A method for screening the suitability of urban neighborhood building roofs for low-altitude logistics access

Abstract

This invention proposes a method for screening the suitability of urban block building rooftops for low-altitude logistics access. Using urban blocks as evaluation units, this invention treats building rooftops within a block as potential take-off and landing nodes, integrating them into the overall spatial structure of the block for systematic screening. This invention places rooftop nodes within the overall spatial structure comprised of their surrounding building height relationships, obstacle environments, and internal logistics accessibility conditions. It further establishes spatial topological relationships between rooftop nodes, introduces block-scale density and height constraints, establishes a node collaborative judgment mechanism, and constructs a set of judgment and screening processes for rooftop take-off and landing sites at the block scale. This invention can systematically explore the potential of existing building rooftops as take-off and landing sites for community small-item logistics drones without adding new urban land or altering the functions of existing buildings, significantly improving the overall adaptability and feasibility of the screening results in high-density urban built environments.

Description

Technical Field

[0001] This invention relates to the field of low-altitude logistics and urban building application technology, and in particular to a method for screening the suitability of urban block building roofs for low-altitude logistics access. Background Technology

[0002] With the development of low-altitude logistics technology, the use of drones for community parcel delivery has gradually become an important way to improve the efficiency of urban last-mile logistics. Depending on the operating radius and the target users, take-off and landing sites for logistics drones can be set up at various spatial scales, including centralized take-off and landing sites on the outskirts of cities, dedicated sites at the park or land use level, and street-level take-off and landing points directly embedded in the urban built environment. Different scales of take-off and landing sites have significant differences in spatial conditions, service efficiency, and implementation costs.

[0003] For small-item, high-frequency delivery scenarios targeting communities, logistics tasks are often characterized by short delivery radii, high response time requirements, and dispersed service points. While centralized or ground-based take-off and landing sites offer advantages in spatial integrity and operational organization, they often require additional ground transportation connections, making it difficult to effectively shorten the "last mile" or even the "last hundred meters" of delivery, thus weakening the technological advantages of low-altitude logistics in terms of timeliness and efficiency. In contrast, setting up take-off and landing points for logistics drones on building rooftops at the urban block scale can fully utilize existing building resources without increasing land use, achieving spatial proximity between the take-off and landing points and community service recipients. Building rooftops have natural advantages in height, continuity, and relative independence, which helps reduce interference from ground personnel and vehicles, simplify safety isolation measures, and shorten the connection path between drones and internal community logistics nodes. Therefore, rooftops, as block-scale take-off and landing sites for logistics drones, have higher feasibility and operational efficiency in high-density urban environments.

[0004] However, due to factors such as the wide variation in roof conditions of existing urban buildings, the complexity of surrounding obstacles, and diverse safety and operational requirements, not all rooftops are suitable for direct use as take-off and landing sites for logistics drones. Existing research mostly focuses on the design and capacity analysis of ground-based or station-level vertical take-off and landing facilities, or relies on experience with individual buildings, lacking a technical method that can quickly identify suitable rooftops with minimal information, based on urban blocks and focusing on rooftops. This, to some extent, restricts the efficient deployment of low-altitude logistics at the block scale in existing cities. Summary of the Invention

[0005] The purpose of this invention is to solve the problems in the prior art and to propose a method for screening the suitability of urban block building roofs for low-altitude logistics access.

[0006] This invention is achieved through the following technical solution: This invention proposes a method for screening the suitability of urban block building rooftops for low-altitude logistics access, the method comprising: Step S1: Obtaining basic roof conditions information: Taking urban blocks as evaluation units, obtain basic roof conditions information for each building roof within the block. The basic conditions information includes at least: roof type and continuous effective area that can be used for UAV take-off, landing and handover, distribution of obstacles around the roof, safe distance conditions between the available area of ​​the roof and the roof edge, and reachability path conditions between the roof and the interior space of the building. Step S2: Screening based on safety and operability baseline conditions: According to the preset safety and operability judgment rules, the rooftops obtained in Step S1 are initially screened to remove rooftops that do not meet the basic requirements for low-altitude logistics drone access; among them, when the rooftop does not have a continuous effective area that meets the minimum take-off and landing requirements, or the surrounding obstacles of the rooftop do not meet the take-off and landing safety requirements, or the safety distance at the edge of the rooftop is insufficient, or there is no continuous reachable path between the rooftop and the interior space of the building, the rooftop is determined to be an unusable rooftop; Step S3: Capability classification and attribute assignment of available roof nodes: For the roof nodes determined in step S2, the capabilities of the roof nodes are classified based on their continuous effective area, the complexity of the surrounding obstacle environment, and the margin of safety distance at the roof edge. The corresponding classification results are used as attribute parameters of the roof nodes to characterize their operational capabilities and service priorities in the block-scale low-altitude logistics system. Step S4: Construction of the roof node set at the block scale and introduction of spatial constraints: The roof nodes determined in step S2 are collected into a set of roof nodes at the block scale; and based on the building coverage, floor area ratio and building height distribution of the buildings in the block, spatial density constraints and height constraints at the block scale are constructed to limit the coordination mode and accessibility relationship between roof nodes. Step S5: Construction of rooftop node spatial topology: Based on the relative spatial position and relative height relationships between rooftop nodes, and combined with the spatial density constraints described in Step S4, construct the spatial topology reachability relationships between rooftop nodes at the block scale to form a rooftop node topology network; Step S6: Rooftop node combination screening based on topological collaboration: In the rooftop node topology network, based on the capability classification results of the rooftop nodes, identify rooftop nodes or combinations of rooftop nodes with collaborative operation potential, and jointly determine the rooftop nodes or combinations of rooftop nodes according to the coverage and operational redundancy requirements of low-altitude logistics services at the neighborhood scale, and determine the recommended access scheme.

[0007] Further, step S1 specifically includes: Step 1.1: Determining the evaluation unit and object; Step 1.2: Obtaining roof shape information; Step 1.3: Obtain information about the surrounding environment of the roof; Step 1.4: Obtaining roof accessibility data and constructing a database based on building attributes.

[0008] Further, step S2 specifically includes: Step 2.1, Minimum Continuous Effective Area Determination: Determine whether the roof has a continuous effective area that meets the preset minimum take-off and landing requirements; Step 2.2, Determining the clearance of surrounding obstacles; Based on the environmental information of the roof surroundings obtained in Step 1.3, taking the continuous and effective area of ​​the roof that can be used for UAV take-off and landing as a benchmark, a take-off and landing clearance space model is constructed, and spatial overlay analysis and height comparison technology are used to determine whether fixed obstacles around the roof intrude into the take-off and landing clearance space. Step 2.3: Based on the preliminary area obtained in step S2.1, the airworthiness safety boundary determination method with multi-dimensional constraint coupling is used to achieve refined screening of the available roof area; Step 2.4: Roof accessibility estimation based on building attributes; Based on the building's functional attributes and construction date, the possibility of forming a logistics channel between the roof and the building's interior space is estimated.

[0009] Further, step 2.3 specifically involves: First, dynamically calculating the horizontal safe setback distance at the roof edge based on the UAV model specifications and building height. Then, by offsetting the outer contour of the building roof inwards, candidate airworthiness zones are generated to eliminate the risk of high-altitude falls. Next, using spatial Boolean operations, the physical projection area occupied by structures is removed from the candidate airworthiness zones. Combined with digital surface model normal vector analysis, non-flat areas with slopes exceeding a preset threshold are eliminated, generating a preliminary effective take-off and landing zone. Finally, using the preliminary effective take-off and landing zone as a benchmark, a three-dimensional clearance envelope space is constructed upwards. LiDAR point cloud data is used to search for slender intrusions within the envelope space, and simultaneously, it is verified whether there are obstacles affecting link quality within the horizontal line-of-sight. Finally, a safe and usable take-off and landing zone meeting the three-dimensional airworthiness requirements is determined through a recursive correction algorithm.

[0010] Furthermore, step S3 specifically includes: Step 3.1, Area Redundancy Evaluation: Based on the relationship between the continuous effective area of ​​the roof and the preset minimum take-off and landing area threshold, evaluate the redundancy of the roof in terms of take-off and landing space. Step 3.2, Environmental Complexity Evaluation: Based on the distribution characteristics of obstacles around the roof and the degree of environmental openness, evaluate the environmental complexity during the take-off, landing, and operation of the UAV; Step 3.3, Safety margin evaluation; Based on the degree of safety setback distance at the roof edge, evaluate the stability and fault tolerance of the roof in terms of take-off and landing safety; Step 3.4, Applicability level classification.

[0011] Further, step S4 specifically includes: Step 4.1: Construct the set of rooftop nodes at the block scale; For all rooftop nodes within the block scale range, read the baseline condition determination results from step S2; Step 4.2: Construction of Block Spatial Density Constraints; Based on the building coverage and floor area ratio information of buildings within the block, the overall building spatial density characteristics of the block are characterized. According to the building spatial density characteristics, spatial density constraints at the block scale are constructed to limit the overall distribution density and collaborative scale of roof nodes at the block scale. Step 4.3: Based on the height distribution information of buildings within the block, construct a building height field at the block scale to characterize the overall distribution characteristics and vertical differences of building heights within the block.

[0012] Further, step S5 specifically includes: Step 5.1: Generating candidate relationships for rooftop node pairs; In the set of rooftop nodes at the block scale, any two different rooftop nodes constitute a candidate node pair; For each candidate node pair, obtain the relative positional relationship of the corresponding rooftop nodes in the planar space and the relative height relationship in the vertical direction, as the basic input for subsequent topology accessibility determination. Step 5.2, Planar accessibility determination based on spatial location relationship; For each candidate roof node pair generated in step 5.1, determine whether the two roof nodes meet the preset planar accessibility conditions based on their relative position relationship in the planar space. Step 5.3, Vertical accessibility determination based on height relationship; For the candidate roof node pairs that have passed the planar accessibility determination in Step 5.2, determine whether the preset vertical accessibility conditions are met between the two roof nodes based on their relative height relationship. Step 5.4, Rooftop Node Topology Confirmation and Network Construction: For rooftop node pairs that pass both Step 5.2 Planar Accessibility Determination and Step 5.3 Vertical Accessibility Determination, confirm that they have spatial topological accessibility at the neighborhood scale.

[0013] Further, step S6 specifically includes: Step 6.1: Generation of candidate rooftop node combinations for collaboration; Based on the rooftop node topology network constructed in step S5, identify rooftop node pairs or multi-node connected subsets that have spatial topological reachability relationships in the network, and use them as candidate combinations of collaborative rooftop nodes. Step 6.2: Feasibility screening of node combinations based on capability classification; Step 6.3: Validity determination of node combinations based on service coverage requirements; For the candidate collaborative rooftop node combinations determined in Step 6.2, evaluate the validity of the combinations in terms of spatial service range based on the low-altitude logistics service coverage requirements at the neighborhood scale. Step 6.4: Node combination optimization and scheme determination based on operational redundancy requirements; For the candidate collaborative rooftop node combinations determined in Step 6.3, the candidate combinations are further screened and optimized based on the operational redundancy requirements of the block-scale low-altitude logistics system.

[0014] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the urban block building roof suitability screening method for low-altitude logistics access.

[0015] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for screening the suitability of urban block building roofs for low-altitude logistics access.

[0016] The beneficial effects of this invention are: 1. Compared with existing technologies, this invention uses urban blocks as evaluation units and building rooftops within blocks as screening objects to construct a rooftop take-off and landing site screening method for community-based small-parcel logistics drone access. This method overcomes the limitations of existing technologies that make isolated judgments based on ground stations or individual buildings, and systematically identifies rooftop nodes within the overall spatial structure at the block scale. This allows the screening results to reflect the true availability of rooftop resources in a high-density urban built environment, significantly improving the applicability of the method in community-scale low-altitude logistics deployment.

[0017] 2. This invention sets minimum safety and operability conditions to quickly eliminate rooftops that do not meet basic access conditions. Based on this, it introduces quantitative indicators such as area redundancy, environmental complexity, and safety condition redundancy to classify and recommend available rooftops based on their suitability, thus realizing a system transformation from "whether it can be used" to "priority use".

[0018] 3. Based on the basic screening and capability classification of rooftop nodes, this invention introduces spatial density and height constraints at the neighborhood scale to construct spatial topological reachability relationships between rooftop nodes and further identify combinations of rooftop nodes with collaborative operation potential. Through this neighborhood-scale topology and combination screening mechanism, this invention can elevate judgment from a single-point perspective to a multi-node collaborative level, providing a structured decision-making basis for the overall layout and redundancy configuration of community-scale low-altitude logistics systems. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart of a method for screening the suitability of urban block building rooftops for low-altitude logistics access, as described in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Existing methods for evaluating the suitability of building rooftops for low-altitude logistics access primarily focus on the physical attributes of individual building rooftops, employing a piecemeal, independent screening approach. This lacks a systematic consideration of the spatial relationships and collaborative potential between multiple rooftops at the neighborhood scale. In high-density built-up areas, the effective operation of low-altitude logistics networks relies on a collaborative system comprised of multiple rooftop nodes. Its feasibility depends not only on individual point conditions but also on whether these nodes can form a continuous and stable topological network within the constraints of the overall neighborhood morphology. Existing methods cannot assess the spatial interconnectivity and overall service capacity of rooftop clusters, leading to fragmented screening results that are difficult to support networked layouts.

[0023] To address this, this invention proposes a collaborative screening method for rooftop clusters, using urban blocks as the evaluation unit, for low-altitude logistics access. This method overcomes the limitations of existing single-unit screening methods by introducing spatial topological relationships within building clusters and constraints on the overall morphology of the blocks, treating multiple rooftops as a set of associated nodes for joint judgment and combination optimization.

[0024] Specifically, in combination Figure 1 This invention proposes a method for screening the suitability of urban block building rooftops for low-altitude logistics access, the method comprising: Step S1: Obtaining Basic Roof Condition Information: Using urban blocks as evaluation units, obtain basic roof condition information for each building roof within the block. The basic condition information includes at least: roof type and continuous effective area available for UAV take-off, landing and handover, distribution of obstacles around the roof, safe distance conditions between the available roof area and the roof edge, and reachability path conditions between the roof and the building's interior space; wherein, the continuous effective area is the area within the available roof area that remains continuous and meets the minimum take-off and landing requirements after excluding the areas affected by parapet walls or shadows, the areas covered by lightning rods and their rotation or protection, and the high-temperature turbulence areas formed by air vents or exhaust facilities in the computer room. Step S2: Screening based on safety and operability baseline conditions: According to the preset safety and operability judgment rules, the rooftops obtained in Step S1 are initially screened to remove rooftops that do not meet the basic requirements for low-altitude logistics drone access; among them, when the rooftop does not have a continuous effective area that meets the minimum take-off and landing requirements, or the surrounding obstacles of the rooftop do not meet the take-off and landing safety requirements, or the safety distance at the edge of the rooftop is insufficient, or there is no continuous reachable path between the rooftop and the interior space of the building, the rooftop is determined to be an unusable rooftop; Step S3: Capability classification and attribute assignment of available roof nodes: For the roof nodes determined in step S2, the capabilities of the roof nodes are classified based on their continuous effective area, the complexity of the surrounding obstacle environment, and the margin of safety distance at the roof edge. The corresponding classification results are used as attribute parameters of the roof nodes to characterize their operational capabilities and service priorities in the block-scale low-altitude logistics system. Step S4: Construction of the roof node set at the block scale and introduction of spatial constraints: The roof nodes determined in step S2 are collected into a set of roof nodes at the block scale; and based on the building coverage, floor area ratio and building height distribution of the buildings in the block, spatial density constraints and height constraints at the block scale are constructed to limit the coordination mode and accessibility relationship between roof nodes. Step S5: Construction of rooftop node spatial topology: Based on the relative spatial position and relative height relationships between rooftop nodes, and combined with the spatial density constraints described in Step S4, construct the spatial topology reachability relationships between rooftop nodes at the block scale to form a rooftop node topology network; Step S6: Rooftop node combination screening based on topological collaboration: In the rooftop node topology network, based on the capability classification results of the rooftop nodes, identify rooftop nodes or combinations of rooftop nodes with collaborative operation potential, and jointly determine the rooftop nodes or combinations of rooftop nodes according to the coverage and operational redundancy requirements of low-altitude logistics services at the neighborhood scale, and determine the recommended access scheme.

[0025] Further, step S1 specifically includes: Step 1.1: Evaluation Unit and Object Determination; Using urban blocks as evaluation units, the spatial range of the urban blocks is determined through urban plot boundary data, street block division data, or road enclosure boundaries; within the evaluation unit range, all building entities are identified and extracted as candidate building objects, and the roofs of each candidate building are further used as specific objects for landing field suitability screening. High-resolution building roof morphology contours are obtained based on semantic segmentation and edge detection.

[0026] Step 1.2: Roof Morphology Information Acquisition; For each building roof identified in Step 1.1, acquire its basic morphological information, specifically including: roof type; roof plan outline range; and distribution of roof surface structures. Building function type is obtained based on GIS spatial analysis and POI data matching. The roof morphology outline and roof surface structure distribution are obtained based on airborne LiDAR laser scanning. On this basis, the roof plan is divided into regions, and equipment, structures, and structural protrusions are removed.

[0027] Step 1.3: Obtaining information on the surrounding environment of the rooftops; Building clusters and height data in the block are obtained using drone swarm aerial photography or airborne LiDAR point cloud scanning, with planned grid-like flight paths covering the entire block area. Digital surface models (DSM) and digital elevation models (DEM) are generated in batches using Pix4D. The height of each building (H=DSM-DEM) is calculated in batches using ArcGIS Pro's "Batch Stretch Tool" based on the building coverage area data. Low-altitude obstacle batch identification data is obtained by automatically extracting the 3D coordinates and dimensions of obstacles such as high-voltage cables, tower cranes, billboards, and trees within the block based on LiDAR classification point cloud data. For drone no-fly zones, a backpack-mounted 3D laser scanner is used for street sweeping, and the collected data is seamlessly stitched with the aerial point cloud data. The terrain data is used to extract slope and elevation undulation information from the digital elevation model; the land parcel function information is obtained by identifying different function types using remote sensing image data combined with supervised classification methods; simultaneously, road and green space vector data provided by public geographic information platforms are used to supplement the spatial structure and functional attributes of the block.

[0028] Step 1.4: Roof Accessibility Data Acquisition and Database Construction Based on Building Attributes. Building functional attributes include public buildings, residential buildings, and industrial buildings. The construction year is determined based on urban surveying and real estate data. A building code constraint library is established. This library is a structured knowledge base that links building attributes with roof accessibility configuration rules. Its core function is to quickly match corresponding vertical transportation (stairs, elevators), roof entrances / exits, and passageway settings with corresponding code requirements and typical configurations based on the building's functional type, construction year, and number of floors. The building code constraint library includes primary functional type, secondary functional type, construction year segmentation, number of floors range, vertical transportation code requirements, roof entrance / exit requirements, passageway configuration requirements, safety facilities, code source, rule type, typical configuration characteristics, and special instructions.

[0029] Further, step S2 specifically includes: Step 2.1: Minimum Continuous Effective Area Determination; Determining whether the roof has a continuous effective area that meets the preset minimum take-off and landing requirements; the determination process specifically includes: using the model with the largest outer dimension among the community small-parcel logistics drones serving the target service as the design model, obtaining the maximum outer dimension parameters of the design model, and determining the minimum take-off and landing space scale required for the drone's vertical take-off and landing based on these parameters. On this basis, combined with a preset safety buffer distance, a minimum effective take-off and landing area required for the drone's take-off and landing is constructed. The size of the minimum effective take-off and landing area is jointly determined by the outer dimension of the design model and the safety buffer distance, and is used to cover the safety space required for the drone's vertical take-off and landing and operation. Further, the size of the minimum effective take-off and landing area is used as a minimum take-off and landing area threshold to determine whether there is a continuous area within the roof plane that is not separated by equipment, structures, or structural protrusions, and whose area is not less than the minimum take-off and landing area threshold. When there is no continuous effective area on the roof that meets the above minimum take-off and landing area threshold, the roof is determined to be an unusable roof; when there is a continuous effective area that meets the conditions, the subsequent screening steps are performed.

[0030] Step 2.2, Obstacle Clearance Determination: Based on the rooftop surrounding environment information obtained in Step 1.3, and taking the continuous effective area of ​​the rooftop suitable for UAV take-off and landing as a benchmark, a take-off and landing clearance space model is constructed. Spatial overlay analysis and height comparison techniques are used to determine whether fixed obstacles around the rooftop intrude into the take-off and landing clearance space. These fixed obstacles include, but are not limited to, adjacent buildings, rooftop structures, equipment, or other physical structures extending above the roof plane. When a fixed obstacle intrudes into the preset take-off and landing clearance range, significantly interfering with the UAV's vertical take-off and landing or entry / exit path, the rooftop is determined not to meet the obstacle clearance requirements and is deemed unusable. If none of the above applies, the rooftop passes the obstacle clearance determination.

[0031] Step 2.3: For the preliminary area obtained in Step S2.1, a refined screening of the available roof area is achieved through a multi-dimensional constraint-coupled airworthiness safety boundary determination method. Specifically, Step 2.3 involves: First, dynamically calculating the horizontal safety clearance distance at the roof edge based on the UAV model specifications and building height. By offsetting the outer contour of the building roof inward, candidate airworthiness zones that exclude the risk of high-altitude falls are generated. Then, using spatial Boolean operations, the physical projection area occupied by structures such as elevator machine rooms, water tanks, and ventilation openings is removed from the candidate airworthiness zones. Combined with digital surface model normal vector analysis, non-flat areas with slopes greater than a preset threshold are removed, generating a preliminary effective take-off and landing zone. Finally, based on the preliminary effective take-off and landing zone, a three-dimensional clearance envelope space is constructed upward. LiDAR point cloud data is used to search for the presence of slender intrusions such as antennas, lightning rods, and billboards within the envelope space. Simultaneously, it is verified whether there are obstacles affecting link quality within the horizontal line of sight. Finally, a safe and available take-off and landing zone that meets the three-dimensional airworthiness requirements is determined through a recursive correction algorithm.

[0032] Step 2.4: Roof Accessibility Assumption Based on Building Attributes; Based on the building's functional attributes and construction date, the possibility of forming a logistics channel between the roof and the building's interior space is estimated. The building's functional attributes include public buildings, residential buildings, and industrial buildings, and the construction date is determined based on urban surveying and real estate data. Based on the building's functional attributes and construction date, a pre-established building code constraint library is invoked. When a building meets the preset roof accessibility conditions, it is determined that the roof has the structural potential to form a logistics channel with the building's interior space; when a building does not meet the conditions, its roof accessibility is determined to be limited, and its capability rating is lowered or it is treated as an unusable roof node in subsequent steps.

[0033] Furthermore, step S3 specifically includes: Step 3.1, Area Redundancy Evaluation; Based on the relationship between the continuous effective area of ​​the roof and the preset minimum take-off and landing area threshold, evaluate the redundancy of the roof in terms of take-off and landing space; based on the continuous effective area A of the roof eff With minimum takeoff and landing area threshold A min The relationship between the area surplus index R and the area surplus index R is calculated. area This is used to characterize the extent of the roof's slack in terms of take-off and landing space.

[0034] Define parameters: A eff : Continuous effective area of ​​the roof; A min Minimum takeoff and landing area threshold (determined by step 2.1); Formula for calculating area surplus

[0035] R area ≥0: indicates that the minimum takeoff and landing requirements are met; R area The larger the value, the greater the amount of extra space the roof provides for take-off and landing.

[0036] Step 3.2, Environmental Complexity Evaluation: Based on the distribution characteristics of obstacles around the roof and the degree of environmental openness, evaluate the environmental complexity during the take-off, landing, and operation of the UAV; calculate the environmental complexity index C by statistically analyzing the obstacles around the roof and combining the spatial relationships of the obstacles. env It is used to evaluate the environmental complexity during the take-off, landing and operation of drones.

[0037] Define parameters: Within the preset analysis range, a total of n obstacles were identified, and the influence weight of the i-th obstacle was w. i Environmental complexity index calculation formula

[0038] C env The larger the value, the more complex the environment; the influence weight w of the i-th obstacle. i It can be determined by its spatial relationship with the take-off and landing area, including the horizontal distance between it and the center point of the take-off and landing area and the height difference of its height relative to the roof take-off and landing plane.

[0039]

[0040] Wherein: H i D represents the height difference between the i-th obstacle and the roof landing plane. i It represents the horizontal distance from the i-th obstacle to the center point of the take-off and landing area.

[0041] Step 3.3, Safety margin evaluation; Based on the degree of safety setback distance at the roof edge, evaluate the stability and fault tolerance of the roof in terms of take-off and landing safety; Define parameters: D edge : Minimum actual distance from the continuous effective takeoff and landing area to the outer edge of the roof; D safe : Preset safe retreat distance threshold; Safety margin calculation formula

[0042] R safe ≥0: indicates that the safety clearance requirement is met, R safe The larger the value, the more sufficient the safety redundancy.

[0043] Step 3.4, Applicability Level Classification. Based on the evaluation indicators obtained in steps 3.1–3.3, a comprehensive rooftop applicability scoring function is constructed to achieve applicability level classification and recommendation order generation. The weight coefficients can be determined based on multi-indicator decision-making methods, including but not limited to the Analytic Hierarchy Process (AHP), expert scoring methods, or parameter calibration methods based on historical sample data. The specific value selection method is not limited. The calculation method is as follows: S=α R area β C env +γ R safe Where S is the overall suitability score for the roof, and R... area C is an indicator of area surplus. env R is an indicator of environmental complexity. safe The safety margin index is defined by α, β, and γ, which are preset weighting coefficients.

[0044] Further, step S4 specifically includes: Step 4.1: Constructing a set of rooftop nodes at the neighborhood scale; For all rooftop nodes within the neighborhood scale, read the baseline condition determination results from step S2; Rooftop nodes that pass the determination in step S2 and meet the basic conditions for take-off, landing and handover of low-altitude logistics drones are uniformly identified as available rooftop nodes; All available rooftop nodes are aggregated to construct a set of rooftop nodes at the neighborhood scale, which is used to characterize the total number of rooftop nodes that can participate in low-altitude logistics collaborative operation within the neighborhood.

[0045] Step 4.2: Construction of Block Spatial Density Constraints; Based on the building coverage and floor area ratio information of buildings within the block, the overall building spatial density characteristics of the block are characterized. According to the building spatial density characteristics, block-scale spatial density constraints are constructed to limit the overall distribution density and collaborative scale of roof nodes at the block scale. The spatial density constraints are used to reflect the degree of aggregation or dispersion that roof nodes may form in space within the block, and serve as the constraint input for subsequent determination of roof node collaborative methods.

[0046] Step 4.3: Based on the building height distribution information within the block, construct a block-scale building height field to characterize the overall distribution characteristics and vertical differences of building heights within the block. Based on this building height field, construct block-scale height constraints to limit the relative accessibility and collaborative relationships between rooftop nodes in the vertical direction. These height constraints reflect potential limitations between rooftop nodes in terms of height differences, line-of-sight obstruction, and vertical connection, and serve as important constraint inputs for subsequent construction of rooftop node spatial topology relationships.

[0047] Further, step S5 specifically includes: Step 5.1: Generating candidate relationships for rooftop node pairs; In the set of rooftop nodes at the block scale, any two different rooftop nodes constitute a candidate node pair; For each candidate node pair, obtain the relative positional relationship of the corresponding rooftop nodes in the planar space and the relative height relationship in the vertical direction, as the basic input for subsequent topology accessibility determination. Step 5.2: Planar Accessibility Determination Based on Spatial Relationship; For each candidate rooftop node pair generated in Step 5.1, based on their relative positional relationship in planar space, determine whether the two rooftop nodes meet the preset planar accessibility conditions; the planar accessibility conditions are used to characterize whether the two rooftop nodes have the spatial possibility of forming a cooperative running path in the horizontal direction, and the determination process includes at least: determining whether the horizontal distance between the two rooftop nodes is within the preset accessible distance range; determining whether the connection between the two rooftop nodes is significantly restricted by the neighborhood space density constraint conditions. When the candidate rooftop node pair meets the planar accessibility conditions, the node pair is determined to pass the planar accessibility determination; otherwise, the node pair is determined not to pass the planar accessibility determination.

[0048] Step 5.3: Vertical Accessibility Determination Based on Height Relationship; For candidate rooftop node pairs that passed the planar accessibility determination in Step 5.2, based on their relative height relationship, determine whether the two rooftop nodes meet the preset vertical accessibility conditions; the vertical accessibility conditions are used to characterize whether the two rooftop nodes have the height conditions to form a cooperative operating relationship in the vertical direction, and the determination process includes: determining whether the height difference between the two rooftop nodes is within a preset acceptable range; determining whether the height difference is restricted by the block height constraint conditions. When the candidate rooftop node pair meets the vertical accessibility conditions, the node pair is determined to pass the vertical accessibility determination; otherwise, the node pair is determined to fail the vertical accessibility determination.

[0049] Step 5.4: Rooftop Node Topology Confirmation and Network Construction; For rooftop node pairs that pass both the planar accessibility determination in Step 5.2 and the vertical accessibility determination in Step 5.3, confirm their spatial topological accessibility at the neighborhood scale. Use the rooftop nodes that pass the determination as nodes in the topological network, and the spatial topological accessibility relationships between the rooftop node pairs as edges in the network, constructing a neighborhood-scale rooftop node topological network. This rooftop node topological network is used to characterize the potential collaborative connection structure between rooftop nodes at the neighborhood scale and serves as the input basis for subsequent rooftop node collaborative combination screening.

[0050] Further, step S6 specifically includes: Step 6.1: Generation of candidate rooftop node combinations for collaboration; Based on the rooftop node topology network constructed in step S5, identify rooftop node pairs or multi-node connected subsets that have spatial topological reachability relationships in the network as candidate combinations of collaborative rooftop nodes; Among them, a single rooftop node can be used as a single-node candidate combination; Any two or more rooftop nodes that are directly or indirectly connected in the topology network through edges can be used as multi-node candidate collaborative combinations.

[0051] Step 6.2: Feasibility screening of node combinations based on capability classification; For each candidate collaborative rooftop node combination generated in Step 6.1, read the capability classification results of each rooftop node included in it obtained in Step S3. Based on preset capability combination rules, determine whether the candidate combination meets the basic capability requirements for collaborative operation. The capability combination rules include at least: whether the capability classification of a single node combination reaches a preset minimum capability level; whether a multi-node combination contains at least one rooftop node with a higher capability classification, or whether the capability classifications of multiple nodes are complementary at the combination level. When a candidate combination does not meet the capability combination rules, it is determined to be an infeasible combination; when it does, proceed to the subsequent determination steps.

[0052] Step 6.3: Validity Determination of Node Combinations Based on Service Coverage Requirements; For the candidate collaborative rooftop node combinations determined in Step 6.2, the validity of the combination in terms of spatial service range is evaluated based on the low-altitude logistics service coverage requirements at the block scale; the service coverage requirements are used to characterize the spatial distribution characteristics of low-altitude logistics service objects within the block. Based on the location distribution of the rooftop nodes in the candidate combination within the block space, it is determined whether the service coverage range formed by it can meet the preset service coverage requirements. When the service coverage range formed by the candidate combination is insufficient to meet the service coverage requirements, the candidate combination is determined to be an insufficient coverage combination; when the service coverage requirements are met, the subsequent determination steps are proceeded.

[0053] Step 6.4: Node Combination Optimization and Scheme Determination Based on Operational Redundancy Requirements; For the candidate collaborative rooftop node combinations determined in Step 6.3, further screening and optimization are performed on the candidate combinations based on the operational redundancy requirements of the block-scale low-altitude logistics system. The operational redundancy requirements characterize the need for the low-altitude logistics system to maintain basic operational capabilities when a single rooftop node is unavailable or operationally restricted. Based on the number, spatial distribution, and topological connectivity of rooftop nodes in the candidate combinations, it is determined whether the combination has alternative or backup nodes. Among the candidate combinations that meet service coverage requirements, those with higher operational redundancy capabilities are preferentially selected as the recommended low-altitude logistics access scheme.

[0054] This invention proposes a method and object system for screening take-off and landing sites for low-altitude logistics drones, oriented towards the neighborhood scale and guided by building rooftops. Unlike existing research on low-altitude logistics or urban air transportation, which often focuses on ground-based stations or individual buildings and relies primarily on empirical judgment or single-point feasibility analysis, this invention, for the first time, uses urban neighborhoods as evaluation units, treating building rooftops within the neighborhood as potential take-off and landing nodes and systematically screening them within the overall spatial structure of the neighborhood. This invention places rooftop nodes within the overall spatial structure comprised of their surrounding building height relationships, obstacle environments, and internal logistics accessibility conditions, constructing a neighborhood-scale judgment and screening process for rooftop take-off and landing sites. This breaks through the traditional approach of isolated judgments based solely on individual rooftops or station-level facilities. By introducing the neighborhood scale, this invention can systematically explore the potential of existing building rooftops as take-off and landing sites for small-item logistics drones without adding new urban land or altering the functions of existing buildings, significantly improving the overall adaptability and feasibility of the screening results in high-density urban built-up environments.

[0055] This invention constructs a minimum information set screening method for rooftop take-off and landing sites for community small-parcel logistics drones. Addressing the issue that existing methods generally rely on high-precision 3D modeling, complex weather simulations, or operational scheduling data, this invention proposes a rooftop screening method based on a minimum information set. The judgment can be completed based solely on the following essential information: continuous effective roof area; spatial distribution of surrounding fixed obstacles; safe setback distance at the roof edge; and accessibility of the rooftop-to-building logistics path. This minimum information set avoids dependence on high-cost data and complex models, enabling the method to be rapidly applied to large-scale urban blocks in the early stages of low-altitude logistics deployment. It provides a lightweight and scalable technical approach for low-altitude logistics planning in existing building environments.

[0056] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the urban block building roof suitability screening method for low-altitude logistics access.

[0057] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for screening the suitability of urban block building roofs for low-altitude logistics access.

[0058] The memory in this application embodiment can be volatile memory or non-volatile memory, or it can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the methods described in this invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0059] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0060] In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are omitted here.

[0061] It should be noted that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the integrated logic circuitry in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied as execution by a hardware decoding processor, or as a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0062] The above provides a detailed description of the urban block building rooftop suitability screening method for low-altitude logistics access proposed in this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for screening the suitability of urban neighborhood building rooftops for low-altitude logistics access, characterized by, The method includes: Step S1: Obtaining basic roof conditions information: Taking urban blocks as evaluation units, obtain basic roof conditions information for each building roof within the block. The basic conditions information includes at least: roof type and continuous effective area that can be used for UAV take-off, landing and handover, distribution of obstacles around the roof, safe distance conditions between the available area of ​​the roof and the roof edge, and reachability path conditions between the roof and the interior space of the building. Step S2: Screening based on safety and operability baseline conditions: According to the preset safety and operability judgment rules, the rooftops obtained in Step S1 are initially screened to remove rooftops that do not meet the basic requirements for low-altitude logistics drone access; among them, when the rooftop does not have a continuous effective area that meets the minimum take-off and landing requirements, or the surrounding obstacles of the rooftop do not meet the take-off and landing safety requirements, or the safety distance at the edge of the rooftop is insufficient, or there is no continuous reachable path between the rooftop and the interior space of the building, the rooftop is determined to be an unusable rooftop; Step S3: Capability classification and attribute assignment of available roof nodes: For the roof nodes determined in step S2, the capabilities of the roof nodes are classified based on their continuous effective area, the complexity of the surrounding obstacle environment, and the margin of safety distance at the roof edge. The corresponding classification results are used as attribute parameters of the roof nodes to characterize their operational capabilities and service priorities in the block-scale low-altitude logistics system. Step S4: Construction of the roof node set at the block scale and introduction of spatial constraints: The roof nodes determined in step S2 are collected into a set of roof nodes at the block scale; and based on the building coverage, floor area ratio and building height distribution of the buildings in the block, spatial density constraints and height constraints at the block scale are constructed to limit the coordination mode and accessibility relationship between roof nodes. Step S5: Construction of rooftop node spatial topology: Based on the relative spatial position and relative height relationships between rooftop nodes, and combined with the spatial density constraints described in Step S4, construct the spatial topology reachability relationships between rooftop nodes at the block scale to form a rooftop node topology network; Step S6: Rooftop node combination screening based on topological collaboration: In the rooftop node topology network, based on the capability classification results of the rooftop nodes, identify rooftop nodes or combinations of rooftop nodes with collaborative operation potential, and jointly determine the rooftop nodes or combinations of rooftop nodes according to the coverage and operational redundancy requirements of low-altitude logistics services at the neighborhood scale, and determine the recommended access scheme.

2. The method of claim 1, wherein, Step S1 specifically includes: Step 1.1: Determining the evaluation unit and object; Step 1.2: Obtaining roof shape information; Step 1.3: Obtain information about the surrounding environment of the roof; Step 1.4: Obtaining roof accessibility data and constructing a database based on building attributes.

3. The method of claim 2, wherein, Step S2 specifically includes: Step 2.1, Minimum Continuous Effective Area Determination: Determine whether the roof has a continuous effective area that meets the preset minimum take-off and landing requirements; Step 2.2, Determining the clearance of surrounding obstacles; Based on the environmental information of the roof surroundings obtained in Step 1.3, taking the continuous and effective area of ​​the roof that can be used for UAV take-off and landing as a benchmark, a take-off and landing clearance space model is constructed, and spatial overlay analysis and height comparison technology are used to determine whether fixed obstacles around the roof intrude into the take-off and landing clearance space. Step 2.3: Based on the preliminary area obtained in step S2.1, the airworthiness safety boundary determination method with multi-dimensional constraint coupling is used to achieve refined screening of the available roof area; Step 2.4: Roof accessibility estimation based on building attributes; Based on the building's functional attributes and construction date, the possibility of forming a logistics channel between the roof and the building's interior space is estimated.

4. The method of claim 3, wherein, Step 2.3 specifically involves: First, based on the UAV model specifications and building height, dynamically calculate the horizontal safety clearance distance at the roof edge, and generate candidate airworthiness zones that exclude the risk of falling from heights by offsetting the outer contour of the building roof inwards. Subsequently, using spatial Boolean operations, the physical projection area occupied by structures is removed from the candidate airworthiness zones. Combined with digital surface model normal vector analysis, non-flat areas with slopes greater than a preset threshold are removed to generate preliminary effective take-off and landing zones. Finally, based on the preliminary effective take-off and landing zones, a three-dimensional clearance envelope space is constructed upwards. LiDAR point cloud data is used to search for whether there are slender intrusions in the envelope space, and at the same time, it is verified whether there are obstacles that affect link quality within the horizontal line of sight. Through a recursive correction algorithm, a safe and usable take-off and landing zone that meets the three-dimensional airworthiness requirements is finally determined.

5. The method according to claim 1, characterized in that, Step S3 specifically includes: Step 3.1, Area Redundancy Evaluation: Based on the relationship between the continuous effective area of ​​the roof and the preset minimum take-off and landing area threshold, evaluate the redundancy of the roof in terms of take-off and landing space. Step 3.2, Environmental Complexity Evaluation: Based on the distribution characteristics of obstacles around the roof and the degree of environmental openness, evaluate the environmental complexity during the take-off, landing, and operation of the UAV; Step 3.3, Safety margin evaluation; Based on the degree of safety setback distance at the roof edge, evaluate the stability and fault tolerance of the roof in terms of take-off and landing safety; Step 3.4, Applicability level classification.

6. The method according to claim 1, characterized in that, Step S4 specifically includes: Step 4.1: Construct the set of rooftop nodes at the block scale; For all rooftop nodes within the block scale range, read the baseline condition determination results from step S2; Step 4.2: Construction of Block Spatial Density Constraints; Based on the building coverage and floor area ratio information of buildings within the block, the overall building spatial density characteristics of the block are characterized. According to the building spatial density characteristics, spatial density constraints at the block scale are constructed to limit the overall distribution density and collaborative scale of roof nodes at the block scale. Step 4.3: Based on the height distribution information of buildings within the block, construct a building height field at the block scale to characterize the overall distribution characteristics and vertical differences of building heights within the block.

7. The method according to claim 1, characterized in that, Step S5 specifically includes: Step 5.1: Generating candidate relationships for rooftop node pairs; In the set of rooftop nodes at the block scale, any two different rooftop nodes constitute a candidate node pair; For each candidate node pair, obtain the relative positional relationship of the corresponding rooftop nodes in the planar space and the relative height relationship in the vertical direction, as the basic input for subsequent topology accessibility determination. Step 5.2, Planar accessibility determination based on spatial location relationship; For each candidate roof node pair generated in step 5.1, determine whether the two roof nodes meet the preset planar accessibility conditions based on their relative position relationship in the planar space. Step 5.3, Vertical accessibility determination based on height relationship; For the candidate roof node pairs that have passed the planar accessibility determination in Step 5.2, determine whether the preset vertical accessibility conditions are met between the two roof nodes based on their relative height relationship. Step 5.4, Rooftop Node Topology Confirmation and Network Construction: For rooftop node pairs that pass both Step 5.2 Planar Accessibility Determination and Step 5.3 Vertical Accessibility Determination, confirm that they have spatial topological accessibility at the neighborhood scale.

8. The method according to claim 1, characterized in that, Step S6 specifically includes: Step 6.1: Generation of candidate rooftop node combinations for collaboration; Based on the rooftop node topology network constructed in step S5, identify rooftop node pairs or multi-node connected subsets that have spatial topological reachability relationships in the network, and use them as candidate combinations of collaborative rooftop nodes. Step 6.2: Feasibility screening of node combinations based on capability classification; Step 6.3: Validity determination of node combinations based on service coverage requirements; For the candidate collaborative rooftop node combinations determined in Step 6.2, evaluate the validity of the combinations in terms of spatial service range based on the low-altitude logistics service coverage requirements at the neighborhood scale. Step 6.4: Node combination optimization and scheme determination based on operational redundancy requirements; For the candidate collaborative rooftop node combinations determined in Step 6.3, the candidate combinations are further screened and optimized based on the operational redundancy requirements of the block-scale low-altitude logistics system.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium for storing computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-8.

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