Goods taking and delivering track design method and system of multi-network-connected unmanned aerial vehicle logistics system

Abstract

The invention discloses a goods taking and delivering track design method for a multi-network-connected unmanned aerial vehicle logistics system. The method comprises the following steps: acquiring a radio map, unmanned aerial vehicle speeds, the number of unmanned aerial vehicles and access position nodes of the unmanned aerial vehicles; and calculating an optimal path matrix and an effective path matrix through the radio map, the unmanned aerial vehicle speed, the unmanned aerial vehicle number and the access position nodes of the unmanned aerial vehicles, and calculating a multi-unmanned aerial vehicle access sequence track by adopting the unmanned aerial vehicle number, the optimal path matrix and the effective path matrix. According to the method, factors of distance, time and power consumption are comprehensively considered, and an optimal path between multiple unmanned aerial vehicle access position nodes is calculated; and then a multi-unmanned aerial vehicle access sequence trajectory is calculated by adopting an unmanned aerial vehicle user group pairing scheme, so that the multi-unmanned aerial vehicle flight trajectory design is optimized, and the unmanned aerial vehicle logistics efficiency is improved under the limited unmanned aerial vehicle cruising ability.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for designing pickup and delivery trajectories for a multi-networked unmanned aerial vehicle logistics system. Background Art

[0002] In recent years, with the dramatic increase in demand for express delivery services, significant investment is required across all parts of the service chain. Drone delivery offers significant advantages in logistics scenarios. Compared to manual delivery, drones offer advantages such as small size, low cost, high speed, and ease of operation. These advantages can significantly reduce logistics delivery pressures while also minimizing unnecessary human contact. Compared to unmanned vehicle delivery, drones offer advantages such as the ability to avoid urban traffic congestion and cross complex terrain such as rivers and lakes. Therefore, their application in the logistics industry holds great promise.

[0003] Due to the limited endurance of drones, maximizing drone power consumption and effectively planning the flight trajectories of multiple drones are key issues. Existing multi-drone flight trajectory designs fail to consider flight-related information constraints and inter-drone collaboration, resulting in inefficient multi-drone allocation and trajectory optimization, and suboptimal results in practical applications. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a method and system for designing pickup and delivery trajectories for a multi-networked drone logistics system. The technical problems to be solved by the present invention are achieved through the following technical solutions:

[0005] A method for designing pickup and delivery trajectories for a multi-networked drone logistics system, comprising:

[0006] Data acquisition: obtain radio maps, drone speed, number of drones, and the location nodes visited by drones;

[0007] Optimal path calculation: obtaining an optimal path matrix and a validity path matrix based on the radio map and the visited location nodes of the UAV;

[0008] Sequential trajectory calculation: obtaining a multi-UAV access sequential trajectory according to the number of UAVs, the optimal path matrix, and the validity path matrix;

[0009] Among them, the access location nodes of the drone include: a drone hangar location node, a pickup location node and a delivery location node.

[0010] In a specific embodiment, obtaining the optimal path matrix and the validity path matrix according to the radio map, the speed of the drone, the number of drones, and the visited location nodes of the drones includes:

[0011] Obtaining the UAV outage time based on the radio map;

[0012] Obtaining energy consumption of the drone according to the speed of the drone;

[0013] Obtaining the moving distance of the drone according to the visited location node of the drone;

[0014] Obtaining an optimal path between any two access location nodes based on the drone interruption time, the drone energy consumption, and the drone movement distance;

[0015] Obtaining an optimal path matrix according to the optimal path between any two access location nodes;

[0016] Obtaining a validity path matrix according to any two visited location nodes;

[0017] The optimal path between any two access location nodes includes: an optimal path starting from the drone hangar location node and ending at the pickup location node, an optimal path starting from the pickup location node and ending at the delivery location node, an optimal path starting from the delivery location node and ending at the pickup location node, an optimal path starting from the pickup location node and ending at the pickup location node, and an optimal path starting from the delivery location node and ending at the delivery location node;

[0018] The row coordinates of each element in the optimal path matrix are the starting point between any two access location nodes, the column coordinates are the end point between any two access location nodes, and the value of each element is the optimal path value from the starting point to the end point between any two access location nodes.

[0019] The row coordinates of each element in the validity path matrix are the starting point between any two access location nodes, the column coordinates are the end point between any two access location nodes, and the value of each element is the validity parameter between any two access location nodes.

[0020] In a specific embodiment, the optimal path calculation formula between any two access location nodes is obtained based on the drone interruption time, the drone energy consumption and the drone movement distance:

[0021] in, is the optimal path of the kth UAV from the i-position node to the j-position node, w1 is the weight of the UAV energy consumption, w2 is the weight of the UAV moving distance, and w3 is the weight of the UAV communication delay. Among them, w1, w2 and w3 are configured differently according to the environment where the UAV is located.

[0022] In a specific embodiment, obtaining the multi-UAV access sequence trajectory according to the number of UAVs, the optimal path matrix, and the validity path matrix includes:

[0023] Obtaining the number of user groups according to the validity path matrix;

[0024] Obtaining a drone user group pairing solution based on the total number of user groups and the number of drones;

[0025] The multi-UAV access sequence trajectory is obtained according to the UAV user group pairing scheme and the optimal path matrix.

[0026] In a specific embodiment, obtaining the multi-UAV access sequence trajectory according to the UAV user group pairing scheme and the optimal path matrix includes:

[0027] Obtain the number of user groups for each drone according to the drone user group pairing scheme;

[0028] The optimal sequential trajectory of the corresponding drone access is obtained according to the number of user groups of each drone and the optimal path matrix, wherein the calculation formula of the optimal sequential trajectory of the corresponding drone access is: By iterating the step parameter set t and the step parameter set c, the optimal sequential trajectory OD of the k-th UAV is obtained. k , and record the iteration step parameter set t and step parameter set c, N is the element sum of the validity path matrix, k i,j is the risk weight of the kth drone from location node i to location node j, A i,j is the validity of the path from the i-position node to the j-position node;

[0029] A multi-UAV access sequence trajectory is obtained according to the optimal sequential trajectories of all the UAV accesses. When the UAV user group pairing scheme includes multiple schemes, a scheme with the smallest multi-UAV access sequence trajectory among all the schemes is selected as the final multi-UAV access sequence trajectory.

[0030] The present invention also provides a delivery trajectory design system for a multi-networked drone logistics system, including:

[0031] A data acquisition module is used to obtain radio maps, drone speed, number of drones, and the location nodes visited by drones;

[0032] An optimal path calculation module, configured to obtain an optimal path matrix and a validity path matrix based on the radio map and the visited location nodes of the UAV;

[0033] A sequential trajectory calculation module, configured to obtain a multi-UAV access sequential trajectory according to the number of UAVs, the optimal path matrix, and the validity path matrix;

[0034] The access location nodes of the drone include: drone hangar location node, pickup location node and delivery location node.

[0035] In a specific embodiment, the optimal path calculation module includes:

[0036] an interruption time calculation submodule, configured to obtain the UAV interruption time according to the radio map;

[0037] The energy consumption calculation submodule is used to obtain the energy consumption of the drone according to the speed of the drone;

[0038] The moving distance calculation submodule is used to obtain the moving distance of the drone based on the location node visited by the drone;

[0039] An optimal path calculation submodule, configured to obtain an optimal path between any two access location nodes based on the UAV interruption time, the UAV energy consumption, and the UAV movement distance;

[0040] An optimal path matrix calculation submodule, configured to obtain an optimal path matrix based on the optimal path between any two access location nodes;

[0041] A validity path matrix calculation submodule, configured to obtain a validity path matrix between any two access location nodes;

[0042] The optimal path between any two access location nodes includes: an optimal path starting from the drone hangar location node and ending at the pickup location node, an optimal path starting from the pickup location node and ending at the delivery location node, an optimal path starting from the delivery location node and ending at the pickup location node, an optimal path starting from the pickup location node and ending at the pickup location node, and an optimal path starting from the delivery location node and ending at the delivery location node;

[0043] The row coordinates of each element in the optimal path matrix are the starting point between any two access location nodes, the column coordinates are the end point between any two access location nodes, and the value of each element is the optimal path value from the starting point to the end point between any two access location nodes.

[0044] The row coordinates of each element in the validity path matrix are the starting point between any two access location nodes, the column coordinates are the end point between any two access location nodes, and the value of each element is the validity parameter between any two access location nodes.

[0045] In a specific embodiment, the calculation formula of the optimal path calculation submodule is:

[0046]

[0047] in, is the optimal path of the kth UAV from the i-position node to the j-position node, w1 is the weight of the UAV energy consumption, w2 is the weight of the UAV moving distance, and w3 is the weight of the UAV communication delay. Among them, w1, w2 and w3 are configured differently according to the environment where the UAV is located.

[0048] In a specific embodiment, the sequential trajectory calculation module includes:

[0049] A submodule for calculating the number of user groups, configured to obtain the number of user groups according to the validity path matrix;

[0050] a pairing solution calculation submodule, configured to obtain a drone user group pairing solution based on the total number of user groups and the number of drones;

[0051] The sequential trajectory calculation submodule is used to obtain the sequential trajectory of multiple drone visits based on the drone user group pairing scheme and the optimal path matrix.

[0052] In a specific embodiment, the sequential trajectory calculation submodule includes:

[0053] a single user group number calculation subunit, configured to obtain the number of user groups for each drone according to the drone user group pairing scheme;

[0054] The single sequence trajectory calculation subunit obtains the corresponding drone access optimal sequence trajectory according to the number of user groups of each drone and the optimal path matrix, wherein the calculation formula of the corresponding drone access optimal sequence trajectory is: By iterating the step parameter set t and the step parameter set c, the optimal sequential trajectory OD of the k-th UAV is obtained. k , and record the iteration step parameter set t and step parameter set c, N is the element sum of the validity path matrix, k i,j is the risk weight of the kth drone from location node i to location node j, A i,j is the validity of the path from the i-position node to the j-position node;

[0055] The full sequence trajectory calculation subunit obtains a multi-UAV access sequence trajectory based on the optimal sequence trajectories of all the UAV accesses. When the UAV user group pairing scheme includes multiple schemes, the scheme with the smallest multi-UAV access sequence trajectory among all the schemes is selected as the final multi-UAV access sequence trajectory.

[0056] Beneficial effects of the present invention:

[0057] The present invention provides a method and system for designing pickup and delivery trajectories for a multi-networked drone logistics system. By obtaining a radio map, drone speed, number of drones, and the location nodes visited by drones, and comprehensively considering factors such as distance, time, and power consumption, the method first calculates the optimal path between the location nodes visited by multiple drones; then, a drone user group pairing scheme is used to calculate the sequential trajectories of the multiple drone visits.

[0058] Finally, the present invention optimizes the flight trajectory design of multiple drones and improves the efficiency of drone logistics under limited drone endurance.

[0059] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a method and system flow chart for designing pickup and delivery trajectories for a multi-networked drone logistics system provided by an embodiment of the present invention;

[0061] Figure 2 This is a module block diagram of a method and system for designing pickup and delivery trajectories for a multi-networked drone logistics system provided by an embodiment of the present invention; DETAILED DESCRIPTION

[0062] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0063] Example 1

[0064] In one embodiment, see Figure 1 , Figure 1 The present invention is a pickup and delivery trajectory design method and system flow chart for a multi-networked drone logistics system.

[0065] S1: To ensure the safety, efficiency and compliance of drone flights, the data acquisition module is required to provide the drone system with necessary data information. Through this data information, the drone control system can better control the drone, optimize flight missions, and improve overall operational efficiency. Therefore, the present invention uses the data acquisition module as a system module to obtain radio maps, drone speeds, drone numbers, and drone location nodes visited, where:

[0066] The radio map provides wireless communication coverage in the drone's operating area, helping the drone avoid areas with weak signals or strong radio interference, ensuring stable data transmission. It is crucial for planning the drone's flight path and ensuring communication reliability. The radio map includes information such as the height of ground buildings, flight restrictions, regional communication quality strength, and regional communication quality fluctuations.

[0067] The drone speed is necessary for flight control and path planning. It not only helps the drone maintain a stable flight state, but also can be used to calculate flight time, predict arrival time, and adjust flight strategies.

[0068] The number of drones mentioned above helps the system avoid air traffic conflicts and ensure flight safety. In the case of multiple drones working together, the number information is crucial for task allocation and coordination.

[0069] The drone's access location nodes facilitate accurate flight route planning and mission execution, record the drone's flight history, optimize future flight plans, and perform geospatial analysis;

[0070] The access location nodes of the drone include: drone hangar location node, pickup location node and delivery location node;

[0071] S2: In a multi-drone logistics system, calculating the optimal path between the drone's visitor location nodes is crucial, as it directly affects the drone's flight efficiency, energy consumption, flight safety, and regulatory compliance. Regarding improving efficiency: determining the shortest or optimal flight path can reduce the drone's flight time when performing a mission, thereby improving the efficiency of logistics distribution. Regarding saving energy: by flying along the shortest path, drones can save battery power, extend flight time, and reduce the number of charging cycles, which is particularly important for long-distance distribution. Regarding reducing costs: reducing flight distance and time can reduce the costs associated with drone operations, including energy consumption, maintenance, and labor costs. Regarding ensuring safety: optimizing flight paths helps drones avoid high-risk areas, such as densely populated areas, restricted airspace, or adverse weather conditions, ensuring flight safety. Regarding regulatory compliance: in some areas, drone flights may be subject to regulatory restrictions, such as altitude restrictions or no-fly zones in specific airspace. Optimal path calculation can help drones comply with these regulations.

[0072] See Figure 2 , Figure 2 This is a module block diagram of a method for designing pickup and delivery trajectories for a multi-networked drone logistics system and the system. Therefore, the present invention uses an optimal path calculation module as a system module, and obtains an optimal path matrix and a validity path matrix based on the radio map, drone speed, number of drones, and the visited location nodes of the drones, including:

[0073] S21: Using an interruption time calculation submodule, for obtaining the UAV interruption time according to the radio map, wherein the UAV interruption time is a communication delay time, and the communication delay specifically includes:

[0074] Weather conditions: unforeseen flight delays or cancellations due to air traffic control or inclement weather;

[0075] Mechanical failure: flight delay or cancellation due to drone malfunction;

[0076] Base station communication: flight delay caused by the signal strength of the communication base station;

[0077] S22: An energy consumption calculation submodule is used to obtain the energy consumption of the drone based on the drone speed. In addition to the drone speed, the following factors must also be considered:

[0078] Drone Weight: The weight of a drone's payload affects the force required to propel it.

[0079] Flight altitude: Atmospheric density at different altitudes affects propulsion efficiency.

[0080] Environmental conditions: Wind speed, temperature, and humidity all affect energy consumption.

[0081] Battery efficiency: battery charging and discharging efficiency and aging degree.

[0082] Propulsion system efficiency: The efficiency of the motor and propeller.

[0083] Flight mode: Level flight, climbing, descending, or hovering have different energy consumption;

[0084] S23: using a moving distance calculation submodule to obtain the moving distance of the drone based on the visited location node of the drone;

[0085] S231: using the optimal path calculation submodule to obtain the optimal path between any two access location nodes according to the UAV interruption time, the UAV energy consumption and the UAV movement distance;

[0086] S232: Using the optimal path matrix calculation submodule, obtain an optimal path matrix according to the optimal path between any two access location nodes; the calculation formula of the optimal path calculation submodule is:

[0087]

[0088] in, is the optimal path of the kth UAV from the i-position node to the j-position node, w1 is the weight of the UAV energy consumption, w2 is the weight of the UAV movement distance, and w3 is the weight of the UAV communication delay, where w1, w2 and w3 are configured differently according to the environment where the UAV is located;

[0089] Taking a plain urban area as an example, since there are many communication base stations in the city, the drone interruption time can be ignored, so w3 is set to 0; since the terrain is plain and the drones have a unified signal, w1 for each drone is set only according to the difference in wind resistance, and w1 is proportional to the wind resistance; since drones in urban delivery all take off and land vertically, only the straight-line distance between any two visited location nodes is considered, so w2 is proportional to the straight-line distance between any two visited location nodes.

[0090] S233: using a validity path matrix calculation submodule to obtain a validity path matrix according to any two access location nodes;

[0091] The optimal path between any two access location nodes includes: an optimal path starting from the drone hangar location node and ending at the pickup location node, an optimal path starting from the pickup location node and ending at the delivery location node, an optimal path starting from the delivery location node and ending at the pickup location node, an optimal path starting from the pickup location node and ending at the pickup location node, and an optimal path starting from the delivery location node and ending at the delivery location node;

[0092] The row coordinates of each element in the optimal path matrix are the starting point between any two access location nodes, the column coordinates are the end point between any two access location nodes, and the value of each element is the optimal path value from the starting point to the end point between any two access location nodes.

[0093] The row coordinates of each element in the validity path matrix are the starting point between any two access position nodes, the column coordinates are the end point between any two access position nodes, and the value of each element is the validity parameter between any two access position nodes;

[0094] S3: After obtaining the optimal path between any two access location nodes, maximizing the number of deliveries by multiple drones, while maintaining a constant total power consumption, becomes the key issue. This can be broken down into calculating the sequential trajectory of each drone's access. Therefore, the present invention uses a sequential trajectory calculation module as a system module to calculate the sequential trajectory of multiple drones based on the number of drones, the optimal path matrix, and the validity path matrix, including:

[0095] A submodule for calculating the number of user groups, configured to obtain the number of user groups according to the validity path matrix;

[0096] a pairing solution calculation submodule, configured to obtain a drone user group pairing solution based on the total number of user groups and the number of drones;

[0097] The sequential trajectory calculation submodule is used to obtain the sequential trajectory of multiple drones according to the drone user group pairing scheme and the optimal path matrix, including

[0098] A single user group number calculation subunit is configured to set a plurality of the drone user group pairing schemes to obtain the number of user groups for each drone;

[0099] The single sequence trajectory calculation subunit obtains the corresponding drone access optimal sequence trajectory according to the number of user groups of each drone and the optimal path matrix, wherein the calculation formula of the corresponding drone access optimal sequence trajectory is: By iterating the step parameter set t and the step parameter set c, the optimal sequential trajectory OD of the k-th UAV is obtained. k , and record the iteration step parameter set t and step parameter set c, N is the element sum of the validity path matrix, k i,j is the risk weight of the kth drone from location node i to location node j, A i,j is the path validity from location node i to location node j; taking UAV delivery in mountainous areas as an example, the iterative step parameter set t includes multiple different t components and the step parameter set c includes multiple different c components, A i,j The size of the cloud is inversely proportional to the number of mountains passed during the flight.

[0100] The full sequence trajectory calculation subunit calculates the optimal sequence trajectory of all drones' access according to each plan to obtain the multi-drone access sequence trajectory, and selects the plan with the smallest multi-drone access sequence trajectory among all plans as the final multi-drone access sequence trajectory.

[0101] The present invention provides a method and system for designing pickup and delivery trajectories for a multi-networked drone logistics system. By obtaining a radio map, drone speed, number of drones, and the location nodes visited by drones, and comprehensively considering factors such as distance, time, and power consumption, the method first calculates the optimal path between the location nodes visited by multiple drones; then, a drone user group pairing scheme is used to calculate the sequential trajectories of the multiple drone visits.

[0102] Finally, the present invention optimizes the flight trajectory design of multiple drones and improves the efficiency of drone logistics under limited drone endurance.

[0103] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0104] Although the present application is described herein with reference to various embodiments, those skilled in the art will be able to understand and implement other variations of the disclosed embodiments in practicing the claimed application by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality.

[0105] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for designing delivery trajectories for a multi-networked drone logistics system, characterized in that: Obtain radio maps, drone speed, number of drones, and the location nodes visited by drones; Obtaining an optimal path matrix and a validity path matrix according to the radio map, the speed of the drone, the number of drones, and the visited location nodes of the drones; Obtaining a multi-UAV access sequence trajectory according to the number of UAVs, the optimal path matrix, and the validity path matrix; Among them, the access location nodes of the drone include: a drone hangar location node, a pickup location node and a delivery location node.

2. The method for designing delivery trajectories of a multi-networked drone logistics system according to claim 1, characterized in that: The obtaining of the optimal path matrix and the validity path matrix according to the radio map, the speed of the drone, the number of drones and the visited location nodes of the drones comprises: Obtaining the UAV outage time according to the radio map; Obtaining energy consumption of the drone according to the speed of the drone; Obtaining the moving distance of the drone according to the visited location node of the drone; Obtaining an optimal path between any two access location nodes according to the drone interruption time, the drone energy consumption and the drone movement distance; Obtaining an optimal path matrix according to the optimal path between any two access location nodes; Obtaining a validity path matrix according to any two visited location nodes; Among them, the optimal path between any two access location nodes includes: the optimal path starting from the drone hangar location node and ending at the pickup location node, the optimal path starting from the pickup location node and ending at the delivery location node, the optimal path starting from the delivery location node and ending at the pickup location node, the optimal path starting from the pickup location node and ending at the pickup location node, and the optimal path starting from the delivery location node and ending at the delivery location node; The row coordinates of each element in the optimal path matrix are the starting point between any two access location nodes, the column coordinates are the end point between any two access location nodes, and the value of each element is the optimal path value from the starting point to the end point between any two access location nodes. The row coordinates of each element in the validity path matrix are the starting point between any two access position nodes, the column coordinates are the end point between any two access position nodes, and the value of each element is the validity parameter between any two access position nodes.

3. The method for designing delivery trajectories of a multi-networked drone logistics system according to claim 2, characterized in that: The optimal path calculation formula between any two access location nodes obtained according to the UAV interruption time, the UAV energy consumption and the UAV moving distance is: in, is the optimal path of the kth UAV from the i-position node to the j-position node, w1 is the weight of the UAV energy consumption, w2 is the weight of the UAV moving distance, and w3 is the weight of the UAV communication delay, where w1, w2 and w3 are configured differently according to the environment where the UAV is located.

4. The method for designing delivery trajectories of a multi-networked drone logistics system according to claim 3, characterized in that: The obtaining of the multi-UAV access sequence trajectory according to the number of UAVs, the optimal path matrix and the validity path matrix includes: Obtaining the number of user groups according to the effectiveness path matrix; Obtaining a drone user group pairing solution according to the total number of user groups and the number of drones; The multi-UAV access sequential trajectory is obtained according to the UAV user group pairing scheme and the optimal path matrix.

5. The method for designing delivery trajectories of a multi-networked drone logistics system according to claim 4, characterized in that: The obtaining of the multi-UAV access sequence trajectory according to the UAV user group pairing scheme and the optimal path matrix includes: Obtaining the number of user groups for each drone according to the drone user group pairing scheme; The corresponding optimal sequence trajectory of drone access is obtained according to the number of user groups of each drone and the optimal path matrix, wherein the calculation formula of the corresponding optimal sequence trajectory of drone access is: By iterating the step parameter set t and the step parameter set c, the optimal sequential trajectory OD of the kth UAV is obtained. k , and record the iteration step parameter set t and step parameter set c, N is the element sum of the validity path matrix, k i,j is the risk weight of the kth drone from location node i to location node j, A i,j is the path validity from the i-position node to the j-position node; The multi-UAV access sequence trajectory is obtained according to the optimal sequential trajectories of all the UAV accesses, wherein, when the UAV user group pairing scheme includes multiple schemes, a scheme with the smallest multi-UAV access sequence trajectory among all the schemes is selected as the final multi-UAV access sequence trajectory.

6. A delivery trajectory design system for a multi-network UAV logistics system, characterized by: include: A data acquisition module, used to obtain radio maps, drone speeds, drone numbers, and visited location nodes of drones; An optimal path calculation module, used to obtain an optimal path matrix and a validity path matrix according to the radio map and the access location nodes of the UAV; A sequential trajectory calculation module, used for obtaining a multi-UAV access sequential trajectory according to the number of UAVs, the optimal path matrix and the validity path matrix; Among them, the access location nodes of the drone include: a drone hangar location node, a pickup location node and a delivery location node.

7. A delivery trajectory design system for a multi-networked drone logistics system according to claim 6, characterized in that: The optimal path calculation module includes: An interruption time calculation submodule, used to obtain the interruption time of the UAV according to the radio map; The energy consumption calculation submodule is used to obtain the energy consumption of the drone according to the speed of the drone; The moving distance calculation submodule is used to obtain the moving distance of the drone according to the visited location nodes of the drone; An optimal path calculation submodule, used to obtain an optimal path between any two access location nodes according to the UAV interruption time, the UAV energy consumption and the UAV movement distance; An optimal path matrix calculation submodule, used to obtain an optimal path matrix according to the optimal path between any two access location nodes; A validity path matrix calculation submodule, used for obtaining a validity path matrix between any two access location nodes; Among them, the optimal path between any two access location nodes includes: the optimal path starting from the drone hangar location node and ending at the pickup location node, the optimal path starting from the pickup location node and ending at the delivery location node, the optimal path starting from the delivery location node and ending at the pickup location node, the optimal path starting from the pickup location node and ending at the pickup location node, and the optimal path starting from the delivery location node and ending at the delivery location node; The row coordinates of each element in the optimal path matrix are the starting point between any two access location nodes, the column coordinates are the end point between any two access location nodes, and the value of each element is the optimal path value from the starting point to the end point between any two access location nodes. The row coordinates of each element in the validity path matrix are the starting point between any two access position nodes, the column coordinates are the end point between any two access position nodes, and the value of each element is the validity parameter between any two access position nodes.

8. The delivery trajectory design system for a multi-networked drone logistics system according to claim 7, characterized in that: The calculation formula of the optimal path calculation submodule is: in, is the optimal path of the kth UAV from the i-position node to the j-position node, w1 is the weight of the UAV energy consumption, w2 is the weight of the UAV moving distance, and w3 is the weight of the UAV communication delay, where w1, w2 and w3 are configured differently according to the environment where the UAV is located.

9. A delivery trajectory design system for a multi-networked drone logistics system according to claim 8, characterized in that: The sequential trajectory calculation module comprises: A total user group number calculation submodule, used to obtain the number of user groups according to the validity path matrix; A pairing scheme calculation submodule, used to obtain a drone user group pairing scheme according to the total number of user groups and the number of drones; The sequential trajectory calculation submodule is used to obtain the multiple drone access sequential trajectories according to the drone user group pairing scheme and the optimal path matrix.

10. A delivery trajectory design system for a multi-networked drone logistics system according to claim 9, characterized in that: The sequential trajectory calculation submodule includes: A single user group number calculation subunit, used to obtain the number of user groups for each drone according to the drone user group pairing scheme; The single sequence trajectory calculation subunit obtains the corresponding optimal sequence trajectory of the drone access according to the number of user groups of each drone and the optimal path matrix, wherein the calculation formula of the corresponding optimal sequence trajectory of the drone access is: By iterating the step parameter set t and the step parameter set c, the optimal sequential trajectory OD of the kth UAV is obtained. k , and record the iteration step parameter set t and step parameter set c, N is the element sum of the validity path matrix, k i,j is the risk weight of the kth drone from location node i to location node j, A i,j is the path validity from the i-position node to the j-position node; The full sequence trajectory calculation subunit obtains the multi-UAV access sequence trajectory according to the optimal sequence trajectory of all the UAV accesses, wherein, when the UAV user group pairing scheme includes multiple schemes, a scheme with the smallest multi-UAV access sequence trajectory among all the schemes is selected as the final multi-UAV access sequence trajectory.

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