1 N (FZ n ) / N.

[0125] In order to ensure that the delivery efficiency and delivery intensity of the drones at each take-off and landing point are as balanced as possible, this embodiment hopes that the prediction scores of the candidate take-off and landing points between the selected target drone take-off and landing points are as consistent as possible.

[0126] In the above, by measuring the differences between the candidate take-off and landing point prediction scores between the M candidate drone take-off and landing points, the screening scores FS of the M candidate drone take-off and landing points are obtained to characterize the screening accuracy of the M candidate drone take-off and landing points, which is used as the basis for updating the initial weight set to re-screen the M take-off and landing points as the target drone take-off and landing points, and provide setting locations for drone take-off and landing facilities, so as to achieve 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.

[0127] 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 at this time is used to screen the target UAV take-off and landing point from the N initial UAV take-off and landing points, FS 0 Refers to the preset screening score threshold.

[0128] Among them, the preset screening score threshold FS 0 The specific value can be set by the implementer according to the actual situation.

[0129] The candidate priority corresponding to each initial UAV take-off and landing point is obtained by comprehensively considering 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 point, as well as the preset initial weights. Therefore, the accuracy of the preset initial weights affects the accuracy of the candidate priority, and thus affects the screening accuracy of the candidate UAV take-off and landing points.

[0130] 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 when the default initial weight set is replaced by the updated QZ, and repeat step S2000 to obtain new M candidate drone take-off and landing points until FS ≤ FS 0 , get FS≤FS 0 The 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 screening the UAV take-off and landing points.

[0131] As described above, 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}, according to HX, M candidate drone take-off and landing points are selected from the 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 between 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, which is used as the basis for updating the initial weight set and the candidate priority set to screen 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 distribution efficiency and distribution intensity of the UAVs corresponding to each target UAV take-off and landing point, and improving the overall work efficiency of the UAVs.

[0132] Embodiment 3

[0133] This embodiment 3 provides a complex network model construction system for selecting the location of drone take-off and landing points. Figure 3 As shown, the complex network model construction system for selecting the take-off and landing points of unmanned aerial vehicles includes a processor and a memory storing a computer program, and the memory also stores an initial set of take-off and landing points of unmanned aerial vehicles 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, ..., C n , ..., 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 A n The corresponding i-th predicted flight route, H n A n The corresponding take-off and landing point location importance, C n A n The corresponding first traffic flow, G n 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 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.

[0134] When the computer program is executed by a processor, the following steps are performed:

[0135] S1, based on 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:

[0136] 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, α3 refers to the preset building interference weight, and e refers to a natural constant.

[0137] The specific values ​​of α1, α2 and α3 can be set by the implementer according to actual conditions.

[0138] In the above, the importance of the take-off and landing point location, the influence of 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 are measured, and the take-off and landing point weights corresponding to each initial drone take-off and landing point are obtained, which improves the accuracy of the take-off and landing point weights, 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 points.

[0139] 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 at the corresponding two initial drone take-off and landing points.

[0140] S3, based on B and H, obtain the route location importance set K corresponding to B = {K1, K2, ..., K n , ..., K N}, where B n The corresponding route location importance list K n ={K n1 , K n2 , ..., K ni , ..., K nI(n)}, K ni Equal to B ni The average value of the importance of the take-off and landing point locations of the two corresponding initial UAV take-off and landing points.

[0141] S4, based on 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:

[0142] 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

[0143] 1 refers to the preset abnormal 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.

[0144] As mentioned above, the influence 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 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.

[0145] S5, 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 to obtain the complex network model T, wherein the complex network model T is used to screen the target UAV take-off and landing point from A.

[0146] Among them, the complex network model T is used to screen the target UAV take-off and landing points from A. The target UAV take-off and landing points can be used as the location for setting up UAV take-off and landing facilities to provide support for UAV delivery logistics and other tasks.

[0147] As mentioned above, based on H, C and G, we can obtain 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 degree 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 guarantee of the corresponding predicted flight route for the transportation of goods by drones. 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 taken as the node, the predicted flight route is taken as the edge, the take-off and landing point weight corresponding to the initial UAV take-off and landing point is taken as the node value of the corresponding node, and the route weight corresponding to the predicted flight route is taken 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 screening accuracy 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.

[0148] Embodiment 4

[0149] This fourth embodiment provides a method for generating an initial location for a take-off and landing point of a UAV based on multiple factors, such as Figure 4As shown, 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:

[0150] S10, obtain Θ original UAV take-off and landing points, several predicted flight routes corresponding to each original UAV take-off and landing point, and the route length, second traffic flow and route abnormality degree corresponding to each predicted flight route, wherein the second traffic flow refers to the average value of the first traffic flow corresponding to the two original UAV take-off and landing points corresponding to the predicted flight route, and the route abnormality degree is used to characterize the degree of damage to the outside world when a UAV encounters a flight abnormality on the corresponding predicted flight route, and Θ is an integer greater than 0.

[0151] In a specific implementation, S10 further includes the following steps:

[0152] S11, obtaining 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.

[0153] 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.

[0154] Among them, implementers can adopt 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.

[0155] Among them, map data may include factors such as terrain data, obstacle data, and weather condition data.

[0156] S20, according to a 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, 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 are obtained.

[0157] Among them, for any original UAV take-off and landing point, the sum of the route lengths of all predicted flight routes corresponding to the current original UAV take-off and landing point is determined as the total route length corresponding to the current original UAV take-off and landing point, and the sum of the second traffic flows of all predicted flight routes corresponding to the current original UAV take-off and landing point is determined as the total traffic volume corresponding to the current original UAV take-off and landing point, which serves as the basis for screening UAV take-off and landing points.

[0158] S30, according to 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 UAV take-off and landing point, select ζ take-off and landing points to be merged from Θ original UAV take-off and landing points, where 0<ζ<Θ.

[0159] Among them, the specific values ​​of the preset route quantity threshold, the preset route length threshold and the preset traffic threshold can be set by the implementer according to actual conditions.

[0160] In a specific implementation, S30 further includes the following steps:

[0161] 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.

[0162] S32, traverse Θ original UAV take-off and landing points, and obtain ζ take-off and landing points to be merged.

[0163] Among them, the more predicted flight routes corresponding to the current original UAV take-off and landing point, the longer the total length of the routes, and the larger the total traffic volume, it means that the original UAV take-off and landing point has a lower demand for UAV take-off and landing facilities, and the possibility of setting up UAV take-off and landing facilities at the original UAV take-off and landing point is higher. Conversely, it means that the demand for UAV take-off and landing facilities is higher, and the possibility of setting up UAV take-off and landing facilities at the original UAV take-off and landing point is lower.

[0164] 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 setting rationality of drone take-off and landing facilities by merging take-off and landing points.

[0165] 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.

[0166] 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 n-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 n-1 original UAV take-off and landing points, the route length priority corresponding to the intermediate take-off and landing point is obtained.

[0167] In a specific implementation, S50 further includes the following steps:

[0168] S51, determining 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.

[0169] S52, determining 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.

[0170] 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.

[0171] S54, determining 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.

[0172] Among them, the first route length can represent the average distance from the current take-off and landing point to be merged directly to the other n-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 n-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, the current take-off and landing point to be merged first to the intermediate take-off and landing point and then to the other n-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 n-1 original drone take-off and landing points, when the intermediate take-off and landing point is used as the transit point from the current take-off and landing point to the other n-1 original drone take-off and landing points, the drone's flight distance is shorter, which means 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 n-1 original drone take-off and landing points is small.

[0173] 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.

[0174] In the above, according to 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 and the intermediate take-off and landing points, as well as the route lengths between the intermediate take-off and landing point and the other n-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 n-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, which 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 merger.

[0175] S60, according to the route abnormality degree of the predicted flight route 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, and the route abnormality degree of the predicted flight route between the intermediate take-off and landing point and the other n-1 original UAV take-off and landing points, obtain the route abnormality degree priority corresponding to the intermediate take-off and landing point.

[0176] In a specific implementation, S60 further includes the following steps:

[0177] S61, determining the average value of the route abnormality degree 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 abnormality degree.

[0178] S62, determining the average value of the route abnormality degree 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 degree.

[0179] 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.

[0180] S64, determining 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.

[0181] Among them, the first route abnormality degree can characterize the average abnormality degree of the drone from the current take-off and landing point to be merged directly to the other η-1 original drone take-off and landing points, the second route abnormality degree can characterize the average abnormality degree of the drone from the current intermediate take-off and landing point directly to the other η-1 original drone take-off and landing points, and the third route abnormality degree can characterize the average abnormality degree of the drone from the current take-off and landing point to be merged 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 be merged to the other η-1 original drone take-off and landing points, when the intermediate take-off and landing point is used as the transfer point from the current take-off and landing point to be merged to the other η-1 original drone take-off and landing points, the flight abnormality degree of the drone is lower, which 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 to the intermediate take-off and landing point, the impact on the safety of the drone is small.

[0182] 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.

[0183] In the above, according to the route abnormality 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 abnormality degree between the intermediate take-off and landing point and the other n-1 original UAV take-off and landing points, the route abnormality 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 with the current take-off and landing point to be merged, and the impact on the safety of the UAV after merging the take-off and landing point to be merged into the intermediate take-off and landing point, as the basis for merging the take-off and landing points to be merged, the rationality of the take-off and landing point merger is improved.

[0184] S70, obtaining the merge priority corresponding to the current intermediate take-off and landing point according to the route length priority and the route abnormality priority.

[0185] In a specific implementation, S70 further includes the following steps:

[0186] S71, obtaining a preset route length weight and a preset abnormality degree weight.

[0187] S72, obtaining 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 degree priority and the preset anomaly degree weight.

[0188] 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, and 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.

[0189] Among them, the specific values ​​of the preset route length weight and the preset abnormality degree weight can be set by the implementer according to the actual situation.

[0190] As described above, in combination with the route length priority, the preset route length weight, the route anomaly degree priority and the preset anomaly degree weight, the merging priority corresponding to the current intermediate take-off and landing point is obtained to characterize the priority of merging the current take-off and landing point to be merged into the current intermediate take-off and landing point, which serves as the basis for merging the current take-off and landing point to be merged, thereby improving the rationality of the take-off and landing point merging.

[0191] 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.

[0192] S90: Merge the current take-off and landing point to be merged with the original UAV take-off and landing point corresponding to the largest merging priority, and determine the merged take-off and landing point as the initial UAV take-off and landing point.

[0193] 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, which have high demand for drone take-off and landing facilities and high possibility of setting up drone take-off and landing facilities, and 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 site selection, so that the setting locations of drone take-off and landing facilities match the take-off and landing point requirements, thereby improving the relative coverage and setting rationality of drone take-off and landing facilities.

[0194] In the above, according to the predicted number of flight routes, total route length and total traffic volume corresponding to each original UAV take-off and landing point obtained, 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, and according to the route length and route abnormality 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 abnormality 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 abnormality 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 with a low possibility of setting up drone take-off and landing facilities, 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 point, 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.

[0195] 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 drone take-off and landing point selection system based on the priority of candidate take-off and landing points, characterized in that: The UAV 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, wherein the memory also stores a preset prediction model, an initial UAV 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 A n The corresponding initial take-off and landing point code, B n = {B n1 , B n2 , ..., B ni , ..., B nI(n) }, B ni A n The corresponding i-th predicted flight route, Q n 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 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 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, 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, the last N-Щ elements are value 0, Щ refers to the preset number of target drone take-off and landing points, when the computer program is executed by the processor, the following steps are implemented: 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 It refers to the initial UAV take-off and landing point with the prediction priority of σth, PX Щ It refers to the initial UAV take-off and landing point with the predicted priority of the Щth position, σ=1, 2, …, Щ; 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 PX σ The corresponding first take-off and landing point code, BM Щ PX Щ The corresponding first take-off and landing point code; S400, transposed vector of GJ and BM 1 Multiply them 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 Loss until Loss=0, 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 initial UAV take-off and landing points in the target priority sorting vector as the target UAV take-off and landing points.

2. The UAV take-off and landing point selection system based on the take-off and landing point candidate priority according to claim 1 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 A n The corresponding take-off and landing point location importance, C n A n The corresponding first traffic flow, G n A n The corresponding first building interference degree and the take-off and landing point weight set Q are obtained by the following steps: S1, based on 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, α3 refers to the preset building interference weight, and e refers to a natural constant.

3. The UAV take-off and landing point selection system based on the take-off and landing point candidate priority according to claim 2 is characterized in that: The memory also stores a route anomaly 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: 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, based on B and H, obtain the route location importance set K corresponding to B = {K1, K2, ..., K n , ..., K N }, where B n The corresponding route location importance list K n ={K n1 , K n2 , ..., K ni , ..., K nI(n) }, K ni Equal to B ni The average of the importance of the take-off and landing points of the two corresponding initial UAV take-off and landing points; S4, based on 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 a preset abnormality weight, β2 refers to a preset route length weight, β3 refers to a preset second traffic weight, and β4 refers to a preset second position weight.

4. The UAV take-off and landing point selection system based on the take-off and landing point candidate priority according to claim 3 is characterized in that: The node betweenness set JS is obtained through the following steps: S5, according to A, B, Q and P, the initial UAV take-off and landing points are taken as nodes, the predicted flight routes are taken as edges, the take-off and landing point weights corresponding to the initial UAV take-off and landing points are taken as the node values ​​of the corresponding nodes, and the route weights corresponding to the predicted flight routes are taken as the edge values ​​of the corresponding edges, and a complex network model T is obtained, wherein the complex network model T is used to screen the target UAV take-off and landing points from A; S61, according to T and E, obtain the shortest path between any two nodes in T; S62, determining the number of shortest paths passing through the nodes corresponding to the nth initial UAV take-off and landing point 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 }.

5. The system for selecting the take-off and landing points of unmanned aerial vehicles based on the priority of candidate take-off and landing points according to claim 3, characterized in that: The memory also stores a clustering coefficient set JL={JL1, JL2, . . . , JL n , ..., JL N }, JL n A n The corresponding clustering coefficient, M preset key take-off and landing points are obtained through the following steps: S2000: According to A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, a 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: HX n =QZ1×Q n +QZ2×∑ i＝1 I(n) (P ni / I(n))+QZ3×I(n)+QZ4×JS n +QZ5×JL n ; S3000, based on HX, selects M candidate UAV take-off and landing points from N initial UAV take-off and landing points; 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: FZ n =β1×(Σ i＝1 I(n) e^(-D ni ))+β2×(∑ n＝1 N (S i＝1 I(n) E ni ))+β3×(∑ n＝1 N (S i＝1 I(n) F ni )), among them,b 1 refers to the preset abnormal weight, β2 refers to the preset route length weight, and β3 refers to the preset second flow weight; S5000: According to FZ, the screening scores FS of M candidate drone take-off and landing points are obtained, where FS meets the following conditions: FS=(Σ n＝1 N (FZ n -FZ0) 2 / N, where the candidate take-off and landing point prediction mean FZ0 ​​= Σ n＝1 N (FZ n ) / N; S7000, 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 , FS≤FS 0 The M candidate UAV take-off and landing points at the time are determined as M key take-off and landing points, FS 0 Refers to the preset screening score threshold.

6. The UAV take-off and landing point selection system based on the take-off and landing point candidate priority according to claim 1 is characterized in that: S300 also includes the following steps: 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 as PX σ The corresponding first take-off and landing point code; 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 as PX σ The corresponding first take-off and landing point code; 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).

7. The system for selecting the take-off and landing points of unmanned aerial vehicles based on the priority of candidate take-off and landing points according to claim 6, characterized in that: S500 also includes the following steps: 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; 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; S530, BM 3 The elements in the Щ+1th to Nth columns are set to 0; 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 .

8. The system for selecting the take-off and landing points of unmanned aerial vehicles based on the priority of candidate take-off and landing points according to claim 7, characterized in that: YZ=M 2 。

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