Travel order recommendation method and system for manned drone travel

Abstract

This application relates to the field of manned drone flight control and discloses a travel order recommendation method and system for manned drone travel. The method includes: obtaining the starting and ending points of a user's manned drone travel through a user device, determining multiple combined routes for the manned drone flight, each combined route including multiple sub-routes for the manned drone flight; determining the obstacle avoidance complexity of each sub-route based on the three-dimensional obstacle avoidance index and route congestion index of each sub-route; summing the obstacle avoidance complexities of the multiple sub-routes corresponding to each combined route to obtain the total obstacle avoidance complexity of each combined route; sending the multiple combined routes and the total obstacle avoidance complexity corresponding to each combined route to the user device, and displaying the multiple combined routes on the user device in ascending order of the total obstacle avoidance complexity corresponding to the multiple combined routes for user selection. This application can efficiently and scientifically plan combined flight routes for drones.

Description

Technical Field

[0001] The present application relates to the field of manned drone flight control technology, and more specifically, to a travel order recommendation method and system for manned drone travel. Background Art

[0002] The development of manned drone flights in the low-altitude economy has benefited from the integrated application of a range of advanced technologies. Drone flight control systems have undergone significant improvements. Today's drones are equipped with high-precision inertial navigation systems, global positioning systems, and sensors such as lidar, enabling autonomous flight and precise positioning and obstacle avoidance in complex environments. Drone propulsion systems have also undergone significant innovations, significantly improving their endurance and payload capacity, making long-term manned flights possible. Advances in communications technology provide a solid foundation for manned drone flights. The application of 5G and satellite communications ensures real-time data transmission between the ground and drones, making in-flight monitoring and control more efficient and reliable.

[0003] Despite significant technological advancements in manned drone flight, numerous challenges remain. As drone manned flight becomes commercialized, efficient route planning for these flights remains a valuable research topic. For example, patent application CN117336677A (application number: CN202311324733.6) provides a method for low-altitude route planning for manned general aviation aircraft based on drones. The user terminal generates route requirement information based on general aviation missions and transmits it to the ground terminal. The ground terminal then transmits this route requirement information to the air terminal, which collects terrain data and transmits it to the ground terminal. The ground terminal then performs route planning based on the terrain data, generates an optimal flight path, and transmits the optimal path to the user terminal, which then executes the general aviation mission based on the optimal path. While the method described in patent application CN117336677A enables flight route planning for manned drones, it cannot effectively plan combined flight routes for scenarios involving multiple flight segments. Summary of the Invention

[0004] The purpose of this application is to provide a travel order recommendation method and system for manned drone travel, which solves the technical problem of not being able to plan the combined flight routes of drones and achieves the technical effect of efficiently and scientifically planning the combined flight routes of drones.

[0005] An embodiment of the present application provides a travel order recommendation method for manned drone travel, the method comprising: obtaining, through a user device, a starting point and an end point of a user's travel via a manned drone, determining a plurality of combined routes for the manned drone's flight, each combined route comprising a plurality of sub-routes for the manned drone's flight; determining the obstacle avoidance complexity of each sub-route based on a three-dimensional obstacle avoidance index and a route congestion index of each sub-route; summing the obstacle avoidance complexities of the plurality of sub-routes corresponding to each combined route as the total obstacle avoidance complexity of each combined route; wherein the three-dimensional obstacle avoidance index is used to characterize the three-dimensional obstacle avoidance difficulty of the manned drone flying on the sub-route, and the route congestion index is used to characterize the route congestion difficulty of the manned drone flying on the sub-route; sending the plurality of combined routes and the total obstacle avoidance complexity corresponding to each combined route to the user device, and displaying the plurality of combined routes on the user device in descending order of the total obstacle avoidance complexity corresponding to the plurality of combined routes for user selection.

[0006] In one possible implementation, the obstacle avoidance complexity of each sub-route is determined based on the three-dimensional obstacle avoidance conditions and route congestion conditions of each sub-route, including: obtaining the travel weather conditions during the time period when the user travels by manned drone; when the travel weather conditions meet the first travel weather conditions, summing the product of the three-dimensional obstacle avoidance index of each sub-route and the weight of the first three-dimensional obstacle avoidance index, and the product of the route congestion index and the weight of the first route congestion index as the obstacle avoidance complexity of each sub-route; when the travel weather conditions meet the second travel weather conditions, summing the product of the three-dimensional obstacle avoidance index of each sub-route and the weight of the second three-dimensional obstacle avoidance index, and the product of the route congestion index and the weight of the second route congestion index as the obstacle avoidance complexity of each sub-route.

[0007] In another possible implementation, the method further includes: obtaining take-off and landing nodes, safe alternate landing nodes, and regional obstacle avoidance indexes corresponding to the multiple sub-routes of each combined route; wherein the take-off and landing node is the starting point or end point of the sub-route, the safe alternate landing node is the safe alternate landing area along the sub-route, and the regional obstacle avoidance index is used to characterize the obstacle avoidance difficulty corresponding to the route of the manned unmanned aerial vehicle on the sub-route flying to the safe alternate landing node; determining node distribution characteristics of the take-off and landing nodes and the safe alternate landing nodes of each sub-route, and when the node distribution characteristics meet preset node distribution conditions, determining a total alternate landing distance between each sub-route and the safe alternate landing node, and determining the product of the total alternate landing distance of each sub-route and the regional obstacle avoidance index corresponding to the safe alternate landing node as the safe alternate landing index; determining a first-tier flight insurance level corresponding to each combined route based on the sum of the safe alternate landing indices corresponding to the multiple sub-routes of each combined route, and recommending flight insurance corresponding to the first-tier flight insurance level for each combined route based on the first-tier flight insurance level corresponding to each combined route; wherein different first-tier flight insurance levels correspond to different flight insurance amounts.

[0008] In another possible implementation, the method also includes: when the node distribution characteristics do not meet the preset node distribution conditions, determining the second-gradient flight insurance level corresponding to each combination route according to the total obstacle avoidance complexity of each combination route, and recommending flight insurance corresponding to the second-gradient flight insurance level for each combination route according to the second-gradient flight insurance level corresponding to each combination route; wherein, the insurance amounts of flight insurance corresponding to different second-gradient flight insurance levels are different, and the insurance amount of flight insurance corresponding to the second-gradient flight insurance level is higher than the insurance amount of flight insurance corresponding to the first-gradient flight insurance level.

[0009] In another possible implementation, the method also includes: obtaining the user's selection rate for different flight insurances corresponding to the first level of flight insurance levels through historical data, and displaying multiple combination routes for user selection on the user device in sequence according to the selection rate of different flight insurances corresponding to the first level of flight insurance levels from high to low; obtaining the user's selection rate for different flight insurances corresponding to the second level of flight insurance levels through historical data, and displaying multiple combination routes for user selection on the user device in sequence according to the selection rate of different flight insurances corresponding to the second level of flight insurance levels from high to low.

[0010] In another possible implementation, the method also includes: when the travel weather conditions meet the first travel weather conditions, multiple combination routes are displayed in sequence on the user device for user selection in order of the selection rates of different flight insurances corresponding to the first gradient of flight insurance levels from high to low; when the travel weather conditions meet the second travel weather conditions, multiple combination routes are displayed in sequence on the user device for user selection in order of the selection rates of different flight insurances corresponding to the first gradient of flight insurance levels and the selection rates of different flight insurances corresponding to the second gradient of flight insurance levels from high to low.

[0011] An embodiment of the present application also provides a travel order recommendation system for manned drone travel, including a unit for executing any of the methods described above.

[0012] An embodiment of the present application also provides a travel order recommendation system for manned drone travel, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any one of the above items is implemented.

[0013] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method as described in any one of the above items is implemented.

[0014] An embodiment of the present application further provides a computer program product, including a computer program, which implements the steps of any of the above methods when executed by a processor.

[0015] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0016] An embodiment of the present application provides a travel order recommendation method for manned drone travel, the method comprising: obtaining the starting point and end point of a user's travel by manned drone through a user device, determining multiple combined routes for the manned drone's flight, each combined route including multiple sub-routes for the manned drone's flight; determining the obstacle avoidance complexity of each sub-route based on the three-dimensional obstacle avoidance index and route congestion index of each sub-route; summing the obstacle avoidance complexities of the multiple sub-routes corresponding to each combined route as the total obstacle avoidance complexity of each combined route; wherein the three-dimensional obstacle avoidance index is used to characterize the three-dimensional obstacle avoidance difficulty of the manned drone flying on the sub-route, and the route congestion index is used to characterize the route congestion difficulty of the manned drone flying on the sub-route; sending the multiple combined routes and the total obstacle avoidance complexity corresponding to each combined route to the user device, and displaying the multiple combined routes on the user device in order of the total obstacle avoidance complexity corresponding to the multiple combined routes from low to high for user selection. The method in the embodiment of the present application can effectively plan multiple routes for the flight of a manned drone, and can scientifically plan the drone routes with multiple routes by combining the three-dimensional obstacle avoidance index and route congestion index of the manned drone during flight, thereby improving the route planning effect of the manned drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flowchart of a method for recommending travel orders for manned drone travel provided in an embodiment of the present application;

[0019] Figure 2 A flowchart of a second method for recommending travel orders for manned drone travel provided in an embodiment of the present application;

[0020] Figure 3 A flowchart of a third method for recommending travel orders for manned drone travel provided in an embodiment of the present application;

[0021] Figure 4 A schematic diagram of the logical structure of a travel order recommendation system for manned drone travel provided in an embodiment of the present application;

[0022] Figure 5 A schematic diagram of the physical structure of a travel order recommendation system for manned drone travel provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0024] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0026] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0028] Existing technologies can realize flight route planning of manned UAVs, but for situations involving multiple flight routes, it is not possible to effectively plan the combined flight routes of the UAVs.

[0029] Based on the above reasons, an embodiment of the present application provides a travel order recommendation method for manned drone travel, the method comprising: obtaining the starting point and end point of the user's travel by manned drone through a user device, determining multiple combined routes for the manned drone flight, each combined route including multiple sub-routes for the manned drone flight; determining the obstacle avoidance complexity of each sub-route based on the three-dimensional obstacle avoidance index and route congestion index of each sub-route; summing the obstacle avoidance complexities of the multiple sub-routes corresponding to each combined route as the total obstacle avoidance complexity of each combined route; wherein the three-dimensional obstacle avoidance index is used to characterize the three-dimensional obstacle avoidance difficulty of the manned drone flying on the sub-route, and the route congestion index is used to characterize the route congestion difficulty of the manned drone flying on the sub-route; sending multiple combined routes and the total obstacle avoidance complexity corresponding to each combined route to the user device, and displaying the multiple combined routes on the user device in order from low to high of the total obstacle avoidance complexity corresponding to the multiple combined routes for user selection. The method in the embodiment of the present application can effectively plan multiple routes for the flight of a manned drone, and can scientifically plan the drone routes with multiple routes by combining the three-dimensional obstacle avoidance index and route congestion index of the manned drone during flight, thereby improving the route planning effect of the manned drone.

[0030] In some scenarios, a travel order recommendation method for manned drone travel in an embodiment of the present application can be applied to the route planning of manned drones, which can improve the planning effect of multiple routes of manned drones and improve the convenience of manned drone travel.

[0031] The following describes in detail a method for recommending travel orders for manned drone travel provided in an embodiment of the present application with reference to specific examples.

[0032] Figure 1 A flow chart of a method for recommending travel orders for manned drone travel provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the method includes S110 to S130, and S110 to S130 are described in detail below.

[0033] S110: Obtain, through the user device, the starting point and the ending point of the user's travel via the manned drone, and determine multiple combined routes of the manned drone's flight, each combined route including multiple sub-routes of the manned drone's flight.

[0034] During the flight of a manned drone, due to limitations such as power limitations and route segment design, the route of the manned drone needs to be planned into multiple segments to improve the convenience of users traveling by drone.

[0035] When planning the route of the drone, the user's starting point and end point of the manned drone trip can be obtained through the user's device, and the drone's travel route can be planned based on the starting point and end point.

[0036] When planning the travel route of the drone based on the starting point and the end point, multiple combined routes for the manned drone flight can be determined, each combined route includes multiple sub-routes for the manned drone flight, and the multiple sub-routes are connected in sequence to form a combined route.

[0037] For example, when determining multiple combined routes for the flight of a manned UAV, the combined routes for the flight of the UAV can be automatically planned according to a path planning algorithm.

[0038] S120. Determine the obstacle avoidance complexity of each sub-route based on the 3D obstacle avoidance index and route congestion index of each sub-route. Sum the obstacle avoidance complexities of the multiple sub-routes corresponding to each combined route to obtain the total obstacle avoidance complexity of each combined route. The 3D obstacle avoidance index represents the 3D obstacle avoidance difficulty of a manned UAV flying on the sub-route, and the route congestion index represents the route congestion difficulty of a manned UAV flying on the sub-route.

[0039] During the flight of a manned UAV, it is necessary to avoid obstacles in three-dimensional space. Therefore, it is necessary to calculate the three-dimensional obstacle avoidance difficulty of the manned UAV to improve the safety of the manned UAV route planning and the reliability of the manned UAV route operation.

[0040] After obtaining multiple combined routes each including multiple sub-routes, the three-dimensional obstacle avoidance index and route congestion index of each sub-route can be obtained. The three-dimensional obstacle avoidance index is used to characterize the three-dimensional obstacle avoidance difficulty of the manned UAV flying on the sub-route, and the route congestion index is used to characterize the route congestion degree of the manned UAV flying on the sub-route. Then, the combined route can be planned based on the three-dimensional obstacle avoidance index and route congestion index of each sub-route.

[0041] For example, when determining the three-dimensional obstacle avoidance index, it can be calculated based on the obstacle height and obstacle density on each sub-route. The larger the three-dimensional obstacle avoidance index, the more difficult it is for the manned drone to avoid obstacles; the smaller the three-dimensional obstacle avoidance index, the easier it is for the manned drone to avoid obstacles.

[0042] For example, when determining the three-dimensional obstacle avoidance index, the three-dimensional obstacle avoidance index of each sub-route may be determined using GIS geographic information including building information.

[0043] For example, when calculating the three-dimensional obstacle avoidance index by using the obstacle height and obstacle density on each sub-route, the product of the total obstacle height and obstacle density on each sub-route can be used as the three-dimensional obstacle avoidance index.

[0044] For example, when determining the line congestion index, it can be determined by the historical congestion level on each sub-line. The larger the line congestion index, the greater the congestion level when the manned drone flies on the sub-line; the smaller the line congestion index, the smaller the congestion level when the manned drone flies on the sub-line.

[0045] After obtaining the three-dimensional obstacle avoidance index and route congestion index, the obstacle avoidance complexity of each sub-route can be determined based on the three-dimensional obstacle avoidance index and route congestion index of each sub-route. The obstacle avoidance complexity represents the complexity of the manned UAV flying on the sub-route.

[0046] After obtaining the obstacle avoidance complexity of each sub-route, the obstacle avoidance complexities of multiple sub-route corresponding to each combined route can be summed up as the total obstacle avoidance complexity of each combined route, and then each combined route can be selected based on the total obstacle avoidance complexity of each combined route.

[0047] S130: Send the multiple combined lines and the total obstacle avoidance complexity corresponding to each combined line to the user equipment, and display the multiple combined lines on the user equipment in descending order of the total obstacle avoidance complexity corresponding to the multiple combined lines for user selection.

[0048] After obtaining the total obstacle avoidance complexity of each combination line, the multiple combination lines and the total obstacle avoidance complexity corresponding to each combination line can be sent to the user device, and the multiple combination lines can be displayed on the user device in order from low to high in terms of the total obstacle avoidance complexity corresponding to the multiple combination lines for user selection.

[0049] Exemplarily, when multiple combination lines are displayed on the user device for user selection in descending order of the total obstacle avoidance complexity corresponding to the multiple combination lines, they can be displayed from top to bottom on the user device in descending order of the total obstacle avoidance complexity corresponding to the multiple combination lines.

[0050] The beneficial effect brought about by the above-mentioned implementation method is that it can effectively plan multiple routes for manned drone flights, and can scientifically plan drone routes with multiple routes by combining the three-dimensional obstacle avoidance index and route congestion index of the manned drone during flight, thereby improving the route planning effect of the manned drone.

[0051] The beneficial effect brought about by the above implementation method is that the obstacle avoidance complexity of each sub-route is determined according to the three-dimensional obstacle avoidance index and route congestion index of each sub-route, and the display order of the combined routes is determined according to the obstacle avoidance complexity of each sub-route, thereby improving the scientific nature of the combined route planning and improving the efficiency of manned drone route operation.

[0052] Figure 2A flow chart of a second method for recommending travel orders for manned drone travel provided in an embodiment of the present application is shown as follows: Figure 2 As shown, in the above S120, the obstacle avoidance complexity of each sub-route is determined according to the three-dimensional obstacle avoidance condition and the route congestion condition of each sub-route, including S121 to S122. S121 to S122 are described in detail below.

[0053] S121. Obtain the travel weather conditions during the time period when the user travels by the manned drone.

[0054] Since different weather conditions may affect the safety of manned drone flights, when planning combined routes, in order to further improve the scientific nature of the combined route planning of drones, it is also possible to obtain the travel weather conditions during the time period when users travel by manned drones, and then optimize the scientific nature of the combined route planning based on the travel weather conditions.

[0055] For example, when obtaining the travel weather conditions during the time period when the user travels by a manned drone, the travel time period input by the user can be obtained, and the weather forecast information during the travel time period can be obtained, and the weather forecast information can be used as the travel weather conditions.

[0056] For example, the travel weather conditions can be obtained by obtaining the local weather forecast through the Internet.

[0057] S122. When the travel weather conditions meet the first travel weather conditions, sum the product of the three-dimensional obstacle avoidance index of each sub-route and the weight of the first three-dimensional obstacle avoidance index, and the product of the route congestion index and the weight of the first route congestion index, to obtain the obstacle avoidance complexity of each sub-route. When the travel weather conditions meet the second travel weather conditions, sum the product of the three-dimensional obstacle avoidance index of each sub-route and the weight of the second three-dimensional obstacle avoidance index, and the product of the route congestion index and the weight of the second route congestion index, to obtain the obstacle avoidance complexity of each sub-route.

[0058] After obtaining the travel weather conditions, the recommended order of the combination routes can be optimized according to the travel weather conditions to improve the scientific nature of the recommendation of the combination routes.

[0059] When the travel weather conditions meet the first travel weather conditions, the product of the three-dimensional obstacle avoidance index of each sub-route and the weight of the first three-dimensional obstacle avoidance index, and the product of the line congestion index and the weight of the first line congestion index can be summed as the obstacle avoidance complexity of each sub-route, and then the total obstacle avoidance complexity of the combined route can be determined based on the obstacle avoidance complexity of each sub-route.

[0060] For example, the first travel weather condition may be rainy weather, foggy weather, or other weather conditions. Under the first travel weather condition, the weight of the first three-dimensional obstacle avoidance index may be 0.7 to 0.8, and the weight of the first line congestion index may be 0.2 to 0.3, so that under poor weather conditions, the first three-dimensional obstacle avoidance index is larger and the weight of the first line congestion index is smaller. This can increase the weight of the three-dimensional obstacle avoidance index under poor weather conditions and reduce the weight of the line congestion index, thereby improving the safety of manned UAV route planning under poor weather conditions.

[0061] When the travel weather conditions meet the second travel weather conditions, the product of the three-dimensional obstacle avoidance index of each sub-route and the weight of the second three-dimensional obstacle avoidance index, and the product of the line congestion index and the weight of the second line congestion index can be summed as the obstacle avoidance complexity of each sub-route, and then the total obstacle avoidance complexity of the combined route can be determined based on the obstacle avoidance complexity of each sub-route.

[0062] For example, the second travel weather condition may be a weather condition with better weather and higher visibility. Under the second travel weather condition, the weight of the second three-dimensional obstacle avoidance index may be 0.2 to 0.3, and the weight of the second line congestion index may be 0.7 to 0.8, so that under better weather conditions, the second three-dimensional obstacle avoidance index is smaller and the weight of the second line congestion index is larger. The weight of the three-dimensional obstacle avoidance index under better weather conditions can be reduced and the weight of the line congestion index can be increased, thereby reducing the congestion of the route planned for the manned drone under better weather conditions, thereby improving the transportation efficiency of the manned drone.

[0063] The beneficial effect brought about by the above-mentioned implementation method is that different weights are assigned to the three-dimensional obstacle avoidance index and the line congestion index under different weather conditions, which can improve the scientific nature of the combined route planning of manned UAVs, avoid excessive obstacles that affect the safety of manned UAVs under poor weather conditions, and improve the UAV's carrying efficiency under good weather conditions.

[0064] Figure 3 A flow chart of a third method for recommending travel orders for manned drone travel provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the above method further includes S210 to S230, and S210 to S230 are described in detail below.

[0065] S210: Obtain the take-off and landing nodes, safe landing nodes, and regional obstacle avoidance indices corresponding to the safe landing nodes for the multiple sub-routes of each combined route. The take-off and landing nodes are the starting or ending points of the sub-route, the safe landing nodes are the safe landing areas along the sub-route, and the regional obstacle avoidance index is used to represent the obstacle avoidance difficulty of the manned UAV on the sub-route flying to the safe landing node.

[0066] When planning a combined route, in order to further improve the safety of the combined route planning, the take-off and landing nodes, safe alternate landing nodes, and regional obstacle avoidance indexes corresponding to the multiple sub-routes of each combined route can be obtained. The take-off and landing nodes are the starting point or end point of the sub-route.

[0067] During planning, the safe alternate landing node is the safe alternate landing area along the sub-line. When a manned drone fails, the manned drone can make an alternate landing in the safe alternate landing area corresponding to the safe alternate landing node to ensure the safe handling of the drone failure.

[0068] For example, the safe alternate landing area may be an area suitable as an alternate landing site, such as a stadium or a square.

[0069] During planning, the regional obstacle avoidance index is used to characterize the obstacle avoidance difficulty corresponding to the route from the manned UAV on the sub-route to the safe alternate landing node, so as to plan the obstacle avoidance process of the manned UAV flying on the sub-route.

[0070] For example, for the first safe alternate landing node of the first sub-route, a first regional obstacle avoidance index of the manned UAV on the first sub-route flying to the first safe alternate landing node can be obtained.

[0071] S220. Determine the node distribution characteristics of the take-off and landing nodes and the safety alternate landing nodes of each sub-route. When the node distribution characteristics meet the preset node distribution conditions, determine the total alternate landing distance between each sub-route and the safety alternate landing node, and determine the product of the total alternate landing distance of each sub-route and the regional obstacle avoidance index corresponding to the safety alternate landing node as the safety alternate landing index.

[0072] During planning, the node distribution characteristics of the take-off and landing nodes and the safe alternate landing nodes of each sub-route can be determined. The node distribution characteristics characterize the positional relationship between the safe alternate landing nodes and the take-off and landing nodes on the sub-route. When the node distribution characteristics meet the preset node distribution conditions, it means that the positions of the take-off and landing nodes and the safe alternate landing nodes meet the safe alternate landing requirements of the manned UAV on the sub-route, and the effective safe alternate landing of the manned UAV on the sub-route can be achieved through the safe alternate landing nodes.

[0073] Exemplarily, the preset node distribution condition may be that the take-off and landing nodes, safety alternate landing nodes, and the spacing between adjacent safety alternate landing nodes on the sub-line are less than a preset distance, and the preset distance may be 3 km to 5 km.

[0074] When the node distribution characteristics meet the preset node distribution conditions, the total alternate landing distance between each sub-route and the safe alternate landing node can be determined. The total alternate landing distance represents the flight distance of the manned UAV on the sub-route to the safe alternate landing node, and the product of the total alternate landing distance of each sub-route and the regional obstacle avoidance index corresponding to the safe alternate landing node is determined as the safe alternate landing index. The safe alternate landing index represents the influence of the distance factor and obstacle avoidance factor of the manned UAV on the sub-route on the safe alternate landing process.

[0075] Exemplarily, when calculating the total alternate distance between each sub-route and the safety alternate node, the sum of the shortest straight-line distances from the sub-route to all safety alternate nodes can be calculated as the total alternate distance.

[0076] S230: Determine a first-tier flight insurance level corresponding to each combined route based on the sum of the safety diversion indices corresponding to the multiple sub-routes of each combined route, and recommend flight insurance corresponding to the first-tier flight insurance level for each combined route based on the first-tier flight insurance level. Different first-tier flight insurance levels correspond to different insurance premiums.

[0077] After obtaining the safety diversion index of the sub-route, the safety diversion index of all sub-routes of the combined route can be used to represent the difficulty of safe diversion of the manned UAV on the combined route during manned flight based on the sum of the safety diversion indexes corresponding to multiple sub-routes of each combined route.

[0078] During operation, the first-gradient flight insurance level corresponding to each combined line can be determined based on the sum of the safety diversion indices corresponding to multiple sub-lines of each combined line, thereby achieving a one-to-one correspondence between the sum of the safety diversion indices of each combined line and the first-gradient flight insurance level.

[0079] Exemplarily, the greater the safety diversion index of a sub-route, the greater the insurance amount corresponding to the first-level flight insurance level corresponding to the sub-route may be.

[0080] During operation, flight insurance corresponding to the first-level flight insurance level can be recommended for each combined route based on the first-level flight insurance level corresponding to each combined route. The insurance amounts of flight insurance corresponding to different first-level flight insurance levels are different.

[0081] For example, after obtaining the first flight insurance level of the first gradient corresponding to the first combination line, the first flight insurance corresponding to the first flight insurance level of the first gradient can be determined, and then the first flight insurance for the first combination line can be displayed on the user device for user selection.

[0082] Exemplarily, when flight insurance corresponding to the first-level flight insurance level is recommended for each combined route, the flight insurance corresponding to the first-level flight insurance level corresponding to each combined route can be displayed on the user device for the user to select appropriate flight insurance.

[0083] The beneficial effect brought about by the above implementation method is that the distance factor and obstacle avoidance factor of the safe diversion node can be calculated to determine the safe diversion difficulty of the safe diversion process, and the corresponding insurance level and flight insurance can be determined according to the safe diversion difficulty of the manned UAV during manned flight, thereby improving the scientific nature of recommending flight insurance to users.

[0084] The beneficial effect brought about by the above-mentioned implementation method is that by verifying the node distribution characteristics, when the node distribution characteristics meet the preset node distribution conditions, the scientific nature of recommending the corresponding safe diversion difficulty to the user based on the safe diversion difficulty is realized, thereby realizing the verification of the node distribution characteristics and improving the scientific nature of recommending flight insurance to the user.

[0085] In some implementations, the method further includes: when the node distribution characteristics do not meet the preset node distribution conditions, determining a second-tier flight insurance level corresponding to each combined route based on the total obstacle avoidance complexity of each combined route, and recommending flight insurance corresponding to the second-tier flight insurance level for each combined route based on the second-tier flight insurance level. Different second-tier flight insurance levels correspond to different flight insurance amounts, and the flight insurance amount corresponding to the second-tier flight insurance level is higher than the flight insurance amount corresponding to the first-tier flight insurance level.

[0086] During planning, when the node distribution characteristics of a sub-route do not meet the preset node distribution conditions, it means that the distribution of the take-off and landing nodes and the safe alternate landing nodes of the sub-route is not suitable for safe alternate landing, and that the safe alternate landing node distribution conditions of the sub-route are not suitable as a recommendation basis for flight insurance. Flight insurance recommendations can be made based on the total obstacle avoidance complexity of the combined route including the sub-route.

[0087] When making flight insurance recommendations, the second-level flight insurance level corresponding to each combination route can be determined based on the total obstacle avoidance complexity of each combination route, and based on the second-level flight insurance level corresponding to each combination route, a one-to-one correspondence between the total obstacle avoidance complexity of each combination route and the second-level flight insurance level is achieved.

[0088] During operation, the flight insurance corresponding to the second-level flight insurance grade can be recommended for each combined route. The insurance amounts of the flight insurance corresponding to different second-level flight insurance grades are different, thus realizing the scientific recommendation of the insurance amounts of the insurance grades.

[0089] For example, when the total obstacle avoidance complexity of the combined route is greater, the insurance amount corresponding to the second gradient flight insurance level corresponding to the combined route may be greater.

[0090] During operation, when the distribution conditions of the safe alternate landing nodes of the sub-route are poor and are not suitable as a recommended basis for flight insurance, the insurance amount of the flight insurance corresponding to the recommended second-level flight insurance level is higher than the insurance amount of the flight insurance corresponding to the first-level flight insurance level, so that the insurance amount is larger when the distribution conditions of the safe alternate landing nodes are poor, thereby improving the scientific nature of the determination of the flight insurance amount.

[0091] Exemplarily, the insurance amount of the flight insurance corresponding to the second-level flight insurance level may correspond to the interval [A1, B1], and the insurance amount of the flight insurance corresponding to the first-level flight insurance level may correspond to the interval [A2, B2], and A1 is greater than B2.

[0092] For example, after obtaining the first total obstacle avoidance complexity of the first combination route, the first flight insurance levels of the second gradient corresponding to the first combination routes can be determined, and the first flight insurance corresponding to the first flight insurance level of the second gradient can be determined, and then the first flight insurance for the first combination route can be displayed on the user device for user selection.

[0093] The beneficial effect brought about by the above implementation method is that when the distribution conditions of the safe alternate landing nodes of a sub-route are poor and are not suitable as a basis for recommending flight insurance, flight insurance is recommended based on the total obstacle avoidance complexity of the combined route including the sub-route, thereby ensuring the scientific nature of the flight insurance recommendation.

[0094] The beneficial effect brought about by the above-mentioned implementation method is that when the distribution conditions of the safe alternate landing nodes of the sub-route are poor and are not suitable as the basis for recommending flight insurance, the insurance amount of the flight insurance corresponding to the recommended second-level flight insurance level is higher than the insurance amount of the flight insurance corresponding to the first-level flight insurance level, thereby improving the scientificity and rationality of the flight insurance recommendation.

[0095] In some implementations, the method further includes: obtaining, from historical data, user selection rates for different flight insurance policies corresponding to a first tier of flight insurance levels, and sequentially displaying, on the user device, a plurality of route combinations for user selection, in descending order of the selection rates for the different flight insurance policies corresponding to the first tier of flight insurance levels. Obtaining, from historical data, user selection rates for different flight insurance policies corresponding to a second tier of flight insurance levels, and sequentially displaying, on the user device, a plurality of route combinations for user selection, in descending order of the selection rates for the different flight insurance policies corresponding to the second tier of flight insurance levels.

[0096] When recommending flight insurance, the recommended flight insurance can be determined based on the user's preferences. Specifically, the user's selection rate for different flight insurance corresponding to the first-tier flight insurance level can be obtained through historical data, and multiple combination routes can be displayed in sequence on the user device for the user to choose from in descending order of the selection rate of different flight insurance corresponding to the first-tier flight insurance level. This achieves the purpose of displaying the recommended order of combination routes according to the flight insurance selection rate, increases the probability of closing an order, and improves the user experience.

[0097] Similarly, when recommending flight insurance, historical data can be used to obtain the user's selection rate for different flight insurance corresponding to the second-tier flight insurance level, and multiple combination routes can be displayed in sequence on the user device for the user to choose from in order from high to low according to the selection rate of different flight insurance corresponding to the second-tier flight insurance level. This achieves the purpose of displaying the recommended order of combination routes according to the flight insurance selection rate, increases the probability of closing an order, and improves the user experience.

[0098] The beneficial effect brought about by the above implementation method is that the recommended order of combined routes is displayed according to the flight insurance selection rate, the probability of success is increased, and the user experience is improved.

[0099] In some implementations, the method further includes: when the travel weather conditions meet the first travel weather conditions, sequentially displaying multiple route combinations on the user device for user selection in descending order of selection rates of different flight insurance policies corresponding to the first tier of flight insurance levels. When the travel weather conditions meet the second travel weather conditions, sequentially displaying multiple route combinations on the user device for user selection in descending order of selection rates of different flight insurance policies corresponding to the first tier of flight insurance levels and selection rates of different flight insurance policies corresponding to the second tier of flight insurance levels.

[0100] When the travel weather conditions meet the first travel weather conditions, the weather conditions corresponding to the first travel weather conditions are poor. Multiple combination routes can be displayed in sequence on the user device for user selection according to the selection rates of different flight insurances corresponding to the first-tier flight insurance levels from high to low. The insurance amount of the first-tier flight insurance level is higher, so that flight insurance with a higher insurance amount is recommended to the user when the weather conditions are poor, thereby improving the scientificity and rationality of the flight insurance of the manned drone.

[0101] For example, the first travel weather condition may be rainy weather, foggy weather, or other weather conditions. Under the first travel weather condition, the safety of manned UAV route planning can be improved under poor weather conditions, and flight insurance with a higher insurance amount can be recommended to users for rationality when weather conditions are poor.

[0102] During operation, when the travel weather conditions meet the second travel weather conditions, the weather conditions of the second travel weather conditions are better. Multiple combination routes can be displayed in sequence on the user device for user selection according to the selection rates of different flight insurances corresponding to the first level of flight insurance levels and the selection rates of different flight insurances corresponding to the second level of flight insurance levels, in order from high to low. This realizes the recommendation of flight insurance of all levels to the user according to the user's historical preferences when the weather conditions are better, which can be more fully in line with the user's usual usage habits and ensure the user's experience in using flight insurance.

[0103] For example, the second travel weather condition may be a sunny day with high visibility.

[0104] The beneficial effect of the above implementation method is to improve the safety of manned UAV route planning under poor weather conditions and to achieve the rationality of recommending flight insurance with higher insurance amounts to users when weather conditions are poor.

[0105] The beneficial effect brought about by the above implementation method is that, when the weather conditions are good, all levels of flight insurance are recommended to users according to their historical preferences, which can be more in line with the user's usual usage habits and ensure the user's experience of using flight insurance.

[0106] An embodiment of the present application also provides a travel order recommendation system for manned drone travel, including a unit for executing any of the methods described above.

[0107] Figure 4 This is a logical structure diagram of a travel order recommendation system for manned drone travel provided by an embodiment of the present application, such as Figure 4 As shown, the system 1 of this embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12, and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects brought about by the embodiment of the present application have been described in the above method and will not be repeated here.

[0108] An embodiment of the present application also provides a travel order recommendation system for manned drone travel, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any one of the above items is implemented.

[0109] Figure 5 This is a schematic diagram of the physical structure of a travel order recommendation system for manned drone travel provided by an embodiment of the present application, such as Figure 5 As shown, the system 2 of this embodiment includes: at least one processor 20 ( Figure 5 Only one processor 20 is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20. When the processor 20 executes the computer program 22, the steps of any of the above-mentioned method embodiments are implemented. The beneficial effects brought about by the embodiments of the present application have been described in the above-mentioned methods and will not be repeated here.

[0110] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by 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. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0112] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0113] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0115] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0116] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0118] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0119] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A travel order recommendation method for manned drone travel, characterized in that: The method comprises: Obtaining, through the user device, the starting point and the ending point of the user's trip via the manned drone, and determining multiple combined routes of the manned drone's flight, each combined route including multiple sub-routes of the manned drone's flight; The obstacle avoidance complexity of each sub-route is determined based on its 3D obstacle avoidance index and route congestion index. The obstacle avoidance complexities of the multiple sub-routes corresponding to each combined route are summed to form the total obstacle avoidance complexity of each combined route. The 3D obstacle avoidance index is used to characterize the 3D obstacle avoidance difficulty of a manned UAV flying on a sub-route, and the route congestion index is used to characterize the route congestion difficulty of a manned UAV flying on a sub-route. Sending the plurality of combined routes and the total obstacle avoidance complexity corresponding to each combined route to the user device, and displaying the plurality of combined routes on the user device in ascending order of the total obstacle avoidance complexity corresponding to the plurality of combined routes for selection by the user; Based on the three-dimensional obstacle avoidance conditions and route congestion conditions of each sub-route, the obstacle avoidance complexity of each sub-route is determined, including: Obtain the weather conditions during the time period when the user travels by manned drone; When the travel weather conditions meet the first travel weather conditions, the product of the three-dimensional obstacle avoidance index of each sub-route and the weight of the first three-dimensional obstacle avoidance index, and the product of the line congestion index and the weight of the first line congestion index are summed to serve as the obstacle avoidance complexity of each sub-route; when the travel weather conditions meet the second travel weather conditions, the product of the three-dimensional obstacle avoidance index of each sub-route and the weight of the second three-dimensional obstacle avoidance index, and the product of the line congestion index and the weight of the second line congestion index are summed to serve as the obstacle avoidance complexity of each sub-route; The method further comprises: Obtain the take-off and landing nodes, safe alternate landing nodes, and regional obstacle avoidance indexes corresponding to the multiple sub-routes of each combined route; the take-off and landing nodes are the starting or ending points of the sub-route, and the safe alternate landing nodes are the safe alternate landing areas along the sub-route. The regional obstacle avoidance index is used to represent the obstacle avoidance difficulty corresponding to the route of the manned UAV on the sub-route flying to the safe alternate landing node; Determine the node distribution characteristics of the take-off and landing nodes and the safety alternate landing nodes of each sub-route. When the node distribution characteristics meet the preset node distribution conditions, determine the total alternate landing distance between each sub-route and the safety alternate landing node, and determine the product of the total alternate landing distance of each sub-route and the regional obstacle avoidance index corresponding to the safety alternate landing node as the safety alternate landing index; Based on the sum of the safety diversion indices corresponding to multiple sub-routes of each combined route, the first-level flight insurance level corresponding to each combined route is determined, and based on the first-level flight insurance level corresponding to each combined route, flight insurance corresponding to the first-level flight insurance level is recommended for each combined route; wherein, different first-level flight insurance levels correspond to different insurance amounts of flight insurance.

2. The method according to claim 1, wherein The method further comprises: When the node distribution characteristics do not meet the preset node distribution conditions, the second-level flight insurance level corresponding to each combination route is determined according to the total obstacle avoidance complexity of each combination route, and based on the second-level flight insurance level corresponding to each combination route, flight insurance corresponding to the second-level flight insurance level is recommended for each combination route; wherein, the insurance amounts of flight insurance corresponding to different second-level flight insurance levels are different, and the insurance amount of flight insurance corresponding to the second-level flight insurance level is higher than the insurance amount of flight insurance corresponding to the first-level flight insurance level.

3. The method according to claim 2, wherein The method further comprises: The selection rates of different flight insurances corresponding to the first-tier flight insurance levels by users are obtained through historical data, and multiple combination routes are displayed on the user device in sequence from high to low order of the selection rates of different flight insurances corresponding to the first-tier flight insurance levels for the user to select; the selection rates of different flight insurances corresponding to the second-tier flight insurance levels by users are obtained through historical data, and multiple combination routes are displayed on the user device in sequence from high to low order of the selection rates of different flight insurances corresponding to the second-tier flight insurance levels for the user to select.

4. The method according to claim 3, wherein The method further comprises: When the travel weather conditions meet the first travel weather conditions, multiple combination routes are displayed on the user device in sequence for user selection according to the selection rates of different flight insurances corresponding to the first level of flight insurance levels from high to low; when the travel weather conditions meet the second travel weather conditions, multiple combination routes are displayed on the user device in sequence for user selection according to the selection rates of different flight insurances corresponding to the first level of flight insurance levels and the selection rates of different flight insurances corresponding to the second level of flight insurance levels from high to low.

5. A travel order recommendation system for manned drone travel, characterized in that: Comprising means for performing the method according to any one of claims 1 to 4.

6. A travel order recommendation system for manned drone travel, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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