CN-121599572-B - Low-altitude logistics vehicle-machine collaborative configuration and transregional carrying matching method and system

CN121599572BCN 121599572 BCN121599572 BCN 121599572BCN-121599572-B

Abstract

The invention discloses a method and a system for collaborative configuration and transregional carrying matching of a low-altitude logistics vehicle and machine, and belongs to the technical field of low-altitude logistics. The invention adopts a three-level optimization flow of single-region optimization- > multi-region vehicle coordination- > vehicle-machine cross-region carrying matching. The single-zone parallel optimization ensures local optimization and calculation efficiency, solves the optimal combination of the number of vehicles and unmanned aerial vehicles for each logistics zone by adopting an optimization algorithm, creatively proposes the concept of virtual vehicles, realizes cross-zone flexible sharing and optimization of vehicle resources, integrates resources at a higher level by multi-zone vehicle coordination, combines virtual vehicle demands of a plurality of zones to be optimized by using a clustering algorithm, is satisfied by one actual vehicle through cross-zone flow, pursues global optimal deployment of vehicle resources, and finally carries out matching treatment on a vehicle-mounted machine to refine a synergistic problem, dynamically evaluates the cost of two schemes of carrying the unmanned aerial vehicle on the fly and carrying the vehicle back for each overload zone, and selects the optimal vehicle.

Inventors

QI JIANHUAI
HU JINHUA
ZHANG LI
XU GUOQIAN
ZHENG WEIFAN
CHENG YANG

Assignees

深圳市永达电子信息股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260130

Claims (10)

1. The method for matching the collaborative configuration and the cross-region carrying of the low-altitude logistics vehicular machine is characterized by comprising the following steps of: The method comprises the steps of S1, carrying out cooperative configuration on vehicles and machines in all logistics areas, namely building a vehicle-machine cooperative logistics cost model integrating traffic efficiency coefficients according to basic information, order information, unmanned aerial vehicle performance parameters and vehicle performance parameters of the logistics areas, solving ideal numbers of vehicles and unmanned aerial vehicles in the logistics areas by using an optimizing algorithm based on the vehicle-machine cooperative logistics cost model with logistics cost minimized as a target, wherein the ideal numbers of the unmanned aerial vehicles are integers, the ideal numbers of the vehicles are floating points, downwards taking the integer from the ideal numbers of the vehicles to configure actual vehicle numbers, and setting the part exceeding the actual vehicle numbers as virtual vehicles; Step S2, carrying out joint optimization configuration on the vehicles in the logistics areas to be optimized, wherein according to the vehicle cross-area circulation cost and the virtual vehicle-to-actual vehicle cost, whether the virtual vehicle in the logistics areas to be optimized is converted into an actual vehicle or is combined with the virtual vehicles in other logistics areas to be optimized into an actual vehicle is determined; the method comprises the steps of forming a cluster in each region to be optimized, calculating the cross-region income among all region pairs, finding out the region pairs with the cross-region income larger than zero and the maximum value, merging the clusters of the two regions in the region pairs into a new cluster, and obtaining a final clustering result through iterative calculation and merging; Step S3, summarizing the final vehicle quantity and the unmanned aerial vehicle quantity in all logistics areas, outputting vehicles needing to be subjected to cross-zone circulation and service area sets thereof, carrying unmanned aerial vehicles exceeding the total carrying capacity of all vehicles in the logistics areas, specifically, generating an unmanned aerial vehicle cross-zone flight carrying scheme and a vehicle cross-zone circulation carrying scheme, respectively calculating increment cost of the two schemes, comparing the increment cost, selecting a scheme with lower increment cost, considering virtual vehicles for increasing the carrying capacity of the unmanned aerial vehicles corresponding to the exceeding capacity for the overload area if the minimum increment cost is still higher than the cost of one vehicle newly added in the overload area, returning to step S2 for cross-zone vehicle optimization, and returning to step S2 for cross-zone vehicle optimization by adopting a strategy for increasing the virtual vehicles for increasing the carrying capacity of the unmanned aerial vehicles corresponding to the exceeding capacity for the rest unmanned aerial vehicles if the two schemes cannot fully distribute all unmanned aerial vehicles due to distance and capacity reasons.
2. The method for matching the collaborative configuration and the cross-region carrying of the low-altitude logistics vehicular machine according to claim 1, wherein, The concrete method for constructing the vehicle-machine collaborative logistics cost model according to the logistics area basic information, the order information, the unmanned aerial vehicle performance parameters and the vehicle performance parameters comprises the following steps: The vehicle-machine cooperative logistics cost is C (V, D) =C_ (V) +C_ (D); Wherein V is the number of vehicles, D is the number of unmanned aerial vehicles, vehicle resource cost C_ (V) =c_v×V×T_v, c_v is the cost per unit time of the vehicle, and T_v is the time of the vehicle mission; T_v= (m_v×d_avg_v)/(v×v_v×te_v×μ_v), where m_v is the number of orders allocated to the vehicle, d_avg_v is the average delivery distance of the vehicle, v_v is the average running speed of the vehicle, te_v is the vehicle traffic efficiency coefficient, the value range is 0-1, μ_v is the vehicle loading efficiency coefficient, and the value range is 0-1; TE_v = (1 - CI) v_v/v_v_max, wherein CI is a congestion index of a logistics area, the value range is 0-1,0 represents smooth, 1 represents serious congestion, and v_v_max is the maximum value of road speed limit of the area; m_v=mx (v×n_v×μ_v)/(v×n_v×μ_v+d×n_d×μ_d), M is the total number of orders, n_v is the payload capacity of a single vehicle, n_d is the payload capacity of a single vehicle, μ_d is the unmanned aerial vehicle loading efficiency coefficient, and the range of values is 0 to 1; Unmanned resource cost c_ (D) =c_d x D x T D, c_d is the unit time cost of the unmanned aerial vehicle, and T_d is the unmanned aerial vehicle task time; t_d= (m_d_avg_d)/(d×v_d×te_d×μ_d), where m_d is the number of orders allocated to the unmanned aerial vehicle, m_d=m-m_v, d_avg_d is the average delivery distance of the unmanned aerial vehicle, v_d is the average flight speed of the unmanned aerial vehicle, te_d is the traffic efficiency coefficient of the unmanned aerial vehicle, and the range of values is 0-1; TE_d = f(ρ_b, σ_h2) v_d/L_d_max, wherein ρ_b is the building density of the logistics area, sigma_h2 is the elevation variance of the logistics area, and L_d_max is the maximum single flight distance of the unmanned aerial vehicle; f(ρ_b, σ_h2) = exp(-α1 ρ_b - β1 σ_h2), where α1, β1 are weight parameters, α1 e [0.5,2], β1 e [0.1,0.5].
3. The method for matching the collaborative configuration and the cross-region carrying of the low-altitude logistics vehicular machine according to claim 2, wherein, The ideal number of vehicles and drones to solve the logistics area needs to meet the following constraints: order completion quantity constraint: m_v+m d=m; order completion time constraint max (T_v, T_d) is less than or equal to completion time limit; vehicle-mounted capacity constraint of m_v N/M ≤ V n_v μ_v,m_d N/M≤ D n_d μ_d; Vehicle number constraint min (V0, (N/(n_v) μ_v TE_v)))≥V≥ 0; min(D0,(N / (n_d μ_d Te_d))) is greater than or equal to D is greater than or equal to 0, and D is an integer; Where V0 is the number of available drones, D0 is the number of available vehicles, N is the total weight of the order, and N/M is used to approximate the weight of the individual order.
4. The method for matching the collaborative configuration and the cross-region carrying of the low-altitude logistics vehicular machine according to claim 1, wherein, The optimizing algorithm comprises one or more of heuristic algorithm, hierarchical enumeration and searching method.
5. The method for matching the collaborative configuration and the cross-region carrying of the low-altitude logistics vehicular machine according to claim 1, wherein, The specific method for determining whether the virtual vehicle in the logistics area to be optimized is changed into an actual vehicle or is combined with the virtual vehicles in other logistics areas into the actual vehicle according to the vehicle cross-area circulation cost and the virtual vehicle-to-actual vehicle cost comprises the following steps: initializing, namely forming each area pk to be optimized into a cluster; Iterative merging, namely calculating the cross-region benefits among all the region pairs to construct a cross-region benefit matrix, namely evaluating cross-region benefits B (i, j) of merging any two regions pi and pj to be optimized into one cluster, wherein B (i, j) is the cost of virtually converting two vehicles into real vehicle to subtract the cost of merging the two vehicles into one vehicle cross-region circulation, and finding out the region pair (i) with the maximum value of B (i, j) >0 , j ) Region i And j The cluster is merged into a new cluster, a cluster list is updated, and the merging benefits among the new cluster and all other clusters are recalculated; for each single-zone cluster, the virtual vehicle changes into an actual vehicle, and an actual vehicle is newly added for the zone; For each multi-zone cluster, the decision is that the virtual vehicles are combined into the actual vehicle cross-zone circulation, and the required actual vehicles are newly added for the zone set.
6. The method for matching low-altitude logistics vehicular machine collaborative configuration and cross-region carrying according to claim 5, wherein, The trans-regional benefit is calculated by B (r) =Σ_ {1 to K } delta C1 (k) -delta V ΔC2(r); ΔC1 (k) represents the cost of increasing the imaginary rotation of the current region k, ΔV is the result of rounding the sum of the number of virtual vehicles, ΔC2 (r) is the cost of the cross-region due to the combination of the virtual vehicles, and ΔC2 (r) =T_route (r) C_v, T_route (r) is the total time required by the vehicle to access each region in the set r in the optimal sequence of path planning, and c_v is the cost per unit time of the vehicle.
7. The method for matching low-altitude logistics vehicular machine collaborative configuration and cross-region carrying according to claim 5, wherein, The determining whether the virtual vehicle of the logistics area to be optimized is converted into an actual vehicle or is combined with the virtual vehicles of other logistics areas into the constraint condition of the actual vehicle according to the vehicle cross-area circulation cost and the virtual vehicle-to-actual vehicle cost comprises the following steps: each region pk to be optimized must and can only belong to one cluster; The order completion time constraint is satisfied, wherein for the k & gtth access logistics area, the order completion time is satisfied with constraint of max (T_v (k) +Σ_ {2 to k } T (k-1, k), T_d (k)) & ltoreq completion time limit value, T_v (k) is vehicle task time of the k-th logistics area, T (k-1, k) is time required for the vehicle to travel from the logistics area k-1 to the logistics area k, and T_d (k) is unmanned plane task time of the k-th logistics area.
8. The method for matching the collaborative configuration and the cross-region carrying of the low-altitude logistics vehicular machine according to claim 1, wherein, The specific method for carrying the unmanned aerial vehicle with the exceeding capacity on the vehicles in the most suitable logistics area in the logistics area with the number of unmanned aerial vehicles exceeding the total carrying capacity of all the vehicles is as follows: Traversing all logistics areas, if a logistics area i meets D_i > V_i×n_vd, adding the logistics area i into the overload area set O as an overload area, and recording the number E_i of unmanned aerial vehicles exceeding capacity, wherein D_i is the number of unmanned aerial vehicles in the logistics area i, V_i is the number of vehicles in the logistics area i, and n_vd is the number of unmanned aerial vehicles which can be carried by each vehicle; Traversing all logistics areas, adding a certain logistics area j into the enrichment area set R if the logistics area j meets D_j < V_j×n_vd, and recording the residual carrying capacity S_j, wherein S_j=V_j×n_vd-D_j, D_j is the number of unmanned aerial vehicles in the logistics area j, and V_j is the number of vehicles in the logistics area j; Setting and comparing a selected carrying scheme for each overload zone, namely calculating the cross-zone flight time T_d (O, R) and the vehicle circulation travel time T_v (O, R) of the unmanned aerial vehicle from the overload zone O to each rich zone R epsilon R for each overload zone O in an overload zone set O, ordering the rich zones from small to large according to T_d (O, R) to generate a flight sequence S_d, ordering the rich zones from small to large according to T_v (O, R) to generate a circulation sequence S_v, and respectively generating two preliminary carrying allocation schemes based on the sequences: Starting from the first rich area of the sequence S_d, distributing as many unmanned aerial vehicles as possible, if the capacity of the area is used up, continuing to distribute to the next rich area in sequence, repeating the process until all unmanned aerial vehicles exceeding the capacity in the overload area o are distributed, wherein in the process, the selected rich area r is required to meet max (T_v, T_d+T_d (o, r))lessthan or equal to the completion time limit value of the overload area o, T_d (o, r) is required to meet the constraint of less than the maximum cross-area flight time, and the maximum cross-area flight time is set based on the maximum flight time and the task time of the unmanned aerial vehicles; Scheme B, starting from the first rich region of the sequence S_v, executing the same distribution logic as that of scheme A, wherein in the process, the selected rich region r is required to meet max (T_v+T_v (o, r), and T_d is less than or equal to the completion time limit value of the rich region r; Calculating the total increment cost of two schemes respectively, wherein the increment cost of the scheme A is delta C_A=Σ_ { R epsilon R (A) } (the number of the distributed excessive unmanned aerial vehicles is multiplied by T_d (o, R) multiplied by c_d), the increment cost of the scheme B is delta C_B=Σ_ { R epsilon R (A) } (the number of vehicles in circulation is multiplied by X (T_v (o, R) +delta T_v (o, R))multipliedby c_v), and delta T_v (o, R) represents the difference between the time of the vehicle from the distributed center to R and the time of the vehicle from the distributed center to o, c_v is the cost of the vehicle unit time, if delta C_A is less than delta C_B, the scheme A is selected, and if delta C_A is more than or equal to delta C_B is selected; If the minimum incremental cost is still higher than the cost of a new vehicle in the overload zone o, virtual vehicles corresponding to the number and the capacity of the unmanned aerial vehicles exceeding the capacity are added for the overload zone o, and the step S2 is returned to perform cross-zone vehicle optimization; if all unmanned aerial vehicles cannot be completely distributed due to the distance and capacity, the strategy of increasing virtual vehicles corresponding to the number and capacity of unmanned aerial vehicles exceeding the capacity is adopted for the rest unmanned aerial vehicles, and the step S2 is returned to perform cross-region vehicle optimization.
9. The method for matching the collaborative configuration and the cross-region carrying of the low-altitude logistics vehicular machine according to claim 8, wherein, When a plurality of overload areas compete for the residual capacity of the same rich area: Sequencing overload areas in the overload area O set according to the overload severity or the task emergency, and preferentially selecting a rich area in the overload area with high priority; And dynamically updating the capacity, namely updating the value of the residual carrying capacity of one capacity-rich area immediately after the residual capacity of the one capacity-rich area is allocated, and only using the updated residual capacity when the subsequent overload area is in a preparation scheme.
10. A low-altitude logistics vehicular machine collaborative configuration and cross-region carrying matching system for realizing the low-altitude logistics vehicular machine collaborative configuration and cross-region carrying matching method as set forth in any one of claims 1-9, the system comprising: the data preprocessing module is configured to acquire original data, clean, convert, count and normalize the original data and then output the data to the post-stage module, wherein the original data comprises logistics area basic data, order data, unmanned aerial vehicle and vehicle performance data and history and experience data; The single-zone configuration optimizing module is configured to calculate the quantity of vehicles and unmanned aerial vehicles when the logistics cost is the lowest according to the data output by the data preprocessing module for all single logistics zones, and perform preliminary real vehicle and virtual vehicle decision and zone type marking, wherein the zone types comprise a common logistics zone and a logistics zone to be optimized; The multi-region vehicle joint optimization module is configured to determine whether a virtual vehicle of a logistics region marked as a logistics region to be optimized is to be converted into a real vehicle or is combined with virtual vehicles of other regions to be optimized through cross-region cluster analysis; The unmanned aerial vehicle cross-region carrying optimization module is configured to arrange unmanned aerial vehicles exceeding capacity on vehicles in other logistics areas with surplus carrying capacity or add virtual vehicles through optimization decisions.

Description

Low-altitude logistics vehicle-machine collaborative configuration and transregional carrying matching method and system Technical Field The invention relates to the technical field of low-altitude logistics, in particular to a method and a system for collaborative configuration and transregional carrying matching of low-altitude logistics vehicles and machines. Background With low-altitude economy being positioned as an emerging industry, low-altitude logistics represented by unmanned aerial vehicles are coming to a key opportunity for large-scale and commercial development. The method has the advantages that an efficient and economic low-altitude logistics network is constructed, deep fusion and collaborative operation of the unmanned aerial vehicle and the existing ground logistics system are realized, and the method becomes a core challenge of releasing low-altitude economic potential and improving urban and rural logistics efficiency. The traditional logistics vehicle goods taking mode is low in efficiency when facing to scenes such as urban traffic jams, mountainous terrain complicacy and the like. Unmanned aerial vehicle has nimble, not influenced by ground traffic as emerging low altitude commodity circulation instrument, but its use cost is higher and the navigation is limited. To expand the service area, it is often necessary to deliver it by a vehicle to the vicinity of the target area for release, i.e. "co-operation of vehicles and machines". In areas of dense buildings and traffic congestion, unmanned aerial vehicles have significant efficiency advantages over vehicles, while in open areas, vehicles may be more cost effective. In logistics scenes such as front-end distribution and collection, reasonable vehicle and unmanned aerial vehicle quantity are arranged in different logistics areas according to traffic geographic characteristics, and better logistics effects can be achieved. However, the planning of the coordination of the vehicles and the machines in the prior art is often limited to simple regional loading, namely, the vehicles are loaded with unmanned planes with limited capacity to go to the same logistics area to execute logistics tasks, the number ratio of the vehicles and the machines is fixed, and the resource allocation is stiff. Therefore, the prior art lacks a flexible vehicle-machine cooperative configuration and a vehicle-machine dynamic carrying matching model crossing a material flow area, and is difficult to realize the optimal balance of the overall cost and the efficiency of the material flow. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a method and a system for collaborative configuration and transregional carrying matching of a low-altitude logistics vehicle and machine, which are used for solving the problems of low efficiency and insufficient resource utilization rate caused by fixed proportioning of the vehicle and machine in the prior art, flexibly configuring vehicle and unmanned aerial vehicle resources, optimizing transregional carrying matching and realizing efficient collaboration of logistics resources. In order to achieve the above purpose, the invention provides a method for matching cooperative configuration and transregional carrying of a low-altitude logistics vehicle machine, which comprises the following steps: The method comprises the steps of S1, carrying out cooperative configuration on vehicles and machines in all logistics areas, namely building a vehicle-machine cooperative logistics cost model integrating traffic efficiency coefficients according to basic information, order information, unmanned aerial vehicle performance parameters and vehicle performance parameters of the logistics areas, solving ideal numbers of vehicles and unmanned aerial vehicles in the logistics areas by using an optimizing algorithm based on the vehicle-machine cooperative logistics cost model with logistics cost minimized as a target, wherein the ideal numbers of the unmanned aerial vehicles are integers, the ideal numbers of the vehicles are floating points, downwards taking the integer from the ideal numbers of the vehicles to configure actual vehicle numbers, and setting the part exceeding the actual vehicle numbers as virtual vehicles; step S2, carrying out joint optimization configuration on the vehicles in the logistics areas to be optimized, wherein according to the vehicle cross-area circulation cost and the virtual vehicle-to-actual vehicle cost, whether the virtual vehicle in the logistics areas to be optimized is converted into an actual vehicle or is combined with the virtual vehicles in other logistics areas to be optimized into an actual vehicle is determined; And step S3, summarizing the final vehicle number and the unmanned aerial vehicle number of all logistics areas, outputting vehicles needing to be subjected to cross-area circulation and service area sets thereof, and carrying unmanned aerial vehicles excee