A complex network model construction system for UAV take-off and landing point selection

Abstract

The present invention relates to the field of drone technology, and in particular to a complex network model construction system for drone take-off and landing point site selection, comprising a processor and a memory storing a computer program, an initial drone take-off and landing point set, a predicted flight route set, a take-off and landing point location importance set, a first traffic flow set, a first building interference degree set, a route anomaly degree set, and a route length set. When the computer program is executed by the processor, take-off and landing point weights corresponding to the initial drone take-off and landing points and route weights corresponding to the predicted flight routes are obtained, and the initial drone take-off and landing points are used as nodes, the predicted flight routes are used as edges, the take-off and landing point weights are used as node values, and the route weights are used as edge values ​​of the corresponding edges to obtain a complex network model for screening target drone take-off and landing points from the initial drone take-off and landing point set, thereby improving the construction accuracy of the complex network model and thereby improving the rationality of site selection for the drone take-off and landing points.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a complex network model construction system for selecting take-off and landing points for UAVs. Background Art

[0002] With the rapid development of drone technology, drones are increasingly being used in various fields, such as logistics and distribution, aerial photography and mapping, and environmental monitoring. Based on the take-off and landing facilities set up within cities, drones can, to a certain extent, alleviate the pressure on ground transportation, optimize the work structure of logistics delivery personnel, and meet the logistics needs of urban residents for high-value, small-volume, and time-sensitive items such as express delivery, takeout, and pharmaceuticals.

[0003] The execution of various drone missions relies on the rational construction of complex network models and the appropriate location of drone takeoff and landing points. By finding the optimal flight path from the takeoff point to the target point, not only can flight distance and time be reduced, energy consumption can be lowered, and endurance can be improved, thereby completing more missions under limited energy conditions, but airspace can also be rationally divided and planned, improving airspace utilization and avoiding airspace chaos and conflicts. This helps air traffic management departments regulate drone flights and ensure airspace safety and order. Therefore, with the rapid development and widespread application of various drone technologies, the construction of complex network models for drone takeoff and landing point location selection has become crucial.

[0004] In existing technologies, the construction of complex network models usually relies on manual experience or simple rules for judgment. For example, based on the operator's experience, a relatively intuitive route is planned between known take-off and landing points, and then a complex network model is constructed based on the shortest straight-line distance between the take-off and landing points. This approach lacks scientific theoretical basis and quantitative analysis, and has great uncertainty and subjectivity in the construction process, which may lead to unreasonable selection of take-off and landing points, affecting the flight efficiency, safety and reliability of the drone. For example, the take-off and landing points may be too remote or have inconvenient transportation, resulting in increased transportation and maintenance costs for the drone; or there may be significant interference from buildings around the take-off and landing points, affecting the flight safety of the drone; or the route planning between the take-off and landing points may be unreasonable, resulting in excessively long flight distances and significant damage to the outside world when abnormal situations occur.

[0005] Therefore, constructing a complex network model for the site selection of drone take-off and landing points is very important to meet the needs of urban logistics. How to improve the accuracy of the construction of the complex network model and thus 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 response to the above technical problems, the technical solution adopted by the present invention is a complex network model construction system for selecting the location of drone take-off and landing points. The complex network model construction system includes a processor and a memory storing a computer program. The memory also stores an 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 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 Refers to A n The corresponding i-th predicted flight route, H n Refers to A n The importance of the corresponding take-off and landing point position, C n Refers to A n The corresponding first traffic flow, G n Refers to A n The corresponding first building interference level, D n ={D n1 , D n2 ,……,D ni ,……,D nI(n)}, D ni Refers to B ni The corresponding route abnormality degree is used to characterize the degree of damage to the outside world when the UAV encounters a flight abnormality on the corresponding predicted flight route. n ={E n1 , E n2 ,……,E ni ,……,E nI(n)}, Eni 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. When the computer program is executed by the processor, the following steps are implemented:

[0007] S1, according to H, C and G, obtain the take-off and landing point weight set Q = {Q1, Q2, ..., Q n ,……,Q N}, where A n The corresponding take-off and landing point weight Q n Meet the following conditions:

[0008] Q n =α1×H n +α2×C n +α3×e^(-G n ), where 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.

[0009] 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.

[0010] S3, according to B and H, obtain the route position importance set K corresponding to B = {K1, K2, ..., K n ,……,K N}, where B n Corresponding route location importance list K n ={K n1 , K n2 ,……,K ni ,……,K nI(n)}, K ni Equal to B ni The average importance of the take-off and landing point locations of the two corresponding initial UAV take-off and landing points.

[0011] S4, according to B, D, E, F and K, obtain the route weight set P corresponding to B = {P1, P2, ..., P n ,……,P N}, where P n ={P n1 , P n2 ,……,P ni ,……,P nI(n)}, B ni The corresponding route weight P ni Meet the following conditions:

[0012] 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

[0013] 1 refers to the preset abnormality weight, β2 refers to the preset route length weight, β3 refers to the preset second traffic weight, and β4 refers to the preset second position weight.

[0014] S5, based on A, B, Q and P, takes the initial UAV take-off and landing points as nodes, the predicted flight routes as edges, the take-off and landing point weights corresponding to the initial UAV take-off and landing points as the node values ​​of the corresponding nodes, and the route weights corresponding to the predicted flight routes as the edge values ​​of the corresponding edges, to obtain the complex network model T, where the complex network model T is used to screen the target UAV take-off and landing points from A.

[0015] The present invention has at least the following beneficial effects: According to H, C and G, the take-off and landing point weight set Q = {Q1, Q2, ..., Q n ,……,Q N}, weigh the influence of the importance of the take-off and landing point location, the first traffic flow and the first building interference level on the possibility of setting up drone take-off and landing facilities at the initial drone take-off and landing point, obtain the take-off and landing point weight corresponding to each initial drone take-off and landing point, improve the accuracy of the take-off and landing point weight, and obtain the second traffic flow set F corresponding to B according to C = {F1, F2, ..., F n ,……,F N}, to characterize the degree of protection of the corresponding predicted flight route for the drone's cargo transportation. According to B and H, the importance set of the route position corresponding to B is obtained, K = {K1, K2, ..., K n,……,K N}, according to B, D, E, F and K, obtain the route weight set P corresponding to B = {P1, P2, ..., P n ,……,P N}, measure the impact of the route abnormality, route length, second traffic flow and route location importance on the possibility of the predicted flight route as the flight path of the UAV, obtain the route weight corresponding to each predicted flight route, and improve the accuracy of the route weight. According to A, B, Q and P, the initial UAV take-off and landing point is used as the node, the predicted flight route is used as the edge, the take-off and landing point weight corresponding to the initial UAV take-off and landing point is used as the node value of the corresponding node, and the route weight corresponding to the predicted flight route is used as the edge value of the corresponding edge. The complex network model T is obtained and used to screen the target UAV take-off and landing point from A, which improves the construction accuracy of the complex network model T, and then improves the rationality of the screening of the target UAV take-off and landing point, that is, improves the rationality of the site selection of the UAV take-off and landing point. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 1 A flowchart of a computer program for executing a complex network model construction system for selecting a take-off and landing point for a UAV provided in the first embodiment of the present invention;

[0018] Figure 2 A flowchart of a method for generating an initial location for a UAV take-off and landing point based on multiple factors provided in the second embodiment of the present invention;

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

[0020] Figure 4 A flowchart of a computer program for executing a system for selecting a take-off and landing point for a UAV based on the priority of candidate take-off and landing points provided in accordance with the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It is understood that, where appropriate, the above-mentioned terms used to distinguish similar objects can be interchanged so that the present invention can also implement other embodiments other than the above-mentioned illustrated embodiments or described embodiments. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] Example 1

[0024] This embodiment provides a complex network model construction system for selecting the location of drone take-off and landing points. Figure 1 As shown, the complex network model construction system for selecting the take-off and landing points of UAVs includes a processor and a memory storing a computer program, and the memory also stores an initial set of UAV take-off and landing points A = {A1, A2, ..., A n ,……,A N}、Predicted flight route set B={B1,B2,……,B n ,……,B N}、The importance set of take-off and landing point locations H={H1,H2,……,H n ,……,H N}, the first traffic flow set C = {C1, C2, ..., 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 ={Bn1 , B n2 ,……,B ni ,……,B nI(n)}, B ni Refers to A n The corresponding i-th predicted flight route, H n Refers to A n The importance of the corresponding take-off and landing point position, C n Refers to A n The corresponding first traffic flow, G n Refers to A n The corresponding first building interference level, D n ={D n1 , D n2 ,……,D ni ,……,D nI(n)}, D ni Refers to B ni The corresponding route abnormality degree is used to characterize the degree of damage to the outside world when the UAV encounters a flight abnormality on the corresponding predicted flight route. n ={E n1 , E n2 ,……,E ni ,……,E nI(n)}, E ni Refers to B ni The corresponding route length, n = 1, 2, ..., N, N refers to the total number of initial drone take-off and landing points, i = 1, 2, ..., I(n), I(n) refers to the total number of predicted flight routes corresponding to the nth initial drone take-off and landing point.

[0025] Among them, the initial drone take-off and landing point can refer to a take-off and landing point that can be used as the layout location of the drone take-off and landing facility in the geographical area where the drone take-off and landing facility needs to be selected. There are predicted flight routes suitable for drone flights to transport goods between the initial drone take-off and landing points. The location of the initial drone take-off and landing point and the trajectory of the predicted flight route can be determined by the implementer based on actual conditions.

[0026] The importance of the take-off and landing point location can be used to characterize the importance of the location of the corresponding initial drone take-off and landing point in the drone transportation mission. The first traffic flow can refer to the traffic flow within a certain range corresponding to the initial drone take-off and landing point, which can be used to characterize the demand for drone transportation of goods at the initial drone take-off and landing point. The first building interference level can be used to characterize the degree of interference of buildings near the initial drone take-off and landing point on the take-off and landing of the drone when the drone takes off and lands at the corresponding initial drone take-off and landing point. The route abnormality level can be used to characterize the degree of damage to the outside world when the drone encounters a flight abnormality in the corresponding predicted flight route, for example, the degree of damage to buildings, human life and property.

[0027] When the computer program is executed by a processor, the following steps are performed:

[0028] S1, according to H, C and G, obtain the take-off and landing point weight set Q = {Q1, Q2, ..., Q n ,……,Q N}, where A n The corresponding take-off and landing point weight Q n Meet the following conditions:

[0029] Q n =α1×H n +α2×C n +α3×e^(-G n ), where 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.

[0030] Among them, the greater the importance of the take-off and landing point location corresponding to the initial drone take-off and landing point, the greater the first traffic flow, and the smaller the first building interference level, the higher the possibility of setting up drone take-off and landing facilities at the initial drone take-off and landing point.

[0031] 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.

[0032] The specific values ​​of α1, α2 and α3 can be set by the implementer according to actual conditions.

[0033] In the above, the influence of the importance of the take-off and landing point location, the first traffic flow and the first building interference level on the possibility of setting up drone take-off and landing facilities at the initial drone take-off and landing point is measured, and the take-off and landing point weight corresponding to each initial drone take-off and landing point is obtained, which improves the accuracy of the take-off and landing point weight, thereby improving the accuracy of the construction of the complex network model and the rationality of the site selection of the drone take-off and landing point.

[0034] In a specific embodiment, the memory further stores a position coordinate set Z={Z1, Z2, . . . , Z n ,……,Z N}, where Z n Refers to A n The corresponding position coordinates and the take-off and landing point position importance set H are obtained through the following steps:

[0035] S01, based on B and Z, obtain the take-off and landing point location importance set H corresponding to A = {H1, H2, ..., Hn ,……,H N}, where A n The importance of the corresponding take-off and landing point position H n Meet the following conditions:

[0036] H n =(2 / ((1+e^(-I(n)))-1))×(e^(-∑ j＝1 N d(Z n , Z j )) / N), where e is the natural index, d(Z n , Z j ) refers to Z n and Z j The distance between them.

[0037] The location coordinates may refer to the geographical location coordinates of the corresponding initial UAV take-off and landing point, including the longitude and latitude of the location corresponding to the initial UAV take-off and landing point.

[0038] The more the number of other initial UAV take-off and landing points connected to the nth initial UAV take-off and landing point and the closer the distance between the nth initial UAV take-off and landing point and the other initial UAV take-off and landing points, the higher the importance of the take-off and landing point position corresponding to the nth initial UAV take-off and landing point.

[0039] In the above, based on the number of other initial UAV take-off and landing points connected to the initial UAV take-off and landing point, and the distance between the initial UAV take-off and landing point and other initial UAV take-off and landing points, the importance of the take-off and landing point position corresponding to each initial UAV take-off and landing point is measured, which serves as the basis for obtaining the take-off and landing point weight corresponding to each initial UAV take-off and landing point, thereby improving the accuracy of the take-off and landing point weight.

[0040] In a specific embodiment, the memory further stores a first building density set J 1 ={J 1 1, J 1 2, ..., J 1 n ,……,J 1 N} and the first building height set J 2 ={J 2 1, J 2 2, ..., J 2 n ,……,J 2 N}, where J 1 n Refers to A n The first building density within the corresponding first preset range, J 2 n ={J2 n1 , J 2 n2 ,……,J 2 nu ,……,J 2 nU(n)}, J 2 nu Refers to A n The height of the u-th first building within the corresponding first preset range, J 2 nu >J 0 , J 0 Refers to the preset building height threshold, u=1, 2, ..., U(n), U(n) refers to A n The total number of first buildings within the corresponding first preset range and the first building interference degree set G are obtained by the following steps:

[0041] S02, according to J 1 and J 2 , obtain the first building interference level set G = {G1, G2, ..., G n ,……,G N}, where A n The corresponding first building interference level G n Meet the following conditions:

[0042] G n =(2 / ((1+e^(-J 1 n ))-1))×(γ1×(Σ u＝1 U(n) J 2 nu ) / U(n)+γ2×max(J 2 n )), wherein max() refers to the maximum value function, γ1 refers to the preset first height weight, and γ2 refers to the preset second height weight.

[0043] Among them, the first preset range can refer to a ground area range with the initial UAV take-off and landing point as the center and the first preset length as the radius or side length. There are several buildings within the first preset range. When the density and height of the buildings in the first preset range are different, different degrees of interference will be caused to the UAV when taking off and landing at the corresponding initial UAV take-off and landing point. Therefore, the first building density and the first building height within the first preset range corresponding to the initial UAV take-off and landing point are used to measure the interference degree of the first building corresponding to the initial UAV take-off and landing point, thereby improving the accuracy of obtaining the take-off and landing point weight.

[0044] The specific values ​​of the first preset length and the preset building height threshold can be set by the implementer according to actual conditions.

[0045] Since the buildings within the first preset range have different heights, lower buildings have less interference with the take-off and landing of drones. Therefore, this embodiment sets a preset building height threshold J 0 Buildings within the first preset range are screened, and taller buildings are selected to characterize the interference degree of the first building corresponding to the initial drone take-off and landing point, so as to reduce the impact of more low buildings on the interference degree of the first building, thereby improving the measurement accuracy of the interference degree of the first building.

[0046] The specific values ​​of γ1 and γ2 can be set by the implementer according to actual conditions.

[0047] In one embodiment, γ1>γ2.

[0048] 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.

[0049] Among them, 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 protection of the corresponding predicted flight route for drone transportation of goods, and serve as the basis for obtaining the route weight corresponding to the predicted flight route.

[0050] S3, according to B and H, obtain the route position importance set K corresponding to B = {K1, K2, ..., K n ,……,K N}, where B n Corresponding route location importance list K n ={K n1 , K n2 ,……,K ni ,……,K nI(n)}, K ni Equal to B ni The average importance of the take-off and landing point locations of the two corresponding initial UAV take-off and landing points.

[0051] Among them, the average value of the take-off and landing point position importance of the two initial UAV take-off and landing points corresponding to each 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] S4, according to B, D, E, F and K, obtain the route weight set P corresponding to B = {P1, P2, ..., P n ,……,P N}, where P n ={P n1 , P n2 ,……,P ni ,……,P nI(n)}, B ni The corresponding route weight P ni Meet the following conditions:

[0053] 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

[0054] 1 refers to the preset abnormality weight, β2 refers to the preset route length weight, β3 refers to the preset second traffic weight, and β4 refers to the preset second position weight.

[0055] Among them, 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.

[0056] 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 UAV take-off and landing points.

[0057] The specific values ​​of β1, β2, β3 and β4 can be set by the implementer according to actual conditions.

[0058] In the above, the influence of the route abnormality degree, route length, second traffic flow and route location importance on the possibility of the predicted flight route as the flight path of the UAV is measured, and the route weight corresponding to each predicted flight route is obtained, which improves the accuracy of the route weight, thereby improving the accuracy of the construction of the complex network model and the rationality of the site selection of the UAV take-off and landing points.

[0059] In a specific embodiment, the memory further stores a second building density set R 1 ={R 1 1, R 1 2, ..., R 1 n ,……,R 1 N}, the second building height set R 2 ={R 2 1, R 2 2, ..., R 2 n ,……,R 2 N}、Population density set Y 1 ={Y 1 1, Y 1 2, ..., Y 1 n ,……,Y 1 N} and the population category importance set Y 2 ={Y 2 1, Y 2 2, ..., Y 2 n ,……,Y 2 N}, where R 1 n ={R 1 n1 , R 1 n2 ,……,R 1 ni ,……,R 1 nI(n)}, R 1 ni Refers to B ni The corresponding second building density within the second preset range, R 2 n ={R 2 n1 , R 2 n2 ,……,R 2 ni ,……,R 2 nI(n)}, R2 ni ={R 2 ni1 , R 2 ni2 ,……,R 2 niv ,……,R 2 niV(ni)}, R 2 niv Refers to B ni The height of the vth second building within the corresponding second preset range, Y 1 n ={Y 1 n1 , Y 1 n2 ,……,Y 1 ni ,……,Y 1 nI(n)}, Y 1 ni Refers to B ni The corresponding population density within the second preset range, Y 2 n ={Y 2 n1 , Y 2 n2 ,……,Y 2 ni ,……,Y 2 nI(n)}, Y 2 ni ={Y 2 ni1 , Y 2 ni2 ,……,Y 2 niw ,……,Y 2 niW(ni)}, Y 2 niw Refers to B ni The importance of the category corresponding to the wth population in the corresponding second preset range, v = 1, 2, ..., V(n), V(n) refers to A n The total number of second buildings within the corresponding second preset range, w = 1, 2, ..., W (ni), W (ni) refers to B ni The total population within the corresponding second preset range and the route anomaly degree set D are obtained by the following steps:

[0060] S03, according to R 1 、R 2 、Y 1 and Y 2, get the route abnormality degree set D = {D1, D2, ..., D n ,……,D N}, where D n ={D n1 , D n2 ,……,D ni ,……,D nI(n)}, B ni The corresponding route abnormality degree D ni Meet the following conditions:

[0061] D ni =δ1×D 1 ni +δ2×D 2 ni , where δ1 refers to the preset building weight, and δ2 refers to the preset first population weight,

[0062] D 1 ni =(2 / ((1+e^(-R 1 ni ))-1))×(γ1×(Σ v＝1 V(n) R 2 niv ) / V(n)+γ2×max(R 2 ni )),

[0063] D 2 ni =(2 / ((1+e^(-Y 1 ni ))-1))×(ε1×(Σ w＝1 W(n) Y 2 niv ) / W(n)+ε2×max(Y 2 ni )), ε1 refers to the preset second population weight, and ε2 refers to the preset third population weight.

[0064] The importance of a population category may refer to the degree of security required for the population in the corresponding category, or may refer to the severity of the security impact on the population in the corresponding category.

[0065] The second preset range may refer to a three-dimensional airspace area based on the trajectory of the predicted flight route, with a second preset length away from each trajectory point of the predicted flight route. There are several buildings and several people within the second preset range. When the density, height, population density and importance of population categories of the buildings within the second preset range are different, it will affect the degree of damage to buildings, human life and property when the drone encounters a flight abnormality in the corresponding predicted flight route.

[0066] Therefore, the degree of route abnormality corresponding to each predicted flight route is measured according to the second building density, second building height, population density and importance of population category corresponding to the predicted flight route, thereby improving the accuracy of the route weight.

[0067] The specific values ​​of δ1, δ2, ε1 and ε2 can be set by the implementer according to actual conditions.

[0068] S5, based on A, B, Q and P, takes the initial UAV take-off and landing points as nodes, the predicted flight routes as edges, the take-off and landing point weights corresponding to the initial UAV take-off and landing points as the node values ​​of the corresponding nodes, and the route weights corresponding to the predicted flight routes as the edge values ​​of the corresponding edges, to obtain the complex network model T, where the complex network model T is used to screen the target UAV take-off and landing points from A.

[0069] Among them, the complex network model T is used to screen the target drone take-off and landing points from A. The target drone take-off and landing points can be used as the location for setting up drone take-off and landing facilities to provide support for drone delivery logistics and other tasks.

[0070] As mentioned above, based on H, C and G, we can get the take-off and landing point weight set Q = {Q1, Q2, ..., Q n ,……,Q N}, weigh the influence of the importance of the take-off and landing point location, the first traffic flow and the first building interference level on the possibility of setting up drone take-off and landing facilities at the initial drone take-off and landing point, obtain the take-off and landing point weight corresponding to each initial drone take-off and landing point, improve the accuracy of the take-off and landing point weight, and obtain the second traffic flow set F corresponding to B according to C = {F1, F2, ..., F n ,……,F N}, to characterize the degree of protection of the corresponding predicted flight route for the drone's cargo transportation. According to B and H, the importance set of the route position corresponding to B is obtained, K = {K1, K2, ..., K n ,……,K N}, according to B, D, E, F and K, obtain the route weight set P corresponding to B = {P1, P2, ..., P n ,……,P N}, measure the impact of the route abnormality, route length, second traffic flow and route location importance on the possibility of the predicted flight route as the flight path of the UAV, obtain the route weight corresponding to each predicted flight route, and improve the accuracy of the route weight. According to A, B, Q and P, the initial UAV take-off and landing point is used as the node, the predicted flight route is used as the edge, the take-off and landing point weight corresponding to the initial UAV take-off and landing point is used as the node value of the corresponding node, and the route weight corresponding to the predicted flight route is used as the edge value of the corresponding edge. The complex network model T is obtained and used to screen the target UAV take-off and landing point from A, which improves the construction accuracy of the complex network model T, and then improves the 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.

[0071] Example 2

[0072] This second embodiment provides a method for generating an initial location for a drone take-off and landing point based on multiple factors, such as Figure 2 As shown in FIG, the method for generating the initial site selection of the take-off and landing points of the UAV based on multiple factors includes the following steps:

[0073] S10, obtain Θ original drone take-off and landing points, several predicted flight routes corresponding to each original drone take-off and landing point, and the route length, second traffic flow and route abnormality degree corresponding to each predicted flight route, where the second traffic flow refers to the average of the first traffic flow corresponding to the two original drone take-off and landing points corresponding to the predicted flight route. The route abnormality degree is used to characterize the degree of damage to the outside world when a drone encounters a flight abnormality on the corresponding predicted flight route. Θ is an integer greater than 0.

[0074] In a specific embodiment, S10 further includes the following steps:

[0075] S11, obtaining map data corresponding to Θ original drone take-off and landing points and a first traffic flow corresponding to each original drone take-off and landing point;

[0076] 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.

[0077] Among them, implementers can use path planning algorithms, such as A* search algorithm, Dijkstra algorithm, genetic algorithm, etc., on the basis of determining the original UAV take-off and landing points, according to the map data and the first traffic flow corresponding to the original UAV take-off and landing points, quickly generate UAV routes between the original UAV take-off and landing points, as predicted flight routes for the site selection task of UAV take-off and landing points.

[0078] Among them, map data may include factors such as terrain data, obstacle data, and weather condition data.

[0079] S20, based on the number of predicted flight routes corresponding to each original UAV take-off and landing point, the route length corresponding to each predicted flight route and the second traffic flow, obtain the number of predicted flight routes, the total route length and the total traffic volume corresponding to each original UAV take-off and landing point.

[0080] Among them, for any original drone take-off and landing point, the sum of the route lengths of all predicted flight routes corresponding to the current original drone take-off and landing point is determined as the total route length corresponding to the current original drone take-off and landing point, and the sum of the second traffic flow of all predicted flight routes corresponding to the current original drone take-off and landing point is determined as the total traffic volume corresponding to the current original drone take-off and landing point, which serves as the basis for screening drone take-off and landing points.

[0081] S30, based on the predicted number of flight routes, total route length, total traffic volume, preset route number threshold, preset route length threshold, and preset traffic threshold corresponding to each original drone take-off and landing point, select ζ take-off and landing points to be merged from Θ original drone take-off and landing points, where 0<ζ<Θ.

[0082] The specific values ​​of the preset route quantity threshold, the preset route length threshold, and the preset traffic flow threshold can be set by the implementer according to actual conditions.

[0083] In a specific embodiment, S30 further includes the following steps:

[0084] 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.

[0085] S32, traverse Θ original drone take-off and landing points, and obtain ζ take-off and landing points to be merged.

[0086] Among them, the more predicted flight routes corresponding to the current original drone take-off and landing point, the longer the total length of the routes, and the greater the total traffic volume, it means that the original drone take-off and landing point has a lower demand for drone take-off and landing facilities, and the higher the possibility of setting up drone take-off and landing facilities at the original drone take-off and landing point. Conversely, it means that the demand for drone take-off and landing facilities is higher, and the possibility of setting up drone take-off and landing facilities at the original drone take-off and landing point is lower.

[0087] Therefore, for the original drone take-off and landing points with low demand and possibility for setting up drone take-off and landing facilities, they can be used as take-off and landing points to be merged and merged into the take-off and landing points connected to them with high demand and possibility for setting up drone take-off and landing facilities, thereby improving the correlation between the take-off and landing points, and ensuring that the possibility of setting up drone take-off and landing facilities at the merged take-off and landing points is high, and improving the coverage and rationality of the drone take-off and landing facilities by merging the take-off and landing points.

[0088] 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.

[0089] S50, for any take-off and landing point to be merged, any original UAV take-off and landing point connected to the current take-off and landing point to be merged is used as the intermediate take-off and landing point, and the route length between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points and the intermediate take-off and landing point, as well as the route length between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, the route length priority corresponding to the intermediate take-off and landing point is obtained.

[0090] In a specific embodiment, S50 further includes the following steps:

[0091] S51: Determine the average of the route lengths between the current take-off and landing point to be merged and the other n-1 original UAV take-off and landing points as the first route length.

[0092] S52: Determine the average of the route lengths between the middle take-off and landing point and the other n-1 original UAV take-off and landing points as the second route length.

[0093] 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.

[0094] S54: Determine the ratio of the first route length to the third route length as the route length priority corresponding to the intermediate take-off and landing point.

[0095] Among them, the first route length can represent the average distance from the current take-off and landing point to the other η-1 original drone take-off and landing points, the second route length can represent the average distance from the current intermediate take-off and landing point to the other η-1 original drone take-off and landing points, and the third route length can represent the average distance from the intermediate take-off and landing point as a transit point, and then to the other η-1 original drone take-off and landing points. If the third route length is less than the corresponding first route length, it means that compared with the current take-off and landing point to be merged directly to the other η-1 original drone take-off and landing points, the drone's flight distance is shorter when the intermediate take-off and landing point is used as a transit point from the current take-off and landing point to the other η-1 original drone take-off and landing points. This can indicate that the intermediate take-off and landing point has a higher priority than the current take-off and landing point to be merged, and after the take-off and landing point to be merged is merged into the intermediate take-off and landing point, the impact on the drone flight demand from the current take-off and landing point to the other η-1 original drone take-off and landing points is small.

[0096] 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.

[0097] In the above, based on the route lengths between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points and the intermediate take-off and landing point, as well as the route lengths between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, the route length priority corresponding to the intermediate take-off and landing point is obtained to characterize the priority of the intermediate take-off and landing point compared to the current take-off and landing point to be merged, and the degree of influence on the UAV flight demand from the current take-off and landing point to be merged to the other η-1 original UAV take-off and landing points after the take-off and landing point to be merged is merged into the intermediate take-off and landing point. This serves as the basis for merging the take-off and landing points to be merged, thereby improving the rationality of the take-off and landing point merging.

[0098] S60, based on the route abnormality degree of the predicted flight route between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points and the intermediate take-off and landing point, and the route abnormality degree of the predicted flight route between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, obtain the route abnormality degree priority corresponding to the intermediate take-off and landing point.

[0099] In a specific embodiment, S60 further includes the following steps:

[0100] S61: Determine the average of the route anomaly levels between the current take-off and landing point to be merged and the other n-1 original UAV take-off and landing points as the first route anomaly level.

[0101] S62: Determine the average of the route abnormality levels between the middle take-off and landing point and the other n-1 original UAV take-off and landing points as the second route abnormality level.

[0102] 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.

[0103] S64: Determine the ratio of the first route abnormality level to the third route abnormality level as the route abnormality level priority corresponding to the intermediate take-off and landing point.

[0104] Among them, the first route abnormality degree can represent the average abnormality degree of the drone directly from the current take-off and landing point to the other η-1 original drone take-off and landing points, the second route abnormality degree can represent the average abnormality degree of the drone directly from the current intermediate take-off and landing point to the other η-1 original drone take-off and landing points, and the third route abnormality degree can represent the average abnormality degree of the drone using the intermediate take-off and landing point as a transit point, from the current take-off and landing point to the intermediate take-off and landing point and then to the other η-1 original drone take-off and landing points. If the third route abnormality degree is less than the corresponding first route abnormality degree, it means that compared with the drone directly from the current take-off and landing point to the other η-1 original drone take-off and landing points, the drone's flight abnormality degree is lower when the intermediate take-off and landing point is used as a transit point from the current take-off and landing point to the other η-1 original drone take-off and landing points. This can indicate that the intermediate take-off and landing point has a higher priority than the current take-off and landing point to be merged, and after the take-off and landing point to be merged is merged into the intermediate take-off and landing point, the impact on the safety of the drone is smaller.

[0105] 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.

[0106] In the above, based on the route anomaly degree between the current take-off and landing point to be merged and the other η-1 original drone take-off and landing points and the intermediate take-off and landing point, as well as the route anomaly degree between the intermediate take-off and landing point and the other η-1 original drone take-off and landing points, the route anomaly degree priority corresponding to the intermediate take-off and landing point is obtained to characterize the priority of the intermediate take-off and landing point compared to the current take-off and landing point to be merged, and the impact on the safety of the drone after merging the take-off and landing point to be merged into the intermediate take-off and landing point. This serves as the basis for merging the take-off and landing points to be merged, thereby improving the rationality of the take-off and landing point merging.

[0107] S70: Obtain the merge priority corresponding to the current intermediate take-off and landing point based on the route length priority and the route abnormality priority.

[0108] In a specific embodiment, S70 further includes the following steps:

[0109] S71, obtaining a preset route length weight and a preset abnormality degree weight.

[0110] S72: Obtain the merge priority corresponding to the current intermediate take-off and landing point according to the route length priority, the preset route length weight, the route anomaly priority, and the preset anomaly weight.

[0111] Among them, the first product of the route length priority and the preset route length weight, as well as the second product of the route anomaly degree priority and the preset anomaly degree weight, are calculated. The sum of the first product and the second product is determined as the merging priority corresponding to the current intermediate take-off and landing point, representing the priority of merging the current take-off and landing point to be merged into the current intermediate take-off and landing point.

[0112] The specific values ​​of the preset route length weight and the preset abnormality degree weight can be set by the implementer according to actual conditions.

[0113] The above-mentioned route length priority, the preset route length weight, the route anomaly priority, and the preset anomaly weight are combined to obtain the merging priority corresponding to the current intermediate take-off and landing point. This represents the priority of merging the current take-off and landing point to be merged into the current intermediate take-off and landing point. This serves as the basis for merging the current take-off and landing point to be merged, thereby improving the rationality of take-off and landing point merging.

[0114] 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.

[0115] S90: Merge the current take-off and landing point to be merged with the original UAV take-off and landing point corresponding to the highest merging priority, and determine the merged take-off and landing point as the initial UAV take-off and landing point.

[0116] Among them, the take-off and landing points to be merged, which have low demand for drone take-off and landing facilities and low possibility of setting up drone take-off and landing facilities, are merged into other take-off and landing points with high demand for drone take-off and landing facilities and high possibility of setting up drone take-off and landing facilities. The initial drone take-off and landing points are obtained as the setting locations of drone take-off and landing facilities, thereby generating a batch of initial points that can be used for drone take-off and landing site selection, so that the setting locations of drone take-off and landing facilities match the take-off and landing point demand, thereby improving the relative coverage and setting rationality of drone take-off and landing facilities.

[0117] In the above, according to the obtained predicted number of flight routes, total route length and total traffic volume corresponding to each original UAV take-off and landing point, combined with the preset route number threshold, the preset route length threshold and the preset traffic threshold, z take-off and landing points to be merged are screened out from Θ original UAV take-off and landing points, and according to all predicted flight routes corresponding to each take-off and landing point to be merged, n original UAV take-off and landing points connected to each take-off and landing point to be merged are obtained. For any take-off and landing point to be merged, any original UAV take-off and landing point connected to the current take-off and landing point to be merged is used as the intermediate take-off and landing point. According to the route length and route anomaly degree between the current take-off and landing point to be merged and the other n-1 original UAV take-off and landing points and the intermediate take-off and landing point, as well as the route length and route anomaly degree between the intermediate take-off and landing point and the other n-1 original UAV take-off and landing points, the route length priority and route anomaly degree corresponding to the intermediate take-off and landing point are obtained. According to the route length priority and the route abnormality priority, the merging priority corresponding to the current intermediate take-off and landing point is obtained, the current take-off and landing point to be merged is merged with the original drone take-off and landing point corresponding to the largest merging priority, and the merged take-off and landing point is determined as the initial drone take-off and landing point. It can be seen that the take-off and landing points to be merged, which have a low possibility of setting up drone take-off and landing facilities and whose predicted number of flight routes is less than the preset route number threshold, the total route length is less than the preset route length threshold, and the total traffic volume is less than the preset flow threshold, are merged into other take-off and landing points with a higher possibility of setting up drone take-off and landing facilities, thereby generating a batch of initial points that can be used for drone take-off and landing site selection, so that when drone take-off and landing facilities are set at the initial drone take-off and landing points, they can match the take-off and landing point requirements, thereby improving the relative coverage and setting rationality of drone take-off and landing facilities.

[0118] Example 3

[0119] This embodiment 3 provides a method for determining the priority of candidate take-off and landing points of drones based on a complex network model. Figure 3 As shown in FIG, the method for determining the priority of candidate take-off and landing points of UAVs based on a complex network model includes:

[0120] 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,……,JSn ,……,JS N}、Clustering coefficient set JL={JL1,JL2,……,JL n ,……,JL N}、Route abnormality degree set D={D1,D2,……,D n ,……,D N}、Route length set E={E1,E2,……,E n ,……,E N} and the second traffic flow set F={F1,F2,……,F n ,……,F N}, where A n Refers to the nth initial drone take-off and landing point, B n ={B n1 , B n2 ,……,B ni ,……,B nI(n)}, B ni Refers to A n The corresponding i-th predicted flight route, Q n Refers to A n The corresponding take-off and landing point weight, P n ={P n1 , P n2 ,……,P ni ,……,P nI(n)}, P ni Refers to B ni The corresponding route weight, JS n Refers to A n The corresponding node betweenness, JL n Refers to A n The corresponding clustering coefficient, D n ={D n1 , D n2 ,……,D ni ,……,D nI(n)}, D ni Refers to B ni The corresponding route abnormality degree, E n ={E n1 , E n2 ,……,E ni ,……,E nI(n)}, E ni Refers to B ni The corresponding route length, F n ={F n1 , F n2 ,……,F ni ,……,F nI(n)}, F ni Refers to B niThe 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.

[0121] The take-off and landing point weight represents the likelihood of installing drone take-off and landing facilities at the corresponding initial drone take-off and landing point, the route weight represents the likelihood of the predicted flight route being used as the drone's flight path, and the node betweenness represents the influence of the corresponding initial drone take-off and landing point relative to other initial drone take-off and landing points. The clustering coefficient represents the degree of clustering between the corresponding initial drone take-off and landing point and other initial drone take-off and landing points.

[0122] S2000: According to A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, the candidate priority set HX = {HX1, HX2, ..., HX n ,……,HX N}, where QZ1 refers to the preset weight of the take-off and landing point weight, QZ2 refers to the preset weight of the route weight, QZ3 refers to the preset weight of the number of routes, QZ4 refers to the preset weight of the node betweenness, QZ5 refers to the preset weight of the clustering coefficient, and A n Corresponding candidate priority HX n Meet the following conditions:

[0123] HX n =QZ1×Q n +QZ2×∑ i＝1 I(n) (P ni / I(n))+QZ3×I(n)+QZ4×JS n +QZ5×JL n .

[0124] Among them, the greater the take-off and landing point weight corresponding to the initial UAV take-off and landing point, the greater the average route weight corresponding to the predicted flight route connected by the initial UAV take-off and landing point, the more routes of the predicted flight route connected by the initial UAV take-off and landing point, and the greater the node betweenness and clustering coefficient corresponding to the initial UAV take-off and landing point, the higher the possibility of setting up UAV take-off and landing facilities at the initial UAV take-off and landing point.

[0125] Therefore, combining Q, P, JS, JL and the corresponding preset initial weights, the candidate priority corresponding to each initial UAV take-off and landing point is obtained, which indicates that the corresponding initial UAV take-off and landing point is determined as the target UAV take-off and landing point to set the priority of the UAV take-off and landing facilities.

[0126] Among them, the specific values ​​of QZ1, QZ2, QZ3, QZ4 and QZ5 can be set by the implementer according to actual conditions.

[0127] As mentioned above, the take-off and landing point weights, average route weights, predicted number of flight routes, node betweenness and clustering coefficient corresponding to the initial UAV take-off and landing points, as well as the preset initial weights are comprehensively considered to measure the candidate priority of each initial UAV take-off and landing point to be determined as the target UAV take-off and landing point, thereby improving the accuracy of the candidate priority and thus improving the accuracy of the target UAV screening.

[0128] S3000, based on HX, selects M candidate UAV take-off and landing points from N initial UAV take-off and landing points.

[0129] In a specific implementation, S3000 further includes the following steps:

[0130] 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.

[0131] 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:

[0132] 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

[0133] refers to the preset abnormality weight, β2 refers to the preset route length weight, and β3 refers to the preset second traffic weight.

[0134] Among them, the smaller the route abnormality degree of all predicted flight routes corresponding to the candidate UAV take-off and landing point, the longer the route length, and the larger the second traffic flow, which means that the candidate UAV take-off and landing point has a lower demand for UAV take-off and landing facilities, and the higher the possibility of setting up UAV take-off and landing facilities at the candidate UAV take-off and landing point. Correspondingly, the larger the candidate take-off and landing point prediction score corresponding to the candidate UAV take-off and landing point.

[0135] As described above, based on D, E, and F, the possibility of setting up drone take-off and landing facilities at the candidate drone take-off and landing points is measured, and then the candidate take-off and landing point prediction score set FZ is obtained, which improves the accuracy of the candidate take-off and landing point prediction scores.

[0136] S5000: Based on FZ, obtain the screening scores FS of M candidate drone take-off and landing points, where FS meets the following conditions:

[0137] FS=(Σ n＝1 N (FZ n -FZ0) 2 / N, where the predicted mean value of candidate take-off and landing points FZ0 = Σ n＝1 N (FZ n ) / N.

[0138] In order to ensure that the delivery efficiency and delivery intensity of drones at each take-off and landing point are as balanced as possible, this embodiment hopes that the candidate take-off and landing point prediction scores between the selected target drone take-off and landing points are as consistent as possible.

[0139] In the above, by measuring the differences between the candidate take-off and landing point prediction scores of M candidate drone take-off and landing points, the screening score FS of the M candidate drone take-off and landing points is obtained to characterize the screening accuracy of the M candidate drone take-off and landing points. This is used as the basis for updating the initial weight set to re-screen M take-off and landing points as the target drone take-off and landing points, providing setting locations for drone take-off and landing facilities, thereby achieving the effect of balancing the delivery efficiency and delivery intensity of the drones corresponding to each target drone take-off and landing point, thereby improving the overall work efficiency of the drones.

[0140] S6000, if FS>FS 0 , then update QZ, and use the updated QZ to replace the preset initial weight set, and repeat step S2000 until FS≤FS 0 , get FS≤FS 0 The candidate priority set HX when FS≤FS 0 The candidate priority set HX is used to filter the target UAV take-off and landing points from the N initial UAV take-off and landing points, FS 0 Refers to the preset screening score threshold.

[0141] Among them, the preset screening score threshold FS 0 The specific value of can be set by the implementer according to the actual situation.

[0142] The candidate priority corresponding to each initial UAV take-off and landing point is obtained by comprehensively considering the take-off and landing point weight, average route weight, predicted number of flight routes, node betweenness and clustering coefficient corresponding to the initial UAV take-off and landing point, as well as the preset initial weight. Therefore, the accuracy of the preset initial weight affects the accuracy of the candidate priority, and thus affects the rationality of the screening of candidate UAV take-off and landing points.

[0143] The larger the screening score FS of the M candidate drone take-off and landing points, the greater the difference in delivery efficiency and delivery intensity between the M candidate drone take-off and landing points. Therefore, when FS>FS 0 Update QZ and use the updated QZ to replace the preset initial weight set, repeat step S2000 to obtain new M candidate drone take-off and landing points until FS≤FS 0 , get FS≤FS 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 the screening of UAV take-off and landing points.

[0144] As described above, based on A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, the candidate priority set HX = {HX1, HX2, ..., HX n ,……,HX N}, according to HX, M candidate drone take-off and landing points are selected from N initial drone take-off and landing points, and according to D, E and F, the candidate take-off and landing point prediction score set FZ = {FZ1, FZ2, ..., FZ n ,……,FZ N}, according to FZ, obtain the screening score FS of M candidate drone take-off and landing points, if FS>FS 0 , then update QZ, and use the updated QZ to replace the preset initial weight set, and repeat the above steps until FS≤FS 0 , FS≤FS 0 The M candidate UAV take-off and landing points at the time are determined as the target UAV take-off and landing points. It can be seen that by measuring the differences between the candidate take-off and landing point prediction scores among the M candidate UAV take-off and landing points, the screening scores FS of the M candidate UAV take-off and landing points are obtained to characterize the screening accuracy of the M candidate UAV take-off and landing points. This is used as the basis for updating the initial weight set and the candidate priority set to screen out the target UAV take-off and landing points and provide a setting location for the UAV take-off and landing facilities, thereby achieving the effect of balancing the delivery efficiency and delivery intensity of the UAVs corresponding to each target UAV take-off and landing point, thereby improving the overall work efficiency of the UAVs.

[0145] Example 4

[0146] This fourth embodiment provides a drone take-off and landing point selection system based on the priority of candidate take-off and landing points. Figure 4 As shown, the drone take-off and landing point selection system based on the take-off and landing point candidate priority includes a processor and a memory storing a computer program, and the memory also stores a preset prediction model, an initial drone take-off and landing point set A = {A1, A2, ..., A n ,……,A N}, Initial UAV take-off and landing point code set A 1 ={A 1 1, A 1 2, ..., A 1 n ,……,A 1 N}、Predicted flight route set B={B1,B2,……,B n ,……,B N}、The take-off and landing point weight set Q={Q1,Q2,……,Q n ,……,Q N}、Route weight set P={P1,P2,……,P n ,……,P N}、Node betweenness set JS={JS1,JS2,……,JS n ,……,JS N}、Preset key take-off and landing point code set GJ={GJ1,GJ2,……,GJ μ ,……,GJ M} and preset reference vector YS=(1,1,……,1,0,0,……,0), where A n Refers to the nth initial drone take-off and landing point, A 1 n Refers to A n The corresponding initial take-off and landing point code, B n ={B n1 , B n2 ,……,B ni ,……,B nI(n)}, B ni Refers to A n The corresponding i-th predicted flight route, Q n Refers to A n The corresponding take-off and landing point weight, P n ={P n1 , P n2 ,……,P ni ,……,P nI(n)}, P ni Refers to B ni The corresponding route weight, JS nRefers to A n The corresponding node betweenness, GJ μ It refers to the key take-off and landing point code corresponding to the preset μth key take-off and landing point, n = 1, 2, ..., N, N refers to the total number of initial UAV take-off and landing points, i = 1, 2, ..., I(n), I(n) refers to the total number of predicted flight routes corresponding to the nth initial UAV take-off and landing point, μ = 1, 2, ..., M, M refers to the total number of preset key take-off and landing points, the first Щ elements in YS are value 1, and the last N-Щ elements are value 0, Щ refers to the preset number of target UAV take-off and landing points.

[0147] Among them, the initial drone take-off and landing point code can be a code pre-set by the implementer according to actual conditions, which is used to identify the initial drone take-off and landing point, such as 1, 2, 3...

[0148] The preset key take-off and landing point codes may be codes pre-set by implementers based on actual conditions, and are used to identify the key take-off and landing points, such as -1, -2, -3, ... . . .

[0149] The preset prediction model is used to extract and map 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.

[0150] The preset reference vector YS = (1, 1, ..., 1, 0, 0, ..., 0), the first N elements in YS are the value 1, and the last N-N elements are the value 0, which is used to process the prediction priority sorting vector, and extract the first N elements in the prediction priority sorting vector as the evaluation of the degree of match between the sorting result of the prediction priority sorting vector and the premise condition.

[0151] When the computer program is executed by a processor, the following steps are performed:

[0152] S100: Input A, B, Q, P and JS into the preset prediction model to obtain the prediction priority ranking vector PX=(PX1, PX2, ..., PX n ,……,PX N ), where PX n It refers to the initial UAV take-off and landing point with the predicted priority of n.

[0153] 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.

[0154] In a specific embodiment, the memory also stores a clustering coefficient set JL={JL1, JL2, ..., JL n,……,JL N}, JL n Refers to A n The corresponding clustering coefficients of the M preset key take-off and landing points are obtained through the following steps:

[0155] S2000: According to A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, the candidate priority set HX = {HX1, HX2, ..., HX n ,……,HX N}, where QZ1 refers to the preset weight of the take-off and landing point weight, QZ2 refers to the preset weight of the route weight, QZ3 refers to the preset weight of the number of routes, QZ4 refers to the preset weight of the node betweenness, QZ5 refers to the preset weight of the clustering coefficient, and A n Corresponding candidate priority HX n Meet the following conditions:

[0156] HX n =QZ1×Q n +QZ2×∑ i＝1 I(n) (P ni / I(n))+QZ3×I(n)+QZ4×JS n +QZ5×JL n .

[0157] S3000, based on HX, selects M candidate UAV take-off and landing points from N initial UAV take-off and landing points.

[0158] 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:

[0159] 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

[0160] refers to the preset abnormality weight, β2 refers to the preset route length weight, and β3 refers to the preset second traffic weight.

[0161] S5000: Based on FZ, obtain the screening scores FS of M candidate drone take-off and landing points, where FS meets the following conditions:

[0162] FS=(Σ n＝1 N (FZ n -FZ0) 2 / N, where the predicted mean value of candidate take-off and landing points FZ0 = Σ n＝1 N (FZ n ) / N.

[0163] 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.

[0164] S200, perform vector multiplication on PX and YS to obtain the take-off and landing point reference vector CK 1 =(PX1, PX2, ..., PX σ ,……,PX Щ ,0,0,……,0), where PX σ Refers to the initial UAV take-off and landing point with the predicted priority of σ, PX Щ It refers to the initial UAV take-off and landing point with the predicted priority of Щ, σ=1, 2, …, Щ.

[0165] Among them, based on the task of screening out the target UAV take-off and landing points, vector multiplication of PX and YS is performed to obtain the take-off and landing point reference vector CK 1 =(PX1, PX2, ..., PX σ ,……,PX Щ , 0, 0, ..., 0), in order to extract the initial UAV take-off and landing points with the top prediction priority as the basis for measuring the sorting effect of the prediction priority sorting vector PX.

[0166] S300, according to CK 1 、A 1 and GJ, get CK 1 The corresponding first take-off and landing point coding reference vector BM 1 =(BM1, BM2, ..., BM σ,……,BM Щ ,0,0,……,0), where BM σ Refers to PX σ The corresponding first take-off and landing point code, BM Щ Refers to PX Щ The corresponding first take-off and landing point code.

[0167] In a specific embodiment, S300 further includes the following steps:

[0168] S310, if PX σ If it is a key take-off and landing point, PX σ The corresponding key take-off and landing point code is determined to be PX σ The corresponding first take-off and landing point code.

[0169] S320, if PX σ If it is not a key take-off and landing point, PX σ The corresponding initial take-off and landing point code is determined to be PX σ The corresponding first take-off and landing point code.

[0170] 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).

[0171] Among them, according to the type of the extracted top Щ initial UAV take-off and landing points, that is, key take-off and landing points or non-key take-off and landing points, the first take-off and landing point code corresponding to each initial UAV take-off and landing point in the top Щ initial UAV take-off and landing points is obtained, so as to judge the degree of matching between the top Щ initial UAV take-off and landing points and the prerequisites through the coding, and provide a data basis for measuring the sorting effect of the predicted priority sorting vector PX.

[0172] 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.

[0173] As mentioned above, the first Щ initial UAV take-off and landing points are converted into corresponding take-off and landing point codes, which provides a data basis for judging the matching degree between the first Щ initial UAV take-off and landing points and the prerequisites through coding, and then measuring the sorting effect of the prediction priority sorting vector PX.

[0174] S400, transposed vector of GJ and BM 1 Multiply to obtain the second take-off and landing point coding reference vector BM 2 .

[0175] Among them, the matrix dimension of GJ is 1×M, and the transposed vector of GJ (GJ) T The dimension is M×1, BM 1 The matrix dimension is 1×N, then (GJ) T ×BM 1 The second take-off and landing point coding reference vector BM obtained 2 The matrix dimension is M×N.

[0176] Among them, (GJ) T The codes of each key take-off and landing point are less than 0, BM 1 The first take-off and landing point code of the corresponding key take-off and landing point is less than 0, BM 1 If the first take-off and landing point code of the corresponding non-critical take-off and landing point is greater than 0, then BM 1 The first take-off and landing point code of the corresponding key take-off and landing point is (GJ) T The product of the codes of each key take-off and landing point is greater than 0, BM 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 represents the matching degree between the corresponding prediction priority ranking vector PX and the prerequisite.

[0177] For example, BM 2 The number of elements greater than 0 in is M×Amount, then we can determine that BM 1 The corresponding number of key take-off and landing points is Amount.

[0178] S500, for BM 2 Perform binarization processing to obtain the third take-off and landing point coding reference vector BM 3 .

[0179] In a specific implementation, S500 further includes the following steps:

[0180] 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.

[0181] 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.

[0182] S530, BM 3The elements in columns Щ+1 to N are set to 0.

[0183] 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 .

[0184] Among them, BM 2 Perform binary processing, set the elements greater than 0 to 1, and the elements less than or equal to 0 to 0, then BM 3 The data scale is normalized to avoid the influence of the value of each element on the prediction loss, thereby improving the accuracy of the prediction loss and further improving the accuracy of the judgment of the matching degree between the prediction priority sorting vector PX and the prerequisite.

[0185] 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 .

[0186] Among them, the premise of this embodiment is that the prediction priorities corresponding to the M key take-off and landing points in the final prediction priority sorting vector belong to the top Щ position, SUM(BM 3 ) can characterize BM 3 The number of elements 1 in BM 1 The number of key take-off and landing points corresponding to , that is, the number of key take-off and landing points in the prediction priority sorting vector PX, and the more key take-off and landing points in the prediction priority sorting vector PX, the higher the degree of matching between the prediction priority sorting vector PX and the prerequisite.

[0187] 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.

[0188] In one embodiment, YZ=M 2 .

[0189] In one embodiment, p=1000.

[0190] The specific value of ρ can be set by the implementer according to the actual situation, for example, ρ = 100, 1000, 10000, etc., to amplify the prediction loss, improve the training effect of the prediction model, and improve the prediction accuracy of the prediction model.

[0191] As mentioned above, increasing the prediction loss to characterize the degree of matching between the prediction priority ranking vector PX and the precondition provides a data basis for updating the parameters of the prediction model to improve the prediction accuracy of the prediction model.

[0192] S700: Update the parameters of the preset prediction model according to Loss until Loss=0, and obtain the target prediction model.

[0193] S800: Input A, B, Q, P and JS into the target prediction model to obtain the target priority ranking vector.

[0194] S900: 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.

[0195] Among them, the target priority sorting vector obtained when Loss = 0 indicates that the number of key take-off and landing points in the predicted priority sorting vector PX is M, which means that the target priority sorting vector matches the prerequisite. Therefore, the first Щ initial drone take-off and landing points in the target priority sorting vector are determined as the target drone take-off and landing points, which serve as the location for setting up drone take-off and landing facilities to provide support for drone delivery logistics and other tasks.

[0196] As mentioned above, A, B, Q, P and JS are input into the preset prediction model to obtain the prediction priority ranking vector PX=(PX1, PX2, ..., PX n ,……,PX N ), perform vector multiplication on PX and YS to obtain the take-off and landing point reference vector CK 1 =(PX1, PX2, ..., PX σ ,……,PX Щ ,0,0,……,0), according to CK 1 、A 1 and GJ, get CK 1 The corresponding first take-off and landing point coding reference vector BM 1 =(BM1, BM2, ..., BM σ ,……,BM Щ ,0,0,……,0), the transposed vector of GJ and BM 1 Multiply to obtain the second take-off and landing point coding reference vector BM 2 , for BM 2 Perform binarization processing to obtain the third take-off and landing point coding reference vector BM3 , according to BM 3 And the preset encoding threshold YZ, the prediction loss Loss = ρ × (YZ-SUM (BM 3 )), update the parameters of the preset prediction model according to Loss until Loss = 0, obtain the target prediction model, input A, B, Q, P and JS into the target prediction model, obtain the target priority sorting vector, and determine the first Щ initial UAV take-off and landing points in the target priority sorting vector as the target UAV take-off and landing points. It can be seen that through BM 3 The number of elements 1 in the , reverse BM 2 The number of elements greater than 0 in , and then reverse 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 matrix 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 premise conditions, thereby improving the rationality of the target UAV take-off and landing points.

[0197] 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 complex network model construction system for selecting the location of drone take-off and landing points, characterized by: The complex network model construction system includes a processor and a memory storing a computer program, wherein the memory also stores an 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 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 Refers to A n The corresponding i-th predicted flight route, H n Refers to A n The importance of the corresponding take-off and landing point position, C n Refers to A n The corresponding first traffic flow, G n Refers to A n The corresponding first building interference level, D n ={D n1 , D n2 ,……,D ni ,……,D nI(n) }, D ni Refers to B ni The corresponding route abnormality degree is used to characterize the degree of damage to the outside world when the UAV encounters a flight abnormality on the corresponding predicted flight route. n ={E n1 , E n2 ,……,E ni ,……,E nI(n) }, E ni Refers to B ni The corresponding route length, n=1, 2, ..., N, N refers to the total number of initial drone take-off and landing points, i=1, 2, ..., I(n), I(n) refers to the total number of predicted flight routes corresponding to the nth initial drone take-off and landing point. When the computer program is executed by a processor, the following steps are implemented: S1, according to H, C and G, obtain the take-off and landing point weight set Q = {Q1, Q2, ..., Q n ,……,Q N }, where A n The corresponding take-off and landing point weight Q n Meet the following conditions: Q n =α1×H n +α2×C n +α3×e^(-G n ), where α1 refers to a preset first location weight, α2 refers to a preset first flow weight, α3 refers to a preset building interference weight, and e refers to a natural constant; S2, according to C, obtain the second traffic flow set F corresponding to B = {F1, F2, ..., F n ,……,F N }, where B n The corresponding second traffic flow list F n ={F n1 , F n2 ,……,F ni ,……,F nI(n) }, F ni Equal to B ni The average value of the first traffic flow of the two corresponding initial drone take-off and landing points; S3, according to B and H, obtain the route position importance set K corresponding to B = {K1, K2, ..., K n ,……,K N }, where B n Corresponding route location importance list K n ={K n1 , K n2 ,……,K ni ,……,K nI(n) }, K ni Equal to B ni The average importance of the take-off and landing points of the two corresponding initial UAV take-off and landing points; S4, according to B, D, E, F and K, obtain the route weight set P corresponding to B = {P1, P2, ..., P n ,……,P N }, where P n ={P n1 , P n2 ,……,P ni ,……,P nI(n) }, B ni The corresponding route weight P ni Meet the following conditions: P ni =β1×e^(-D ni )+β2×E ni / ((∑ n＝1 N (S i＝1 I(n) E ni )) / (S n＝1 N I(n)))+β3×F ni +β4×K ni , among them, β1 refers to the preset anomaly weight, β2 refers to the preset route length weight, β3 refers to the preset second flow weight, and β4 refers to the preset second position weight; S5, based on A, B, Q and P, takes the initial UAV take-off and landing point as the node, the predicted flight route as the edge, the take-off and landing point weight corresponding to the initial UAV take-off and landing point as the node value of the corresponding node, and the route weight corresponding to the predicted flight route as the edge value of the corresponding edge, to obtain the complex network model T, wherein the complex network model T is used to filter the target UAV take-off and landing point from A.

2. The complex network model construction system for selecting the location of a UAV take-off and landing point according to claim 1 is characterized in that: The initial drone take-off and landing points are obtained through the following steps: S10, obtaining Θ original UAV take-off and landing points, a number of predicted flight routes corresponding to each original UAV take-off and landing point, and a route length, a second traffic flow, and a route anomaly degree corresponding to each predicted flight route, wherein the second traffic flow refers to the average of the first traffic flows corresponding to the two original UAV take-off and landing points corresponding to the predicted flight route, and the route anomaly degree is used to represent the degree of damage to the outside world when a UAV flight anomaly occurs on the corresponding predicted flight route, and Θ is an integer greater than 0; S20, obtaining 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 based on the number of predicted flight routes corresponding to each original UAV take-off and landing point, the route length corresponding to each predicted flight route, and the second traffic flow; S30, based on the predicted number of flight routes, total route length, total traffic volume, a preset route number threshold, a preset route length threshold, and a 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; S40, based on all predicted flight routes corresponding to each take-off and landing point to be merged, obtaining 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; 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 an intermediate take-off and landing point. Based on 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 point, and 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. S60, obtaining a route anomaly priority corresponding to the intermediate take-off and landing point based on the route anomaly 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 anomaly 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; S70, obtaining a merge priority corresponding to the current intermediate take-off and landing point based on the route length priority and the route abnormality priority; 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; S90: Merge the current take-off and landing point to be merged with the original UAV take-off and landing point corresponding to the highest merging priority, and determine the merged take-off and landing point as the initial UAV take-off and landing point.

3. The complex network model construction system for selecting the location of UAV take-off and landing points according to claim 2 is characterized in that: S10 further includes the following steps: S11, obtaining map data corresponding to the Θ original drone take-off and landing points and a first traffic flow corresponding to each original drone take-off and landing point; S12, input the map data corresponding to the Θ original drone take-off and landing points and the first traffic flow corresponding to the 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.

4. The complex network model construction system for selecting the location of a UAV take-off and landing point according to claim 1 is characterized in that: The memory also stores a position coordinate set Z={Z1, Z2, . . . , Z n ,……,Z N }, where Z n Refers to A n The corresponding position coordinates and the take-off and landing point position importance set H are obtained through the following steps: S01, based on B and Z, obtain the position importance set H corresponding to A = {H1, H2, ..., H n ,……,H N }, where A n The corresponding position importance H n Meet the following conditions: H n =(2 / ((1+e^(-I(n)))-1))×(e^(-∑ j＝1 N d(Z n , Z j )) / N), where e is the natural index, d(Z n , Z j ) refers to Z n and Z j The distance between them.

5. The complex network model construction system for selecting the location of a UAV take-off and landing point according to claim 1 is characterized in that: The memory also stores a first building density set J 1 ={J 1 1, J 1 2, ..., J 1 n ,……,J 1 N } and the first building height set J 2 ={J 2 1, J 2 2, ..., J 2 n ,……,J 2 N }, where J 1 n Refers to A n The first building density within the corresponding first preset range, J 2 n ={J 2 n1 , J 2 n2 ,……,J 2 nu ,……,J 2 nU(n) }, J 2 nu Refers to A n The height of the u-th first building within the corresponding first preset range, J 2 nu >J 0 , J 0 Refers to the preset building height threshold, u=1, 2, ..., U(n), U(n) refers to A n The total number of first buildings within the corresponding first preset range and the first building interference degree set G are obtained by the following steps: S02, according to J 1 and J 2 , obtain the first building interference level set G = {G1, G2, ..., G n ,……,G N }, where A n The corresponding first building interference level G n Meet the following conditions: G n =(2 / ((1+e^(-J 1 n ))-1))×(γ1×(Σ u＝1 U(n) J 2 nu ) / U(n)+γ2×max(J 2 n )), where max() refers to Take the maximum value function, γ1 refers to the preset first height weight, and γ2 refers to the preset second height weight.

6. The complex network model construction system for selecting the location of a UAV take-off and landing point according to claim 5 is characterized in that: The memory also stores a second building density set R 1 ={R 1 1, R 1 2, ..., R 1 n ,……,R 1 N }, the second building height set R 2 ={R 2 1, R 2 2, ..., R 2 n ,……,R 2 N }、Population density set Y 1 ={Y 1 1, Y 1 2, ..., Y 1 n ,……,Y 1 N } and the population category importance set Y 2 ={Y 2 1, Y 2 2, ..., Y 2 n ,……,Y 2 N }, where R 1 n ={R 1 n1 , R 1 n2 ,……,R 1 ni ,……,R 1 nI(n) }, R 1 ni Refers to B ni The corresponding second building density within the second preset range, R 2 n ={R 2 n1 , R 2 n2 ,……,R 2 ni ,……,R 2 nI(n) }, R 2 ni ={R 2 ni1 , R 2 ni2 ,……,R 2 niv ,……,R 2 niV(ni) }, R 2 niv Refers to B ni The height of the vth second building within the corresponding second preset range, Y 1 n ={Y 1 n1 , Y 1 n2 ,……,Y 1 ni ,……,Y 1 nI(n) }, Y 1 ni Refers to B ni The corresponding population density within the second preset range, Y 2 n ={Y 2 n1 , Y 2 n2 ,……,Y 2 ni ,……,Y 2 nI(n) }, Y 2 ni ={Y 2 ni1 , Y 2 ni2 ,……,Y 2 niw ,……,Y 2 niW(ni) }, Y 2 niw Refers to B ni The importance of the category corresponding to the wth population in the second preset range, v = 1, 2, ..., V (n), V (n) refers to A n The total number of second buildings within the corresponding second preset range, w=1, 2, ..., W(ni), W(ni) refers to B ni The total population within the corresponding second preset range and the route anomaly degree set D are obtained by the following steps: S03, according to R 1 、R 2 、Y 1 and Y 2 , get the route abnormality degree set D = {D1, D2, ..., D n ,……,D N }, where D n ={D n1 , D n2 ,……,D ni ,……,D nI(n) }, B ni The corresponding route abnormality degree D ni Meet the following conditions: D ni =δ1×D 1 ni +δ2×D 2 ni , where δ1 refers to the preset building weight, and δ2 refers to the preset first population weight, D 1 ni J(2 / ((1+e^(-R 1 ni ))-1))×(γ1×(Σ v＝1 V(n) R 2 niv ) / V(n)+γ2×max(R 2 ni )), D 2 ni =(2 / ((1+e^(-Y 1 ni ))-1))×(ε1×(Σ w＝1 W(n) Y 2 niv ) / W(n)+ε2×max(Y 2 ni )), ε1 refers to the preset second population weight, and ε2 refers to the preset third population weight.

7. The complex network model construction system for selecting the location of a UAV take-off and landing point according to claim 1 is characterized in that: The memory also stores a node betweenness set JS={JS1, JS2, ..., JS n ,……,JS N } and clustering coefficient set JL = {JL1, JL2, ..., JL n ,……,JL N }, JS n Refers to A n The corresponding node betweenness, JL n Refers to A n The corresponding clustering coefficient, when the computer program is executed by a processor, further implements the following steps: S61, based on T and E, obtain the shortest path between any two nodes in T; S62, the number of shortest paths passing through the nodes corresponding to the nth initial UAV take-off and landing point is determined as the node betweenness JS corresponding to the nth initial UAV take-off and landing point n ; S63, traverse n=1, 2, ..., N, and obtain the node betweenness set JS={JS1, JS2, ..., JS n ,……,JS N }; S2000: According to A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, the candidate priority set HX = {HX1, HX2, ..., HX n ,……,HX N }, where QZ1 refers to the preset weight of the take-off and landing point weight, QZ2 refers to the preset weight of the route weight, QZ3 refers to the preset weight of the number of routes, QZ4 refers to the preset weight of the node betweenness, QZ5 refers to the preset weight of the clustering coefficient, and A n Corresponding candidate priority HX n Meet the following conditions: 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 abnormality weight, β2 refers to the preset route length weight, and β3 refers to the preset second flow weight; S5000: Based on FZ, obtain the screening scores FS of M candidate drone take-off and landing points, where FS meets the following conditions: FS=(Σ n＝1 N (FZ n -FZ0) 2 / N, where the predicted mean value of candidate take-off and landing points FZ0 = Σ n＝1 N (FZ n ) / N; S6000, if FS>FS 0 , then update QZ, and use the updated QZ to replace the preset initial weight set, and repeat step S2000 until FS≤FS 0 , get FS≤FS 0 The candidate priority set HX when FS≤FS 0 The candidate priority set HX is used to filter the target UAV take-off and landing points from the N initial UAV take-off and landing points, FS 0 Refers to the preset screening score threshold; S6100, set 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.

8. The complex network model construction system for selecting the location of a UAV take-off and landing point according to claim 7, characterized in that: The memory also stores a preset prediction model, an initial UAV take-off and landing point code set A 1 ={A 1 1, A 1 2, ..., A 1 n ,……,A 1 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 1 n Refers to A n Corresponding initial take-off and landing point code, GJ μ refers to the key take-off and landing point code corresponding to the μth key take-off and landing point, μ=1, 2, ..., M, M refers to the total number of key take-off and landing points, the first M elements in YS are value 1, and the last NM elements are value 0. When the computer program is executed by a processor, the following steps are also 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 Refers to the initial UAV take-off and landing point with the predicted priority of σ, PX M It refers to the initial UAV take-off and landing point with the predicted priority of Щ, 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 Refers to PX σ The corresponding first take-off and landing point code, BM Щ Refers to PX Щ The corresponding first take-off and landing point code; S400, transposed vector of GJ and BM 1 Multiply to obtain the second take-off and landing point coding reference vector BM 2 ; S500, for BM 2 Perform binarization processing to obtain the third take-off and landing point coding reference vector BM 3 ; S600, according to BM 3 And the preset encoding threshold YZ, the prediction loss Loss = ρ × (SUM (BM 3 )-YZ), where ρ refers to the preset loss coefficient, SUM(BM 3 ) means BM 3 The sum of all elements in ; S700, updating the parameters of the preset prediction model according to the Loss until the Loss converges, and obtaining the target prediction model; S800, inputting A, B, Q, P and JS into the target prediction model to obtain a target priority ranking vector; S900: Determine the first 10 initial UAV take-off and landing points in the target priority sorting vector as target UAV take-off and landing points.

9. The complex network model construction system for selecting the location of a UAV take-off and landing point according to claim 8, 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 to be 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 to be 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).

10. The complex network model construction system for selecting the location of a UAV take-off and landing point according to claim 9, characterized in that: 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.

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