A truck-multi-unmanned aerial vehicle contactless delivery collaborative scheduling method, device, medium and product

Abstract

The application discloses a truck-multiple unmanned aerial vehicle non-contact delivery collaborative scheduling method, equipment, medium and product, relates to the field of logistics distribution, and the method comprises the following steps: acquiring delivery order data, ground traffic data, airspace control data and containment event data, and time-varying restriction regions, containment event regions, customer side parameters, carrier parameters and cost parameters in each period; determining the traffic feasibility of ground arcs and air arcs in the corresponding period based on the time-varying restriction regions in each period, and determining whether the unmanned aerial vehicle flight is feasible; determining the time correction coefficient and the energy consumption correction coefficient of the take-off section and the recovery section respectively, and correcting the nominal flight time and the nominal energy consumption; constructing a collaborative optimization model and solving it to obtain a collaborative scheduling scheme; the collaborative scheduling scheme is executed by the vehicle-mounted terminal and the airborne terminal, and the execution feedback data is received to update the collaborative scheduling scheme. The application improves the efficiency and execution accuracy of collaborative distribution in the low-altitude economic scenario.

Description

Technical Field

[0001] This application relates to the field of logistics and distribution, and in particular to a method, equipment, medium, and product for contactless delivery coordination between trucks and multiple drones. Background Technology

[0002] With the development of e-commerce, instant retail, and emergency logistics, last-mile delivery has placed higher demands on timeliness, flexibility, and safety. Against the backdrop of the rapid development of the low-altitude economy, truck-drone collaborative delivery, as a new delivery model integrating ground transportation and low-altitude flight capabilities, has become an important development direction for smart logistics due to its ability to balance trunk transportation efficiency with last-mile delivery flexibility, especially in contactless delivery scenarios.

[0003] Common delivery methods include truck delivery, truck-single-drone collaborative delivery, truck-multiple-drone collaborative delivery, and dynamic path planning methods that consider no-fly zones, road blockages, or time window constraints. While these methods improve delivery efficiency to some extent, they still have shortcomings. First, existing methods are mostly based on static road networks or static airspace modeling, lacking a unified characterization of time-varying restricted areas, temporary lockdown events, and time-segmented accessibility, making it difficult to accurately reflect the true feasibility of ground paths and air sorties in dynamic environments. Second, existing methods typically treat drone flight time and energy consumption parameters as fixed values, or use only a single coefficient for overall correction, failing to distinguish the differences between takeoff and recovery phases in terms of congestion, restricted boundaries, and flight conditions, thus easily leading to inaccurate estimates of mission duration and energy consumption. Third, existing methods provide a relatively coarse characterization of the collaborative relationship between trucks and drones, lacking fine constraints on the synchronization relationship between truck parking slots, drone takeoff times, service times, and recovery times, easily causing mission time conflicts or execution mismatches. Furthermore, existing dynamic optimization methods do not adequately utilize feedback information from the execution phase, typically failing to comprehensively consider factors such as time deviations, energy consumption deviations, and communication anomalies. They also lack corresponding trigger-based replanning mechanisms, resulting in untimely responses to operational disturbances. In addition, existing technologies do not adequately consider risk control in contactless delivery scenarios, failing to incorporate customer risk levels, service mode priorities, and fallback strategies in infeasibility scenarios into a unified optimization framework, making it difficult to balance delivery efficiency, service safety, and solution feasibility.

[0004] Therefore, there is an urgent need for a truck-multi-drone contactless delivery collaborative scheduling method that can simultaneously consider time-varying regional restrictions, airspace congestion impacts, vehicle-machine synchronization constraints, and execution feedback closed-loop updates, in order to improve the efficiency and execution accuracy of collaborative delivery in low-altitude economic scenarios. Summary of the Invention

[0005] The purpose of this application is to provide a truck-multi-drone contactless delivery collaborative scheduling method, equipment, medium and product to improve the efficiency and execution accuracy of collaborative delivery in low-altitude economic scenarios.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a truck-multi-drone contactless delivery collaborative scheduling method, including: Acquire delivery order data, ground traffic data, airspace control data, and lockdown event data, as well as time-varying restricted areas for each time period, mapping areas corresponding to lockdown events, customer-side parameters, vehicle parameters, and cost parameters; determine the delivery center departure node, delivery center return node, customer node set, candidate truck docking node set, truck set, drone set, and discrete time period set; the time-varying restricted areas include ground time-varying restricted areas and air time-varying restricted areas; the customer-side parameters include customer demand, service window, and risk parameters; the vehicle parameters include truck load capacity parameters and drone capacity parameters; the cost parameters include ground arc cost parameters, drone sortie cost parameters, nominal flight time, and nominal energy consumption; Based on the time-varying ground and air time-varying restricted areas for each time period, the feasibility of ground arcs and air arcs for passage in the corresponding time period is determined, and ground accessibility markers and air accessibility markers are generated; and the feasibility of drone sorties is determined based on the air accessibility markers; the drone sortie is a task unit that takes off from the docking point, serves the customer, and returns to the docking point; Using a congestion agent estimator, the time correction coefficient and energy consumption correction coefficient for the takeoff and recovery phases are output, and the nominal flight time and nominal energy consumption are corrected in segments to obtain the corrected mission duration and corrected mission energy consumption. Based on delivery order data, ground traffic data, airspace control data, lockdown event data, time-varying restricted areas for each time period, mapping areas corresponding to lockdown events, customer-side parameters, vehicle parameters, cost parameters, delivery center departure nodes, delivery center return nodes, customer node set, candidate truck docking node set, truck set, drone set, discrete time period set, traffic feasibility, traffic accessibility, and modified task duration and energy consumption, a collaborative optimization model is constructed. The collaborative optimization model includes an objective function and constraints aimed at minimizing total cost. Total cost includes truck travel cost, drone operation cost, energy consumption cost, time window default penalty cost, and risk penalty cost. Constraints include: service mode constraints, drone affiliation constraints, accessibility and feasibility constraints, truck route and load constraints, drone capability constraints, truck docking time slot constraints, truck time recursion constraints, time period boundary constraints, takeoff and recovery point access constraints, takeoff and recovery window constraints, drone flight and service time constraints, customer service completion time constraints, time window default relaxation constraints, drone resource mutual exclusion constraints, and sub-loop elimination constraints. Solving the aforementioned collaborative optimization model yields a collaborative scheduling scheme; The collaborative scheduling scheme is sent to the vehicle-mounted and airborne terminals for execution, and execution feedback data is received. When the execution deviation exceeds the preset threshold, communication is abnormal, the next scheduling cycle is entered, or the restriction information is updated, the collaborative scheduling scheme is updated based on the execution feedback data of the previous moment. The scheme is updated by "determining the passability of the ground arc and the air arc in the corresponding time period based on the ground time-varying restriction area and the air time-varying restriction area in each time period, generating ground accessibility identifiers and air accessibility identifiers, and determining whether the UAV sorties are feasible based on the air accessibility identifiers".

[0007] In one implementation, ground accessibility is indicated as: ; in, The ground arc at time t within the time-varying constraint region of the ground Accessibility; For ground arc sets; For ground arc The geometric trajectory; This is a time-varying restricted area on the ground. This refers to the mapped area corresponding to the ground lockdown event.

[0008] In one implementation, air reachability is identified as: ; in, The aerial arc at time t within the time-varying constraint region in the air Accessibility; For an aerial arc set; For the arc in the air The geometric trajectory; This is a time-varying restricted area in the air; This is the mapping area corresponding to the air traffic control event.

[0009] In one implementation, determining the feasibility of a drone sortie based on air reachability indicators specifically includes: Using formula Determine the feasibility of drone sorties; among them, regarding drone sorties... Air Arc Index Take respectively and ; To assess the feasibility of drone sorties; The aerial arc at time t within the time-varying constraint region in the air Accessibility; The aerial arc at time t within the time-varying constraint region in the air Accessibility; When the aerial arc is within the time-varying restriction region at time t Accessibility and time-varying airborne arc at time t within the airborne time-limited region When any of the reachability parameters is 0, the feasibility of drone sorties is 0.

[0010] In one embodiment, the nominal flight time and nominal energy consumption are adjusted in segments to obtain the adjusted mission duration and adjusted mission energy consumption, specifically including: Using formula The nominal flight time and nominal energy consumption are corrected in segments to obtain the corrected mission duration and corrected mission energy consumption. in, To adjust the task duration; This is a correction factor for the nominal flight time during the takeoff phase; This is a correction factor for the nominal flight time of the recovery segment; The nominal flight time for the takeoff segment; The nominal flight time for the recovery segment; The service duration for customer n; To correct the task's energy consumption; This is a correction factor for the nominal energy consumption during takeoff. This is a correction factor for the nominal energy consumption of the recovery section; This refers to the nominal energy consumption during takeoff. This represents the nominal energy consumption of the recovery section.

[0011] In one embodiment, the total cost is: ; ; ; ; ; ; in, Total cost; For truck operating costs; K represents the weight of truck operating costs; K is the set of trucks; T is the set of discrete time periods; For ground arc sets; For ground arc cost parameters; For truck arc variables; Cost of drone operations; D represents the weight of drone operation costs; H represents the set of drones; N represents the set of candidate truck docking nodes; and N represents the set of customer nodes. This refers to the cost parameters for each drone sortie; For the number of drone sorties; Energy consumption cost of drones; Weighting of drone energy consumption costs; To correct the task's energy consumption; The cost of penalties for breach of contract during the time window; Weighting of penalty costs for breach of contract within the time window; The weighting parameter for the penalty for service failure when service completion is later than the upper bound of the service window; For late arrival time window relaxation variables; The weighting parameter for the penalty for service failure when the service is completed earlier than the lower bound of the service window; For the early arrival time window relaxation variable; For the cost of risk penalties; Risk penalty cost weighting; The weighting parameter for risk penalty costs; For risk parameters; For truck service mode variables.

[0012] In one embodiment, the service mode constraint is as follows: in, For truck service mode variables; For the contactless service method of drones; Assign variables to customer-truck; Let be the number of drone flights; D be the set of drones; H be the set of candidate truck docking nodes; K be the set of trucks; T be the set of discrete time periods; and N be the set of customer nodes. The drone affiliation constraint is as follows: in, For the drone-truck attribution variable; Enable variables for the vehicle; The accessibility and feasibility constraints are as follows: in, For truck arc variables; The ground arc at time t within the time-varying constraint region of the ground Accessibility; To assess the feasibility of drone sorties; The truck route and load constraints are as follows: in, For trucks During the period Starting point from the distribution center Drive to the node The arc variable; For trucks During the period From node Return node after reaching the distribution center The arc variable; For trucks During the period From node Drive to the node The arc variable; For trucks During the period From node Drive to the node The arc variable; For truck arrival variables; For the set of ground nodes; The departure node of the distribution center; Return node to distribution center; Parameters required by the customer; For truck load capacity parameters; The drone capability constraints are as follows: in, For drones Load capacity parameters; To adjust the task duration; For drones Maximum task duration; It is a constant; To correct the task's energy consumption; For drones Maximum task power consumption limit; The truck docking time slot constraint is as follows: in, The moment the truck leaves the stop point h; The time when the truck arrives at the stopping point h; This refers to the length of the docking time slot; This is the upper limit of the preset docking time slot; The truck time recursion constraint is: ; in, The moment the truck arrives at stop point j; The moment the truck leaves stop point i; For the ground arc in time period The following travel time; The time period boundary constraint is: ; ; in, The lower boundary of time; For the upper boundary of time; This represents the contraction amount at the time boundary. The takeoff time; The takeoff and recovery point access constraints are as follows: in, For trucks Did you visit the drone take-off and docking point? Variables; For trucks Did you visit the drone recycling docking station? Variables; The takeoff and recovery window constraints are as follows: in, This is the time for recycling; The constraints on the drone flight and service time are as follows: in, The time when the service is completed; This is a correction factor for the nominal flight time during the takeoff phase; This is a correction factor for the nominal flight time of the recovery segment; The nominal flight time for the takeoff segment; The nominal flight time for the recovery segment; The service duration for customer n; The customer service completion time constraint is as follows: in, The moment of completion for serving customers; The time when the truck arrives at customer node n; The time-window default relaxation constraint is: in, For late arrival time window relaxation variables; For the early arrival time window relaxation variable; For customers The upper bound of the service window; For customers The service window bottom; The mutual exclusion constraint for the drone resources is as follows: The sub-loop constraint elimination method is as follows: in, For trucks At the node Sub-loops eliminate order variables; For trucks At the node Sub-loops eliminate order variables; For trucks At the node The sub-circuit eliminates the order variable.

[0013] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described truck-multi-drone contactless delivery collaborative scheduling method.

[0014] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described truck-multi-drone contactless delivery collaborative scheduling method.

[0015] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned truck-multi-drone contactless delivery collaborative scheduling method.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a truck-multi-drone contactless delivery collaborative scheduling method, equipment, medium, and product. It acquires delivery order data, ground traffic data, airspace control data, and lockdown event data, as well as time-varying restricted areas for each time period, mapping areas corresponding to lockdown events, customer-side parameters, vehicle parameters, and cost parameters. It determines the delivery center departure node, delivery center return node, customer node set, candidate truck docking node set, truck set, drone set, and discrete time period set. Based on the ground time-varying and air time-varying restricted areas for each time period, it determines the passability of ground arcs and air arcs for the corresponding time periods, generating ground accessibility markers. The system identifies and identifies airborne reachability markers; it then determines the feasibility of drone sorties based on these markers; using a congestion proxy estimator, it outputs time and energy correction coefficients for the takeoff and recovery phases, and performs segmented corrections on nominal flight time and nominal energy consumption to obtain corrected task duration and energy consumption; based on the above data, it constructs and solves a collaborative optimization model to obtain a collaborative scheduling scheme; the collaborative scheduling scheme is then distributed to vehicle-mounted and airborne terminals for execution, and execution feedback data is received; when the execution deviation exceeds a preset threshold, communication fails, the next scheduling cycle begins, or restriction information is updated, the collaborative scheduling scheme is updated based on the execution feedback data from the previous moment. This application can simultaneously consider time-varying regional restrictions, airspace congestion impacts, vehicle-drone synchronization constraints, and closed-loop updates of execution feedback, improving the efficiency and execution accuracy of collaborative delivery in low-altitude economic scenarios. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a truck-multi-drone contactless delivery collaborative scheduling method provided in an embodiment of this application; Figure 2This is a diagram illustrating the collaborative scheduling results of truck-multi-drone contactless delivery provided in one embodiment of this application. Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] In one exemplary embodiment, such as Figure 1 As shown, a truck-multi-drone contactless delivery collaborative scheduling method is provided, including the following steps: S1: Acquire delivery order data, ground traffic data, airspace control data, and lockdown event data, as well as time-varying restricted areas for each time period, mapping areas corresponding to lockdown events, customer-side parameters, vehicle parameters, and cost parameters; determine the delivery center departure node, delivery center return node, customer node set, candidate truck docking node set, truck set, drone set, and discrete time period set; the time-varying restricted areas include ground time-varying restricted areas and air time-varying restricted areas; the customer-side parameters include customer demand, service window, and risk parameters; the vehicle parameters include truck load capacity parameters and drone capacity parameters; the cost parameters include ground arc cost parameters, drone sortie cost parameters, nominal flight time, and nominal energy consumption.

[0022] In this embodiment, delivery order data, ground traffic data, and airspace control data are collected and unified into a single coordinate system. Based on this, the departure node of the delivery center is determined. , distribution center return node Customer node set Candidate truck docking node set Truck collection drone collection and discrete time period set .in, For each time period Given its time boundary All time periods are arranged in chronological order, do not overlap, and satisfy the following conditions: .

[0023] Define the set of ground nodes as: And obtain the ground arc set The ground arc is a connecting section where vehicles are allowed to pass directly; therefore, This represents the set of all feasible ground connections.

[0024] Get the set of aerial arcs An aerial arc is a connection segment through which drones are permitted to fly directly. Simultaneously, drone missions are limited to a two-segment structure: the drone must take off from a designated docking point, fly to the customer node to complete its service, and then return to the docking point for recovery.

[0025] Calculate the demand density for each time period based on order data. .in, For time period The strength of demand or the concentration of orders within a given time period is typically measured in the number of orders per unit of time; average truck speeds at different times are calculated based on ground traffic data. And further determine the time period of the ground arc. The following travel time Determine the drone's cruising speed based on its parameters. Determine the time period based on airspace control data. The width of the airspace channel below With partition angle .

[0026] Obtaining the time-varying restricted area on the ground Time-varying restricted areas in the air And the mapping area corresponding to the ground lockdown event. The mapping area corresponding to the air traffic control event The aforementioned areas all vary by time period. Changes. Methods for generating the mapping area include: directly using the announcement polygon of the lockdown event as the boundary of the lockdown area; or dividing the event location points by radius. Buffering can be used to form polygonal control zones; or events can be mapped to a set of ground road segments and a set of airspace grid occupancy sets; where... And the units are consistent with the trajectory coordinate system.

[0027] Obtain customer-side parameters, including: customer demand parameters Service window parameters ,in Risk parameters And customer service duration .

[0028] Obtain vehicle parameters, including: truck load capacity parameters. and drone capability parameters ,in, For drones Load capacity parameters; For drones Maximum task duration; For drones The maximum energy consumption limit for a given task.

[0029] Obtain cost and flight-related parameters, including: ground arc cost parameters. Drone sortie cost parameters Nominal flight time Nominal energy consumption .in, It consists of both fixed operating costs and mileage costs; It can be calculated from flight distance and cruise speed, or calibrated from historical average data; It can be calculated from an energy consumption model or given by a calibration curve.

[0030] Obtain model control and feedback update parameters, including: weight parameters. A sufficiently large constant Preset upper limit of docking time slot Feedback to update window length Time period boundary contraction amount ,in: and termination threshold Maximum number of iterations Early stopping threshold Feedback deviation threshold and communication trigger threshold .

[0031] A sufficiently large constant satisfy: And for have , The quantiles of historical samples are adaptively determined or given by preset boundaries.

[0032] S2: Based on the time-varying ground and air time-varying restricted areas for each time period, determine the passability of the ground arc and air arc for the corresponding time period, and generate ground accessibility markers and air accessibility markers; and determine whether the drone sortie is feasible based on the air accessibility markers; the drone sortie is a task unit that takes off from the docking point, serves the customer, and returns to the docking point.

[0033] In this embodiment, a time-varying accessibility function (ground accessibility identifier and air accessibility identifier) ​​is constructed based on the time-varying restricted area: ; ; in, The ground arc at time t within the time-varying constraint region of the ground Accessibility; For ground arc sets; For ground arc The geometric trajectory; This is a time-varying restricted area on the ground. The mapped area corresponding to the ground lockdown event; The aerial arc at time t within the time-varying constraint region in the air Accessibility; For an aerial arc set; For the arc in the air The geometric trajectory; This is a time-varying restricted area in the air; This refers to the mapped region corresponding to the air traffic control event. The trajectory and the time-varying restricted region are intersected under a unified coordinate system or unified spatial representation, and intersection is considered complete if the boundary contact is included. Ground arc geometric trajectory. The geometric trajectory of an arc in the air is a broken line or a set of its segments representing the centerline of a road. It can be a two-dimensional line segment projection or a three-dimensional height layer trajectory; when using a three-dimensional space or a height layer grid, the determination is made according to the intersection relationship in the corresponding height layer.

[0034] In this embodiment, determining the feasibility of a drone sortie based on air reachability indicators specifically includes: Using formula Determine the feasibility of drone sorties; among them, regarding drone sorties... Air Arc Index Take respectively and ; To assess the feasibility of drone sorties; The aerial arc at time t within the time-varying constraint region in the air Accessibility; The aerial arc at time t within the time-varying constraint region in the air Accessibility. The number of drone sorties is from the docking point. Takeoff service customers At the stop Recycling, among which and They can be the same or different, and when This indicates the number of round-trip flights that take off, land, and recover at the same docking point. (UAV flight feasibility indicator) Implementation methods include lookup masks or Boolean AND operations to equivalently represent two aerial arc segments. and During the period The conditions that must be met simultaneously for accessibility.

[0035] When the aerial arc is within the time-varying restriction region at time t Accessibility and time-varying airborne arc at time t within the airborne time-limited region When any of the reachability parameters is 0, the feasibility of drone sorties is 0.

[0036] The candidate set includes the candidate arc set. With candidate flight set ;Will and Used to limit the domain of variables, in step S4 of constructing the collaborative optimization model, to limit the truck arc variable. and drone sortie variables The range of values ​​for makes hour , hour .

[0037] The candidate set can be generated by using either the nominal travel time or the corrected time output from the previous scheduling cycle as weights. Shortest path search; or use column generation to gradually expand the candidate set.

[0038] S3: Using a congestion proxy estimator, output the time and energy correction coefficients for both the takeoff and recovery phases. Then, perform segmented corrections on the nominal flight time and nominal energy consumption to obtain the corrected mission duration and energy consumption. (Congestion proxy estimator) For a learning model that includes physical constraints, the physical constraints include at least the airspace grid task flow intensity conservation residual term and the zero flux or continuity boundary residual term at the no-fly zone boundary.

[0039] In this embodiment, a congestion proxy estimator is obtained based on historical samples and online feedback training or calibration. Construct congestion feature vector It includes at least and spatial related features or the distance of the takeoff segment and the distance of the recovery section Output two-segment spatial state correction coefficients The correction coefficient is subject to validity constraints or anomaly handling to ensure it is positive and falls within a preset valid range, and this applies to each time period. The task is fixed and updated in the next time period or the next scheduling cycle; and the duration and energy consumption of the corrected task are calculated accordingly. ; in, To adjust the task duration; This is a correction factor for the nominal flight time during the takeoff phase; This is a correction factor for the nominal flight time of the recovery segment; The nominal flight time for the takeoff segment; The nominal flight time for the recovery segment; The service duration for customer n; To correct the task's energy consumption; This is a correction factor for the nominal energy consumption during takeoff. This is a correction factor for the nominal energy consumption of the recovery section; This refers to the nominal energy consumption during takeoff. This represents the nominal energy consumption of the recovery section.

[0040] The time-based reachability function is used to trim the feasible domain of ground arcs and UAV sorties, while the two-segment airspace state correction is used to adjust the mission duration, energy consumption, and recovery synchronization window boundaries of retained sorties; due to the takeoff segment With recycling section The geometric structure, congestion conditions, and limited boundaries may differ, requiring separate time and energy consumption corrections, which together alter the feasible region and objective function of the collaborative optimization model.

[0041] S4: Based on delivery order data, ground traffic data, airspace control data, lockdown event data, time-varying restricted areas for each time period, mapping areas corresponding to lockdown events, customer-side parameters, vehicle parameters, cost parameters, delivery center departure nodes, delivery center return nodes, customer node set, candidate truck docking node set, truck set, drone set, discrete time period set, traffic feasibility, traffic accessibility, and modified task duration and modified task energy consumption, a collaborative optimization model is constructed. The collaborative optimization model includes an objective function and constraints aimed at minimizing total cost. The constraints include: service mode constraints, drone affiliation constraints, accessibility and feasibility constraints, truck route and load constraints, drone capability constraints, truck docking time slot constraints, truck time recursion constraints, time period boundary constraints, takeoff and recovery point access constraints, takeoff and recovery window constraints, drone flight and service time constraints, customer service completion time constraints, time window default relaxation constraints, drone resource mutual exclusion constraints, and sub-loop elimination constraints.

[0042] In this embodiment, a collaborative optimization model is constructed and solved to obtain a collaborative scheduling scheme. The collaborative optimization model includes at least decision variables: vehicle activation variables. Service method variables Customer-Truck Assignment Variable Drone-Truck Attribution Variable Truck arc variable (Only for) Definition), Truck arrival variable ( ), Truck arrival time With departure time ( ), docking time slot length ( Sub-ring elimination of order variables ( Customer service completion time Time window relaxation variable Variables of drone sorties Departure time Service completion time With recycling time The collaborative optimization model aims to minimize the total cost, which includes at least the truck's operating cost, the drone's operational cost, the drone's energy consumption cost, the time window default penalty cost, and the risk penalty cost, and is written as: ; ; ; ; ; ; in, Total cost; For truck operating costs; Weighting for truck operating costs; Cost of drone operations; Weighting of drone operation costs; Energy consumption cost of drones; Weighting of drone energy consumption costs; The cost of penalties for breach of contract during the time window; Weighting of penalty costs for breach of contract within the time window; The weighting parameter for the penalty for service failure when service completion is later than the upper bound of the service window; For late arrival time window relaxation variables, representing the customer The service completion time exceeds the upper limit of the service window by an excess amount; The weighting parameter for the penalty for service failure when the service is completed earlier than the lower bound of the service window; For the early arrival time window relaxation variable, representing the customer The service completion time is earlier than the lower bound of the service window; For the cost of risk penalties; Risk penalty cost weighting; The weighting parameter for risk penalty costs; For risk parameters; For truck service mode variables, a value of 1 indicates the customer. Serviced by trucks. Weight Other weights are .

[0043] And satisfy the following constraints: Service method constraints: in, For the contactless drone service method, a value of 1 represents the customer. Serviced by drones.

[0044] Drone ownership constraints: Among them, drones can be left unused, and drones that have not been assigned a home and have not performed any missions are considered idle.

[0045] Accessibility and feasibility constraints: And it satisfies communication reliability constraints: when the predicted communication delay deviation, number of consecutive retransmission failures, or timeout duration of a link corresponding to a certain flight exceeds the corresponding threshold, the selection of the corresponding flight variable is prohibited in the next round of collaborative optimization. .

[0046] Truck routing and load constraints: Time period index Used to identify the departure time of an arc, and to indicate waiting times across time slots for passing through stop slots. Depiction; in, For trucks During the period Starting point from the distribution center Drive to the node The arc variable; For trucks During the period From node Return node after reaching the distribution center The arc variable; For trucks During the period From node Drive to the node The arc variable; For trucks During the period From node Drive to the node The arc variable. The cargo served by the drone sortie is carried to the takeoff and docking point by its assigned truck. After loading is completed within the docking window at the designated takeoff and docking point, the vehicle will take off.

[0047] Unmanned aerial vehicle (UAV) capability constraints: Truck parking time slot constraints: in, The moment the truck leaves the stop point h; The time when the truck arrives at the stopping point h.

[0048] in, Let k be the arrival time of truck k at departure node 0 of the distribution center; Let k be the departure time of truck k from node 0 of the distribution center; Let k be the departure time of truck k at node 0', which is the return node of the distribution center. Let K be the arrival time of truck k at node 0' of the return trip from the distribution center.

[0049] Truck time recursion constraint: ; in, The moment the truck arrives at stop point j; The moment the truck leaves stop point i.

[0050] Time period boundary constraints: ; ; in, The lower boundary of time; This represents the upper boundary of time.

[0051] Takeoff and recovery point access constraints: in, For trucks Did you visit the drone take-off and docking point? Variables; For trucks Did you visit the drone recycling docking station? Variables.

[0052] Takeoff and recovery window constraints: Unmanned aerial vehicle (UAV) flight and service time constraints: Customer service completion time constraints: in, Let n be the time when the truck arrives at customer node n.

[0053] Time window default slack constraint: in, For customers The upper limit of the service window, i.e., the latest allowed service time; For customers The lower bound of the service window, i.e., the earliest allowed service time.

[0054] Drone resource mutual exclusion constraints: And satisfy based on and The task non-overlap constraint ensures that the sorties performed by the same drone at different time periods do not overlap in time.

[0055] Sub-loop constraint elimination: Sub-loops are eliminated using MTZ constraints or cut-plane constraints, and a constraint is added to each pair of node arcs that they pass through at most once within the same scheduling cycle. For trucks At the node Sub-loops eliminate order variables; For trucks At the node Sub-loops eliminate order variables; For trucks At the node The sub-circuit eliminates the order variable.

[0056] In another embodiment, the collaborative optimization model can also take a two-level form and use value functions to estimate variables. Represents the optimal value of the lower level ;in At least including: And by generating cut constraints to approximate the value function and updating it. In fixed Then, linear relaxation is performed on the lower-level subproblem containing the following variables and their linkage constraints to obtain dual information: And generate the optimal cut: .in, The optimal cut intercept term is obtained by linear relaxation of the lower-level subproblems; This is the transpose / cut slope term of the corresponding dual multiplier vector.

[0057] S5: Solve the aforementioned collaborative optimization model to obtain the collaborative scheduling scheme. For the above mixed-integer linear programming model (collaborative optimization model), solve it using intlinprog in MATLAB, where MTZ constraints are used to eliminate sub-loops.

[0058] S6: Distribute the collaborative scheduling scheme to the vehicle-mounted terminal and airborne terminal for execution, and receive execution feedback data; when the execution deviation exceeds the preset threshold, communication is abnormal, the next scheduling cycle is entered, or the restriction information is updated, based on the execution feedback data of the previous moment, return "Based on the ground time-varying restriction area and air time-varying restriction area of ​​each time period, determine the passability of the ground arc and air arc in the corresponding time period, generate ground accessibility identifier and air accessibility identifier; and determine whether the UAV sortie is feasible based on the air accessibility identifier", and update the collaborative scheduling scheme.

[0059] In this embodiment, scheduling instructions are generated based on a collaborative scheduling scheme and sent to the vehicle-mounted execution terminal and the airborne execution terminal to control the truck departure time and the drone take-off / recovery time. The system executes contactless delivery actions for customers and sends back delivery status, which includes at least success, failure, timeout, and verification. It receives execution feedback and calculates time deviation, energy consumption deviation, and communication latency deviation. If any deviation exceeds a corresponding threshold... , or Or the number of consecutive retransmission failures exceeds the threshold. Or the communication timeout duration exceeds the threshold. Replanning is triggered either upon entering the next scheduling cycle or upon detecting an update to the constraint information, i.e., utilizing the most recent... The execution feedback of each scheduling cycle is adjusted according to the effective range of the correction coefficient based on the quantile recalibration, and based on the most recent Order and execution feedback are updated on a rolling basis for each scheduling cycle. Update the communication restricted flight markers or restricted area information according to the communication anomaly record, and re-execute steps S2 to S5.

[0060] Execution feedback deviations that trigger replanning include time deviations, energy consumption deviations, or communication delay deviations, where time deviations, energy consumption deviations, and communication delay deviations are defined as follows: and Calibrate using the same dimensionless deviation caliber; when any deviation exceeds the corresponding threshold, calibrate according to the most recent caliber. Feedback for each scheduling cycle , Quantile recalibration of the effective range and Rolling updates are performed, and when the communication delay deviation, the number of consecutive retransmission failures, or the timeout duration exceeds the corresponding threshold, the corresponding flight is marked as a communication-restricted flight and is prohibited from being selected in the next round of replanning.

[0061] As a risk enhancement control measure for contactless delivery, the contactless delivery strategy is based on risk parameters. With threshold Implement hierarchical control to satisfy: when Forced ;when Time priority When in a fixed position Under the condition that the lower-level subproblem has no feasible solution, the customer is rolled back to... And included in the risk penalty item; among which .

[0062] like Figure 2 The diagram shows the scheduling results for customer nodes, distribution centers, truck docking points, time-varying restricted areas, controlled areas, and truck-drone collaborative delivery routes. Black dots: represent client nodes, for example: These are customers who require delivery services, and each customer has a different demand (e.g.: ).

[0063] Black square: Indicates the starting point of the distribution center ( ) and return point ( These are the starting and ending points for trucks and drones.

[0064] Black diamond: Indicates a truck stop, similar to... and These points serve as transfer hubs between drones and trucks.

[0065] Circular areas are used to represent time-varying restricted areas that affect the feasibility of delivery routes. These areas change depending on the time of day, specifically: The red dashed circles represent restricted areas for trucks. According to the patented method, these circles indicate areas that trucks cannot traverse at specific times, subject to time-varying ground traffic.

[0066] Blue dashed circles: Indicate restricted airspace areas. These circles change over time, representing restricted areas for drone flight, limited by the width and angle of airspace passageways.

[0067] Black dashed circles: Represent buffer zones for a lockdown event. In this embodiment, these areas are used to indicate lockdown areas caused by unforeseen events (such as weather, no-fly zones, etc.), which may temporarily prohibit all delivery operations.

[0068] Different lines represent the delivery path or the status of connection nodes, with the following specific meanings: The solid blue lines represent reachable paths between connected nodes, typically representing the paths of trucks or drones. In the patented method, these connections are defined by time-varying reachability functions (such as...). or This is calculated to indicate whether travel is possible between nodes during a specific time period.

[0069] Orange dashed line: Indicates the drone's path, such as from the truck docking point. To customer node The path. According to the patent, the orange dashed line shows the drone's mission path, connecting transportation between trucks and customers.

[0070] Alternating solid blue lines and dashed orange lines: These alternating lines represent the paths along which trucks and drones work together. For example, a truck from... arrive Then the drone from arrive , then return Finally, the goods were delivered by truck. .

[0071] X and Y represent the normalized horizontal and vertical positions. From Figure 2As can be seen, the route planning and resource scheduling of the entire delivery system are optimized based on strict time-varying area restrictions and airspace congestion correction strategies. Through dynamic restrictions on ground and airspace, customer demand, task allocation at delivery centers, and collaborative operations between drones and trucks, the system ensures that each delivery task is executed optimally while meeting time window requirements, risk levels, and resource constraints.

[0072] The truck-multi-drone contactless delivery collaborative scheduling method of this application has the following advantages: 1) This application introduces a time-varying restricted accessibility function, which can take into account time-limited restricted areas in ground and air transportation in real time and update the delivery route in a timely manner.

[0073] 2) This application adopts a two-stage airspace state correction method to adjust the flight time and energy consumption of the UAV from takeoff to recovery, reflecting the asymmetry and variability of airspace congestion.

[0074] 3) This application designs a vehicle-machine synchronization window to ensure that the take-off, service and recovery times of trucks and drones can be accurately synchronized, avoiding time conflicts and resource waste.

[0075] 4) This application is based on a multi-threshold feedback triggering replanning mechanism to adjust the route and strategy in real time during the delivery process, ensuring that the solution can cope with deviations and anomalies during execution.

[0076] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described truck-multi-drone contactless delivery collaborative scheduling method.

[0077] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described truck-multi-drone contactless delivery collaborative scheduling method.

[0078] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described truck-multi-drone contactless delivery collaborative scheduling method.

[0079] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a truck-multi-drone contactless delivery collaborative scheduling method.

[0080] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0081] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0082] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0083] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0084] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A truck-multi-drone contactless delivery collaborative scheduling method, characterized in that, include: Acquire delivery order data, ground traffic data, airspace control data, and lockdown event data, as well as time-varying restricted areas for each time period, mapping areas corresponding to lockdown events, customer-side parameters, vehicle parameters, and cost parameters; The system determines the departure node of the distribution center, the return node of the distribution center, the set of customer nodes, the set of candidate truck docking nodes, the set of trucks, the set of drones, and the set of discrete time periods; the time-varying restricted areas include ground time-varying restricted areas and air time-varying restricted areas; the customer-side parameters include customer demand, service window, and risk parameters; the vehicle parameters include truck load capacity parameters and drone capacity parameters; the cost parameters include ground arc cost parameters, drone sortie cost parameters, nominal flight time, and nominal energy consumption. Based on the time-varying ground and air time-varying restricted areas for each time period, the passability of the ground arc and air arc in the corresponding time period is determined, and ground accessibility markers and air accessibility markers are generated. And determine the feasibility of drone sorties based on air accessibility indicators; The drone sortie is a mission unit that takes off from the docking point, serves the customer, and returns to the docking point; Using a congestion agent estimator, the time correction coefficient and energy consumption correction coefficient for the takeoff and recovery phases are output, and the nominal flight time and nominal energy consumption are corrected in segments to obtain the corrected mission duration and corrected mission energy consumption. Based on delivery order data, ground traffic data, airspace control data, lockdown event data, time-varying restricted areas for each time period, mapping areas corresponding to lockdown events, customer-side parameters, vehicle parameters and cost parameters, delivery center departure nodes, delivery center return nodes, customer node set, candidate truck docking node set, truck set, drone set, discrete time period set, traffic feasibility, traffic accessibility, and modified task duration and modified task energy consumption, a collaborative optimization model is constructed. The collaborative optimization model includes an objective function and constraints aimed at minimizing total cost. Total cost includes truck driving cost, drone operation cost, energy consumption cost, time window default penalty cost, and risk penalty cost. The constraints include: service mode constraints, drone affiliation constraints, accessibility and feasibility constraints, truck route and load constraints, drone capability constraints, truck docking time slot constraints, truck time recursion constraints, time period boundary constraints, takeoff and recovery point access constraints, takeoff and recovery window constraints, drone flight and service time constraints, customer service completion time constraints, time window default relaxation constraints, drone resource mutual exclusion constraints, and sub-loop elimination constraints. Solving the aforementioned collaborative optimization model yields a collaborative scheduling scheme; The collaborative scheduling scheme is sent to the vehicle-mounted and airborne terminals for execution, and execution feedback data is received. When the execution deviation exceeds the preset threshold, communication is abnormal, the next scheduling cycle is entered, or the restriction information is updated, the collaborative scheduling scheme is updated based on the execution feedback data of the previous moment. The scheme is updated by "determining the passability of the ground arc and the air arc in the corresponding time period based on the ground time-varying restriction area and the air time-varying restriction area in each time period, generating ground accessibility identifiers and air accessibility identifiers, and determining whether the UAV sortie is feasible based on the air accessibility identifiers".

2. The truck-multi-drone contactless delivery collaborative scheduling method according to claim 1, characterized in that, Ground accessibility signage is as follows: ； in, The ground arc at time t within the time-varying constraint region of the ground Accessibility; For ground arc set; For ground arc The geometric trajectory; This is a time-varying restricted area on the ground. This refers to the mapped area corresponding to the ground lockdown event.

3. The truck-multi-drone contactless delivery collaborative scheduling method according to claim 1, characterized in that, Air accessibility is indicated as: ； in, The aerial arc at time t within the time-varying constraint region in the air Accessibility; For an aerial arc set; For the arc in the air The geometric trajectory; This is a time-varying restricted area in the air; This is the mapping area corresponding to the air traffic control event.

4. The truck-multi-drone contactless delivery collaborative scheduling method according to claim 1, characterized in that, Determining the feasibility of drone sorties based on air reachability indicators specifically includes: Using formula Determine the feasibility of drone sorties; among them, regarding drone sorties... Air Arc Index Take respectively and ; To assess the feasibility of drone sorties; The aerial arc at time t within the time-varying constraint region in the air Accessibility; The aerial arc at time t within the time-varying constraint region in the air Accessibility; When the aerial arc is within the time-varying restriction region at time t Accessibility and time-varying airborne arc at time t within the airborne time-limited region When any of the reachability parameters is 0, the feasibility of drone sorties is 0.

5. The truck-multi-drone contactless delivery collaborative scheduling method according to claim 1, characterized in that, The nominal flight time and nominal energy consumption are adjusted in segments to obtain the adjusted mission duration and adjusted mission energy consumption, specifically including: Using formula The nominal flight time and nominal energy consumption are corrected in segments to obtain the corrected mission duration and corrected mission energy consumption. in, To adjust the task duration; This is a correction factor for the nominal flight time during the takeoff phase; This is a correction factor for the nominal flight time of the recovery segment; The nominal flight time for the takeoff segment; The nominal flight time for the recovery segment; The service duration for customer n; To correct the mission's energy consumption; This is a correction factor for the nominal energy consumption during takeoff. This is a correction factor for the nominal energy consumption of the recovery section; This refers to the nominal energy consumption during takeoff. The nominal energy consumption of the recovery section.

6. The truck-multi-drone contactless delivery collaborative scheduling method according to claim 1, characterized in that, The total cost is: ； ； ； ； ； ； in, Total cost; For truck operating costs; K represents the weight of truck operating costs; K is the set of trucks; T is the set of discrete time periods; For ground arc set; For ground arc cost parameters; For truck arc variables; Cost of drone operations; D represents the weight of drone operation costs; H represents the set of drones; N represents the set of candidate truck docking nodes; and N represents the set of customer nodes. This refers to the cost parameters for each drone sortie; For the number of drone sorties; Energy consumption cost of drones; Weighting of drone energy consumption costs; To correct the mission's energy consumption; The cost of penalties for breach of contract during the time window; Weighting of penalty costs for breach of contract within the time window; The weighting parameter for the penalty for service failure when service completion is later than the upper bound of the service window; For late arrival time window relaxation variables; The weighting parameter for the penalty for service failure when the service is completed earlier than the lower bound of the service window; For the early arrival time window relaxation variable; For the cost of risk penalties; Risk penalty cost weighting; The weighting parameter for risk penalty costs; For risk parameters; For truck service mode variables.

7. The truck-multi-drone contactless delivery collaborative scheduling method according to claim 1, characterized in that, The service method is subject to the following constraints: in, For truck service mode variables; For the contactless service method of drones; Assign variables to customer-truck; Let be the number of drone flights; D be the set of drones; H be the set of candidate truck docking nodes; K be the set of trucks; T be the set of discrete time periods; and N be the set of customer nodes. The drone affiliation constraint is as follows: in, For the drone-truck attribution variable; Enable variables for the vehicle; The accessibility and feasibility constraints are as follows: in, For truck arc variables; The ground arc at time t within the time-varying constraint region of the ground Accessibility; To assess the feasibility of drone sorties; The truck route and load constraints are as follows: in, For trucks During the period Starting point from the distribution center Drive to the node The arc variable; For trucks During the period From node Return node after reaching the distribution center The arc variable; For trucks During the period From node Drive to the node The arc variable; For trucks During the period From node Drive to the node The arc variable; For truck arrival variables; For the set of ground nodes; The departure node of the distribution center; Return node to distribution center; Parameters required by the customer; For truck load capacity parameters; The drone capability constraints are as follows: in, For drones Load capacity parameters; To adjust the task duration; For drones Maximum task duration; It is a constant; To correct the mission's energy consumption; For drones Maximum task power consumption limit; The truck docking time slot constraint is as follows: in, The moment the truck leaves the stop point h; The time when the truck arrives at the stopping point h; This refers to the length of the docking time slot; This is the upper limit of the preset docking time slot; The truck time recursion constraint is: ； in, The moment the truck arrives at stop point j; The moment the truck leaves stop point i; For the ground arc in time period The following travel time; The time period boundary constraint is: ； ； in, The lower boundary of time; For the upper boundary of time; This represents the contraction amount at the time boundary. The takeoff time; The takeoff and recovery point access constraints are as follows: in, For trucks Did you visit the drone take-off and docking point? Variables; For trucks Did you visit the drone recycling docking station? Variables; The takeoff and recovery window constraints are as follows: in, This is the time for recycling; The constraints on the drone flight and service time are as follows: in, The time when the service is completed; This is a correction factor for the nominal flight time during the takeoff phase; This is a correction factor for the nominal flight time of the recovery segment; The nominal flight time for the takeoff segment; The nominal flight time for the recovery segment; The service duration for customer n; The customer service completion time constraint is as follows: in, The moment of completion for serving customers; The time when the truck arrives at customer node n; The time-window default relaxation constraint is: in, For late arrival time window relaxation variables; For the early arrival time window relaxation variable; For customers The upper bound of the service window; For customers The lower bound of the service window; The mutual exclusion constraint for drone resources is as follows: The sub-loop constraint elimination method is as follows: in, For trucks At the node Sub-loops eliminate order variables; For trucks At the node Sub-loops eliminate order variables; For trucks At the node The sub-circuit eliminates the order variable.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the truck-multi-drone contactless delivery collaborative scheduling method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the truck-multi-drone contactless delivery collaborative scheduling method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the truck-multi-drone contactless delivery collaborative scheduling method as described in any one of claims 1-7.

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