A light small unmanned aerial vehicle city logistics route structure design and optimization method

Abstract

The application discloses a kind of light small unmanned aerial vehicle city logistics route structure design and optimization method, comprising: design city logistics route concept;Based on city logistics route concept, build initial route configuration;Build the parameterized model of the connection mode between route structure and section;According to the pre-constructed feasible rule library, the change range of variable structure parameters in each initial route configuration parameterized model is filtered to form a feasible search space;Based on parameterized model, build route structure evaluation model;Based on the feasible search space, the route structure evaluation model is solved, the optimal variable structure parameter is determined, and the optimized route structure is obtained.The application solves the connection problem between approach, departure route and air route, constructs the parameterized representation of route, constructs a general route structure evaluation and optimization method, realizes unmanned aerial vehicle high-density approach safety, reduces total delay time and total delay cost.

Description

Technical Field

[0001] This invention belongs to the field of UAV route structure design and optimization technology, specifically relating to a method for designing and optimizing urban logistics route structures for lightweight UAVs. Background Technology

[0002] Lightweight multi-rotor logistics drones taking off and landing near tall buildings and residential areas are not only limited by noise and public privacy, but are also easily affected by complex wind fields between buildings and unstable communication and navigation signals. Under these circumstances, dense takeoffs and landings undoubtedly pose significant safety risks. To reduce the risks to cities caused by the dense takeoffs and landings of drones performing logistics transportation tasks, drone logistics delivery service operators often designate fixed flight paths in low-altitude airspace, controlling drones to fly along predetermined routes to reduce the risk of collisions. Flight path design and optimization are key technologies for supporting the safe and efficient operation of drones.

[0003] In practice, different flight path designs directly affect the flight efficiency of UAVs. Designing flight path structures with the goals of safety, efficiency, and low energy consumption requires consideration not only of the individual components of the flight path but also of efficient connections between flight segments. Existing flight path designs focus more on macro-level network planning, approach route design, and the design of intersections between air routes, neglecting not only departure routes but also the connections between approach / departure routes and air routes. Summary of the Invention

[0004] Objective: In view of at least one of the above technical problems, this application provides a method for designing and optimizing the urban logistics route structure of lightweight unmanned aerial vehicles (UAVs).

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] In a first aspect, the present invention provides a method for designing and optimizing urban logistics route structures for lightweight unmanned aerial vehicles (UAVs), comprising:

[0007] Step (1): Design the concept of urban logistics routes based on the functional differences of each segment of the route; wherein the urban logistics concept is a round-trip route structure, and the round-trip route includes the outbound route and the return route.

[0008] Step (2): Based on the concept of urban logistics routes, and according to the design principles of traffic flow direction, cruising altitude, and route connection, construct initial route configurations for four different round-trip route structures;

[0009] Step (3): Based on the existence of each flight segment, the shape of the flight segment, the altitude of the highest point of the flight segment, and the number of UAVs that the flight segment can accommodate, construct a parametric model representing the flight route structure and the connection method between flight segments, and determine the variable structural parameters in the parametric model;

[0010] Step (4): Based on the pre-built feasible rule base, determine the range of change of variable structural parameters in the parameterized model of each initial route configuration to form a feasible search space;

[0011] Step (5): Based on the parameterized model, with minimizing the average delay as the objective function and the conflict-free operation of the UAV in the flight segment as the constraint, a flight path structure evaluation model is constructed.

[0012] Step (6): Based on the feasible search space, use the route structure evaluation model to evaluate and optimize, determine the optimal variable structure parameters, and obtain the optimized UAV urban logistics route structure.

[0013] Secondly, this application provides a device for designing and optimizing the urban logistics route structure of a lightweight unmanned aerial vehicle (UAV), including a processor and a storage medium;

[0014] The storage medium is used to store instructions;

[0015] The processor is configured to operate according to the instructions to execute the method according to the first aspect.

[0016] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0017] Fourthly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0018] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0019] Beneficial effects: The present invention provides a method for designing and optimizing urban logistics routes for lightweight drones, which solves the connection problem between arrival, departure and air routes, constructs a parameterized representation of routes, and builds a general method for evaluating and optimizing route structures, thereby achieving safe high-density drone arrival and reducing total delay time and total delay cost. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the design and optimization process of a lightweight unmanned aerial vehicle (UAV) urban logistics route according to an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the conceptual design of an urban logistics route according to an embodiment of the present invention;

[0022] Figure 3 These are schematic diagrams of four initial flight path configurations according to embodiments of the present invention;

[0023] Figure 4 This is a schematic diagram comparing the average delay of four initial flight path configurations according to an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram comparing the average delay optimization results of different initial route configurations in an embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram comparing the average delay of the flight path configuration before and after optimization in an embodiment of the present invention;

[0026] Figure 7 This is a schematic diagram comparing the route structure before and after optimization in an embodiment of the present invention. Detailed Implementation

[0027] The present application will be further described below with reference to the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and should not be used to limit the scope of protection of the present application.

[0028] In the description of this application, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0029] In the description of this application, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0030] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0031] Example 1: This example provides a method for designing and optimizing urban logistics routes using lightweight unmanned aerial vehicles (UAVs), such as... Figure 1 As shown, it includes:

[0032] Step (1): Design the concept of urban logistics routes based on the functional differences of each segment of the route;

[0033] The concept of urban logistics is a round-trip route structure, which includes outbound routes and return routes. Figure 2 This is a schematic diagram illustrating the conceptual design of an urban logistics route according to an embodiment of the present invention. See also... Figure 2 The outbound flight segment r includes a vertical takeoff segment r. t Departure waiting segment r wd Departure segment r d Cruise segment r c Approach segment r a Approach waiting segment r wa Vertical landing segment r l The return route structure is the same, and the return route structure segment r' includes the vertical takeoff segment r. t '、Departure waiting segment r wd '、Departure segment r d '、Cruising segment r c '、Approach Segment r a '、Arrival waiting segment r wa 'Vertical landing segment r' l '.

[0034] See Figure 2 The segments in the aforementioned round-trip route structure should be directed line segments with a starting point and an ending point. The outbound route connection method includes: vertical takeoff segment r t For the takeoff and landing point p t To the takeoff and landing point to be on time p lt Departure waiting segment r wd For from p lt To the departure waiting point p wd Departure segment r d For from p lt or p wd to departure point p d Cruise segment r c For from p d To the entrance point p a Approach segment r a For from p a to the entrance waiting point p wa Or the landing point is aligned with the exact point p. ll Arrival waiting segment r wa For the entry waiting point p wa Arrive at the landing / takeoff point and be on time (p) ll Flight segment r l For from p ll to landing point p lThe return route connection includes a vertical takeoff segment. t 'For the takeoff and landing point p t 'To the takeoff and landing point to align with the schedule p lt ', Departure waiting segment r wd 'For from p lt 'To the departure waiting point p wd ', Departure segment r d 'For from p lt 'or p wd 'To departure point p d ', Cruise segment r c 'For from p d 'To the entrance point p a ', approach segment r a 'For from p a 'To the entrance waiting point p wa 'Or the landing / takeoff point is aligned with the exact date p' ll ', approach segment r wa 'For from p wa 'to p ll ', Vertical landing segment r l 'For from p ll 'To the landing point p l '.

[0035] In some embodiments, in the outbound flight path structure, the vertical takeoff segment r t Its function is to rapidly climb from the ground; departure holding segment r wd The function is to allow departing drones to wait before entering the departure segment; departure segment r d Its function is to ascend to altitude and enter the air route; the cruise segment r c Its function is to fly between takeoff and landing points; approach segment r a Its function is to descend to a lower altitude and fly above the landing / takeoff point; approach holding segment r wa Its function is to wait before the drone lands; vertical landing segment r l The function is to land the drone at the take-off and landing point; the functions are the same for each segment of the return route.

[0036] Step (2): Based on the concept of urban logistics routes, and according to the design principles of traffic flow direction, cruising altitude, and route connection, construct initial route configurations for four different round-trip route structures;

[0037] In this embodiment, the idea behind designing the initial flight path configuration is to combine the arrival and departure flight path configurations with the air flight path configurations, eliminating several impossible scenarios. The design method is as follows.

[0038] Design arrival and departure route configuration: Outbound departure segment r d and return arrival segment ra 'Same route, same altitude, r d and r a 'Different routes at the same altitude, r d and r a 'Different routes at the same cruising altitude, r d and r a Different routes require different cruising altitudes;

[0039] Designing the air route configuration: Outbound cruise segment r c and return cruise segment r c 'The round-trip route at the same cruising altitude overlaps, r c and r c 'The round-trip routes at the same cruising altitude do not overlap, r c and r c 'The round-trip routes do not overlap at different cruising altitudes;'

[0040] By combining the designed arrival and departure route configurations with the air route configurations and eliminating impossible scenarios (such as cruising altitude, combinations of different arrival and departure altitudes, etc.), four initial route configurations with different round-trip route structures are obtained: Configuration 1, Configuration 2, Configuration 3, and Configuration 4. (Refer to...) Figure 3 .

[0041] Step (3): Based on the existence of each flight segment, the shape of the flight segment, the altitude of the highest point of the flight segment, and the number of UAVs that the flight segment can accommodate, construct a parametric model representing the route structure and the connection method between flight segments, and determine the variable structure parameters in the parametric model; the parametric model includes the parametric model Route for the outbound route. d Parametric model of return route Route a ;

[0042] Define a parametric model to represent route structure and segment connections. The route structure is represented in matrix form. The parametric model for the outbound route is Route. d The expression is as follows:

[0043]

[0044] Among them, C d For the outbound route control matrix, S d For the outbound flight path state matrix, C in the control matrix t C wd C d C c C a C wa C l Representing flight segment r t r wd r d rc r a r wa r l Does S exist in the state matrix? t S wd S d S c S a S wa S l Representing flight segment r t r wd r d r c r a r wa r l Horizontal, slanted, and vertical shapes, H t H wd H d H c H a H wa H l Representing flight segment r t r wd r d r c r a r wa r l The highest point height, N t N wd N d N c N a N wa N l Representing flight segment r t r wd r d r c r a r wa r l The number of drones that can be accommodated for operation;

[0045] Parametric model of return route (Route) a The expression is as follows:

[0046]

[0047] Among them, C a For the return route control matrix, S a For the state matrix, C in the control matrix t '、C wd '、C d '、C c '、C a '、Cwa '、C l 'Represents flight segment r respectively t '、r wd '、r d '、r c '、r a '、r wa '、r l Does S exist in the state matrix? t '、S wd '、S d '、S c '、S a '、S wa '、S l 'Represents flight segment r respectively t '、r wd '、r d '、r c '、r a '、r wa '、r l 'Horizontal, slanted, and vertical shapes, H t '、H wd '、H d '、H c '、H a '、H wa '、H l 'Represents flight segment r respectively t '、r wd '、r d '、r c '、r a '、r wa '、r l The highest point height, N t '、N wd '、N d '、N c '、N a '、N wa '、N l 'Represents flight segment r respectively t '、r wd '、r d '、r c '、r a '、r wa '、r l The number of drones that can be operated.

[0048] In this embodiment, the idea behind setting variable structured parameters in the parameterized model is that only structured parameters that exist in the actual operating scenario can be changed. The design method is as follows.

[0049] In actual operation, flight segment r dr d '、r a r a There are three shapes: horizontal, inclined, and vertical. The parameter S corresponding to each flight segment... d S d '、S a S a 'As a variable structuring parameter;'

[0050] In actual operation, some flight routes at the operation site have segment r. wa Check if the corresponding parameter C exists for each waiting segment. wd C wd '、C wa C wa 'As a variable structuring parameter;'

[0051] Therefore, the variable structuring parameter is designed as S. d S' d S a S' a C wd C' wd C wa C' wa .

[0052] Step (4): Based on the pre-built feasible rule base, determine the range of change of variable structural parameters in the parameterized model of each initial route configuration to form a feasible search space;

[0053] Changing the variable structure parameter S in the parameterized model d S' d S a S' a C wd C' wd C wa C' wa Determine whether the modified parameterized model satisfies the pre-built feasible rule base; if it does, add the parameterized model to the feasible search space.

[0054] In this embodiment, the idea of ​​constructing a feasible rule base is to determine the conditions that feasible variable structure parameters should satisfy by combining the variable structure parameters. The design method is as follows.

[0055] Using the status and existence of flight segments above the take-off and landing points as the criteria, we will discuss the scenarios where flight segments are not feasible:

[0056] Infeasible scenario 1: Segment r d Horizontal and segment r a 'The tilt and absence of waiting segments lead to a variety of variable structural parameter combinations that result in multiple takeoff and landing points aligning with the schedule;'

[0057] Infeasible scenario two: Segment r d Vertical and segment r a 'Vertical and without waiting segments or other factors that cause overlapping of round-trip routes;'

[0058] The final feasible rule base can be obtained by combining the variable structure parameters.

[0059] A feasible rule base is constructed. Under the same cruising altitude and route structure configuration, the rules include shape constraints for the approach and departure segments. To ensure that the designed segment structure conforms to the route concept, the shape variation values ​​of the approach and departure segments should be restricted to ensure that the approach and departure segments in the route structure are connected at a single point, as follows:

[0060] S d =1→S' a =1

[0061] S' a =1→S d =1

[0062] S d =2→S' a ≠1

[0063] S' a =2→S d ≠1

[0064] Furthermore, while limiting the shape change values ​​of the approach and departure segments, the existence of waiting segments should also be considered, as follows:

[0065] C wd +C' wa =0→(S d =3→S' a =2)

[0066] C wd +C' wa =0→(S' a =3→S d =2)

[0067] C wd +C' wa ≥1→(S d =3→S' a ≠1)

[0068] C wd +C' wa ≥1→(S' a =3→S d ≠1)

[0069] A feasible rule base is constructed. Under different cruising altitudes and route structure configurations, the rules include shape constraints on the approach and departure segments. To ensure that the designed segment structure conforms to the route concept, the shape variation values ​​of the approach and departure segments should be limited to ensure that the approach and departure segments are connected at a single point in the route structure, as follows:

[0070] S' a =1→S d ≠1

[0071] S' a =2→S d ≠1

[0072] Furthermore, while limiting the shape change values ​​of the approach and departure segments, the existence of waiting segments should also be considered, as follows:

[0073] C wd +C' wa =0→S' a ≠3

[0074] C wd +C' wa ≥1→(S d =3→S' a ≠1)

[0075] Based on whether the outbound and return routes are at the same cruising altitude in the parametric representation of the initial route configuration, the corresponding feasible rule base is selected.

[0076] In this embodiment, four of the variable structural parameters are the shape of the flight segment, with a parameter variation range of 3; the other four are the existence of the flight segment, with a parameter variation range of 2. After permutations and combinations, the total search space should be 1296 configuration schemes. After filtering through the feasible rule base, there are 361 feasible configurations at the same cruising altitude and 484 feasible configurations at different cruising altitudes. The feasible configurations together constitute the feasible search space.

[0077] Step (5): Based on the parameterized model, with minimizing the average delay as the objective function and the conflict-free operation of the UAV in the flight segment as the constraint, a flight path structure evaluation model is constructed.

[0078] The objective function of the route structure evaluation model is to minimize the average delay, expressed as:

[0079]

[0080] Where U represents the set of drones carrying out the corresponding task; Delay iLet represent the delay of the i-th drone; n represent the number of missions; Delay1 represents the takeoff delay of the drone at the takeoff and landing points; and Delay2 represents the arrival delay of the drone at the landing and landing points. For the i-th drone from p t Departure time, ETD i Let be the estimated takeoff time of the i-th drone; This indicates that the i-th drone has arrived at the landing point p. l Time; This represents the flight time of the i-th drone from its takeoff / landing point to its landing / landing point without any delay.

[0081] The constraints of the route structure evaluation model include departure time constraints, same UAV operation constraints, landing and take-off point dwell time interval constraints, conflict-free take-off and landing constraints above the take-off and take-off points, conflict-free take-off and landing constraints above the landing and take-off points, different UAV operation constraints, and cruise segment capacity constraints.

[0082] Furthermore, a flight path structure evaluation model is constructed, with constraints including departure time constraints. The takeoff time of the UAV from the takeoff and landing points should be limited to ensure it is longer than the expected takeoff time, as follows:

[0083]

[0084] in, For the i-th drone from p t Departure time, ETD i Let U be the estimated takeoff time of the i-th drone, and U be the set of drones carrying the corresponding mission.

[0085] A flight path structure evaluation model is constructed, with constraints including the time interval between the departure of the same UAV from the starting point of adjacent flight segments on the outbound flight path. Constraints should be placed on the time when the UAV leaves adjacent flight segments to ensure that the UAV can leave each flight segment according to the planned time, as follows:

[0086]

[0087] in, Let t be the time when the i-th UAV departs from the starting point of flight segment r, and r+1 represent the next flight segment connected to flight segment r. r Let R be the time required for the drone to fly from the starting point to the destination of flight segment r, where R is the set of flight segments in the outbound route.

[0088] A flight path structure evaluation model is constructed, with constraints including the time interval between the departure of the same UAV from the starting point of adjacent flight segments on the return flight path. Constraints should be placed on the time when the UAV leaves adjacent flight segments to ensure that the UAV can leave each flight segment according to the planned time, as follows:

[0089]

[0090] in, Let t be the time when the i-th UAV departs from the starting point of flight segment r', and r'+1 represent the next flight segment connected to flight segment r'. r' R' is the time required for the drone to fly from the starting point to the destination of flight segment r', where R' is the set of flight segments in the return route.

[0091] A flight path structure evaluation model is constructed, with constraints including the time interval constraint for the drone's stay at the landing and take-off points. The time the drone spends at these points should be constrained to ensure that the drone can complete the order delivery process, as follows:

[0092]

[0093] Among them, t T The minimum time interval for a drone to stop at its landing or takeoff / landing point;

[0094] A flight path structure evaluation model is constructed, with constraints including conflict-free takeoff and landing constraints above the takeoff and landing points. Constraints should be placed on the timing of takeoff and landing UAVs above the takeoff and landing points to ensure that UAVs operate within the specified flight segment r. t No head-on conflict will occur, as follows:

[0095]

[0096] in, Let be the maximum value between the time when UAV i departs from the starting point of flight segment r and the time when UAV j departs from the starting point of flight segment r'. Let be the minimum of the time when UAV i leaves from the starting point of flight segment r and the time when UAV j leaves from the starting point of flight segment r'.

[0097] A flight path structure evaluation model is constructed, with constraints including conflict-free takeoff and landing constraints above the landing and takeoff points. Constraints should be placed on the timing of landing and takeoff UAVs above the landing and takeoff points to ensure that UAVs operate within flight segment r. l No head-on conflict will occur, as follows:

[0098]

[0099] Where M is a sufficiently large positive integer, λ i,j It is a 0-1 variable, which is 1 when the time when drone i leaves the take-off and landing point of the outbound route is less than or equal to that of drone j, and 0 otherwise;

[0100] A flight path structure evaluation model is constructed, with constraints including the time interval between different UAVs leaving the starting point of adjacent flight segments on the outbound flight path. Constraints should be placed on the transit times of different UAVs to ensure that UAVs do not conflict on the same flight segment at the same time, as follows:

[0101]

[0102] in, Let be the time when the j-th UAV departs from the starting point of flight segment r;

[0103] A flight path structure evaluation model is constructed, with constraints including the time interval between different UAVs leaving the starting point of adjacent segments on the return flight path. Constraints should be placed on the transit times of different UAVs to ensure that UAVs do not conflict on the same segment at the same time, as follows:

[0104]

[0105] in, Let be the time when the j-th UAV departs from the starting point of flight segment r';

[0106] A flight path structure evaluation model is constructed, with constraints including that the UAV should meet the capacity constraints of the cruise segment in the outbound flight path, as follows:

[0107]

[0108] in, Let be the number of UAVs present in flight segment r at time t;

[0109] A flight path structure evaluation model is constructed, with constraints including that the UAV should meet the capacity constraints of the cruise segment in the return flight path, as follows:

[0110]

[0111] in, Let be the number of UAVs present in flight segment r' at time t;

[0112] In this embodiment, the idea of ​​constructing the route structure evaluation model is to construct an integer programming model that represents the conflict-free operation of UAVs by constructing time constraints for UAVs passing through each waypoint and occupying flight segments. This model is then used for route structure evaluation, that is, the configuration with the minimum delay is optimal under the same transportation task.

[0113] In this embodiment, the GUROBI solver is used to solve the global exact solution of the evaluation model. The final value of the optimization objective in the evaluation model is not affected by the number of trials and the performance of the algorithm, but is only related to the flight path structure. The solution result of the evaluation model can be used as the evaluation index of the flight path structure.

[0114] Step (6): Based on the feasible search space, optimize using the route structure evaluation model to determine the optimal variable structure parameters and obtain the optimized UAV urban logistics route structure.

[0115] In step (6): the route structure evaluation model is input into the solver for optimization, all feasible search spaces are traversed, and the optimal variable structure parameters are determined from the feasible search space, so as to obtain the optimized route structure of each configuration.

[0116] In this embodiment, during the solution process using the GUROBI optimization solver, a heuristic branch and bound algorithm is used in the optimization solver to optimize the route structure evaluation model. The convergence condition for optimization is:

[0117] max|Delay g -Delay g-1 |≤E D

[0118] Among them, Delay g For the value of the optimization objective of the route structure evaluation model during the iterative solution process, Delay g-1 Let m be the value of the optimization objective of the route structure evaluation model in the previous iteration, and E be the value of E. D To solve for the tolerance;

[0119] After meeting the optimization requirements, return the parameterized representation of the optimized route structure and output the existence, shape, highest point altitude, and capacity of each segment.

[0120] To verify the effectiveness of the urban logistics route structure design and optimization method for lightweight UAVs provided in this embodiment of the invention, this invention example selects the Xinghe Central Business District in Longgang District, Shenzhen, Guangdong Province, and designs four initial route configurations. Since all segments of configuration one overlap, parametric representation cannot be used. The parametric representations of the outbound routes for configurations two to four are shown in Table 1. The parametric representation of the return route is referred to... Figure 3 And parameterized representation of content.

[0121] Table 1 Outbound routes with different configurations

[0122]

[0123] Table 1 shows the specific values ​​assigned to the variables in the parameterized representation of UAV flight routes. Variable C is a 0-1 variable, with 1 indicating the existence of the flight segment; variable S is an integer ranging from 0 to 3, where 1 indicates a horizontal flight segment, 2 indicates an inclined flight segment (the inclination angle is related to the connection requirements of the flight segment), and 3 indicates a vertical flight segment; variable H is an integer ranging from 0 to 2, where 1 indicates the highest point of the flight segment is below 70m, and 2 indicates the highest point of the flight segment is below 90m; variable N represents the maximum number of UAVs that a flight segment can accommodate simultaneously; when a parameterized variable is 0, it indicates that the parameter does not exist.

[0124] To demonstrate the effectiveness of the route structure configuration design method provided in this embodiment of the invention, this embodiment generates tasks using a uniform distribution within 30 minutes, generating a random task sequence with the number of tasks ranging from 10 to 100. Each random task sequence represents a task scenario. Under each task scenario, the route structure evaluation model is used to evaluate four initial route configurations, such as... Figure 4 As shown. Using average delay as the evaluation index, the design configurations are ranked from best to worst as follows: Configuration 4, Configuration 3, Configuration 2, and Configuration 1. Based on the average delay of Configuration 1, when the number of tasks is 10, Configurations 2, 3, and 4 show the largest percentage decrease compared to Configuration 1, at 88.9%, 89.2%, and 93.3%, respectively; when the number of tasks is 100, the absolute value of the decrease in average delay compared to Configuration 1 is the largest, at 12195.6s, 13274.6s, and 16301.0s, respectively.

[0125] To demonstrate the effectiveness of the route structure configuration optimization method provided in this embodiment of the invention, the average delay performance before and after route structure configuration optimization at the same cruising altitude and at different cruising altitudes is compared, such as... Figure 5 As shown. Figure 5 As shown in (a), after searching the feasible search space at the same cruise altitude, with the goal of minimizing average delay, the optimal configuration optimization scheme appears at position 198 in the feasible search space, configuration two appears at position 1, and configuration three appears at position 93. When the number of tasks is 60, the average delay of the optimized configurations two and three is reduced by -12.5% ​​compared to configuration two, and by -13.8% compared to configuration three. After searching the feasible search space at different cruise altitudes, with the goal of minimizing average delay, the optimal configuration optimization scheme appears at position 203 in the feasible search space, and configuration four appears at position 379, as shown in (a). Figure 5 As shown in (b), when the number of tasks is 60, the average delay of the optimized configuration 4 is reduced by -3.2% compared to configuration 4.

[0126] To demonstrate the optimization results of the route structure configuration optimization method provided in this embodiment of the invention under different numbers of missions, the average delay performance before and after route structure configuration optimization at the same and different cruise altitudes under different numbers of missions is compared, such as... Figure 6 As shown. Figure 6 As shown in (a), compared to configuration two, the maximum reduction in average delay after optimization is 15.6%; compared to configuration three, the maximum reduction in average delay after optimization is 26.3%. Optimization of configurations two and three shows significant results under different numbers of tasks, with the optimization effect becoming more pronounced as the number of tasks increases. Figure 6 As shown in (b), since configuration 4 is already in a better position in the feasible search space before optimization, the optimization performance for average delay is not significant, but the average delay performance is improved only slightly.

[0127] The optimization results for the initial flight path configuration in this invention example are as follows: Figure 7 As shown. Figure 7 As shown in (a), the optimized configuration adds three waiting segments, especially segment r in the outbound route. wd and flight segment r wa This allows drones to queue in the air as much as possible, reducing their waiting time. For the return flight segment r... d 'and flight segment r a The tilted shape, because drones operate at their slowest speeds during the tilted flight segment, helps maintain efficiency while also ensuring a safe distance between drones. For example... Figure 7 As shown in (b), the optimization results of configuration four are almost identical to those of configurations two and three.

[0128] In summary, this invention proposes a method for designing and optimizing urban logistics flight routes for lightweight unmanned aerial vehicles (UAVs). It presents a more complete definition of urban logistics UAV flight routes, constructs a parameterized representation of the definition, and identifies four round-trip flight route configurations. A method for evaluating and optimizing flight route structures is also developed. This method aims to minimize average delay and evaluates the performance parameters of various configurations under different mission numbers. Combined with a parameterized flight route model, variable structural parameters are adjusted to obtain the optimal flight route configuration. Based on these methods, and through case studies, it is shown that a reasonable flight route configuration design can significantly reduce the variation in average delay. Currently, the widely used configurations in practice are only suitable for small-batch transportation. As the transportation volume increases, delays increase linearly, necessitating more complex configurations to significantly reduce delay levels. Furthermore, this study also demonstrates that the various approach flight route designs proposed in previous studies can be optimized using a more complete flight route structure.

[0129] Example 2: Based on Example 1, this example provides a device for designing and optimizing the urban logistics route structure of a lightweight unmanned aerial vehicle (UAV), including a processor and a storage medium;

[0130] The storage medium is used to store instructions;

[0131] The processor is configured to operate according to the instructions to execute the method according to Embodiment 1.

[0132] Example 3: Based on Example 1, this example provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Example 1.

[0133] Example 4: Based on Example 1, this example provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in Example 1.

[0134] Example 5: Based on Example 1, this example provides a computer program product, including a computer program that, when executed by a processor, implements the method described in Example 1.

[0135] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0139] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A light small unmanned aerial vehicle urban logistics route structure design and optimization method, characterized in that, The method comprises the following steps: Step (1): designing a city logistics route concept according to the function difference of each route section in the route; The city logistics route concept is a round-trip route structure, and the round-trip route comprises a departure route and a return route; Step (2): based on the city logistics route concept, constructing initial route configurations of four different round-trip route structures according to the design principles of route traffic flow direction, cruising altitude and route section connection; Step (3): constructing a parameterized model representing the route structure and the connection mode between route sections according to the existence of each route section, the shape of the route section, the highest point height of the route section and the number of unmanned aerial vehicles that can be accommodated in the route section, and determining the variable structure parameters in the parameterized model, including: Step (301): The parameterized model includes a parameterized model Route d of the outbound flight and a parameterized model Route a of the return flight. wherein C d is a control matrix of the outbound route, S d is a state matrix of the outbound route, C t , C wd , C d , C c , C a , C wa , C l respectively represent whether there is a flight segment r t , r wd , r d , r c , r a , r wa , r l , S t , S wd , S d , S c , S a , S wa , S l respectively represent the horizontal, inclined, vertical shape of the flight segment r t , r wd , r d , r c , r a , r wa , r l , H t , H wd , H d , H c , H a , H wa , H l respectively represent the height of the highest point of the flight segment r t , r wd , r d , r c , r a , r wa , r l , N t , N wd , N d , N c , N a , N wa , N l respectively represent the number of unmanned aerial vehicles that can be accommodated by the flight segment r t , r wd , r d , r c , r a , r wa , r l ; wherein C a is a return route control matrix, S a is a state matrix, C t ', C wd ', C d ', C c ', C a ', C wa ', C l ' respectively indicate whether there is a leg r t ', r wd ', r d ', r c ', r a ', r wa ', r l ' in the return route, S t ', S wd ', S d ', S c ', S a ', S wa ', S l ' respectively indicate the horizontal, inclined, vertical shape of the leg r t ', r wd ', r d ', r c ', r a ', r wa ', r l ', H t ', H wd ', H d ', H c ', H a ', H wa ', H l ' respectively indicate the height of the highest point of the leg r t ', r wd ', r d ', r c ', r a ', r wa ', r l ', N t ', N wd ', N d ', N c ', N a ', N wa ', N l ' respectively indicate the height of the highest point of the leg r t ', r wd ', r d ', r c ', r a ', r wa ', r l the number of drones that can be accommodated to operate; Step (302): Referring to the actual operation scene, the outbound departure flight segment r d , the inbound departure flight segment r d , the outbound approach flight segment r a , the inbound approach flight segment r a There are three shapes: horizontal, inclined, and vertical. Some operation scenes have approach waiting flight segments r wa The variable structured parameter is designed as S d , S d , S a , S a , C wd , C wd , C wa , C wa ; Step (4): determining the change range of the variable structure parameters in the parameterized model of each initial route configuration according to the pre-constructed feasible rule library, and forming a feasible search space; Step (5): based on the parameterized model, taking the minimization of average delay as the objective function and taking the conflict-free operation of unmanned aerial vehicles in the route section as the constraint condition, constructing a route structure evaluation model; the objective function of the route structure evaluation model is: wherein U is a set of UAVs carrying corresponding tasks; Delay i represents the delay of the ith UAV; n represents the number of tasks; Delay1 represents the take-off delay of the UAV at the take-off and landing point; Delay2 represents the arrival delay of the UAV at the landing and take-off point, is the time for the ith UAV to take off from the take-off and landing point p t ; ETD i is the estimated take-off time of the ith UAV; represents the time for the ith UAV to arrive at the landing and take-off point p l ; and represents the flight time of the ith UAV from the take-off and landing point to the landing and take-off point without any delay. Step (6): based on the feasible search space, evaluating and optimizing by using the route structure evaluation model to determine the optimal variable structure parameters and obtain the optimized city logistics route structure of unmanned aerial vehicles. 2.The method of claim 1, wherein, The step (1) designs a city logistics route concept according to the function difference of each route section in the route, which comprises: Step (101): Function analysis of the round trip route, design of each leg in the city logistics route structure, the outbound route structure leg r includes vertical take-off leg r t , departure waiting leg r wd , departure leg r d , cruise leg r c , approach leg r a , approach waiting leg r wa , vertical landing leg r l , the return route structure leg r' includes vertical take-off leg r t ', departure waiting leg r wd ', departure leg r d ', cruise leg r c ', approach leg r a ', approach waiting leg r wa ', vertical landing leg r l '; Step (102): designing the connection mode of each route section, the route section in the route structure is a directed line segment, which has a starting point and an ending point; The outbound route connection comprises a vertical take-off segment r t To align the point p t To align the point p lt , a departure waiting segment r wd From p lt To a departure waiting point p wd , a departure segment r d From p lt Or p wd To a departure point p d , a cruising segment r c From p d To an approach point p a , an approach segment r a From p a To an approach waiting point p wa Or a landing take-off alignment point p ll , an approach segment r wa From p wa To p ll , a vertical landing segment r l From p ll To a landing take-off point p l ; the return route connection comprises a vertical take-off segment r t ' From a take-off take-off point p t ' To a take-off take-off alignment point p lt ', a departure waiting segment r wd ' From p lt ' To a departure waiting point p wd ', a departure segment r d ' From p lt ' Or p wd ' To a departure point p d ', a cruising segment r c ' From p d ' To an approach point p a ', an approach segment r a ' From p a ' To an approach waiting point p wa ' Or a landing take-off alignment point p ll ', an approach segment r wa ' From p wa ' To p ll ', a vertical landing segment r l ' From p ll ' To a landing take-off point p l '. 3.The method of claim 2, wherein, In the step (2), the initial route configurations of four different round-trip route structures are constructed, which comprises: Step (201): Design four kinds of departure and arrival route configurations: r d and arrival route r a ' same route and same altitude, r d and r a ' same route and different altitudes, r d and r a ' different routes and same cruise altitude, r d and r a ' different routes and different cruise altitudes; Step (202): Design three air route configurations: Outbound cruise leg r c and Inbound cruise leg r c with cruise altitude coinciding, r c and r c with cruise altitude not coinciding, r c and r c with cruise altitude not coinciding; Step (203): combining the arrival and departure route configurations and the air route configurations designed in each other to remove impossible cases, and obtaining four initial route configurations of different round-trip route structures, namely configuration one, configuration two, configuration three and configuration four. 4.The method of claim 1, wherein, In step (4), the feasible rule library comprises a feasible rule library under the same cruising altitude route structure configuration and a feasible rule library under the different cruising altitude route structure configuration; The feasible rule library under the same cruising altitude route structure configuration is represented as: S d = 1 → S' a = 1 S' a = 1 → S d = 1 S d = 2 → S' a ≠ 1 S' a = 2 → S d ≠ 1 C wd +C' wa = 0 → (S d = 3 → S' a = 2) C wd +C' wa = 0 → (S a = 3 → S d = 2) C wd +C' wa ≥1→(S d =3→S' a ≠1) C wd +C' wa ≥1→(S' a =3→S d ≠1) Wherein "→" represents that the left proposition can deduce the right proposition; The feasible rule library under the different cruising altitude route structure configuration is represented as: S' a = 1 → S d ≠ 1 S' a = 2 → S d ≠ 1 C wd +C' wa =0→S' a ≠3 C wd +C' wa ≥1→(S d =3→S' a ≠1). 5.The method of claim 1, wherein, In step (4), the feasible rule library comprises a feasible rule library under the same cruising altitude route structure configuration and a feasible rule library under the different cruising altitude route structure configuration, which are selected according to whether the departure route and the return route in the parameterized representation of the initial route configuration are at the same cruising altitude, so as to form a feasible search space of all changed parameterized models meeting the feasible rule library. 6.The method of claim 1, wherein, The constraint conditions of the route structure evaluation model comprise: The departure time constraint limits the takeoff time of the unmanned aerial vehicle from the takeoff and landing point to ensure that it is greater than the expected takeoff time, as follows: wherein, is the take-off and landing point p for the i-th UAV t is the time of take-off, ETD i is the estimated take-off time for the i-th UAV, U is the set of UAVs carrying corresponding tasks; The time interval constraint for a same unmanned aerial vehicle leaving the starting point of adjacent route sections in the departure route is to constrain the time when the unmanned aerial vehicle leaves the adjacent route sections to ensure that the unmanned aerial vehicle can leave each route section according to the planned time, as follows: wherein, ti is the time for the ith UAV to leave the start of the leg r, r+1 denotes the next leg connected to the leg r, t r tr is the time for the UAV to fly from the start to the end of the leg r, R is the set of legs in the outbound route; The time interval constraint of the same UAV leaving the starting point of the adjacent section in the return route should constrain the time of the UAV leaving the adjacent section to ensure that the UAV can leave each section according to the planned time, as follows: wherein, ti is the time for the ith UAV to leave the start of the leg r', r'+1 denotes the next leg connected to the leg r', t r' tr' is the time required for the UAV to fly from the start to the end of the leg r', R' is the set of legs in the return trip; The time interval constraint of the UAV staying at the landing and taking-off point should constrain the time of the UAV staying at the landing and taking-off point to ensure that the UAV can complete the order delivery process, as follows: Wherein, t T is the minimum time interval for the UAV to stay and land at the landing point. The time of take-off and landing of the unmanned aerial vehicles over the take-off and landing point is constrained to ensure that the unmanned aerial vehicles are in the flight section r t Head-on conflicts do not occur, as follows: wherein, is the time at which the drone i departs from the start of the leg r t is the maximum of the time at which the drone j departs from the start of the leg r l ' The landing and taking-off point is not restricted by the conflict of taking-off, and the time of the landing UAV and the taking-off UAV in the air above the landing and taking-off point is restricted to ensure that the UAV is in the flight section r l The head-on conflict does not occur, as follows: where M is a sufficiently large positive integer, λ i,j is a 0-1 variable, equal to 1 if the time at which the UAV i leaves the take-off and landing point of the outbound route is less than or equal to the time of the UAV j, and 0 otherwise; The time interval constraint of different UAVs leaving the starting point of the adjacent section in the outbound route should constrain the time of different UAVs passing through the point to ensure that the UAVs will not conflict in the same section at the same time, as follows: wherein, tj is the time for the jth drone to leave the start of the leg r; The time interval constraint of different UAVs leaving the starting point of the adjacent section in the return route should constrain the time of different UAVs passing through the point to ensure that the UAVs will not conflict in the same section at the same time, as follows: wherein, tj is the time for the jth drone to leave the start of the leg r'; The UAV should meet the capacity constraint of the cruise section in the outbound route: wherein, r(t) is the cruising leg r in the outbound route at time t c the number of drones present in the region; The UAV should meet the capacity constraint of the cruise section in the return route, as follows: wherein, r(t) = cruise leg r in the return leg at time t c the number of drones present in the area.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the method of any one of claims 1 to 6.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the method of any one of claims 1 to 6.

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