Unmanned aerial vehicle delivery planning method, device and system

Abstract

The application discloses a UAV distribution planning method, device and system, wherein the method comprises the following steps: acquiring UAV monitoring information corresponding to a plurality of transport tracks; the transport track is arranged above a target transport area for flight distribution of a transport UAV; determining UAV carrying information corresponding to the transport track according to the UAV monitoring information corresponding to the transport track; determining a UAV scheduling strategy according to track route information corresponding to all the transport tracks and the UAV carrying information; the UAV scheduling strategy is used for scheduling at least one target transport UAV to enter the corresponding transport track for flight, so that the distribution efficiency of the target transport area is improved. It can be seen that the application can realize more efficient and reasonable scheduling of the UAV to improve the distribution efficiency of the distribution area and bring better distribution service experience to users.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle distribution planning method, device and system. BACKGROUND

[0002] An unmanned aerial vehicle is referred to as an unmanned aerial vehicle, as a new technology product, in recent years, has been rapid development, unmanned aerial vehicle not only in the fire, inspection, agriculture, logistics and other fields have been widely used, also gradually accepted by the people, at present, has gradually launched a number of consumer products. Following, some fast food distribution enterprises are also considering, unmanned aerial vehicle into the distribution tool range, in order to provide fast food or semi-finished food distribution for customers.

[0003] But with the development of unmanned aerial vehicle distribution service, more and more unmanned aerial vehicles fly in low altitude, unmanned aerial vehicles in low altitude are more and more dense, and the risk of unmanned aerial vehicle collision becomes more and more big. Let the unmanned aerial vehicle fly in order, will be an effective way to solve the problem, and the distribution planning for unmanned aerial vehicle is the most cost-effective implementation scheme to achieve the goal of orderly flight under the existing technical conditions. How to plan the flight route of unmanned aerial vehicle has become an increasingly urgent problem to be solved. SUMMARY

[0004] The technical problem to be solved by the present application is to provide an unmanned aerial vehicle distribution planning method, device and system, which can determine the carrying capacity of the transportation track based on the monitoring information obtained by multiple unmanned aerial vehicle transportation tracks, and further determine the unmanned aerial vehicle scheduling strategy, so as to realize more efficient and more reasonable scheduling of unmanned aerial vehicles to improve the distribution efficiency of the distribution area and bring better distribution service experience to users.

[0005] In order to solve the above technical problems, the first aspect of the present application discloses an unmanned aerial vehicle distribution planning method, the method comprises:

[0006] Obtain unmanned aerial vehicle monitoring information corresponding to multiple transportation tracks; the transportation track is arranged in the upper space of the target transportation area for the flight distribution of the transportation unmanned aerial vehicle;

[0007] According to the unmanned aerial vehicle monitoring information corresponding to the transportation track, determine the unmanned aerial vehicle carrying information corresponding to the transportation track;

[0008] According to the track line information corresponding to all the transportation tracks and the unmanned aerial vehicle carrying information, determine the unmanned aerial vehicle scheduling strategy; the unmanned aerial vehicle scheduling strategy is used to schedule at least one target transportation unmanned aerial vehicle to enter the corresponding transportation track to fly, so as to improve the distribution efficiency of the target transportation area.

[0009] As an optional implementation, in the first aspect of the present application, the unmanned aerial vehicle monitoring information comprises one or more of the number information, speed information, power information and load information of the transport unmanned aerial vehicle.

[0010] As an optional implementation, in the first aspect of the present application, the determination of the unmanned aerial vehicle carrying information corresponding to the transport track according to the unmanned aerial vehicle monitoring information corresponding to the transport track comprises:

[0011] determining the number information of the transport unmanned aerial vehicle in the transport track according to the unmanned aerial vehicle monitoring information corresponding to the transport track, and determining the number information as the unmanned aerial vehicle carrying information corresponding to the transport track;

[0012] and / or,

[0013] determining the number information of the transport unmanned aerial vehicle in the transport track and the speed information of all the transport unmanned aerial vehicles according to the unmanned aerial vehicle monitoring information corresponding to the transport track;

[0014] calculating the average unmanned aerial vehicle transport speed corresponding to the transport track according to the number information of the transport unmanned aerial vehicle and the speed information of all the transport unmanned aerial vehicles, and determining the average unmanned aerial vehicle transport speed as the unmanned aerial vehicle carrying information corresponding to the transport track;

[0015] and / or,

[0016] determining the power information of all the transport unmanned aerial vehicles in the transport track according to the unmanned aerial vehicle monitoring information corresponding to the transport track;

[0017] calculating the unmanned aerial vehicle suspension operation rate corresponding to the transport track according to the power information of all the transport unmanned aerial vehicles and the remaining running distance of each transport unmanned aerial vehicle on the transport track, and determining the unmanned aerial vehicle suspension operation rate as the unmanned aerial vehicle carrying information corresponding to the transport track;

[0018] and / or,

[0019] determining the load information of all the transport unmanned aerial vehicles in the transport track according to the unmanned aerial vehicle monitoring information corresponding to the transport track;

[0020] determining the scheduling priority information of each transport unmanned aerial vehicle according to the load information of each transport unmanned aerial vehicle;

[0021] calculating the unmanned aerial vehicle transport priority information corresponding to the transport track according to the scheduling priority information of all the transport unmanned aerial vehicles, and determining the unmanned aerial vehicle transport priority information as the unmanned aerial vehicle carrying information corresponding to the transport track.

[0022] As an optional implementation, in the first aspect of the present invention, determining the UAV scheduling strategy based on the track route information corresponding to all the transport tracks and the UAV carrying information includes:

[0023] Based on the UAV carrying information, determine the busyness parameter corresponding to any of the transport tracks;

[0024] Based on the track route information and the route information of the target transport drone, determine the route matching degree between the target transport drone and any of the transport tracks;

[0025] Based on the busyness parameter and the route matching degree, the transportation track corresponding to the target transport drone is determined, so as to determine the drone scheduling strategy.

[0026] As an optional implementation, in the first aspect of the present invention, determining the transport trajectory corresponding to the target transport drone based on the busyness parameter and the route matching degree includes:

[0027] For any of the transport tracks, the track matching parameters between the transport track and the target transport drone are calculated based on the busyness parameter and the route matching degree.

[0028] All the transport tracks are sorted from largest to smallest according to the track matching parameters to obtain a track sequence;

[0029] The transport track corresponding to the target transport drone is determined based on the first preset number of transport tracks in the track sequence.

[0030] As an optional implementation, in the first aspect of the invention, a plurality of charging devices are provided on the transport track; the method further includes:

[0031] Based on the drone monitoring information, determine the remaining battery power of any of the transport drones in any of the transport tracks;

[0032] When it is determined that the remaining power of any of the transport drones is lower than a preset power threshold, a charging instruction corresponding to that transport drone is generated; the charging instruction is used to instruct the transport drone to establish a power supply connection with the nearest charging device.

[0033] As an optional implementation, in the first aspect of the present invention, the UAV monitoring information includes the sensing information of the transport UAV; the sensing information includes one or more of image sensing information, electromagnetic wave sensing information, and sound sensing information; the method further includes:

[0034] Based on the UAV monitoring information, determine the flight status of any of the transport UAVs in any of the transport tracks;

[0035] When any of the transport drones is determined to be in a dangerous state, a rescue command corresponding to that transport drone is generated.

[0036] A second aspect of the present invention discloses a drone delivery planning device, the device comprising:

[0037] The acquisition module is used to acquire drone monitoring information corresponding to multiple transport tracks; the transport tracks are set above the target transport area for transport drones to carry out flight delivery;

[0038] The load determination module is used to determine the UAV load information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track;

[0039] The strategy determination module is used to determine a drone scheduling strategy based on the track route information corresponding to all the transport tracks and the drone carrying information; the drone scheduling strategy is used to schedule at least one target transport drone to fly in the corresponding transport track, so as to improve the delivery efficiency of the target transport area.

[0040] As an optional implementation, in a second aspect of the present invention, the drone monitoring information includes one or more of the following: quantity information, speed information, battery information, and load information of the transport drone.

[0041] As an optional implementation, in a second aspect of the present invention, the specific method by which the load determination module determines the UAV load information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track includes:

[0042] Based on the UAV monitoring information corresponding to the transport track, determine the number of transport UAVs in the transport track, and use the number information as the UAV carrying information corresponding to the transport track;

[0043] And / or,

[0044] Based on the UAV monitoring information corresponding to the transport track, determine the number of transport UAVs in the transport track and the speed information of all transport UAVs;

[0045] Based on the number of transport drones and the speed information of all transport drones, the average drone transport speed corresponding to the transport track is calculated, and the average drone transport speed is determined as the drone carrying information corresponding to the transport track.

[0046] And / or,

[0047] Based on the monitoring information of the UAVs corresponding to the transport track, determine the battery information of all the transport UAVs in the transport track;

[0048] Based on the battery information of all the transport drones and the remaining running distance of each transport drone on the transport track, the drone suspension rate corresponding to the transport track is calculated, and the drone suspension rate is determined as the drone carrying information corresponding to the transport track.

[0049] And / or,

[0050] Based on the UAV monitoring information corresponding to the transport track, determine the load information of all transport UAVs in the transport track;

[0051] Based on the payload information of each transport drone, the scheduling priority information of each transport drone is determined;

[0052] Based on the scheduling priority information of all the transport drones, calculate the drone transport priority information corresponding to the transport track, and determine the drone transport priority information as the drone carrying information corresponding to the transport track.

[0053] As an optional implementation, in a second aspect of the invention, the strategy determination module determines the specific method of the UAV scheduling strategy based on the track route information corresponding to all the transport tracks and the UAV carrying information, including:

[0054] Based on the UAV carrying information, determine the busyness parameter corresponding to any of the transport tracks;

[0055] Based on the track route information and the route information of the target transport drone, determine the route matching degree between the target transport drone and any of the transport tracks;

[0056] Based on the busyness parameter and the route matching degree, the transportation track corresponding to the target transport drone is determined, so as to determine the drone scheduling strategy.

[0057] As an optional implementation, in a second aspect of the invention, the strategy determination module determines the specific method by which it determines the transport track corresponding to the target transport drone based on the busyness parameter and the route matching degree, including:

[0058] For any of the transport tracks, the track matching parameters between the transport track and the target transport drone are calculated based on the busyness parameter and the route matching degree.

[0059] All the transport tracks are sorted from largest to smallest according to the track matching parameters to obtain a track sequence;

[0060] The transport track corresponding to the target transport drone is determined based on the first preset number of transport tracks in the track sequence.

[0061] As an optional implementation, in a second aspect of the invention, a plurality of charging devices are provided on the transport track; the device further includes:

[0062] A power determination module is used to determine the remaining power of any of the transport drones in any of the transport tracks based on the drone monitoring information.

[0063] The charging instruction module is used to generate a charging instruction for any of the transport drones when it is determined that the remaining power of any of the transport drones is lower than a preset power threshold; the charging instruction is used to instruct the transport drone to establish a power supply connection with the nearest charging device.

[0064] As an optional implementation, in a second aspect of the invention, the UAV monitoring information includes sensor information of the transport UAV; the sensor information includes one or more of image sensor information, electromagnetic wave sensor information, and sound sensor information; the device further includes:

[0065] The flight determination module is used to determine the flight status of any of the transport drones in any of the transport tracks based on the drone monitoring information.

[0066] The rescue module is used to generate a rescue command for any of the transport drones when it is determined that the transport drone is in a dangerous state.

[0067] A third aspect of the present invention discloses another drone delivery planning device, the device comprising:

[0068] Memory containing executable program code;

[0069] A processor coupled to the memory;

[0070] The processor calls the executable program code stored in the memory to execute some or all of the steps in the drone delivery planning method disclosed in the first aspect of the present invention.

[0071] A fourth aspect of the present invention discloses a drone delivery system, the system comprising multiple transport tracks, multiple transport drones operating on the transport tracks, and control devices respectively connected to the transport tracks and the transport drones, the control devices being used to execute some or all of the steps in the drone delivery planning method disclosed in the first aspect of the present invention.

[0072] The fifth aspect of the present invention discloses a computer storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements some or all of the steps in the drone delivery planning method disclosed in the first aspect of the present invention.

[0073] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0074] In this embodiment of the invention, monitoring information of drones corresponding to multiple transport tracks is acquired. These transport tracks are set above the target transport area for delivery drones. Based on the drone monitoring information corresponding to each transport track, the drone carrying capacity information for that transport track is determined. Based on the track route information corresponding to all transport tracks and the drone carrying capacity information, a drone scheduling strategy is determined. This drone scheduling strategy is used to schedule at least one target transport drone to fly within its corresponding transport track, thereby improving the delivery efficiency of the target transport area. Therefore, this invention can determine the carrying capacity of transport tracks based on monitoring information acquired from multiple drone transport tracks, and then further determine the drone scheduling strategy, thereby achieving more efficient and reasonable drone scheduling to improve delivery efficiency in the delivery area and provide users with a better delivery service experience. Attached Figure Description

[0075] 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.

[0076] Figure 1 This is a flowchart illustrating a drone delivery planning method disclosed in an embodiment of the present invention;

[0077] Figure 2 This is a schematic diagram of the structure of a drone delivery planning device disclosed in an embodiment of the present invention;

[0078] Figure 3 This is a schematic diagram of another drone delivery planning device disclosed in an embodiment of the present invention. Detailed Implementation

[0079] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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.

[0080] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0081] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0082] This invention discloses a drone delivery planning method, apparatus, and system. Based on monitoring information obtained from multiple drone transport tracks, it determines the load conditions of the transport tracks and further determines the drone scheduling strategy. This enables more efficient and rational drone scheduling, improving delivery efficiency within the delivery area and providing users with a better delivery service experience. Detailed descriptions follow.

[0083] Example 1

[0084] Please see Figure 1 , Figure 1 This is a flowchart illustrating a drone delivery planning method disclosed in an embodiment of the present invention. Wherein, Figure 1 The described method can be applied to corresponding control terminals, control devices, or servers, and the server can be a local server or a cloud server. For example... Figure 1 As shown, the drone delivery planning method may include the following operations:

[0085] 101. Obtain UAV monitoring information corresponding to multiple transport tracks.

[0086] In this embodiment of the invention, a transport track is set above the target transport area for transport drones to carry out flight delivery. Optionally, the transport track can be a closed track, such as a tubular track, in which the drone flies. The closed track can ensure that the drone's flight is not easily affected by external factors, such as in severe weather, allowing the drone to fly normally. At the same time, the safety of the drone can also be guaranteed. For example, if the drone loses power or signal and falls, it will not fall directly to the ground and be damaged, but can be received by the track.

[0087] Optionally, the transport track can be semi-enclosed, such as a track with a semi-circular or arc-shaped cross section. This type of track design can save some costs and can also provide some shielding or support for the drone.

[0088] Optionally, a monitoring module can be installed on the transport track to detect the status of the transport drone. Optionally, the drone monitoring information may include one or more of the following: drone quantity information, speed information, battery level information, and payload information. Correspondingly, the monitoring module may also include one or more of the following: a quantity monitoring unit, a speed monitoring unit, a battery level monitoring unit, or a payload monitoring unit. Specifically, the battery level monitoring unit may also establish a connection with a battery detector installed on the transport drone to obtain the drone's battery level information in real time.

[0089] Optionally, transport drones can be used to deliver prepared food or semi-finished products such as ingredient packets. For example, catering businesses can use transport drones to transport ingredient packets or raw materials to the food preparation area via transport tracks. Automated food processing robots, such as those used for cooking rice, located in the food preparation area, can then process these semi-finished products into finished food. Furthermore, catering businesses can use transport drones to deliver food to customers via transport tracks for their enjoyment.

[0090] Furthermore, the transport track can be connected to the customer's residence or receiving device. For example, a receiving device can be installed in the customer's window to connect to the transport track, allowing the customer to receive the items transported by the drone. Additionally, storage devices can be installed on the transport track to store the items transported by the drone for the customer to retrieve.

[0091] Furthermore, the transport track can also be equipped with a charging module to provide charging services for transport drones with low battery levels. This charging service can be an automatically generated task or a task initiated based on a request from the drone. Optionally, the charging service may require the entity corresponding to the transport drone to pay a certain fee.

[0092] 102. Based on the UAV monitoring information corresponding to the transport track, determine the UAV carrying information corresponding to the transport track.

[0093] 103. Determine the drone scheduling strategy based on the track route information corresponding to all transportation tracks and the drone carrying information.

[0094] In this embodiment of the invention, the drone scheduling strategy is used to schedule at least one target transport drone to fly in the corresponding transport track, so as to improve the delivery efficiency of the target transport area.

[0095] As can be seen, the method described in the embodiments of the present invention can determine the load-bearing status of the transport track based on the monitoring information obtained from multiple UAV transport tracks, and further determine the UAV scheduling strategy, thereby enabling more efficient and reasonable scheduling of UAVs to improve the delivery efficiency of the delivery area and bring a better delivery service experience to users.

[0096] In an optional implementation, step 102 above, determining the UAV carrying information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track, includes:

[0097] Based on the monitoring information of the UAVs corresponding to the transport track, the number of transport UAVs in the transport track is determined, and the number information is used as the UAV carrying information corresponding to the transport track.

[0098] It is evident that implementing this optional implementation method can determine the quantity information as the drone carrying information corresponding to the transport track, thereby more accurately determining the drone carrying information, which is beneficial to improving the accuracy of subsequent drone scheduling strategy calculations, and thus improving the intelligence and accuracy of drone delivery planning.

[0099] In an optional implementation, step 102 above, determining the UAV carrying information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track, includes:

[0100] Based on the drone monitoring information corresponding to the transport track, determine the number of transport drones in the transport track and the speed information of all transport drones;

[0101] Based on the number of transport drones and the speed information of all transport drones, the average drone transport speed corresponding to the transport track is calculated, and the average drone transport speed is determined as the drone carrying information corresponding to the transport track.

[0102] Optionally, the sum of the speed information of all transport drones can be calculated and the ratio of it to the quantity information to obtain the average drone transport speed corresponding to the transport track. The average drone transport speed obtained in this way can be used to indicate the average smoothness of drone transport on the corresponding transport track, and can be further used to indicate the speed at which drones pass through the transport track.

[0103] It is evident that implementing this optional implementation method can calculate the average drone transport speed corresponding to the transport track based on the number of transport drones and the speed information of all transport drones, thereby more accurately determining the average drone transport speed. This is beneficial to improving the accuracy of subsequent drone scheduling strategies, and thus enhancing the intelligence and precision of drone delivery planning.

[0104] In an optional implementation, step 102 above, determining the UAV carrying information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track, includes:

[0105] Based on the monitoring information of the drones corresponding to the transport track, determine the battery information of all transport drones in the transport track;

[0106] Based on the battery information of all transport drones and the remaining operating distance of each transport drone on the transport track, the drone downtime rate corresponding to the transport track is calculated, and the drone downtime rate is determined as the drone carrying information corresponding to the transport track.

[0107] Optionally, the remaining operating distance of each transport drone can be obtained by calculating the distance from its current position along the transport track to the target endpoint.

[0108] Optionally, the remaining driving distance of the transport drone can be determined based on its battery information and the corresponding transport power consumption rules. It can then be determined whether the remaining driving distance is less than the remaining operating distance. If the determination result is yes, the transport drone is determined to be of the expected suspension type, that is, the transport drone will be suspended within the transport track.

[0109] Optionally, the ratio between the number of transport drones of the expected pause type among all transport drones on the transport track and the total number of all transport drones can be calculated to obtain the drone pause operation rate.

[0110] It is evident that implementing this optional implementation method can calculate the drone downtime rate corresponding to the transport track based on the battery information of all transport drones and the remaining operating distance of each transport drone on the transport track, thereby more accurately determining the drone downtime rate. This is beneficial to improving the accuracy of subsequent drone scheduling strategies, and thus improving the intelligence and accuracy of drone delivery planning.

[0111] In an optional implementation, step 102 above, determining the UAV carrying information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track, includes:

[0112] Based on the drone monitoring information corresponding to the transport track, determine the load information of all transport drones in the transport track;

[0113] Based on the payload information of each transport drone, determine the scheduling priority information of each transport drone.

[0114] Based on the scheduling priority information of all transport drones, calculate the drone transport priority information corresponding to the transport track, and determine the drone transport priority information as the drone carrying information corresponding to the transport track.

[0115] Optionally, the scheduling priority information of each transport drone is determined based on its payload information. This can be achieved by determining the scheduling priority information of each transport drone based on its payload information and a preset payload priority rule. Optionally, the payload priority rule is used to define the correspondence between payload weight and priority level. Preferably, the payload priority rule can be multiple priorities corresponding to multiple payload weight ranges. By setting it in this way, transport drones with heavier payloads can be released with higher priority, enabling them to complete their tasks faster and reducing damage to the drones caused by heavy loads.

[0116] Optionally, based on the scheduling priority information of all transport drones, the drone transport priority information corresponding to the transport track can be calculated. This can be done by calculating the ratio of the number of transport drones with high scheduling priority to the total number of all transport drones corresponding to the transport track, in order to obtain the drone transport priority information.

[0117] It is evident that implementing this optional implementation method can calculate the drone transportation priority information corresponding to the transportation track based on the scheduling priority information of all transport drones, thereby more accurately determining the drone transportation priority information. This is beneficial to improving the accuracy of subsequent drone scheduling strategy calculations, and thus improving the intelligence and accuracy of drone delivery planning.

[0118] In an optional implementation, step 103 above, determining the drone scheduling strategy based on the track route information corresponding to all transport tracks and the drone carrying information, includes:

[0119] Based on the information carried by the drone, determine the busyness parameter corresponding to any transport track;

[0120] Based on the track route information and the route information of the target transport drone, determine the route matching degree between the target transport drone and any transport track;

[0121] Based on the busyness parameter and the route matching degree, the corresponding transport track of the target transport drone is determined, so as to determine the drone scheduling strategy.

[0122] Optionally, determining the busyness parameter corresponding to any transport track based on the drone's carrying information may include:

[0123] One or more of the following information included in the drone transport information—the number of transport drones, average drone transport speed, drone downtime rate, and drone transport priority information—are input into a preset busy calculation model to obtain a busyness parameter. The busy calculation model calculates the sum of the products of one or more of these parameters, along with their corresponding weight information. Optionally, the corresponding weight information indicates the importance of the respective parameter information; it can be determined based on experience or experimentation, and the sum of all weight information should be 1.

[0124] Optionally, based on the orbital route information and the route information of the target transport drone, determining the route matching degree between the target transport drone and any transport orbit may include:

[0125] The similarity between the trajectory information of any transport track and the route information of the target transport drone is calculated using a path matching algorithm, and the calculated similarity is determined as the route matching degree between the target transport drone and any transport track.

[0126] As can be seen, implementing this optional implementation method can determine the transport track corresponding to the target transport drone based on the busyness parameter and the route matching degree, so as to determine the drone scheduling strategy. This can achieve more efficient and reasonable drone scheduling to improve the delivery efficiency of the delivery area and bring a better delivery service experience to users.

[0127] In an optional implementation, the step of determining the transport trajectory corresponding to the target transport drone based on the busyness parameter and the route matching degree includes:

[0128] For any transport track, calculate the track matching parameters between the transport track and the target transport drone based on the busyness parameter and the route matching degree;

[0129] For all transport tracks, sort them from largest to smallest according to the track matching parameters to obtain the track sequence;

[0130] Based on the pre-set number of transport tracks in the track sequence, determine the transport track corresponding to the target transport drone.

[0131] Optionally, based on the busyness parameter and the route matching degree, the track matching parameters between the transport track and the target transport drone can be calculated, which may include:

[0132] The traffic congestion level and route matching degree are input into a preset track matching calculation model to obtain track matching parameters. The track matching calculation model calculates the sum of the products of the traffic congestion level and route matching degree with their corresponding weight information. Optionally, the corresponding weight information indicates the importance of the corresponding traffic congestion level or route matching degree; this weight information can be determined based on experience or experiments, and the sum of all weight information should be 1.

[0133] Optionally, determining the transport track corresponding to the target transport drone based on the first preset number of transport tracks in the track sequence may include:

[0134] The first transport track in the track sequence is determined as the transport track corresponding to the target transport drone;

[0135] or,

[0136] The transport track whose entry point is closest to the target transport drone among the first preset number of transport tracks in the track sequence is determined as the transport track corresponding to the target transport drone.

[0137] As can be seen, implementing this optional implementation method can determine the transport track corresponding to the target transport drone based on the busyness parameter and the route matching degree, so as to determine the drone scheduling strategy. This can achieve more efficient and reasonable drone scheduling to improve the delivery efficiency of the delivery area and bring a better delivery service experience to users.

[0138] In one optional implementation, a plurality of charging devices are provided on the transport track. Specifically, the method further includes:

[0139] Based on drone monitoring information, determine the remaining battery power of any transport drone in any transport track;

[0140] When it is determined that the remaining battery power of any transport drone is lower than the preset battery power threshold, a charging command corresponding to that transport drone is generated.

[0141] In this embodiment of the invention, the charging command is used to instruct the transport drone to establish a power supply connection with the nearest charging device. For example, the charging command may be used to instruct the transport drone to take a path to the nearest charging device, or to instruct the nearest charging device to issue a command to notify the transport drone.

[0142] As can be seen, implementing this optional implementation method can generate a charging command for any transport drone when it is determined that the remaining power of any transport drone is lower than a preset power threshold, thereby enabling timely charging of the drone and providing users with a better delivery service experience.

[0143] In one optional implementation, the drone monitoring information includes sensor information of the transport drone; the sensor information includes one or more of image sensor information, electromagnetic wave sensor information, and sound sensor information; specifically, the method further includes:

[0144] Based on drone monitoring information, determine the flight status of any transport drone in any transport track;

[0145] When any transport drone is determined to be in a dangerous state, a rescue command corresponding to that transport drone is generated.

[0146] In this embodiment of the invention, a three-dimensional model of the transport drone can be established using image sensing information and / or electromagnetic wave sensing information from the sensor information of the transport drone, and the flight status of the transport drone can be determined based on the three-dimensional model of the transport drone and a preset three-dimensional analysis algorithm.

[0147] In this embodiment of the invention, the flight status of the transport drone can be determined by using the sound information in the sensor information of the transport drone and the preset fault sound template, and by using a sound matching algorithm. For example, when the similarity between the sound information and the fault sound template is higher than a preset similarity threshold, it is determined that the flight status of the transport drone is in a dangerous state.

[0148] In this embodiment of the invention, the rescue command can be used to instruct the nearest rescue equipment, or the associated personnel or organization of the transport drone, to rescue the transport drone. Optionally, the rescue command may include the location of the transport drone or the expected crash location.

[0149] As can be seen, implementing this optional implementation method can generate a rescue command for any transport drone when it is determined that the drone is in a dangerous state, thereby enabling timely rescue of the drone, ensuring the drone's safety, and bringing a better delivery service experience to users.

[0150] Example 2

[0151] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a drone delivery planning device disclosed in an embodiment of the present invention. Figure 2 The described apparatus can be applied to corresponding control terminals, control devices, or servers, and the server can be a local server or a cloud server; the embodiments of the present invention do not limit this. Figure 2 As shown, the device may include:

[0152] The acquisition module 201 is used to acquire UAV monitoring information corresponding to multiple transport tracks.

[0153] In this embodiment of the invention, a transport track is set above the target transport area for transport drones to carry out flight delivery. Optionally, the transport track can be a closed track, such as a tubular track, in which the drone flies. The closed track can ensure that the drone's flight is not easily affected by external factors, such as in severe weather, allowing the drone to fly normally. At the same time, the safety of the drone can also be guaranteed. For example, if the drone loses power or signal and falls, it will not fall directly to the ground and be damaged, but can be received by the track.

[0154] Optionally, the transport track can be semi-enclosed, such as a track with a semi-circular or arc-shaped cross section. This type of track design can save some costs and can also provide some shielding or support for the drone.

[0155] Optionally, a monitoring module can be installed on the transport track to detect the status of the transport drone. Optionally, the drone monitoring information may include one or more of the following: drone quantity information, speed information, battery level information, and payload information. Correspondingly, the monitoring module may also include one or more of the following: a quantity monitoring unit, a speed monitoring unit, a battery level monitoring unit, or a payload monitoring unit. Specifically, the battery level monitoring unit may also establish a connection with a battery detector installed on the transport drone to obtain the drone's battery level information in real time.

[0156] Optionally, transport drones can be used to deliver prepared food or semi-finished products such as ingredient packets. For example, catering businesses can use transport drones to transport ingredient packets or raw materials to the food preparation area via transport tracks. Automated food processing robots, such as those used for cooking rice, located in the food preparation area, can then process these semi-finished products into finished food. Furthermore, catering businesses can use transport drones to deliver food to customers via transport tracks for their enjoyment.

[0157] Furthermore, the transport track can be connected to the customer's residence or receiving device. For example, a receiving device can be installed in the customer's window to connect to the transport track, allowing the customer to receive the items transported by the drone. Additionally, storage devices can be installed on the transport track to store the items transported by the drone for the customer to retrieve.

[0158] Furthermore, the transport track can also be equipped with a charging module to provide charging services for transport drones with low battery levels. This charging service can be an automatically generated task or a task initiated based on a request from the drone. Optionally, the charging service may require the entity corresponding to the transport drone to pay a certain fee.

[0159] The load determination module 202 is used to determine the UAV load information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track.

[0160] The strategy determination module 203 is used to determine the drone scheduling strategy based on the track route information corresponding to all transport tracks and the drone carrying information; the drone scheduling strategy is used to schedule at least one target transport drone to fly in the corresponding transport track, so as to improve the delivery efficiency of the target transport area.

[0161] As can be seen, the apparatus described in the embodiments of the present invention can determine the load-bearing status of the transport tracks based on monitoring information obtained from multiple drone transport tracks, and further determine the drone scheduling strategy, thereby enabling more efficient and reasonable scheduling of drones to improve the delivery efficiency of the delivery area and bring a better delivery service experience to users.

[0162] As an optional implementation, the carrier determination module 202 determines the specific method of the UAV carrier information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track, including:

[0163] Based on the monitoring information of the UAVs corresponding to the transport track, the number of transport UAVs in the transport track is determined, and the number information is used as the UAV carrying information corresponding to the transport track.

[0164] It is evident that implementing this optional implementation method can determine the quantity information as the drone carrying information corresponding to the transport track, thereby more accurately determining the drone carrying information, which is beneficial to improving the accuracy of subsequent drone scheduling strategy calculations, and thus improving the intelligence and accuracy of drone delivery planning.

[0165] As an optional implementation, the carrier determination module 202 determines the specific method of the UAV carrier information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track, including:

[0166] Based on the drone monitoring information corresponding to the transport track, determine the number of transport drones in the transport track and the speed information of all transport drones;

[0167] Based on the number of transport drones and the speed information of all transport drones, the average drone transport speed corresponding to the transport track is calculated, and the average drone transport speed is determined as the drone carrying information corresponding to the transport track.

[0168] Optionally, the sum of the speed information of all transport drones can be calculated and the ratio of it to the quantity information to obtain the average drone transport speed corresponding to the transport track. The average drone transport speed obtained in this way can be used to indicate the average smoothness of drone transport on the corresponding transport track, and can be further used to indicate the speed at which drones pass through the transport track.

[0169] It is evident that implementing this optional implementation method can calculate the average drone transport speed corresponding to the transport track based on the number of transport drones and the speed information of all transport drones, thereby more accurately determining the average drone transport speed. This is beneficial to improving the accuracy of subsequent drone scheduling strategies, and thus enhancing the intelligence and precision of drone delivery planning.

[0170] As an optional implementation, the carrier determination module 202 determines the specific method of the UAV carrier information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track, including:

[0171] Based on the monitoring information of the drones corresponding to the transport track, determine the battery information of all transport drones in the transport track;

[0172] Based on the battery information of all transport drones and the remaining operating distance of each transport drone on the transport track, the drone downtime rate corresponding to the transport track is calculated, and the drone downtime rate is determined as the drone carrying information corresponding to the transport track.

[0173] Optionally, the remaining operating distance of each transport drone can be obtained by calculating the distance from its current position along the transport track to the target endpoint.

[0174] Optionally, the remaining driving distance of the transport drone can be determined based on its battery information and the corresponding transport power consumption rules. It can then be determined whether the remaining driving distance is less than the remaining operating distance. If the determination result is yes, the transport drone is determined to be of the expected suspension type, that is, the transport drone will be suspended within the transport track.

[0175] Optionally, the ratio between the number of transport drones of the expected pause type among all transport drones on the transport track and the total number of all transport drones can be calculated to obtain the drone pause operation rate.

[0176] It is evident that implementing this optional implementation method can calculate the drone downtime rate corresponding to the transport track based on the battery information of all transport drones and the remaining operating distance of each transport drone on the transport track, thereby more accurately determining the drone downtime rate. This is beneficial to improving the accuracy of subsequent drone scheduling strategies, and thus improving the intelligence and accuracy of drone delivery planning.

[0177] As an optional implementation, the carrier determination module 202 determines the specific method of the UAV carrier information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track, including:

[0178] Based on the drone monitoring information corresponding to the transport track, determine the load information of all transport drones in the transport track;

[0179] Based on the payload information of each transport drone, determine the scheduling priority information of each transport drone.

[0180] Based on the scheduling priority information of all transport drones, calculate the drone transport priority information corresponding to the transport track, and determine the drone transport priority information as the drone carrying information corresponding to the transport track.

[0181] Optionally, the scheduling priority information of each transport drone is determined based on its payload information. This can be achieved by determining the scheduling priority information of each transport drone based on its payload information and a preset payload priority rule. Optionally, the payload priority rule is used to define the correspondence between payload weight and priority level. Preferably, the payload priority rule can be multiple priorities corresponding to multiple payload weight ranges. By setting it in this way, transport drones with heavier payloads can be released with higher priority, enabling them to complete their tasks faster and reducing damage to the drones caused by heavy loads.

[0182] Optionally, based on the scheduling priority information of all transport drones, the drone transport priority information corresponding to the transport track can be calculated. This can be done by calculating the ratio of the number of transport drones with high scheduling priority to the total number of all transport drones corresponding to the transport track, in order to obtain the drone transport priority information.

[0183] It is evident that implementing this optional implementation method can calculate the drone transportation priority information corresponding to the transportation track based on the scheduling priority information of all transport drones, thereby more accurately determining the drone transportation priority information. This is beneficial to improving the accuracy of subsequent drone scheduling strategy calculations, and thus improving the intelligence and accuracy of drone delivery planning.

[0184] As an optional implementation, the strategy determination module 203 determines the specific method of the UAV scheduling strategy based on the track route information corresponding to all transport tracks and the UAV carrying information, including:

[0185] Based on the information carried by the drone, determine the busyness parameter corresponding to any transport track;

[0186] Based on the track route information and the route information of the target transport drone, determine the route matching degree between the target transport drone and any transport track;

[0187] Based on the busyness parameter and the route matching degree, the corresponding transport track of the target transport drone is determined, so as to determine the drone scheduling strategy.

[0188] Optionally, determining the busyness parameter corresponding to any transport track based on the drone's carrying information may include:

[0189] One or more of the following information included in the drone transport information—the number of transport drones, average drone transport speed, drone downtime rate, and drone transport priority information—are input into a preset busy calculation model to obtain a busyness parameter. The busy calculation model calculates the sum of the products of one or more of these parameters, along with their corresponding weight information. Optionally, the corresponding weight information indicates the importance of the respective parameter information; it can be determined based on experience or experimentation, and the sum of all weight information should be 1.

[0190] Optionally, based on the orbital route information and the route information of the target transport drone, determining the route matching degree between the target transport drone and any transport orbit may include:

[0191] The similarity between the trajectory information of any transport track and the route information of the target transport drone is calculated using a path matching algorithm, and the calculated similarity is determined as the route matching degree between the target transport drone and any transport track.

[0192] As can be seen, implementing this optional implementation method can determine the transport track corresponding to the target transport drone based on the busyness parameter and the route matching degree, so as to determine the drone scheduling strategy. This can achieve more efficient and reasonable drone scheduling to improve the delivery efficiency of the delivery area and bring a better delivery service experience to users.

[0193] As an optional implementation, the strategy determination module 203 determines the specific method of the transport track corresponding to the target transport drone based on the busyness parameter and the route matching degree, including:

[0194] For any transport track, calculate the track matching parameters between the transport track and the target transport drone based on the busyness parameter and the route matching degree;

[0195] For all transport tracks, sort them from largest to smallest according to the track matching parameters to obtain the track sequence;

[0196] Based on the pre-set number of transport tracks in the track sequence, determine the transport track corresponding to the target transport drone.

[0197] Optionally, based on the busyness parameter and the route matching degree, the track matching parameters between the transport track and the target transport drone can be calculated, which may include:

[0198] The traffic congestion level and route matching degree are input into a preset track matching calculation model to obtain track matching parameters. The track matching calculation model calculates the sum of the products of the traffic congestion level and route matching degree with their corresponding weight information. Optionally, the corresponding weight information indicates the importance of the corresponding traffic congestion level or route matching degree; this weight information can be determined based on experience or experiments, and the sum of all weight information should be 1.

[0199] Optionally, determining the transport track corresponding to the target transport drone based on the first preset number of transport tracks in the track sequence may include:

[0200] The first transport track in the track sequence is determined as the transport track corresponding to the target transport drone;

[0201] or,

[0202] The transport track whose entry point is closest to the target transport drone among the first preset number of transport tracks in the track sequence is determined as the transport track corresponding to the target transport drone.

[0203] As can be seen, implementing this optional implementation method can determine the transport track corresponding to the target transport drone based on the busyness parameter and the route matching degree, so as to determine the drone scheduling strategy. This can achieve more efficient and reasonable drone scheduling to improve the delivery efficiency of the delivery area and bring a better delivery service experience to users.

[0204] As an optional implementation, several charging devices are provided on the transport track; the device also includes:

[0205] The power determination module is used to determine the remaining power of any transport drone in any transport track based on drone monitoring information.

[0206] The charging indicator module is used to generate a charging instruction for any transport drone when it is determined that the remaining power of any transport drone is lower than a preset power threshold.

[0207] In this embodiment of the invention, the charging command is used to instruct the transport drone to establish a power supply connection with the nearest charging device. For example, the charging command may be used to instruct the transport drone to take a path to the nearest charging device, or to instruct the nearest charging device to issue a command to notify the transport drone.

[0208] As can be seen, implementing this optional implementation method can generate a charging command for any transport drone when it is determined that the remaining power of any transport drone is lower than a preset power threshold, thereby enabling timely charging of the drone and providing users with a better delivery service experience.

[0209] As an optional implementation, the drone monitoring information includes sensor information of the transport drone; the sensor information includes one or more of image sensor information, electromagnetic wave sensor information, and sound sensor information; the device further includes:

[0210] The flight determination module is used to determine the flight status of any transport drone in any transport track based on drone monitoring information.

[0211] The rescue module is used to generate a rescue command for any transport drone when it is determined that the drone is in a dangerous state.

[0212] In this embodiment of the invention, a three-dimensional model of the transport drone can be established using image sensing information and / or electromagnetic wave sensing information from the sensor information of the transport drone, and the flight status of the transport drone can be determined based on the three-dimensional model of the transport drone and a preset three-dimensional analysis algorithm.

[0213] In this embodiment of the invention, the flight status of the transport drone can be determined by using the sound information in the sensor information of the transport drone and the preset fault sound template, and by using a sound matching algorithm. For example, when the similarity between the sound information and the fault sound template is higher than a preset similarity threshold, it is determined that the flight status of the transport drone is in a dangerous state.

[0214] In this embodiment of the invention, the rescue command can be used to instruct the nearest rescue equipment, or the associated personnel or organization of the transport drone, to rescue the transport drone. Optionally, the rescue command may include the location of the transport drone or the expected crash location.

[0215] As can be seen, implementing this optional implementation method can generate a rescue command for any transport drone when it is determined that the drone is in a dangerous state, thereby enabling timely rescue of the drone, ensuring the drone's safety, and bringing a better delivery service experience to users.

[0216] Example 3

[0217] Please see Figure 3 , Figure 3 This is a structural schematic diagram of another drone delivery planning device disclosed in an embodiment of the present invention. (See diagram below.) Figure 3 As shown, the device may include:

[0218] Memory 301 storing executable program code;

[0219] Processor 302 coupled to memory 301;

[0220] The processor 302 calls the executable program code stored in the memory 301 to execute some or all of the steps in the drone delivery planning method disclosed in Embodiment 1 of the present invention.

[0221] Example 4

[0222] This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute some or all of the steps in the drone delivery planning method disclosed in Embodiment 1 of this invention.

[0223] Example 5

[0224] This invention discloses a drone delivery system, which includes multiple transport tracks, multiple transport drones operating on the transport tracks, and control devices connected to the transport tracks and the transport drones respectively. The control devices are used to execute some or all of the steps in the drone delivery planning method disclosed in Embodiment 1 of this invention to realize the delivery planning of multiple transport drones.

[0225] Specifically, the technical details related to the transport track and transport drone in the embodiments of the present invention can be referred to the corresponding description in Embodiment 1, and will not be repeated in the embodiments of the present invention.

[0226] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0227] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0228] Finally, it should be noted that the drone delivery planning method, apparatus, and system disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A drone delivery planning method, characterized in that, The method includes: The system acquires drone monitoring information corresponding to multiple transport tracks; the transport tracks are set above the target transport area for transport drones to fly and deliver goods; the drone monitoring information includes the number, speed, battery level, and payload information of the transport drones. Based on the UAV monitoring information corresponding to the transport track, determine the UAV carrying information corresponding to the transport track; Based on the track route information corresponding to all the transport tracks and the UAV carrying information, a UAV scheduling strategy is determined; the UAV scheduling strategy is used to schedule at least one target transport UAV to fly in the corresponding transport track, so as to improve the delivery efficiency of the target transport area; The step of determining the UAV carrying information corresponding to the transport track based on the UAV monitoring information corresponding to the transport track includes: Based on the UAV monitoring information corresponding to the transport track, determine the quantity information of the transport UAVs in the transport track, and use the quantity information as the UAV carrying information corresponding to the transport track; Based on the UAV monitoring information corresponding to the transport track, determine the number of transport UAVs in the transport track and the speed information of all transport UAVs; based on the number of transport UAVs and the speed information of all transport UAVs, calculate the average UAV transport speed corresponding to the transport track, and determine the average UAV transport speed as the UAV carrying information corresponding to the transport track; Based on the monitoring information of the UAVs corresponding to the transport track, the battery information of all the transport UAVs on the transport track is determined; based on the battery information of all the transport UAVs and the remaining operating distance of each transport UAV on the transport track, the UAV pause rate corresponding to the transport track is calculated, and the UAV pause rate is determined as the UAV carrying information corresponding to the transport track; wherein, the UAV pause rate is the ratio between the number of transport UAVs of the expected pause type on the transport track and the total number of all the transport UAVs; Based on the UAV monitoring information corresponding to the transport track, determine the load information of all transport UAVs in the transport track; based on the load information of each transport UAV, determine the scheduling priority information of each transport UAV; based on the scheduling priority information of all transport UAVs, calculate the UAV transport priority information corresponding to the transport track, and determine the UAV transport priority information as the UAV carrying information corresponding to the transport track. The step of determining the drone scheduling strategy based on the track route information corresponding to all the transport tracks and the drone carrying information includes: The number of transport drones, the average transport speed of drones, the drone downtime rate, and the drone transport priority information included in the drone carrying information are input into a preset busy calculation model to obtain the busyness parameter corresponding to any transport track. The similarity between the track route information of any of the transport tracks and the route information of the target transport drone is calculated using a path matching algorithm, and the calculated similarity is determined as the route matching degree between the target transport drone and any of the transport tracks. Based on the busyness parameter and the route matching degree, the transportation track corresponding to the target transport drone is determined, so as to determine the drone scheduling strategy; The step of determining the transport trajectory corresponding to the target transport drone based on the busyness parameter and the route matching degree includes: For any of the transport tracks, the track matching parameters between the transport track and the target transport drone are calculated based on the busyness parameter and the route matching degree. All the transport tracks are sorted from largest to smallest according to the track matching parameters to obtain a track sequence; The transport track corresponding to the target transport drone is determined based on the first preset number of transport tracks in the track sequence.

2. The drone delivery planning method according to claim 1, characterized in that, The transport track is equipped with several charging devices; the method further includes: Based on the drone monitoring information, determine the remaining battery power of any of the transport drones in any of the transport tracks; When it is determined that the remaining power of any of the transport drones is lower than a preset power threshold, a charging instruction corresponding to that transport drone is generated; the charging instruction is used to instruct the transport drone to establish a power supply connection with the nearest charging device.

3. The drone delivery planning method according to claim 1, characterized in that, The drone monitoring information includes the sensor information of the transport drone; The sensing information includes one or more of image sensing information, electromagnetic wave sensing information, and sound sensing information; the method further includes: Based on the UAV monitoring information, determine the flight status of any of the transport UAVs in any of the transport tracks; When any of the transport drones is determined to be in a dangerous state, a rescue command corresponding to that transport drone is generated.

4. A drone delivery planning device, characterized in that, The device includes: The acquisition module is used to acquire drone monitoring information corresponding to multiple transport tracks; the transport tracks are set above the target transport area for transport drones to carry out flight delivery; The load determination module is used to determine the drone load information corresponding to the transport track based on the drone monitoring information corresponding to the transport track; the drone monitoring information includes the quantity information, speed information, battery information, and load information of the transport drones; The strategy determination module is used to determine a drone scheduling strategy based on the track route information corresponding to all the transport tracks and the drone carrying information; the drone scheduling strategy is used to schedule at least one target transport drone to fly in the corresponding transport track, so as to improve the delivery efficiency of the target transport area; The method by which the load determination module determines the drone load information corresponding to the transport track based on the drone monitoring information corresponding to the transport track includes: Based on the UAV monitoring information corresponding to the transport track, determine the quantity information of the transport UAVs in the transport track, and use the quantity information as the UAV carrying information corresponding to the transport track; Based on the UAV monitoring information corresponding to the transport track, determine the number of transport UAVs in the transport track and the speed information of all transport UAVs; based on the number of transport UAVs and the speed information of all transport UAVs, calculate the average UAV transport speed corresponding to the transport track, and determine the average UAV transport speed as the UAV carrying information corresponding to the transport track; Based on the monitoring information of the UAVs corresponding to the transport track, the battery information of all the transport UAVs on the transport track is determined; based on the battery information of all the transport UAVs and the remaining operating distance of each transport UAV on the transport track, the UAV pause rate corresponding to the transport track is calculated, and the UAV pause rate is determined as the UAV carrying information corresponding to the transport track; wherein, the UAV pause rate is the ratio between the number of transport UAVs of the expected pause type on the transport track and the total number of all the transport UAVs; Based on the UAV monitoring information corresponding to the transport track, determine the load information of all transport UAVs in the transport track; based on the load information of each transport UAV, determine the scheduling priority information of each transport UAV; based on the scheduling priority information of all transport UAVs, calculate the UAV transport priority information corresponding to the transport track, and determine the UAV transport priority information as the UAV carrying information corresponding to the transport track. The strategy determination module determines the specific method of the drone scheduling strategy based on the track route information corresponding to all the transport tracks and the drone carrying information, including: The number of transport drones, the average transport speed of drones, the drone downtime rate, and the drone transport priority information included in the drone carrying information are input into a preset busy calculation model to obtain the busyness parameter corresponding to any transport track. The similarity between the track route information of any of the transport tracks and the route information of the target transport drone is calculated using a path matching algorithm, and the calculated similarity is determined as the route matching degree between the target transport drone and any of the transport tracks. Based on the busyness parameter and the route matching degree, the transport trajectory corresponding to the target transport drone is determined, thereby determining the drone scheduling strategy. The strategy determination module determines the specific method of the transport track corresponding to the target transport drone based on the busyness parameter and the route matching degree, including: For any of the transport tracks, the track matching parameters between the transport track and the target transport drone are calculated based on the busyness parameter and the route matching degree. All the transport tracks are sorted from largest to smallest according to the track matching parameters to obtain a track sequence; The transport track corresponding to the target transport drone is determined based on the first preset number of transport tracks in the track sequence.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 3.

6. A drone delivery system, characterized in that, The system includes multiple transport tracks, multiple transport drones operating on the transport tracks, and control devices connected to the transport tracks and the transport drones respectively, the control devices being used to execute the drone delivery planning method as described in any one of claims 1-3.

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