A method for planning logistics transportation point network in low-altitude airspace

Abstract

The present invention discloses a method for planning a network of logistics transportation points in low-altitude airspace, which relates to the technical field of airport planning, and includes the following steps: S1: based on the optimal location of logistics transportation points in low-altitude airspace, logistics flow, and a collection of alternative points of logistics transportation points, with the goal of maximizing the route attraction fitting degree in the region, determine the location of the core logistics transportation points in the low-altitude airspace; S2: based on the optimal location of logistics transportation points in urban low-altitude airspace, logistics flow, and a collection of alternative points of logistics transportation points, with the goal of minimizing construction and service costs, establish a site selection model, draw a route network, and obtain the number, location, and level of drone logistics transportation points in the region. The present invention adopts the above-mentioned method for planning a network of logistics transportation points in low-altitude airspace, studies the path planning technology of low-altitude urban logistics drones, and provides partial theoretical and methodological support for the autonomy and intelligence of drone low-altitude flight.

Description

Technical Field

[0001] The invention belongs to the technical field of airport planning, and in particular relates to a method for planning a logistics transportation point network in a low-altitude airspace. Background Art

[0002] Currently in the field of civil logistics, compared with traditional vehicle delivery, logistics drone delivery has high efficiency, strong practicality, great flexibility and low environmental pollution. It solves the problem that the delivery model of traditional outlets and couriers is prone to cause road congestion, air pollution and low delivery efficiency in terminal delivery.

[0003] Terminal delivery is an important link that every logistics company must consider in order to improve the industrial chain, connect with end customers, and improve convenience. Most of the existing research on low-altitude urban logistics drone path planning is oriented towards urban airspace. This research field is affected by many restrictive factors, is not specific enough, and has low feasibility.

[0004] Therefore, a method for planning a logistics transportation point network in low-altitude airspace is provided to solve the above problems. Summary of the invention

[0005] The purpose of this invention is to provide a method for network planning of logistics transportation points in low-altitude airspace, to study the path planning technology of low-altitude urban logistics UAVs, to provide some theoretical and methodological support for the autonomy and intelligence of UAV low-altitude flight, to ensure that low-altitude resources can be fully utilized in the future when the demand for low-altitude navigation is becoming increasingly strong, and to realize the safe and smooth flight of civil UAVs.

[0006] To achieve the above-mentioned purpose, the present invention provides a method for planning a network of logistics transportation points in low-altitude airspace, comprising the following steps: S1: based on the optimal location of logistics transportation points in low-altitude airspace, logistics flow, and a set of candidate points of logistics transportation points, with the goal of maximizing the route attraction fitting degree in the region, determining the location of the core logistics transportation point in the low-altitude airspace;

[0007] S2: Based on the optimal location of logistics transportation points, logistics flow, and a collection of alternative logistics transportation points in the urban low-altitude airspace, a site selection model is established with the goal of minimizing construction and service costs, and a route network is drawn to obtain the number, location, and level of drone logistics transportation points in the region.

[0008] Preferably, in step S1, the location of the core logistics transportation point in the low-altitude airspace adopts the following objective function:

[0009]

[0010]

[0011]

[0012] P3={(alt,req)|d altreq ≤R}

[0013] Among them, k alt The decision variable J is introduced as the construction and operation cost of the alternative point alt. alt , select alternative point alt, J alt =1; do not select alternative point alt, J alt =0, constant A is the gravitational balance coefficient, and the route attraction quality at the alternative point alt and the demand point req is the thermal index M of logistics transportation alt With M req express, is the generalized distance function between the alternative point alt and the demand point req, P1 is the set of alternative points alt, P2 is the set of demand points req, and R represents the radius covered by the core logistics transportation point in the low-altitude airspace;

[0014] The convex hull algorithm is used to solve the above objective function and obtain J alt , find out the alternative points where drone logistics transportation points need to be built.

[0015] Preferably, the candidate point P1 set is determined as follows: locations that meet any of the following conditions are selected within the area as candidate points;

[0016] Condition 1: A population settlement area with more than 1,000 people;

[0017] Condition 2: Locations with an average daily logistics demand of more than 500 pieces;

[0018] Condition 3: The location with the highest weekly take-off and landing volume reaching at least 300;

[0019] Condition 4: The road is congested and traffic control requires manual intervention.

[0020] Preferably, in step S2, based on the route utilization rate of the selected area, the thermal index of logistics transportation, the population density of the alternative point area and the performance requirements of the logistics drone itself, the locations and logistics flows of various logistics demand points in the area are determined, and the carrying level of the logistics transportation points that meet the needs of the demand points is determined.

[0021] Preferably, in the carrying level of the logistics transportation point, the small logistics drone take-off and landing point is level D; the medium-sized logistics drone take-off and landing point is level C; the large logistics drone take-off and landing point is level B; and the logistics drone hub take-off and landing platform is level A.

[0022] Preferably, the set of alternative points that meet the demand points in the area is determined as follows: the bearing level of the alternative point alt is not less than the bearing level of the demand point req.

[0023] Preferably, the site selection model is as follows:

[0024]

[0025]

[0026]

[0027] D={(alt',req')|L req' >L alt'}

[0028] X'=(X,…,X) T

[0029] X=(X0,X A ,X B ,X C ,X D )

[0030] Y(L req' +1,req′)=1

[0031] Among them, d alt' Indicates the minimum distance among the alternative points alt' that meet the requirements, d alt'req' represents the distance between the demand point req' and the alternative point alt' that meets the demand, D represents the set of alternative points alt' that meet the demand, P'1 is the set of alternative points alt' in the region, P'2 is the set of demand points req' in the region, X' is the matrix of construction and service costs, Y is the load level matrix, k const is the estimated fixed loss constant of the UAV, R alt' is the logistics transportation volume, L req' is the airport level of the demand point req', L alt' is the airport level of the alternative point alt', X=(X0,X A ,X B ,X C ,X D ) indicates that the airport levels are 0, A, B, C, and D, among which level 0 does not have a logistics transport point.

[0032] Preferably, the decision variable Z is introduced alt'req' , where L req' >L alt' , Z alt'req' =0; where L req' ≤L alt' , Z alt'req' =1, the convex hull algorithm is used to solve the objective function and obtain the level and location of the logistics transportation points in the region.

[0033] Therefore, the present invention adopts the above-mentioned method for planning a logistics transportation point network in low-altitude airspace, which has the following beneficial effects:

[0034] (1) The present invention adopts a site selection model to rationally plan a network of multiple logistics transportation points within a region, which will improve the coordination between logistics transportation points, reduce the cost of building transportation services, and improve the quality of logistics services.

[0035] (2) The present invention uses a convex hull algorithm to solve the objective function and find out the alternative points for building drone logistics transportation points and the level and location of logistics transportation points in the region.

[0036] (3) The present invention provides scientific methodological guidance for the future development of low-altitude aviation logistics industry and helps the transformation and upgrading of the logistics industry.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of a method for planning a network of logistics transportation points in low-altitude airspace according to the present invention;

[0039] Figure 2 A schematic diagram of a regional logistics transportation point coverage model of a logistics transportation point network planning method in low-altitude airspace according to the present invention; DETAILED DESCRIPTION

[0040] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0041] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0042] Example

[0043] like Figure 1-Figure 2As shown, the present invention provides a method for network planning of logistics transportation points in low-altitude airspace, comprising the following steps: S1: based on the optimal position of logistics transportation points in low-altitude airspace, logistics flow and a set of alternative points of logistics transportation points, with the goal of maximizing the route attraction fitting degree in the region, determining the position of the core logistics transportation point in the low-altitude airspace; S2: based on the optimal position of logistics transportation points in urban low-altitude airspace, logistics flow and a set of alternative points of logistics transportation points, with the goal of minimizing construction and service costs, establishing a site selection model, delineating a route network, and obtaining the number, position and level of drone logistics transportation points in the region.

[0044] In step S1, the location of the core logistics transportation point in the low-altitude airspace adopts the following objective function:

[0045]

[0046]

[0047]

[0048] P3={(alt,req)|d altreq ≤R}

[0049] Among them, k alt The decision variable J is introduced as the construction and operation cost of the alternative point alt. alt , select alternative point alt, J alt =1; do not select alternative point alt, J alt =0, constant A is the gravitational balance coefficient, and the route attraction quality at the alternative point alt and the demand point req is the thermal index M of logistics transportation alt With M req express, is the generalized distance function between the alternative point alt and the demand point req, P1 is the set of alternative points alt, P2 is the set of demand points req, and R represents the radius covered by the core logistics transportation point in the low-altitude airspace. The convex hull algorithm is used to solve the above objective function and obtain J alt , find out the alternative points where drone logistics transportation points need to be built.

[0050] The set of candidate points P1 is determined as follows: within the region, locations that meet any of the following conditions are selected as candidate points;

[0051] Condition 1: A population settlement area with more than 1,000 people;

[0052] Condition 2: Locations with an average daily logistics demand of more than 500 pieces;

[0053] Condition 3: The location with the highest weekly take-off and landing volume reaching at least 300;

[0054] Condition 4: The road is congested and traffic control requires manual intervention.

[0055] In step S2, based on the route utilization rate of the selected area, the thermal index of logistics transportation, the population density of the alternative point area and the performance requirements of the logistics drone itself, the locations and logistics flows of various logistics demand points in the area are determined, and the carrying level of the logistics transportation points that meet the needs of the demand points is determined.

[0056] Among the logistics transportation point carrying levels, the small logistics drone take-off and landing point is level D; the medium-sized logistics drone take-off and landing point is level C; the large logistics drone take-off and landing point is level B; the logistics drone hub take-off and landing platform is level A.

[0057] The set of alternative points that meet the demand points in the area is determined as follows: the bearing level of the alternative point alt is not less than the bearing level of the demand point req, and the alternative point alt can provide service capability for the demand point req and is an alternative point that meets the demand point.

[0058] The site selection model is as follows:

[0059]

[0060]

[0061]

[0062] D={(alt',req')|L req' >L alt'}

[0063] X'=(X,…,X) T

[0064] X=(X0,X A ,X B ,X C ,X D )

[0065] Y(L req' +1,req′)=1

[0066] Among them, d alt' Indicates the minimum distance among the alternative points alt' that meet the requirements, d alt'req' represents the distance between the demand point req' and the alternative point alt' that meets the demand, D represents the set of alternative points alt' that meet the demand, P'1 is the set of alternative points alt' in the region, P'2 is the set of demand points req' in the region, X' is the matrix of construction and service costs, Y is the load level matrix, k const is the estimated fixed loss constant of the UAV, R alt'is the logistics transportation volume, L req' is the airport level of the demand point req′, L alt' is the airport level of the alternative point alt′, X=(X0,X A ,X B ,X C ,X D ) indicates that the airport level is 0, A, B, C, D, among which no logistics transportation point is built at level 0. The location selection model is used to rationally plan the network of multiple logistics transportation points within the region, which will improve the coordination between logistics transportation points, reduce the cost of building transportation services, and improve the quality of logistics services.

[0067] Introducing decision variable Z alt′req′ , where L req′ >L alt′ , Z alt′req′ =0; where L req' ≤L alt' , Z alt'req' =1, the convex hull algorithm is used to solve the objective function and obtain the level and location of the logistics transportation points in the region.

[0068] Therefore, the present invention adopts the above-mentioned method for network planning of logistics transportation points in low-altitude airspace to study the path planning technology of low-altitude urban logistics drones, provide some theoretical and methodological support for the autonomy and intelligence of drone low-altitude flight, ensure that low-altitude resources can be fully utilized in the future when the demand for low-altitude aviation is becoming increasingly strong, and realize the safe and smooth flight of civil drones.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for planning a logistics transportation point network in low-altitude airspace, characterized by: The following steps are included: S1: Based on the optimal location of logistics transportation points in low-altitude airspace, logistics flow, and the set of alternative points of logistics transportation points, the location of the core logistics transportation points in low-altitude airspace is determined with the goal of maximizing the route attraction fit within the region; In step S1, the location of the core logistics transportation point in the low-altitude airspace adopts the following objective function: ； ； ； ； in, For the construction and operation cost of the alternative point alt, the decision variable is introduced , select the alternative point alt, =1; do not select alternative point alt, =0, constant A is the gravitational balance coefficient, and the route attraction quality at the alternative point alt and the demand point req is the thermal index of logistics transportation and express, is the generalized distance function between the alternative point alt and the demand point req, is a set of alternative points alt, is the set of demand points req, R represents the radius covered by the core logistics transportation points in the low-altitude airspace; The convex hull algorithm is used to solve the above objective function and we get , find out the alternative points where drone logistics transportation points need to be built; S2: Based on the optimal location of logistics transportation points, logistics flow, and the set of alternative points of logistics transportation points in the low-altitude airspace of the city, a site selection model is established with the goal of minimizing construction and service costs, and a route network is drawn to obtain the number, location, and level of drone logistics transportation points in the region; The site selection model is as follows: ； ； ； ； ； ； ； in, Indicates alternative points that meet the requirements The minimum distance in Indicates demand point Alternative points that meet your needs The distance between Indicates alternative points that meet the requirements A collection of Alternative points in the area A collection of Demand points in the region A collection of is the matrix of construction and service costs, is the carrier level matrix, is the estimated fixed loss constant of the drone, is the logistics transportation volume, For demand point Airport level, For alternative points Airport level, Indicates that the airport level is 0, A, B, C, D, among which level 0 does not have a logistics transportation point; Among the logistics transport point carrying levels, the small logistics drone take-off and landing point is level D; the medium-sized logistics drone take-off and landing point is level C; the large logistics drone take-off and landing point is level B; the logistics drone hub take-off and landing platform is level A; Introducing decision variables ,in , ;in , ,The convex hull algorithm is used to solve the objective function and obtain the level and location of the logistics transportation points built in the area.

2. According to claim 1, a method for planning a network of logistics transportation points in low-altitude airspace is characterized by: The alternative point The set is determined as follows: within the region, locations that meet any of the following conditions are selected as candidate points; Condition 1: A population settlement area with more than 1,000 people; Condition 2: Locations with an average daily logistics demand of more than 500 pieces; Condition 3: The location with the highest weekly take-off and landing volume reaching at least 300; Condition 4: The road is congested and traffic control requires manual intervention.

3. The method for planning a network of logistics transport points in low-altitude airspace according to claim 1, characterized in that: In step S2, based on the route utilization rate of the selected area, the thermal index of logistics transportation, the population density of the alternative point area and the performance requirements of the logistics drone itself, the locations and logistics flows of various logistics demand points in the area are determined, and the logistics transportation point carrying level that meets the needs of the demand points is determined.

4. The method for planning a network of logistics transport points in low-altitude airspace according to claim 1, characterized in that: The set of alternative points that meet the demand points in the area is determined as follows: the bearing level of the alternative point alt is not less than the bearing level of the demand point req.

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