Method, device, medium and equipment for generating initial site selection for UAV take-off and landing points based on multiple factors

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method, apparatus, medium, and equipment for generating initial UAV take-off and landing sites based on multiple factors. The method includes: selecting initial UAV take-off and landing sites with low probability of setting up UAV take-off and landing facilities, where the predicted number of flight routes is less than a route number threshold, the total route length is less than a route length threshold, and the total traffic volume is less than a traffic flow threshold, as potential merged take-off and landing sites; filtering intermediate take-off and landing sites based on these sites; and obtaining the merging priority for each intermediate take-off and landing site based on the route length and route anomaly degree corresponding to the potential merged take-off and landing sites and the intermediate take-off and landing sites. The methods then merge the potential merged take-off and landing sites into the intermediate take-off and landing sites with the highest priority, generating a batch of initial sites suitable for UAV take-off and landing site selection. This ensures that the placement of UAV take-off and landing facilities at these initial sites matches the take-off and landing site requirements, improving the relative coverage and rationality of the UAV take-off and landing facilities.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method, apparatus, medium, and equipment for generating initial locations for UAV take-off and landing based on multiple factors. Background Technology

[0002] The concept of Urban Air Mobility (UAM) has attracted attention from academia, industry, and governments since its inception. Its use of eVTOL (Electric Vertical Takeoff and Landing) and unmanned aerial vehicles (UAVs) for transportation can not only meet the transportation needs of various distances within cities but also improve transportation safety and efficiency. With the rapid development and widespread application of UAV technologies, the rational selection of UAV takeoff and landing sites has become crucial.

[0003] In existing technologies, the methods for selecting drone take-off and landing sites are usually quite simplistic, considering only a few factors such as geographical location and terrain conditions. For example, determining drone take-off and landing sites solely based on distance from the target area or the flatness of the terrain fails to adequately consider the complexity of predicted drone flight paths and important factors such as air traffic flow. This makes it difficult to assess the demand correlation between drone take-off and landing sites, thus hindering the effective integration of drone resources. Consequently, the site selection of drone take-off and landing facilities is often poorly rational and fails to match the drone needs of users within the region.

[0004] Therefore, the planning and layout of drone take-off and landing facilities is crucial to meeting urban logistics needs, and how to improve the rationality of drone take-off and landing site selection has become an urgent problem to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention provides a method for generating initial locations for UAV take-off and landing points based on multiple factors. This method includes the following steps:

[0006] S10: Obtain Θ original UAV take-off and landing points, several predicted flight paths corresponding to each original UAV take-off and landing point, and the flight path length, second traffic flow, and flight path anomaly degree corresponding to each predicted flight path. The second traffic flow refers to the average of the first traffic flow corresponding to the two original UAV take-off and landing points corresponding to the predicted flight path. The flight path anomaly degree is used to characterize the degree of damage to the outside world when the UAV experiences flight anomalies on the corresponding predicted flight path. Θ is an integer greater than 0.

[0007] S20: Based on the several predicted flight routes corresponding to each original UAV take-off and landing point, the length of each predicted flight route, and the second traffic flow, obtain the number of predicted flight routes, the total length of the routes, and the total traffic volume corresponding to each original UAV take-off and landing point.

[0008] 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 flow 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.

[0009] S40: Based on all predicted flight paths corresponding to each take-off and landing point to be merged, obtain η original UAV take-off and landing points connected to each take-off and landing point to be merged, where η is an integer greater than 0.

[0010] S50: For any take-off and landing point to be merged, take any original UAV take-off and landing point connected to the current take-off and landing point as an intermediate take-off and landing point. Based on 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, obtain the route length priority corresponding to the intermediate take-off and landing point.

[0011] S60: Based on the degree of route anomaly of the predicted flight path 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 degree of route anomaly of the predicted flight path between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, obtain the priority of the route anomaly degree corresponding to the intermediate take-off and landing point.

[0012] S70 obtains the merging priority corresponding to the current intermediate take-off and landing point based on the priority of route length and the priority of route anomaly.

[0013] S80: Traverse the η original UAV take-off and landing points connected to the current take-off and landing points to be merged, and obtain η merging priorities.

[0014] S90 merges 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 determines the merged take-off and landing point as the initial UAV take-off and landing point.

[0015] The present invention also provides a multi-factor-based initial location generation device for UAV take-off and landing points, which includes:

[0016] The data acquisition module is used to acquire Θ original UAV take-off and landing points, several predicted flight paths corresponding to each original UAV take-off and landing point, and the flight path length, second traffic flow, and flight path anomaly degree corresponding to each predicted flight path. The second traffic flow refers to the average of the first traffic flow corresponding to the two original UAV take-off and landing points corresponding to the predicted flight path. The flight path anomaly degree is used to characterize the degree of damage to the outside world when the UAV experiences flight anomalies on the corresponding predicted flight path. Θ is an integer greater than 0.

[0017] The data processing module is used to obtain the number of predicted flight routes, the total length of the routes, and the total traffic volume corresponding to each original UAV take-off and landing point, based on several 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.

[0018] The take-off and landing point filtering module is used to filter out ζ take-off and landing points to be merged from Θ original drone take-off and landing points based on the predicted number of flight routes, total length of routes, total traffic volume, preset number of routes threshold, preset route length threshold and preset traffic flow threshold corresponding to each original drone take-off and landing point, where 0 < ζ < Θ.

[0019] The take-off and landing point acquisition module is used to obtain η original UAV take-off and landing points connected to each take-off and landing point to be merged, based on all predicted flight routes corresponding to each take-off and landing point to be merged, where η is an integer greater than 0.

[0020] The first priority acquisition module is used to, for any take-off and landing point to be merged, take any original UAV take-off and landing point connected to the current take-off and landing point to be merged as an intermediate take-off and landing point, and obtain the route length priority corresponding to the intermediate take-off and landing point based on 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.

[0021] The second priority acquisition module is used to obtain the priority of the route anomaly degree 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 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 anomaly 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.

[0022] The third priority acquisition module is used to obtain the merging priority corresponding to the current intermediate take-off and landing point based on the route length priority and the route anomaly degree priority.

[0023] The fourth priority acquisition module is used to traverse the η original UAV take-off and landing points connected to the current take-off and landing point to be merged, and obtain η merging priorities.

[0024] The take-off and landing point merging module is used to 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.

[0025] The present invention also provides a non-transitory computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement the above-described method for generating initial location of UAV take-off and landing points based on multiple factors.

[0026] The present invention also provides an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0027] This invention has at least the following beneficial effects: Based on the number of predicted flight routes, total route length, and total traffic volume corresponding to each original UAV take-off and landing point, and combined with preset route number thresholds, preset route length thresholds, and preset traffic flow thresholds, ζ take-off and landing points to be merged are selected from Θ original UAV take-off and landing points. Based on all predicted flight routes corresponding to each take-off and landing point to be merged, η 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 taken as an intermediate take-off and landing point. Based on the route length and route anomaly degree 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 and route anomaly degree between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, the optimal route length corresponding to the intermediate take-off and landing point is obtained. Priority is determined based on the priority of flight path length and the degree of flight path anomaly. 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 highest merging priority. The merged take-off and landing point is determined as the initial drone take-off and landing point. It can be seen that take-off and landing points with a low probability of setting up drone take-off and landing facilities, such as those with a predicted number of flight paths less than the preset number of flight paths threshold, a total flight path length less than the preset flight path length threshold, and a total traffic volume less than the preset traffic flow threshold, are merged into other take-off and landing points with a higher probability of setting up drone take-off and landing facilities. This generates a batch of initial points that can be used for drone take-off and landing site selection, so that the setting up of drone take-off and landing facilities at the initial drone take-off and landing points can match the take-off and landing point requirements, thereby improving the relative coverage and rationality of the drone take-off and landing facilities. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A flowchart illustrating a method for generating initial locations for UAV take-off and landing points based on multiple factors, provided in Embodiment 1 of the present invention;

[0030] Figure 2 This is a flowchart of the computer program executing a complex network model construction system for UAV take-off and landing point selection provided in Embodiment 2 of the present invention;

[0031] Figure 3 This is a flowchart of a method for determining the priority of candidate take-off and landing points for unmanned aerial vehicles based on a complex network model, provided in Embodiment 3 of the present invention;

[0032] Figure 4 This is a flowchart of the computer program executing a UAV take-off and landing point selection system based on candidate take-off and landing point priority, provided in Embodiment 4 of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 terms used to distinguish similar objects can be interchanged so that the invention can also be implemented in other embodiments besides the illustrated or described embodiments. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. 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 explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0035] Example 1

[0036] This embodiment provides a method for generating initial locations for UAV take-off and landing points based on multiple factors. This method includes the following steps: Figure 1 As shown:

[0037] S10: Obtain Θ original UAV take-off and landing points, several predicted flight paths corresponding to each original UAV take-off and landing point, and the flight path length, second traffic flow, and flight path anomaly degree corresponding to each predicted flight path. The second traffic flow refers to the average of the first traffic flow corresponding to the two original UAV take-off and landing points corresponding to the predicted flight path. The flight path anomaly degree is used to characterize the degree of damage to the outside world when the UAV experiences flight anomalies on the corresponding predicted flight path. Θ is an integer greater than 0.

[0038] The original UAV take-off and landing point can refer to the take-off and landing point in the geographical area where UAV take-off and landing facilities need to be selected, which can serve as the location for the layout of UAV take-off and landing facilities. There are predicted flight routes between the original UAV take-off and landing points that are suitable for UAVs to fly to transport goods. The location of the original UAV take-off and landing point and the trajectory of the predicted flight route can be determined by the implementer according to the actual situation.

[0039] Flight path anomaly can be used to characterize the degree of damage to the outside world when a drone experiences flight anomalies on its predicted flight path, such as damage to buildings, people, and other lives and property.

[0040] The first traffic flow refers to the traffic flow within a certain range corresponding to the original drone take-off and landing point, which can be used to characterize the demand for drone cargo transport at that original drone take-off and landing point. The average of the first traffic flows of the two original drone take-off and landing points corresponding to each predicted flight path can be used as the second traffic flow of the corresponding predicted flight path to characterize the guarantee of drone cargo transport by the corresponding predicted flight path, and serve as the basis for selecting drone take-off and landing points.

[0041] In one specific embodiment, S10 further includes the following steps:

[0042] S101, obtain the map data corresponding to Θ original drone take-off and landing points and the first traffic flow corresponding to each original drone take-off and landing point.

[0043] S102, input the map data corresponding to Θ original UAV take-off and landing points and the first traffic flow corresponding to Θ original UAV take-off and landing points into the preset UAV route setting model to obtain several predicted flight routes corresponding to each original UAV take-off and landing point.

[0044] The implementer can use path planning algorithms, such as A* search algorithm, Dijkstra algorithm, genetic algorithm, etc., to quickly generate drone flight paths between the original drone take-off and landing points based on the map data and the first traffic flow corresponding to the original drone take-off and landing points, and use the predicted flight paths for the drone take-off and landing point selection task.

[0045] Map data can include factors such as terrain data, obstacle data, and weather conditions.

[0046] S20: Based on the several predicted flight routes corresponding to each original UAV take-off and landing point, the length of each predicted flight route, and the second traffic flow, obtain the number of predicted flight routes, the total length of the routes, and the total traffic volume corresponding to each original UAV take-off and landing point.

[0047] Specifically, for any original UAV take-off and landing point, the sum of the lengths of all predicted flight routes corresponding to the current original UAV take-off and landing point is determined as the total length of the flight route corresponding to the current original UAV take-off and landing point, and the sum of the second traffic flow of all predicted flight routes corresponding to the current original UAV take-off and landing point is determined as the total traffic volume corresponding to the current original UAV take-off and landing point, which serves as the basis for selecting UAV take-off and landing points.

[0048] 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 flow 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.

[0049] The specific values ​​of the preset threshold for the number of routes, the preset threshold for the length of routes, and the preset threshold for traffic flow can be set by the implementer according to the actual situation.

[0050] In one specific embodiment, S30 further includes the following steps:

[0051] 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 number of routes threshold, the total length of routes 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 a take-off and landing point to be merged.

[0052] S32, iterate through Θ original drone take-off and landing points to obtain ζ take-off and landing points to be merged.

[0053] The more predicted flight routes, the longer the total length of the routes, and the larger the total traffic volume corresponding to the original drone take-off and landing point, the lower the demand for drone take-off and landing facilities at the original drone take-off and landing point, and the higher the probability of setting up drone take-off and landing facilities at the original drone take-off and landing point. Conversely, the fewer predicted flight routes, the higher the demand for drone take-off and landing facilities, and the lower the probability of setting up drone take-off and landing facilities at the original drone take-off and landing point.

[0054] Therefore, original drone take-off and landing points with low demand and probability of setting up drone take-off and landing facilities can be designated as take-off and landing points to be merged into connected take-off and landing points with high demand and probability of setting up drone take-off and landing facilities. This improves the correlation between take-off and landing points, ensures that the merged take-off and landing points have a higher probability of setting up drone take-off and landing facilities, and improves the coverage and rationality of drone take-off and landing facilities through take-off and landing point merging.

[0055] S40: Based on all predicted flight paths corresponding to each take-off and landing point to be merged, obtain η original UAV take-off and landing points connected to each take-off and landing point to be merged, where η is an integer greater than 0.

[0056] S50: For any take-off and landing point to be merged, take any original UAV take-off and landing point connected to the current take-off and landing point as an intermediate take-off and landing point. Based on 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, obtain the route length priority corresponding to the intermediate take-off and landing point.

[0057] In one specific embodiment, S50 further includes the following steps:

[0058] S51, the average of the flight path lengths between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points is determined as the first flight path length.

[0059] S52, the average of the flight path lengths between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points is determined as the second flight path length.

[0060] S53, 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 is determined as the third route length.

[0061] S54 determines the ratio of the length of the first route to the length of the third route as the route length priority corresponding to the intermediate take-off and landing point.

[0062] The first route length represents 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 represents the average distance from the current intermediate take-off and landing point to the other η-1 original drone take-off and landing points. The third route length represents the average distance 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, with the intermediate take-off and landing point as a transit point. 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 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. This indicates that the intermediate take-off and landing point has a higher priority than the current take-off and landing point, and merging the take-off and landing point to the intermediate take-off and landing point has a smaller 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.

[0063] Correspondingly, the larger the ratio of the length of the first route to the length of the third route, the higher the priority of the route length corresponding to the intermediate take-off and landing points.

[0064] The above-mentioned method obtains the priority of 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, to characterize the priority of the intermediate take-off and landing point compared with the current take-off and landing point to be merged, and the impact of merging the take-off and landing point to be merged into the intermediate take-off and landing point 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. This serves as the basis for merging the take-off and landing points to be merged, thus improving the rationality of take-off and landing point merging.

[0065] S60: Based on the degree of route anomaly of the predicted flight path 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 degree of route anomaly of the predicted flight path between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, obtain the priority of the route anomaly degree corresponding to the intermediate take-off and landing point.

[0066] In one specific embodiment, S60 further includes the following steps:

[0067] S61, the average of the flight path anomaly levels between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points is determined as the first flight path anomaly level.

[0068] S62, the average of the flight path anomaly levels between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points is determined as the second flight path anomaly level.

[0069] S63, the sum of the abnormality level of the current take-off and landing point to be merged and the intermediate take-off and landing point and the abnormality level of the second route is determined as the abnormality level of the third route.

[0070] S64 determines the ratio of the abnormality level of the first route to that of the third route as the priority of the abnormality level of the route corresponding to the intermediate take-off and landing point.

[0071] The first route anomaly level can characterize the average anomaly level of a drone traveling directly from the current take-off and landing point to the other η-1 original drone take-off and landing points. The second route anomaly level can characterize the average anomaly level of a drone traveling directly from the current intermediate take-off and landing point to the other η-1 original drone take-off and landing points. The third route anomaly level can characterize the average anomaly level of a drone traveling 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, using the intermediate take-off and landing point as a transit point. If the third route anomaly level is less than the corresponding first route anomaly level, it means that compared with the drone traveling 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 anomaly level is lower when the intermediate take-off and landing point is used as a transit point. This indicates that the intermediate take-off and landing point has a higher priority than the current take-off and landing point to be merged, and merging the take-off and landing point to be merged into the intermediate take-off and landing point has a smaller impact on the safety of the drone.

[0072] Correspondingly, the higher the ratio of the abnormality level of the first route to that of the third route, the higher the priority of the abnormality level of the route corresponding to the intermediate take-off and landing point.

[0073] The above-mentioned method obtains the priority of the flight path anomaly 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 priority of the flight path anomaly between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points. This is used 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 of merging the take-off and landing point to be merged into the intermediate take-off and landing point on the safety of the UAV. This serves as the basis for merging the take-off and landing points to be merged, thus improving the rationality of the take-off and landing point merging.

[0074] S70 obtains the merging priority corresponding to the current intermediate take-off and landing point based on the priority of route length and the priority of route anomaly.

[0075] In one specific embodiment, S70 further includes the following steps:

[0076] S71, obtain the preset route length weight and the preset anomaly degree weight.

[0077] S72, based on the route length priority, the preset route length weight, the route anomaly priority, and the preset anomaly weight, obtains the merging priority corresponding to the current intermediate take-off and landing point.

[0078] Specifically, the first product of the route length priority and the preset route length weight, and the second product of the route anomaly priority and the preset anomaly 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, which represents the priority of merging the current take-off and landing point to be merged into the current intermediate take-off and landing point.

[0079] The specific values ​​of the preset route length weight and the preset anomaly degree weight can be set by the implementer according to the actual situation.

[0080] The above-mentioned method combines route length priority, preset route length weight, route anomaly priority, and preset anomaly weight to obtain the merging priority corresponding to the current intermediate take-off and landing point. This priority is used to represent the priority of merging the current take-off and landing point to be merged into the current intermediate take-off and landing point, which serves as the basis for merging the current take-off and landing point to be merged and improves the rationality of take-off and landing point merging.

[0081] S80: Traverse the η original UAV take-off and landing points connected to the current take-off and landing points to be merged, and obtain η merging priorities.

[0082] S90 merges 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 determines the merged take-off and landing point as the initial UAV take-off and landing point.

[0083] In this process, take-off and landing points with low demand for drone take-off and landing facilities and low probability of setting up such facilities are merged into other take-off and landing points with high demand for such facilities and high probability of setting up such facilities. This process obtains initial drone take-off and landing points as locations for drone take-off and landing facilities, thereby generating a batch of initial locations that can be used for drone take-off and landing site selection. This ensures that the locations of drone take-off and landing facilities match the demand for take-off and landing points, thereby improving the relative coverage and rationality of drone take-off and landing facilities.

[0084] As described above, based on the number of predicted flight routes, total route length, and total traffic volume corresponding to each original UAV take-off and landing point, and combined with preset route number thresholds, preset route length thresholds, and preset traffic volume thresholds, ζ take-off and landing points to be merged are selected from Θ original UAV take-off and landing points. Based on all predicted flight routes corresponding to each take-off and landing point to be merged, η 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 is taken as an intermediate take-off and landing point. Based on the route length and route anomaly degree 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 and route anomaly degree between the intermediate take-off and landing point and the other η-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. Based on the priority of flight path length and the priority of flight path anomaly, 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 highest 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 take-off and landing points with low probability of setting up drone take-off and landing facilities, such as those with a predicted number of flight paths less than the preset number of flight paths threshold, a total flight path length less than the preset flight path length threshold, and a total traffic volume less than the preset traffic flow threshold, are merged into other take-off and landing points with a higher probability of setting up drone take-off and landing facilities. This generates a batch of initial points that can be used for drone take-off and landing site selection, so that the setting up of drone take-off and landing facilities at the initial drone take-off and landing points can match the take-off and landing point requirements, thereby improving the relative coverage and rationality of the drone take-off and landing facilities.

[0085] Example 2

[0086] This second embodiment provides a complex network model construction system for UAV take-off and landing point selection, such as... Figure 2 As shown, the complex network model construction system for UAV take-off and landing point selection includes a processor and a memory storing computer programs. The memory also stores an initial set of UAV take-off and landing points A = {A1, A2, ..., A...}. n , ..., A N The set of predicted flight paths is B = {B1, B2, ..., B}. n , ..., B N The set of importance of take-off and landing point locations is H = {H1, H2, ..., H...} n H N The first traffic flow set C = {C1, C2, ..., C} n , ..., C N The first set of building interference levels is G = {G1, G2, ..., G...} n , ..., GN The set of abnormality levels of flight routes, D = {D1, D2, ..., D...} n , ..., D N The set of route lengths E = {E1, E2, ..., E} n , ..., E N}, where A n This refers to the nth initial UAV 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 path, H n Refers to A n The corresponding importance of the take-off and landing point location, C n Refers to A n The corresponding first traffic flow, G n Refers to A n The corresponding first level of building interference, D n ={D n1 D n2 , ..., D ni , ..., D nI(n)}, D ni It refers to B ni The corresponding flight path anomaly level, E, is used to characterize the degree of damage to the external environment when a UAV experiences flight anomalies along its predicted flight path. n ={E n1 E n2 , ..., E ni , ..., E nI(n)}, E ni It refers to B ni The corresponding flight path length, n = 1, 2, ..., N, where N refers to the total number of initial UAV take-off and landing points, i = 1, 2, ..., I(n), where I(n) refers to the total number of predicted flight paths corresponding to the nth initial UAV take-off and landing point.

[0087] Among them, the importance of the take-off and landing point location can be used to characterize the importance of the location of the corresponding initial drone take-off and landing point in the drone transportation mission, and the first building interference degree can be used to characterize the degree of interference of the buildings near the initial drone take-off and landing point on the drone take-off and landing when the drone takes off and lands at the corresponding initial drone take-off and landing point.

[0088] When a computer program is executed by a processor, the following steps are performed:

[0089] S1, based on H, C, and G, obtain the set of takeoff and landing point weights Q = {Q1, Q2, ..., Q...}n Q N}, where A n The corresponding take-off and landing point weights Q n The following conditions must be met:

[0090] 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 traffic weight, α3 refers to the preset building interference weight, and e refers to the natural constant.

[0091] The greater the importance of the initial drone take-off and landing point, the greater the first traffic flow, and the less interference from the first building, the higher the likelihood of setting up drone take-off and landing facilities at that initial drone take-off and landing point.

[0092] 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 weights, while the degree of first building interference is negatively correlated with the take-off and landing point weights. Thus, the set of take-off and landing point weights Q can be obtained based on H, C, and G, which serves as the basis for constructing complex network models and selecting UAV take-off and landing point locations.

[0093] The specific values ​​of α1, α2, and α3 can be set by the implementer according to the actual situation.

[0094] The above measures the impact of the importance of the take-off and landing point location, the first traffic flow, and the first building interference on the possibility of setting up drone take-off and landing facilities at the initial drone take-off and landing point, obtains the take-off and landing point weight corresponding to each initial drone take-off and landing point, improves the accuracy of the take-off and landing point weight, and thus improves the accuracy of the construction of complex network models and the rationality of the selection of drone take-off and landing points.

[0095] In one specific embodiment, the memory also stores a set of position coordinates Z = {Z1, Z2, ..., Z...} n , ..., Z N}, where Z n Refers to A n The corresponding location coordinates and the set H of importance of the take-off and landing points are obtained through the following steps:

[0096] S01, Based on B and Z, obtain the set of importance of the take-off and landing points corresponding to A, H = {H1, H2, ..., H...} n H N}, where A n The corresponding importance H of the take-off and landing point location n The following conditions must be met:

[0097] Hn =(2 / ((1+e^(-I(n)))-1))×(e^(-∑ j＝1 N d(Z n Z j )) / N), where e refers to the natural index, d(Z) n Z j ) refers to Z n and Z j The distance between them.

[0098] The location coordinates can refer to the geographical 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.

[0099] The more other initial drone take-off and landing points the nth initial drone take-off and landing point connects to, and the closer the nth initial drone take-off and landing point is to other initial drone take-off and landing points, the higher the importance of the take-off and landing point corresponding to the nth initial drone take-off and landing point.

[0100] The above-mentioned method measures the importance of each initial UAV take-off and landing point by considering the number of other initial UAV take-off and landing points connected to the initial UAV take-off and landing point, as well as the distance between them. This serves as the basis for obtaining the take-off and landing point weight for each initial UAV take-off and landing point, thus improving the accuracy of the take-off and landing point weight.

[0101] In one specific embodiment, 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 corresponding first building density within the 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 This refers to the preset building height threshold, u = 1, 2, ..., U(n), where U(n) refers to A. n The total number of first buildings within the corresponding first preset range, and the set of interference levels of the first buildings G, are obtained through the following steps:

[0102] S02, according to J 1 and J 2 The first set of building interference levels, G = {G1, G2, ..., G...}, is obtained. n , ..., G N}, where A n The corresponding first building disturbance level G n The following conditions must be met:

[0103] 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 the maximum value function, γ1 refers to the preset first height weight, and γ2 refers to the preset second height weight.

[0104] The first preset range can refer to the ground area centered on the initial UAV take-off and landing point, with a 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 within the first preset range are different, they will cause different degrees of interference to the UAV when it takes off and lands at the corresponding initial UAV take-off and landing point. Therefore, the degree of interference of the first building corresponding to the initial UAV take-off and landing point is measured based on the first building density and the first building height within the first preset range corresponding to the initial UAV take-off and landing point, thereby improving the accuracy of obtaining the take-off and landing point weight.

[0105] The specific values ​​of the first preset length and the preset building height threshold can be set by the implementer according to the actual situation.

[0106] Since the buildings within the first preset range have varying heights, the lower buildings cause less interference to the takeoff and landing of drones. Therefore, this embodiment sets a preset building height threshold J. 0Buildings within a first preset range are selected, and higher-ranking buildings are used to characterize the first building interference level corresponding to the initial UAV take-off and landing point. This reduces the impact of many low-ranking buildings on the first building interference level, thereby improving the accuracy of measuring the first building interference level.

[0107] The specific values ​​of γ1 and γ2 can be set by the implementer according to the actual situation.

[0108] In one specific implementation, γ1 > γ2.

[0109] S2, based on C, obtain the second traffic flow set F = {F1, F2, ..., F} corresponding to B. 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 equals B ni The average of the first traffic flow at the two corresponding initial drone take-off and landing points.

[0110] The average of the first traffic flow at the two initial UAV take-off and landing points corresponding to each predicted flight path can be used as the second traffic flow of the corresponding predicted flight path to characterize the degree of guarantee provided by the predicted flight path for UAV cargo transportation, and serve as the basis for obtaining the route weight corresponding to the predicted flight path.

[0111] S3, based on B and H, obtain the set of route location importance corresponding to B, K = {K1, K2, ..., K...} n ..., K N}, where B n List of corresponding route location importance K n ={K n1 K n2 , ..., K ni , ..., K nI(n)}, K ni equals B ni The average importance of the two initial drone take-off and landing points.

[0112] The average of the importance of the two initial UAV take-off and landing points corresponding to each predicted flight path can characterize the importance of the corresponding predicted flight path location, serving as the basis for obtaining the flight path weights corresponding to the predicted flight path.

[0113] S4, based on B, D, E, F, and K, obtain the set of route weights P = {P1, P2, ..., P} corresponding to B. n ..., P N}, where P n ={P n1 P n2 ..., P ni ..., P nI(n)}, B ni The corresponding route weight P ni The following conditions must be met:

[0114] 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

[0115] 1 refers to the preset abnormal weight, β2 refers to the preset route length weight, β3 refers to the preset second traffic weight, and β4 refers to the preset second position weight.

[0116] Among them, the smaller the degree of flight path anomaly, the longer the flight path, the greater the secondary traffic flow, and the higher the importance of the flight path location, the higher the probability that the predicted flight path is the flight path of the UAV.

[0117] Therefore, the predicted flight path weights are negatively correlated with the degree of flight path anomaly and positively correlated with flight path length, secondary traffic flow, and the importance of flight path location. Thus, based on B, D, E, F, and K, the set of flight path weights P corresponding to B can be obtained, which serves as the basis for constructing complex network models and selecting UAV take-off and landing sites.

[0118] The specific values ​​of β1, β2, β3 and β4 can be set by the implementer according to the actual situation.

[0119] The above measures the impact of flight path anomaly, flight path length, secondary traffic flow, and flight path location importance on the likelihood of a predicted flight path serving as the flight path of a UAV. By obtaining the flight path weight corresponding to each predicted flight path, the accuracy of the flight path weight is improved, thereby improving the accuracy of the construction of complex network models and the rationality of the selection of UAV take-off and landing points.

[0120] In one specific embodiment, the memory also stores a second building density set R. 1 ={R 1 1, R 1 2, ..., R 1 n , ..., R 1 N}、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 set of population category importance 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 It 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 It refers to B ni The height of the v-th 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 It 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 It refers to B ni The corresponding category importance of the w-th person within the second preset range, v = 1, 2, ..., V(n), where V(n) refers to A n The total number of second buildings within the corresponding second preset range, w = 1, 2, ..., W(ni), where W(ni) refers to B. ni The total population within the corresponding second preset range, and the set D of flight route anomalies, are obtained through the following steps:

[0121] S03, according to R 1 R 2 Y 1 and Y 2 Obtain the set of abnormality levels of flight routes D = {D1, D2, ..., D...} n , ..., D N}, where D n ={D n1 D n2 , ..., D ni , ..., D nI(n)}, Bni The corresponding flight path anomaly level D ni The following conditions must be met:

[0122] 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.

[0123] 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 )),

[0124] 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.

[0125] The importance of a population category can refer to the level of security required by the corresponding category of population, or it can refer to the severity of the security impact on the corresponding category of population.

[0126] The second preset range can refer to the airspace three-dimensional area based on the trajectory of the predicted flight path, which is a distance of a second preset length from each trajectory point of the predicted flight path. There are several buildings and several people within the second preset range. When the density and height of the buildings, as well as the population density and the importance of the population category within the second preset range are different, it will affect the degree of damage to buildings, people and other lives and property when the UAV experiences flight anomalies on the corresponding predicted flight path.

[0127] Therefore, the degree of route anomaly corresponding to each predicted flight route is measured based on the second building density, second building height, population density, and population category importance corresponding to the predicted flight route, thereby improving the accuracy of route weighting.

[0128] The specific values ​​of δ1, δ2, ε1, and ε2 can be set by the implementer according to the actual situation.

[0129] S5. Based on A, B, Q and P, the initial UAV take-off and landing point is taken as a node, the predicted flight path is taken as an edge, the take-off and landing point weight corresponding to the initial UAV take-off and landing point is taken as the node value of the corresponding node, and the flight path weight corresponding to the predicted flight path is taken as the edge value of the corresponding edge, thus obtaining a complex network model T. The complex network model T is used to filter the target UAV take-off and landing point from A.

[0130] Among them, the complex network model T is used to filter out the target drone take-off and landing points from A. The target drone take-off and landing points can serve as the location for setting up drone take-off and landing facilities to support tasks such as drone delivery logistics.

[0131] Based on the predicted flight path, and according to H, C, and G, the set of takeoff and landing point weights Q = {Q1, Q2, ..., Q} is obtained. n Q N The study measures the impact of the importance of the take-off and landing point location, the first traffic flow, and the first building interference on the probability of setting up drone take-off and landing facilities at the initial drone take-off and landing point. It obtains the take-off and landing point weights corresponding to each initial drone take-off and landing point, improving the accuracy of the take-off and landing point weights. Based on C, it obtains the second traffic flow set F = {F1, F2, ..., F} corresponding to B. n , ..., F N} is used to characterize the degree of assurance that the corresponding predicted flight path provides for the transport of goods by the UAV. Based on B and H, the set of importance of the flight path position corresponding to B is obtained as K = {K1, K2, ..., K}. n , ..., K N Based on B, D, E, F, and K, obtain the set of route weights P = {P1, P2, ..., P} corresponding to B. n ..., P N This method measures the impact of flight path anomaly degree, flight path length, secondary traffic flow, and flight path location importance on the likelihood of a predicted flight path serving as the flight path of a UAV. It obtains the flight path weights corresponding to each predicted flight path, improving the accuracy of these weights. Based on A, B, Q, and P, it uses the initial UAV take-off and landing points as nodes, the predicted flight paths 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 flight path weights corresponding to the predicted flight paths as the edge values ​​of the corresponding edges, thus obtaining a complex network model T. This model is used to filter target UAV take-off and landing points from A, improving the accuracy of the complex network model T construction and consequently enhancing the rationality of the selection of target UAV take-off and landing points, and ultimately improving the rationality of UAV take-off and landing point location selection.

[0132] Example 3

[0133] This third embodiment provides a method for determining the priority of candidate take-off and landing points for unmanned aerial vehicles (UAVs) based on a complex network model, such as... Figure 3 As shown, the method for determining the priority of candidate take-off and landing points for UAVs based on complex network models includes:

[0134] S1000, Obtain the initial set of UAV take-off and landing points A = {A1, A2, ..., A...} n , ..., A N The set of predicted flight paths is B = {B1, B2, ..., B}. n , ..., B N}, The set of takeoff and landing point weights Q = {Q1, Q2, ..., Q} n Q N The set of route weights, P = {P1, P2, ..., P} n ..., P N}, Node betweenness set JS = {JS1, JS2, ..., JS} n , ..., JS N Clustering coefficient set JL = {JL1, JL2, ..., JL} n ..., JL N The set of abnormality levels of flight routes, D = {D1, D2, ..., D...} n , ..., D N The set of route lengths E = {E1, E2, ..., E} n , ..., E N The second traffic flow set F = {F1, F2, ..., F} n , ..., F N}, where A n This refers to the nth initial UAV 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 path, Q n Refers to A n The corresponding takeoff and landing point weights, P n ={P n1 P n2 ..., P ni ..., P nI(n)}, P ni It refers to B ni The corresponding route weights, JS n Refers to A n The corresponding node betweenness, JL n Refers to A nThe corresponding clustering coefficient, D n ={D n1 D n2 , ..., D ni , ..., D nI(n)}, D ni It refers to B ni The corresponding degree of flight route abnormality, E n ={E n1 E n2 , ..., E ni , ..., E nI(n)}, E ni It refers to B ni The corresponding route length, F n ={F n1 F n2 , ..., F ni , ..., F nI(n)}, F ni It refers to B ni The corresponding second traffic flow, n = 1, 2, ..., N, where N refers to the total number of initial UAV take-off and landing points, i = 1, 2, ..., I(n), where I(n) refers to the total number of predicted flight paths corresponding to the nth initial UAV take-off and landing point.

[0135] Among them, the take-off and landing point weights represent the probability of setting up drone take-off and landing facilities at the corresponding initial drone take-off and landing point; the flight path weights represent the probability of the predicted flight path being the drone's flight path; the node betweenness coefficients represent the influence of the corresponding initial drone take-off and landing point relative to other initial drone take-off and landing points; and the clustering coefficients represent the degree to which the corresponding initial drone take-off and landing point clusters with other initial drone take-off and landing points.

[0136] S2000, based on A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, obtain the candidate priority set HX = {HX1, HX2, ..., HX...}. n ..., HX N}, where QZ1 refers to the preset weight of take-off and landing points, QZ2 refers to the preset weight of flight routes, QZ3 refers to the preset weight of the number of flight routes, QZ4 refers to the preset weight of node betweenness, QZ5 refers to the preset weight of clustering coefficient, and A n Corresponding candidate priority HX n The following conditions must be met:

[0137] HX n =QZ1×Q n +QZ2×∑ i＝1 I(n) (P ni / I(n))+QZ3×I(n)+QZ4×JS n +QZ5×JL n .

[0138] Among them, the higher the weight of the initial UAV take-off and landing point, the higher the average route weight of the predicted flight route connected to the initial UAV take-off and landing point, the more routes the predicted flight route connected to the initial UAV take-off and landing point, and the higher the node betweenness number and clustering coefficient of the initial UAV take-off and landing point, the higher the probability of setting up UAV take-off and landing facilities at the initial UAV take-off and landing point.

[0139] Therefore, by 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 facility.

[0140] The specific values ​​of QZ1, QZ2, QZ3, QZ4, and QZ5 can be set by the implementer according to the actual situation.

[0141] The above-mentioned combination of the initial UAV take-off and landing point weight, average flight path weight, number of predicted flight paths, node betweenness and clustering coefficient, and preset initial weights, along with the initial UAV take-off and landing point weights, measures the candidate priority of each initial UAV take-off and landing point as the target UAV take-off and landing point, thereby improving the accuracy of candidate priority and thus improving the rationality of target UAV selection.

[0142] S3000, based on HX, selects M candidate drone take-off and landing points from N initial drone take-off and landing points.

[0143] In one specific embodiment, S3000 further includes the following steps:

[0144] S3100 determines the initial UAV take-off and landing points corresponding to the M highest candidate priorities in HX as candidate UAV take-off and landing points.

[0145] 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 The corresponding candidate takeoff and landing point prediction score FZ n The following conditions must be met:

[0146] 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

[0147] β1 refers to the preset abnormal weight, β2 refers to the preset route length weight, and β3 refers to the preset second traffic weight.

[0148] Among them, the smaller the degree of flight path anomaly, the longer the flight path length, and the greater the second traffic flow of all predicted flight paths corresponding to the candidate drone take-off and landing point, the lower the demand for drone take-off and landing facilities at the candidate drone take-off and landing point, and the higher the probability of setting up drone take-off and landing facilities at the candidate drone take-off and landing point. Correspondingly, the higher the prediction score of the candidate take-off and landing point.

[0149] The above-mentioned method measures the probability of setting up drone take-off and landing facilities at candidate drone take-off and landing points based on D, E, and F, thereby obtaining the candidate take-off and landing point prediction score set FZ, which improves the accuracy of the candidate take-off and landing point prediction scores.

[0150] S5000, based on FZ, obtain the screening scores FS for M candidate UAV take-off and landing points, where FS meets the following conditions:

[0151] FS=(Σ n＝1 N (FZ n -FZ0) 2 / N, where the predicted mean of candidate take-off and landing points FZ0=Σ n＝1 N (FZ n ) / N.

[0152] 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 aims to make the predicted scores of candidate take-off and landing points among the selected target drone take-off and landing points as consistent as possible.

[0153] The above describes how the difference between the predicted scores of the M candidate drone take-off and landing points is measured to obtain the screening score FS of the M candidate drone take-off and landing points. This score represents the rationality of the screening of the M candidate drone take-off and landing points and serves as the basis for updating the initial weight set. This allows for the re-screening of the M take-off and landing points as target drone take-off and landing points, providing locations for drone take-off and landing facilities. This achieves the effect of balancing the delivery efficiency and delivery intensity of drones corresponding to each target drone take-off and landing point, thereby improving the overall working efficiency of drones.

[0154] S6000, if FS > FS0 If the initial weight set is not updated, then QZ is updated and replaced with the updated QZ. Step S2000 is repeated until FS ≤ FS. 0 To obtain FS≤FS 0 The candidate priority set HX is given by FS ≤ FS. 0 The candidate priority set HX is used to filter the target UAV take-off and landing points from N initial UAV take-off and landing points, FS 0 This refers to the preset screening score threshold.

[0155] Among them, the preset screening score threshold FS 0 The specific values ​​can be set by the implementer based on the actual situation.

[0156] The candidate priority for each initial UAV take-off and landing point is obtained by combining the take-off and landing point weight, average flight path weight, number of predicted flight paths, node betweenness coefficient and clustering coefficient, 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 selection of candidate UAV take-off and landing points.

[0157] The larger the screening score FS for the M candidate drone take-off and landing points, the greater the difference in delivery efficiency and delivery intensity among the M candidate drone take-off and landing points. Therefore, when FS > FS 0 Update QZ periodically and replace the preset initial weight set with the updated QZ. Repeat step S2000 to obtain M new candidate UAV take-off and landing points until FS≤FS. 0 To obtain FS≤FS 0 The candidate priority set HX is used to select target drone take-off and landing points from N initial drone take-off and landing points, thereby improving the consistency of delivery efficiency and delivery intensity among target drone take-off and landing points and improving the rationality of drone take-off and landing point selection.

[0158] 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} is obtained. 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. Based on D, E, and F, the predicted score set of the candidate take-off and landing points is obtained as FZ = {FZ1, FZ2, ..., FZ...}. n ..., FZ N According to FZ, obtain the screening score FS for M candidate drone take-off and landing points. If FS > FS 0If the initial weight set is not found, then update QZ and replace it with the updated QZ. Repeat the above steps until FS ≤ FS. 0 FS≤FS 0 M candidate drone take-off and landing points are determined as the target drone take-off and landing points. It can be seen that by measuring the difference between the predicted scores of the candidate take-off and landing points, the screening score FS of the M candidate drone take-off and landing points is obtained to characterize the rationality of the screening of the M candidate drone take-off and landing points. This serves as the basis for updating the initial weight set and the candidate priority set, so as to screen out the target drone take-off and landing points and provide the setting location for drone take-off and landing facilities. This achieves 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 working efficiency of the drones.

[0159] Example 4

[0160] This fourth embodiment provides a UAV take-off and landing point selection system based on candidate take-off and landing point priority, such as... Figure 4 As shown, the UAV take-off and landing point selection system based on candidate priority includes a processor and a memory storing a computer program. The memory also stores a preset prediction model and an initial set of UAV take-off and landing points 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 The set of predicted flight paths is B = {B1, B2, ..., B}. n , ..., B N}, The set of takeoff and landing point weights Q = {Q1, Q2, ..., Q} n Q N The set of route weights, P = {P1, P2, ..., P} n ..., P N}, Node betweenness set JS = {JS1, JS2, ..., JS} n , ..., JS N The preset key take-off and landing point code set GJ = {GJ1, GJ2, ..., GJ} μ ..., GJ M} and a preset reference vector YS = (1, 1, ..., 1, 0, 0, ..., 0), where A n This refers to the nth initial UAV take-off and landing point, A. 1 n Refers to A nThe 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 path, Q n Refers to A n The corresponding takeoff and landing point weights, P n ={P n1 P n2 ..., P ni ..., P nI(n)}, P ni It refers to B ni The corresponding route weights, JS n Refers to A n The corresponding node betweenness, GJ μ YS refers to the key take-off and landing point code corresponding to the preset μ-th key take-off and landing point, n = 1, 2, ..., N, where N is the total number of initial UAV take-off and landing points, i = 1, 2, ..., I(n), where I(n) is the total number of predicted flight paths corresponding to the n-th initial UAV take-off and landing point, μ = 1, 2, ..., M, where M is the total number of preset key take-off and landing points. The first Щ elements in YS are 1, and the last N-Щ elements are 0. Щ is the preset number of target UAV take-off and landing points.

[0161] The initial UAV take-off and landing point code can be a code that is pre-set by the implementer according to the actual situation, used to identify the initial UAV take-off and landing point, such as 1, 2, 3...

[0162] The preset key take-off and landing point codes can be pre-set by the implementer according to the actual situation, and are used to identify key take-off and landing points, such as -1, -2, -3, etc.

[0163] The pre-defined prediction model is used to extract and map features from the input unordered initial UAV take-off and landing points, predicted flight paths, node betweenness, take-off and landing point weights, and flight path weights, and outputs an ordered prediction priority ranking vector of the initial UAV take-off and landing points.

[0164] The preset reference vector YS = (1, 1, ..., 1, 0, 0, ..., 0) has the first 3 elements of YS as the value 1 and the last N-3 elements as the value 0. It is used to process the predicted priority ranking vector and extract the first 3 elements of the predicted priority ranking vector as the evaluation of the degree of matching between the ranking result of the predicted priority ranking vector and the preconditions.

[0165] When a computer program is executed by a processor, the following steps are performed:

[0166] 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 ), of which PX n This refers to the initial drone take-off and landing point with a prediction priority of nth.

[0167] The higher the priority ranking, the higher the likelihood that the corresponding initial drone take-off and landing point will be the target drone take-off and landing point for setting up drone take-off and landing facilities.

[0168] In one 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 and the M preset key take-off and landing points are obtained through the following steps:

[0169] S2000, based on A, B, Q, P, JS, JL and the preset initial weight set QZ = {QZ1, QZ2, QZ3, QZ4, QZ5}, obtain the candidate priority set HX = {HX1, HX2, ..., HX...}. n ..., HX N}, where QZ1 refers to the preset weight of take-off and landing points, QZ2 refers to the preset weight of flight routes, QZ3 refers to the preset weight of the number of flight routes, QZ4 refers to the preset weight of node betweenness, QZ5 refers to the preset weight of clustering coefficient, and A n Corresponding candidate priority HX n The following conditions must be met:

[0170] HX n =QZ1×Q n +QZ2×∑ i＝1 I(n) (P ni / I(n))+QZ3×I(n)+QZ4×JS n +QZ5×JL n .

[0171] S3000, based on HX, selects M candidate drone take-off and landing points from N initial drone take-off and landing points.

[0172] 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 nThe corresponding candidate takeoff and landing point prediction score FZ n The following conditions must be met:

[0173] 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

[0174] β1 refers to the preset abnormal weight, β2 refers to the preset route length weight, and β3 refers to the preset second traffic weight.

[0175] S5000, based on FZ, obtain the screening scores FS for M candidate UAV take-off and landing points, where FS meets the following conditions:

[0176] FS=(Σ n＝1 N (FZ n -FZ0) 2 / N, where the predicted mean of candidate take-off and landing points FZ0=Σ n＝1 N (FZ n ) / N.

[0177] S7000, if FS > FS 0 If the initial weight set is not updated, then QZ is updated and replaced with the updated QZ. Step S2000 is repeated until FS ≤ FS. 0 FS≤FS 0 The M candidate UAV take-off and landing points were determined as the M critical take-off and landing points, FS 0 This refers to the preset screening score threshold.

[0178] S200, perform vector multiplication on PX and YS to obtain the takeoff and landing point reference vector CK. 1 = (PX1, PX2, ..., PX) σ ..., PX Щ ,0,0,...,0), where PX σ This refers to the prediction of the initial UAV take-off and landing point with a priority of σth position, PX Щ This refers to the prediction of the initial UAV take-off and landing point with a priority of Щ, where σ = 1, 2, ..., Щ.

[0179] In this task, based on the selection of Щ target UAV take-off and landing points, PX and YS are multiplied by vector to obtain the take-off and landing point reference vector CK. 1 = (PX1, PX2, ..., PX) σ ..., PX Щ ,0,0,...,0), to extract the initial UAV take-off and landing points ranked in the top Щ positions according to the prediction priority, which serves as the basis for evaluating the ranking effect of the prediction priority ranking vector PX.

[0180] S300, according to CK 1 、A 1 And GJ, obtain CK 1 The corresponding first take-off and landing point coded reference vector BM 1 = (BM1, BM2, ..., BM σ , ..., BM Щ ,0,0,...,0), where BM σ It refers to PX σ The corresponding first takeoff and landing point code, BM Щ It refers to PX Щ The corresponding first take-off and landing point code.

[0181] In one specific embodiment, S300 further includes the following steps:

[0182] S310, if PX σ If it is a critical take-off and landing point, then PX will be used. σ The corresponding critical take-off and landing point code is determined to be PX. σ The corresponding first take-off and landing point code.

[0183] S320, if PX σ If it is not a critical takeoff and landing point, then PX will be used. σ The corresponding initial takeoff and landing point code is determined to be PX. σ The corresponding first take-off and landing point code.

[0184] S330, iterate through PX1, PX2, ..., PX σ ..., PX Щ , obtain CK 1 The corresponding first take-off and landing point coded reference vector BM 1 = (BM1, BM2, ..., BM σ , ..., BM Щ ,0,0,...,0).

[0185] Specifically, based on the type of the initial UAV take-off and landing points extracted from the first 3 positions, i.e., critical take-off and landing points or non-critical 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 first 3 positions is obtained. The code is used to determine the degree of matching between the initial UAV take-off and landing points of the first 3 positions and the preconditions, providing a data basis for measuring the ranking effect of the prediction priority ranking vector PX.

[0186] In one specific implementation, the critical take-off and landing point code is less than 0, and the initial take-off and landing point code is greater than 0.

[0187] The above-mentioned conversion of the initial take-off and landing points of the previous 3 UAVs into corresponding take-off and landing point codes provides a data foundation for judging the degree of matching between the initial take-off and landing points of the previous 3 UAVs and the preconditions through coding, and thus measuring the ranking effect of the prediction priority ranking vector PX.

[0188] S400, the transpose of GJ and BM 1 Multiply to obtain the second take-off and landing point coded reference vector BM 2 .

[0189] Wherein, the matrix dimension of GJ is 1×M, and the transpose of GJ is (GJ). T The dimension is M×1, BM 1 If the matrix dimension is 1×N, then (GJ) T ×BM 1 The obtained second take-off and landing point coded reference vector BM 2 The matrix dimension is M×N.

[0190] Among them, (GJ) T The codes for each key take-off and landing point in the middle are less than 0, BM 1 The first takeoff and landing point code corresponding to the critical takeoff and landing point in the middle is less than 0, BM 1 If the first takeoff and landing point code of the corresponding non-critical takeoff and landing point is greater than 0, then BM 1 The first takeoff and landing point code corresponding to the key takeoff and landing point in (GJ) T The product of the codes for each key take-off and landing point in the BM is greater than 0. 1 The corresponding non-critical takeoff and landing points and (GJ) T The product of the codes for each key takeoff and landing point is less than 0; therefore, it can be determined through BM. 2 The number of elements greater than 0 in the matrix, and the reverse calculation of BM. 1 The number of key take-off and landing points corresponds to the prediction priority ranking vector PX, which in turn represents the degree of matching between the prediction priority ranking vector PX and the preconditions.

[0191] For example, BM 2 If the number of elements greater than 0 is M×Amount, then BM can be determined. 1The number of critical take-off and landing points corresponding to this is Amount.

[0192] S500, compared to BM 2 Binarization is performed to obtain the third take-off and landing point coded reference vector BM. 3 .

[0193] In one specific embodiment, S500 further includes the following steps:

[0194] S510, if BM 2 If the element in the μ-th row and σ-th column is greater than 0, then BM will be... 3 The element in the μ-th row and σ-th column is set to 1.

[0195] S520, if BM 2 If the element in the μ-th row and σ-th column is less than 0, then BM will be... 3 The element in the μ-th row and σ-th column is set to 0.

[0196] S530, BM 3 Set the elements in columns Щ+1 to Д to 0.

[0197] S530, traverse μ = 1, 2, ..., M and σ = 1, 2, ..., Щ to obtain the third take-off and landing point coded reference vector BM. 3 .

[0198] Among them, for BM 2 Binarization is performed, setting elements greater than 0 to 1 and elements less than or equal to 0 to 0. This allows us to modify the BM... 3 The data scale is normalized to avoid the influence of the magnitude of each element value on the prediction loss, thereby improving the accuracy of the prediction loss and thus improving the accuracy of judging the matching degree between the prediction priority ranking vector PX and the preconditions.

[0199] S600, according to BM 3 Given a preset encoding threshold YZ, the prediction loss Loss = ρ × (YZ - SUM(BM)) is obtained. 3 ), where ρ refers to the preset loss coefficient, SUM(BM 3 ) refers to BM 3 The sum of all elements in .

[0200] In this embodiment, the prerequisite is that the prediction priorities of the M key take-off and landing points in the final prediction priority ranking vector belong to the top 3 positions, and SUM(BM) 3 ) can characterize BM 3 The number of element 1s in BM, which represents the number of elements in BM. 1The number of key take-off and landing points in the prediction priority ranking vector PX represents the number of key take-off and landing points in the prediction priority ranking vector PX. The more key take-off and landing points there are in the prediction priority ranking vector PX, the higher the degree of matching between the prediction priority ranking vector PX and the preconditions.

[0201] SUM(BM 3 The range of values ​​for ) is [0, M]. 2 Therefore, based on the prediction loss Loss = ρ × (YZ - SUM(BM) 3 The prediction priority ranking vector PX is used to characterize the degree of matching between the prediction priority ranking vector PX and the preconditions, serving as the basis for updating the prediction model parameters to improve the prediction accuracy of the prediction model.

[0202] In one specific implementation, YZ = M 2 .

[0203] In one specific implementation, ρ = 1000.

[0204] 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.

[0205] The above-mentioned method of improving the prediction loss to characterize the degree of matching between the prediction priority ranking vector PX and the preconditions provides a data basis for updating the parameters of the prediction model to improve the prediction accuracy of the prediction model.

[0206] S700 updates the parameters of the preset prediction model based on the loss until the loss = 0, and obtains the target prediction model.

[0207] S800 inputs A, B, Q, P and JS into the target prediction model to obtain the target priority ranking vector.

[0208] S900 determines the first 3 initial UAV take-off and landing points in the target priority sorting vector as the target UAV take-off and landing points.

[0209] The target priority ranking vector obtained by Loss=0 indicates that the number of key take-off and landing points in the predicted priority ranking vector PX is M. This means that the target priority ranking vector matches the preconditions. Therefore, the first Щ initial UAV take-off and landing points in the target priority ranking vector are determined as the target UAV take-off and landing points, which serve as the location for setting up UAV take-off and landing facilities to support tasks such as UAV delivery logistics.

[0210] As described above, A, B, Q, P, and JS are input into a preset prediction model to obtain the prediction priority ranking vector PX = (PX1, PX2, ..., PX...).n ..., PX N By multiplying PX and YS by vectors, the takeoff and landing point reference vector CK is obtained. 1 = (PX1, PX2, ..., PX) σ ..., PX Щ ,0,0,……,0), according to CK 1 、A 1 And GJ, obtain CK 1 The corresponding first take-off and landing point coded reference vector BM 1 = (BM1, BM2, ..., BM σ , ..., BM Щ (0, 0, ..., 0), the transpose of GJ and BM 1 Multiply to obtain the second take-off and landing point coded reference vector BM 2 , for BM 2 Binarization is performed to obtain the third take-off and landing point coded reference vector BM. 3 According to BM 3 Given a preset encoding threshold YZ, the prediction loss Loss = ρ × (YZ - SUM(BM)) is obtained. 3 The parameters of the preset prediction model are updated based on the loss until loss = 0, thus obtaining the target prediction model. A, B, Q, P, and JS are input into the target prediction model to obtain the target priority ranking vector. The first 3 initial UAV take-off and landing points in the target priority ranking vector are determined as the target UAV take-off and landing points. It can be seen that through BM... 3 The number of elements 1 in the middle, and the reverse calculation of BM. 2 The number of elements greater than 0 in the matrix, and then the BM can be deduced from this. 1 The number of corresponding key take-off and landing points, based on preset encoding thresholds YZ and BM. 3 The number of elements 1 in the middle is used to construct the prediction loss, and the parameters of the prediction model are updated accordingly. This improves the matching degree between the target priority ranking vector and the preconditions, thereby improving the rationality of the target UAV take-off and landing points.

[0211] Example 5

[0212] This fifth embodiment provides a multi-factor-based initial location generation device for UAV take-off and landing points, which includes:

[0213] The data acquisition module 51 is used to acquire Θ original UAV take-off and landing points, several predicted flight routes corresponding to each original UAV take-off and landing point, and the route length, second traffic flow, and route anomaly degree corresponding to each predicted flight route. The second traffic flow refers to the average of the first traffic flow corresponding to the two original UAV take-off and landing points corresponding to the predicted flight route. The route anomaly degree is used to characterize the degree of damage to the outside world when the UAV has a flight anomaly on the corresponding predicted flight route. Θ is an integer greater than 0.

[0214] The data processing module 52 is used to obtain the number of predicted flight routes, the total length of the routes, and the total traffic volume corresponding to each original UAV take-off and landing point based on several predicted flight routes corresponding to each original UAV take-off and landing point, the length of each predicted flight route, and the second traffic flow.

[0215] The take-off and landing point filtering module 53 is used to filter out ζ take-off and landing points to be merged from Θ original drone take-off and landing points based on the predicted number of flight routes, total length of routes, total traffic volume, preset number of routes threshold, preset route length threshold and preset traffic flow threshold corresponding to each original drone take-off and landing point, where 0 < ζ < Θ.

[0216] The take-off and landing point acquisition module 54 is used to obtain η original UAV take-off and landing points connected to each take-off and landing point to be merged, based on all predicted flight routes corresponding to each take-off and landing point to be merged, where η is an integer greater than 0.

[0217] The first priority acquisition module 55 is used to, for any take-off and landing point to be merged, take any original UAV take-off and landing point connected to the current take-off and landing point to be merged as an intermediate take-off and landing point, and obtain the route length priority corresponding to the intermediate take-off and landing point based on 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.

[0218] The second priority acquisition module 56 is used to obtain the priority of the route anomaly degree corresponding to the intermediate take-off and landing point based on the route anomaly degree between the current take-off and landing point 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 anomaly degree between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points.

[0219] The third priority acquisition module 57 is used to obtain the merging priority corresponding to the current intermediate take-off and landing point based on the route length priority and the route anomaly degree priority.

[0220] The fourth priority acquisition module 58 is used to traverse the η original UAV take-off and landing points connected to the current take-off and landing point to be merged, and obtain η merging priorities.

[0221] The take-off and landing point merging module 59 is used to 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.

[0222] In one specific embodiment, the data acquisition module 51 further includes:

[0223] The data acquisition submodule is used to acquire map data corresponding to Θ original drone take-off and landing points and the first traffic flow corresponding to each original drone take-off and landing point.

[0224] The flight path prediction module is used to input the map data corresponding to Θ original UAV take-off and landing points and the first traffic flow corresponding to Θ original UAV take-off and landing points into the preset UAV flight path setting model to obtain several predicted flight paths corresponding to each original UAV take-off and landing point.

[0225] In one specific embodiment, the take-off and landing point screening module 53 further includes:

[0226] The first take-off and landing point filtering submodule is used to determine the current original drone take-off and landing point as a take-off and landing point to be merged if the number of predicted flight routes corresponding to the current original drone take-off and landing point is less than the preset number of flight routes threshold, the total length of flight routes is less than the preset length of flight routes threshold, and the total traffic volume is less than the preset flow threshold.

[0227] The second take-off and landing point filtering submodule is used to traverse N original drone take-off and landing points and obtain ζ take-off and landing points to be merged.

[0228] In one specific embodiment, the first priority acquisition module 55 further includes:

[0229] The first route length determination submodule is used to determine the average of 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 as the first route length.

[0230] The second route length determination submodule is used to determine the average of the route lengths between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points as the second route length.

[0231] The third route length determination submodule is used to determine the third route length by summing the route length between the current take-off and landing point to be merged and the intermediate take-off and landing point with the second route length.

[0232] The length priority determination submodule is used to determine the length priority of the route corresponding to the intermediate take-off and landing point by the ratio of the length of the first route to the length of the third route.

[0233] In one specific embodiment, the second priority acquisition module 56 further includes:

[0234] The first route anomaly acquisition submodule is used to determine the first route anomaly level by averaging the route anomalies between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points.

[0235] The second route anomaly acquisition submodule is used to determine the average of the route anomalies between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points as the second route anomaly.

[0236] The third route anomaly level acquisition submodule is used to determine the third route anomaly level by summing the route anomaly level between the current take-off and landing point to be merged and the intermediate take-off and landing point with the second route anomaly level.

[0237] The anomaly severity priority determination submodule is used to determine the anomaly severity priority of the route corresponding to the intermediate take-off and landing point by the ratio of the anomaly severity of the first route to that of the third route.

[0238] In one specific embodiment, the third priority acquisition module 57 further includes:

[0239] The preset weight acquisition submodule is used to obtain preset route length weights and preset anomaly degree weights.

[0240] The merge priority acquisition submodule is used to obtain the merge priority corresponding to the current intermediate take-off and landing point based on the route length priority, the preset route length weight, the route anomaly priority, and the preset anomaly weight.

[0241] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0242] Example 6

[0243] Embodiment 6 of the present invention provides a non-transitory computer-readable storage medium, which stores at least one instruction or at least one program segment, wherein the at least one instruction or at least one program segment is loaded and executed by a processor to implement the following steps:

[0244] S10: Obtain Θ original UAV take-off and landing points, several predicted flight paths corresponding to each original UAV take-off and landing point, and the flight path length, second traffic flow, and flight path anomaly degree corresponding to each predicted flight path. The second traffic flow refers to the average of the first traffic flow corresponding to the two original UAV take-off and landing points corresponding to the predicted flight path. The flight path anomaly degree is used to characterize the degree of damage to the outside world when the UAV experiences flight anomalies on the corresponding predicted flight path. Θ is an integer greater than 0.

[0245] S20: Based on the several predicted flight routes corresponding to each original UAV take-off and landing point, the length of each predicted flight route, and the second traffic flow, obtain the number of predicted flight routes, the total length of the routes, and the total traffic volume corresponding to each original UAV take-off and landing point.

[0246] 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 flow 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.

[0247] S40: Based on all predicted flight paths corresponding to each take-off and landing point to be merged, obtain η original UAV take-off and landing points connected to each take-off and landing point to be merged, where η is an integer greater than 0.

[0248] S50: For any take-off and landing point to be merged, take any original UAV take-off and landing point connected to the current take-off and landing point as an intermediate take-off and landing point. Based on 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, obtain the route length priority corresponding to the intermediate take-off and landing point.

[0249] S60: Based on the degree of route anomaly of the predicted flight path 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 degree of route anomaly of the predicted flight path between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, obtain the priority of the route anomaly degree corresponding to the intermediate take-off and landing point.

[0250] S70 obtains the merging priority corresponding to the current intermediate take-off and landing point based on the priority of route length and the priority of route anomaly.

[0251] S80: Traverse the η original UAV take-off and landing points connected to the current take-off and landing points to be merged, and obtain η merging priorities.

[0252] S90 merges 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 determines the merged take-off and landing point as the initial UAV take-off and landing point.

[0253] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0254] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0255] Example 7

[0256] Embodiment 7 of the present invention provides an electronic device, which includes a processor and a non-transitory computer-readable storage medium as described in Embodiment 6 of the present invention.

[0257] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for generating initial locations for UAV take-off and landing points based on multiple factors, characterized in that, The method for generating initial locations for UAV take-off and landing points based on multiple factors includes: S10: Obtain Θ original UAV take-off and landing points, several predicted flight paths corresponding to each original UAV take-off and landing point, and the flight path length, second traffic flow, and flight path anomaly degree corresponding to each predicted flight path. The second traffic flow refers to the average of the first traffic flow corresponding to the two original UAV take-off and landing points corresponding to the predicted flight path. The flight path anomaly degree is used to characterize the degree of damage to the outside world when the UAV has a flight anomaly on the corresponding predicted flight path. Θ is an integer greater than 0. S20: Based on the several predicted flight routes corresponding to each original UAV take-off and landing point, the length of each predicted flight route, and the second traffic flow, obtain the number of predicted flight routes, the total length of the routes, and the total traffic volume corresponding to each original UAV take-off and landing point. 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 flow 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 paths corresponding to each take-off and landing point to be merged, obtain η original UAV take-off and landing points connected to each take-off and landing point to be merged, where η is an integer greater than 0; S50: For any take-off and landing point to be merged, take any original UAV take-off and landing point connected to the current take-off and landing point as an intermediate take-off and landing point. Based on 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, obtain the route length priority corresponding to the intermediate take-off and landing point. S60: Based on the degree of route anomaly of the predicted flight path 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 degree of route anomaly of the predicted flight path between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points, obtain the priority of the route anomaly degree corresponding to the intermediate take-off and landing point. S70, based on the route length priority and the route anomaly priority, obtain the merging priority corresponding to the current intermediate take-off and landing point; S80: Traverse the η original drone take-off and landing points connected to the current take-off and landing points to be merged, and obtain η merging priorities; S90 merges 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 determines the merged take-off and landing point as the initial UAV take-off and landing point.

2. The method for generating initial location selection for UAV take-off and landing points based on multiple factors according to claim 1, characterized in that, S10 also includes the following steps: S101, obtain the map data corresponding to the Θ original UAV take-off and landing points and the first traffic flow corresponding to each original UAV take-off and landing point; S102, input the map data corresponding to the Θ original UAV take-off and landing points and the first traffic flow corresponding to the Θ original UAV take-off and landing points into the preset UAV route setting model to obtain several predicted flight routes corresponding to each original UAV take-off and landing point.

3. The method for generating initial location selection for UAV take-off and landing points based on multiple factors according to claim 1, characterized in that, S30 also includes the following steps: 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 number of flight routes threshold, the total length of flight routes is less than the preset length of flight routes 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 a take-off and landing point to be merged. S32, iterate through Θ original drone take-off and landing points to obtain ζ take-off and landing points to be merged.

4. The method for generating initial location selection for UAV take-off and landing points based on multiple factors according to claim 1, characterized in that, S50 also includes the following steps: S51, the average of the flight path lengths between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points is determined as the first flight path length; S52, the average of the flight path lengths between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points is determined as the second flight path length; S53, 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 is determined as the third route length; S54, the ratio of the length of the first route to the length of the third route is determined as the route length priority corresponding to the intermediate take-off and landing point.

5. The method for generating initial location selection for UAV take-off and landing points based on multiple factors according to claim 1, characterized in that, S60 also includes the following steps: S61, the average of the flight path anomaly levels between the current take-off and landing point to be merged and the other η-1 original UAV take-off and landing points is determined as the first flight path anomaly level; S62, the average value of the flight path anomaly between the intermediate take-off and landing point and the other η-1 original UAV take-off and landing points is determined as the second flight path anomaly. S63, the sum of the degree of route abnormality between the current take-off and landing point to be merged and the intermediate take-off and landing point and the degree of route abnormality is determined as the third degree of route abnormality; S64, the ratio of the abnormality level of the first route to the abnormality level of the third route is determined as the route abnormality priority corresponding to the intermediate take-off and landing point.

6. The method for generating initial location selection for UAV take-off and landing points based on multiple factors according to claim 1, characterized in that, S70 also includes the following steps: S71, obtain the preset route length weight and the preset anomaly degree weight; S72, based on the route length priority, the preset route length weight, the route anomaly priority, and the preset anomaly weight, obtain the merging priority corresponding to the current intermediate take-off and landing point.

7. A device for generating initial location selection for UAV take-off and landing points based on multiple factors, characterized in that, The multi-factor-based UAV take-off and landing point initial location generation device includes: The data acquisition module is used to acquire Θ original UAV take-off and landing points, several predicted flight paths corresponding to each original UAV take-off and landing point, and the flight path length, second traffic flow, and flight path anomaly degree corresponding to each predicted flight path. The second traffic flow refers to the average of the first traffic flow corresponding to the two original UAV take-off and landing points corresponding to the predicted flight path. The flight path anomaly degree is used to characterize the degree of damage to the outside world when the UAV has a flight anomaly on the corresponding predicted flight path. Θ is an integer greater than 0. The data processing module is used to obtain the number of predicted flight routes, the total length of the routes, and the total traffic volume corresponding to each original UAV take-off and landing point based on several predicted flight routes corresponding to each original UAV take-off and landing point, the route length corresponding to each predicted flight route, and the second traffic flow. The take-off and landing point filtering module is used to filter out ζ take-off and landing points to be merged from Θ original drone take-off and landing points based on the predicted number of flight routes, total length of routes, total traffic volume, preset number of routes threshold, preset route length threshold and preset traffic flow threshold corresponding to each original drone take-off and landing point, where 0 < ζ < Θ. The take-off and landing point acquisition module is used to obtain the η original UAV take-off and landing points connected to each take-off and landing point to be merged, based on all predicted flight routes corresponding to each take-off and landing point to be merged, where η is an integer greater than 0; The first priority acquisition module is used to, for any take-off and landing point to be merged, take any original UAV take-off and landing point connected to the current take-off and landing point as an intermediate take-off and landing point, and obtain the route length priority corresponding to the intermediate take-off and landing point based on the route length between the current take-off and landing point 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 second priority acquisition module is used to obtain the priority of the route anomaly degree 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 η-1 original UAV take-off and landing points and the intermediate take-off and landing point, as well as the route anomaly 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. The third priority acquisition module is used to obtain the merging priority corresponding to the current intermediate take-off and landing point based on the route length priority and the route anomaly degree priority. The fourth priority acquisition module is used to traverse the η original UAV take-off and landing points connected to the current take-off and landing point to be merged, and obtain η merging priorities; The take-off and landing point merging module is used to 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.

8. The device for generating initial location selection for UAV take-off and landing points based on multiple factors according to claim 7, characterized in that, The take-off and landing point screening module also includes: The first take-off and landing point filtering submodule is used to determine the current original drone take-off and landing point as a take-off and landing point prediction flight route to be merged if the number of predicted flight routes corresponding to the current original drone take-off and landing point is less than the preset number of flight routes threshold, the total length of flight routes is less than the preset length of flight routes threshold, and the total traffic volume is less than the preset flow threshold. The second take-off and landing point filtering submodule is used to traverse Θ original UAV take-off and landing points and obtain ζ take-off and landing points to be merged.

9. A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores at least one instruction or at least one program, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the multi-factor-based UAV take-off and landing point initial location generation method as described in any one of claims 1-6.

10. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 9.

Images