CN112669461B - Airport clearance safety detection method and device, electronic equipment and storage medium - Google Patents
Airport clearance safety detection method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application provides an airport clearance safety detection method, an airport clearance safety detection device, electronic equipment and a storage medium, and relates to the technical field of data detection. The airport clearance safety detection method comprises the following steps: acquiring point cloud data and airport information data; classifying the point cloud data to obtain at least 1 type of point cloud data; acquiring an airport clearance model according to airport data; acquiring overrun points from the point cloud data according to the airport clearance model; and performing cluster analysis on the overrun points to obtain the types of the monomer barriers. According to the embodiment of the application, the point cloud data are subjected to data classification in advance, the classified point cloud data and the airport clearance model are utilized to obtain the overrun point, the monomer obstacle can be directly obtained after cluster analysis is carried out on the overrun point, and finally the monomer obstacle is identified according to the type of the point cloud data, so that the type of the monomer obstacle is obtained. The problem of complicated data format conversion is avoided, and the automation degree of airport clearance safety detection is improved.
Description
Technical Field
The present invention relates to the field of data detection, and in particular, to a method and apparatus for detecting airport clearance security, an electronic device, and a storage medium.
Background
Airport clearance is a space area which extends to the periphery based on a runway, and airport clearance regulation is a limiting requirement on the height of obstacles in the area. With the development of urban economy and the improvement of the living standard of people, more and more high-rise buildings are rising in various cities. Although these high-rise buildings play an important role in local economic life, for cities where civil airports or military airports are built, the existence of the high-rise buildings may destroy airport clearance, and affect the flight safety of the aircraft.
In the prior art, a digital elevation model (Digital Elevation Model, DEM for short) is established by using LiDAR point cloud data, a reference surface model is manufactured according to the requirements of an airport clearance protection area, an ultra-high building is screened out after comparing the two models, and then safety detection of airport clearance is performed by combining image interpretation, a topographic map and the like.
However, the existing detection method needs to combine a topographic map and a plurality of third party platforms to perform calculation and analysis, and format conversion is complicated.
Disclosure of Invention
In order to solve the problems in the prior art, the application provides an airport clearance safety detection method, an airport clearance safety detection device, electronic equipment and a storage medium.
The first aspect of the application provides an airport clearance safety detection method, which comprises the following steps:
acquiring point cloud data and airport information data;
classifying the point cloud data to obtain at least 1 type of point cloud data; at least the class 1 point cloud data comprises: ground points and non-ground points, the non-ground points comprising: buildings, trees, and towers;
acquiring an airport clearance model according to airport data;
acquiring overrun points from the point cloud data according to the airport clearance model, wherein the overrun points indicate points with heights exceeding a preset limit in the point cloud data;
and carrying out cluster analysis on the overrun points to obtain the types of the monomer barriers.
Optionally, the acquiring, according to the airport clearance model, an overrun point from the point cloud data includes:
acquiring a plurality of clearance model sub-surfaces according to the airport clearance model;
intersection point cloud data with intersection points of the clearance model subplane and the external bounding box of the point cloud data are obtained;
judging whether the airport clearance model subplane is a plane or not;
when the airport clearance model subplane is a plane, acquiring the overrun point by using a height difference method, the airport clearance model subplane and the intersection point cloud data;
and when the airport clearance model subplane is non-planar, acquiring the overrun point by using a collision detection method, the airport clearance model subplane and the intersection point cloud data.
Optionally, the classifying the data of the point cloud data to obtain at least 1 type of point cloud data includes:
dividing the point cloud data into ground points and non-ground points through an improved asymptotic triangle network encryption filtering algorithm;
the non-ground points are separated into buildings, trees and towers by a building extraction algorithm based on planar segmentation.
Optionally, the clustering analysis is performed on the overrun point to obtain the type of the monomer obstacle, including:
storing the overrun points according to a k-d tree structure to obtain target overrun point data;
and carrying out cluster analysis on the target overrun point data according to K neighbor query and a region growing algorithm to obtain the type of the single obstacle.
Optionally, after the cluster analysis is performed on the overrun point and the type of the monomer obstacle is obtained, the method further includes:
and drawing the contour line and the ultrahigh line of the single obstacle according to the corresponding points of the single obstacle.
Optionally, after the cluster analysis is performed on the overrun point and the type of the monomer obstacle is obtained, the method further includes:
and generating a visual three-dimensional model corresponding to the single obstacle according to the point corresponding to the single obstacle.
A second aspect of the present application provides an airport clearance safety inspection device comprising: an acquisition unit and a classification unit;
the acquisition unit is used for acquiring point cloud data and airport information data;
the classifying unit is used for classifying the point cloud data to obtain at least 1 type of point cloud data; at least the class 1 point cloud data comprises: ground points and non-ground points, the non-ground points comprising: buildings, trees, and towers;
the acquisition unit is also used for acquiring an airport clearance model according to airport data;
acquiring overrun points from the point cloud data according to the airport clearance model, wherein the overrun points indicate points with heights exceeding a preset limit in the point cloud data;
and carrying out cluster analysis on the overrun points to obtain the types of the monomer barriers.
Optionally, the acquiring unit is specifically configured to acquire a plurality of clearance model subplanes according to the airport clearance model;
intersection point cloud data with intersection points of the clearance model subplane and the external bounding box of the point cloud data are obtained;
judging whether the airport clearance model subplane is a plane or not;
when the airport clearance model subplane is a plane, acquiring the overrun point by using a height difference method, the airport clearance model subplane and the intersection point cloud data;
and when the airport clearance model subplane is non-planar, acquiring the overrun point by using a collision detection method, the airport clearance model subplane and the intersection point cloud data.
Optionally, the classification unit is specifically configured to divide the point cloud data into ground points and non-ground points through a modified asymptotic triangle network encryption filtering algorithm;
the non-ground points are separated into buildings, trees and towers by a building extraction algorithm based on planar segmentation.
Optionally, the acquiring unit is specifically configured to store the overrun point according to a k-d tree structure to obtain target overrun point data;
and carrying out cluster analysis on the target overrun point data according to K neighbor query and a region growing algorithm to obtain the type of the single obstacle.
Optionally, the apparatus further comprises: a drawing unit;
and the drawing unit is used for drawing the contour line and the ultrahigh line of the single obstacle according to the point corresponding to the single obstacle.
Optionally, the apparatus further comprises: a generating unit;
the generation unit is used for generating a visual three-dimensional model corresponding to the single obstacle according to the point corresponding to the single obstacle.
A third aspect of the present application provides an electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating over the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the method as described in the first aspect above.
A fourth aspect of the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method according to the first aspect described above.
The embodiment of the application provides an airport clearance safety detection method, an airport clearance safety detection device, electronic equipment and a storage medium. The airport clearance safety detection method comprises the following steps: acquiring point cloud data and airport information data; classifying the point cloud data to obtain at least 1 type of point cloud data; at least the class 1 point cloud data comprises: ground points and non-ground points, the non-ground points comprising: buildings, trees, and towers; acquiring an airport clearance model according to airport data; acquiring overrun points from the point cloud data according to the airport clearance model, wherein the overrun points indicate points with heights exceeding a preset limit in the point cloud data; and carrying out cluster analysis on the overrun points to obtain the types of the monomer barriers. In the embodiment of the application, the point cloud data are subjected to data classification in advance, the classified point cloud data are utilized to obtain the overrun point with the airport clearance model, the monomer obstacle can be directly obtained after the overrun point is subjected to cluster analysis, the monomer obstacle is finally identified according to the type of the point cloud data, the type of the monomer obstacle is obtained, the problem that in the prior art, the data conversion process is complicated in format conversion by means of a plurality of platforms is avoided, the type of the obstacle is marked, the problem that the airport clearance safety detection is directly obtained by the type of the point cloud data without manual comparison is solved, and the automation degree of the airport clearance safety detection is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of the present disclosure of a method for detecting airport clearance safety;
FIG. 2 is a flow chart of an airport clearance safety detection method according to an embodiment of the present application;
FIG. 3 is a flow chart of an airport clearance safety detection method according to another embodiment of the present application;
FIG. 4 is a flow chart of an airport clearance safety detection method according to another embodiment of the present application;
FIG. 5 is a schematic diagram of an airport clearance safety inspection device according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an airport clearance safety inspection device according to another embodiment of the present application;
FIG. 7 is a schematic diagram of an airport clearance safety inspection device according to another embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the accompanying drawings in the present application are only for the purpose of illustration and description, and are not intended to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. A flowchart, as used in this application, illustrates operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be implemented out of order and that steps without logical context may be performed in reverse order or concurrently. Moreover, one or more other operations may be added to the flow diagrams and one or more operations may be removed from the flow diagrams as directed by those skilled in the art.
In addition, the described embodiments are only some, but not all, of the embodiments of the present application. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the presence of the features stated hereinafter, but not to exclude the addition of other features.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
Furthermore, the terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, without conflict, features in embodiments of the present application may be combined with each other.
Airport clearance is defined as the space in and around an airport where objects that form an obstacle to the operation of an aircraft must be defined, and is a space region that extends around the runway. In general, airport clearance is determined by various clearance limiting surfaces, the height of which determines the upper limit of the height that a local building can construct. With the development of urban economy and the improvement of the living standard of people, the number of high-rise buildings is increased. Although these high-rise buildings play an important role in local economic life, for cities where civil airports or military airports are built, the existence of the high-rise buildings may destroy airport clearance, and affect the flight safety of the aircraft.
Fig. 1 is a flow chart of an existing airport clearance safety detection method according to an embodiment of the present application. As shown in fig. 1, in the prior art, firstly, ground objects below a preset height (for example, 30 meters) of a point cloud are removed, and the removed point cloud data are subjected to point cloud data format conversion; and generating a three-dimensional model of the reference surface according to the airport-related altitude limit rule. Comparing the three-dimensional model of the reference surface with the point cloud data after format conversion to obtain an elevation point exceeding an overrun point; and (5) comparing the height Cheng Dian exceeding the overrun point with the acquired topographic map, and finally completing the ultra-high ground feature screening.
Therefore, the existing detection mode needs to be combined with a topographic map and a plurality of third party platforms for calculation and analysis, and format conversion is complicated. In addition, when the elevation points exceeding the overrun point are compared with the topographic map, human participation is needed, and the degree of automation is not high.
In order to solve the technical problems in the prior art, the present application provides an inventive concept: the method comprises the steps of carrying out data classification on point cloud data in advance, obtaining overrun points by utilizing the classified point cloud data and an airport clearance model, carrying out cluster analysis on the overrun points, directly obtaining single obstacle, finally marking the single obstacle according to the type of the point cloud data, and obtaining the type of the single obstacle, so that the problems that in the prior art, the data conversion process is complicated by means of a plurality of platforms and format conversion are avoided, in addition, the type of the obstacle is marked, manual comparison is not needed, the single obstacle is directly obtained by the type of the point cloud data, and the automation degree of airport clearance safety detection is improved.
The specific technical solutions provided in the present application are described below through possible implementation manners.
Fig. 2 is a flow chart of an airport clearance safety detection method according to an embodiment of the present application, where an execution subject of the method may be a computer, a server, or other devices with processing functions. As shown in fig. 2, the method includes:
s201, acquiring point cloud data and airport information data.
The point cloud data is used to represent a set of vectors in a three-dimensional coordinate system, usually in the form of X, Y, Z three-dimensional coordinates, and is mainly used to represent the shape of the outer surface of an object. The point cloud data in the embodiment of the application is mainly radar reflection information of objects in a certain range around an airport, which is acquired through an airborne radar, and is also called LIDAR point cloud data.
Airport information data may be the national restrictions imposed on altitude in certain areas of airport coverage in order to ensure aircraft flight safety.
S202, classifying the point cloud data to obtain at least 1 type of point cloud data.
Optionally, in the embodiment of the present application, at least 1 type of point cloud data includes: ground points and non-ground points, the non-ground points including: buildings, trees, and towers.
It should be noted that the ground points may include: a point on the ground surface of a planar formation, a mountain surface, or the like.
Classification of ground points and non-ground points can be achieved by adopting a conditional random field, a gradient-based pseudo-scanning line point cloud data filtering algorithm or an improved asymptotic triangular network encryption filtering algorithm, and the embodiment of the application is not limited to a specific classification algorithm.
S203, acquiring an airport clearance model according to airport data.
In the embodiment of the application, according to airport data, the airport clearance model can be drawn by using drawing software according to the limit requirements of the country on the altitude in a certain area of the airport range.
S204, acquiring overrun points from the point cloud data according to the airport clearance model.
In one possible implementation, the overrun point may be obtained by collision detection using the entire airport headroom model and point cloud data.
In another possible implementation manner, the airport clearance model may be further divided into a plurality of clearance model subplanes, and the overrun point under the clearance model subplanes is obtained through the clearance model subplanes and point cloud data corresponding to the clearance model subplanes. And integrating all overrun points under the sub-surfaces of the clearance model to obtain all overrun points.
It should be noted that, in the embodiment of the present application, the overrun point indicates a point in the point cloud data, where the height exceeds a preset limit. Alternatively, the preset limit may be a set altitude contained in the airport clearance model.
The overrun points are individual points, and in order to perform overall analysis on the individual overrun points, a single obstacle is obtained, and in the following embodiment, cluster analysis processing may be performed on the overrun points.
S205, performing cluster analysis on the overrun points to obtain the types of the monomer barriers.
Alternatively, the manner of cluster analysis may employ: a partition-based approach, a hierarchy-based approach, a density-based approach, a grid-based approach, etc. The embodiment of the application does not limit the specific manner of cluster analysis.
In the embodiment of the present application, the same type of overrun points are clustered by using a clustering analysis method, so that the monomer obstacle can be obtained. In addition, the data types of all the point cloud data can be obtained by executing step S202, and the type of the single obstacle is marked according to the type of the point cloud data contained in the single obstacle, so as to obtain the type of the single obstacle. Illustratively, when a certain proportion of the point cloud data types included in the single obstacle are buildings, the single obstacle is marked with a "building".
The embodiment of the application provides an airport clearance safety detection method, which comprises the following steps: acquiring point cloud data and airport information data; classifying the point cloud data to obtain at least 1 type of point cloud data; at least the class 1 point cloud data comprises: ground points and non-ground points, the non-ground points comprising: buildings, trees, and towers; acquiring an airport clearance model according to airport data; acquiring overrun points from the point cloud data according to the airport clearance model, wherein the overrun points indicate points with heights exceeding a preset limit in the point cloud data; and carrying out cluster analysis on the overrun points to obtain the types of the monomer barriers. In the embodiment of the application, the point cloud data are subjected to data classification in advance, the classified point cloud data are utilized to obtain the overrun point with the airport clearance model, the monomer obstacle can be directly obtained after the overrun point is subjected to cluster analysis, the monomer obstacle is finally identified according to the type of the point cloud data, the type of the monomer obstacle is obtained, the problem that in the prior art, the data conversion process is complicated in format conversion by means of a plurality of platforms is avoided, the type of the obstacle is marked, the problem that the airport clearance safety detection is directly obtained by the type of the point cloud data without manual comparison is solved, and the automation degree of the airport clearance safety detection is improved.
When an overrun point is obtained from point cloud data in another possible implementation, the implementation is as shown in the following embodiment.
Fig. 3 is a flow chart of an airport clearance safety detection method according to another embodiment of the present application, as shown in fig. 3, step S204 may specifically further include:
s301, acquiring a plurality of clearance model sub-surfaces according to an airport clearance model.
S302, intersecting the clearance model subplane with an external bounding box of the point cloud data to obtain intersection point cloud data with the clearance model subplane.
S303, judging whether the airport clearance model subplane is a plane or not.
Optionally, in the embodiment of the present application, the airport clearance model may be divided into a plurality of portions according to a construction manner of the airport clearance model, so as to obtain a plurality of clearance model subplanes.
For example, airport headroom models defining a height at the same height may be partitioned into a headroom model sub-plane. Dividing an airport clearance model with limited height on the same inclined plane into another clearance model subplane.
Optionally, before performing the overrun point calculation, flying point data in the point cloud data may also be removed according to a reflection type of the point cloud data, where the flying point data is, for example: reflection data of dust, flying insects, and the like. It can be appreciated that in the embodiment of the application, by removing flying spot data in the point cloud data in advance, interference of the flying spot data on overrun point data can be avoided, and accuracy of obtaining the monomer obstacle is improved.
The circumscribed bounding box of the point cloud data can be a bounding box obtained by extending each point in the point cloud data to the airport clearance model direction.
In one implementation, the clearance model subplanes and the external bounding boxes of the point cloud data can be intersected to obtain intersection point cloud data corresponding to each clearance model subplane. In another implementation manner, the point cloud data can be subjected to octree indexing in advance, and the circumscribed bounding box of the indexed point cloud data is utilized to intersect with the clearance model subplane to obtain intersection point cloud data corresponding to each clearance model subplane.
It can be appreciated that in this embodiment, by making the point cloud data into the octree index in advance, the problem of rendering and scheduling of massive point cloud data can be solved, the calculation efficiency of the overrun point is improved, and the hardware requirement on the server is reduced.
And obtaining the calculation mode of the overrun point by judging the type of the sub-surface of the airport clearance model. The specific operation process is as follows:
and when the airport clearance model subplane is a plane, executing step S304, and acquiring overrun points by using a height difference method, the airport clearance model subplane and intersection point cloud data.
When the airport clearance model subplane is non-planar, step S305 is performed to obtain the overrun point using the collision detection method, the airport clearance model subplane, and the intersection point cloud data.
In the embodiment of the application, when the airport clearance model subplane is a plane, the overrun point is obtained by using a height difference method, the airport clearance model subplane and intersection point cloud data. The specific implementation process of the height difference method is as follows, whether the height value of all points in the intersection point cloud data is higher than the height value of the airport clearance model subplane or not is judged, and the points higher than the airport clearance model subplane in the intersection point cloud data are extracted to be used as overrun points.
And when the airport clearance model subplane is non-planar, acquiring an overrun point by using a collision detection method and the airport clearance model subplane and intersection point cloud data. The collision detection method is specifically implemented as follows: extending each point in the intersection point cloud data to the direction of the airport clearance model subplane until the intersection point cloud data intersects the airport clearance model subplane, and obtaining the extension height. And extracting points which are larger than the extension height of the cross point cloud data from the cross point cloud data to serve as overrun points.
It can be understood that in the embodiment of the application, the airport clearance model is divided into a plurality of airport clearance model subplanes, and the overrun points are acquired by utilizing the airport clearance model subplanes and the corresponding intersection point cloud data, so that the data operation amount is greatly reduced, the operation pressure of processing equipment is reduced, and the hardware requirement of the equipment is reduced.
Optionally, performing data classification on the point cloud data to obtain at least 1 type of point cloud data, including:
dividing the point cloud data into ground points and non-ground points through an improved asymptotic triangle network encryption filtering algorithm;
non-ground points are separated into buildings, trees and towers by a building extraction algorithm based on planar segmentation.
In the embodiment of the application, the point cloud data can be divided into the ground point and the non-ground point by adopting a modified asymptotic triangle network encryption filtering algorithm. In addition, a building extraction algorithm based on planar segmentation may also be used to divide non-ground points into buildings, trees, and towers.
The improved asymptotic triangular network encryption filtering algorithm comprises the following execution steps: and selecting the lowest point in a certain range area of the airport as a seed point to construct an initial triangular network. Then, other laser foot points are judged according to a certain criterion, and the points conforming to the judging criterion are encrypted into the initial triangle network. The criterion is as follows: the distance from the point to be fixed to the nearest triangle patch and the included angle between the connecting line of the point to be fixed and the nearest triangle vertex and the triangle patch are smaller than the set threshold. For points which do not meet the conditions, a mirror image technology is adopted for further judgment. That is, for the above-mentioned points which do not meet the condition, it is determined whether or not the mirror image point of the point meets the requirement. If the mirror point meets the requirement, still regard this point as the ground point; otherwise, as a non-ground point. Thus, the problem of distinguishing ground points such as steep chops, cliffs and the like is partially solved. The filtering process is performed by triangle iterative encryption, and the operation is finished when no new point is added to the triangle network.
The building extraction algorithm based on plane segmentation is executed as follows: extracting non-ground points with height information higher than a preset height in the non-ground point data to construct a target set, wherein the preset height can be 1.5 meters; calculating three-dimensional curvatures of each point and points around the points in the target set, and constructing plane index data based on Euclidean distance and three-dimensional curvatures of each point; clustering adjacent plane index data, determining artificial structure types, namely building and high tower data by judging whether the clustered adjacent planes are regular curves, wherein the rest irregular curve data are tree data, and finally judging whether the non-ground points are specifically building data or high tower data by judging the form of the clustered adjacent planes.
Fig. 4 is a flowchart of an airport clearance safety detection method according to another embodiment of the present application, as shown in fig. 4, step S205 may specifically include:
s401, storing the overrun points according to the k-d tree structure to obtain target overrun point data.
S402, clustering analysis is carried out on the target overrun point data according to K neighbor query and a region growing algorithm, and the type of the single obstacle is obtained.
In the embodiment of the present application, the point cloud data are scattered data points, that is, each data point only includes three-dimensional coordinate values of the point and categories of the point cloud data, and corresponding set topology information is not explicitly given. And constructing a three-dimensional index structure for the point cloud, namely restoring the real distribution of the point cloud in space by analyzing the position relation and the space coordinates of the points in the space of the discrete point cloud. The current method for analyzing the spatial position field relation of the point cloud mainly comprises the steps of constructing index structures such as a quadtree, a triangle network and the like. The k-d tree is a data structure that partitions k-dimensional data space. The advantage is that the Euclidean distance can be obtained quickly for all points in k-dimensional space. For LiDAR point cloud data, k-d trees with different dimensions can be selectively constructed. In the embodiment of the application, in order to improve the clustering efficiency of the overrun points, the overrun points are stored according to the k-d tree structure, and target overrun point data are obtained.
Further, by using K neighbor query (K-neighbor searches), finding out data with a distance smaller than the threshold value from target overrun point data according to a given query point and a threshold value of a query distance (a distance threshold value of a region growing algorithm), merging and clustering the data, and repeating the steps by using newly obtained points as query points until no suitable point exists in a growing threshold value set by the region growing algorithm, stopping calculation, selecting new seed points, repeating the steps, and finally obtaining the monomer obstacle. In addition, the type of the single obstacle is obtained by marking the type of the single obstacle according to the type of the point cloud data.
Optionally, after cluster analysis is performed on the overrun point to obtain the type of the monomer obstacle, the method further includes:
and drawing the contour line and the ultrahigh line of the single obstacle according to the corresponding points of the single obstacle.
In this embodiment of the present application, the contour line and the superelevation line of the single obstacle are drawn according to the points corresponding to the single obstacle, and specifically, the intersection point of each point in the single obstacle and the airport clearance sub-surface may be connected, and the superelevation line of the single obstacle is drawn.
The contour line of the single obstacle can be obtained by drawing the contour line on the periphery of the single obstacle through drawing software.
Optionally, after cluster analysis is performed on the overrun point to obtain the type of the monomer obstacle, the method further includes:
and generating a visual three-dimensional model corresponding to the monomer obstacle according to the point corresponding to the monomer obstacle.
In the embodiment of the application, point data corresponding to all the monomer barriers are input to drawing software to obtain the visual three-dimensional model corresponding to all the monomer barriers in a certain range of an airport, so that visual display can be carried out on a user, and the user can conveniently and intuitively check the point data.
It can be understood that in the embodiment of the application, the detection result of the airport clearance can be intuitively obtained by obtaining the visual three-dimensional model, so that corresponding measures can be timely taken for the obstacle with the height exceeding the limit.
The following describes a device, a storage medium, etc. corresponding to the airport clearance safety detection method provided by the present application, and specific implementation processes and technical effects of the device and the storage medium are referred to above, which are not described in detail below.
Fig. 5 is a schematic diagram of an airport clearance safety detection apparatus according to an embodiment of the present application, as shown in fig. 5, the apparatus may include: an acquisition unit 501 and a classification unit 502;
an acquiring unit 501, configured to acquire point cloud data and airport information data;
the classification unit 502 is configured to perform data classification on the point cloud data, and obtain at least 1 type of point cloud data; at least class 1 point cloud data includes: ground points and non-ground points, the non-ground points including: buildings, trees, and towers;
an obtaining unit 501, configured to obtain an airport clearance model according to airport data;
acquiring overrun points from the point cloud data according to the airport clearance model, wherein the overrun points indicate points with the heights exceeding a preset limit in the point cloud data;
and performing cluster analysis on the overrun points to obtain the types of the monomer barriers.
Optionally, the obtaining unit 501 is specifically configured to obtain a plurality of clearance model subplanes according to an airport clearance model;
intersecting the clearance model subplane with an external bounding box of the point cloud data to obtain intersection point cloud data with an intersection point of the clearance model subplane;
judging whether the airport clearance model subplane is a plane or not;
when the airport clearance model subplane is a plane, acquiring an overrun point by using a height difference method and airport clearance model subplane and intersection point cloud data;
and when the airport clearance model subplane is non-planar, acquiring an overrun point by using a collision detection method and the airport clearance model subplane and intersection point cloud data.
Optionally, the classification unit 502 is specifically configured to divide the point cloud data into a ground point and a non-ground point through a modified asymptotic triangle mesh encryption filtering algorithm;
non-ground points are separated into buildings, trees and towers by a building extraction algorithm based on planar segmentation.
Optionally, the obtaining unit 501 is specifically configured to store the overrun point according to a k-d tree structure, so as to obtain target overrun point data;
and clustering the target overrun point data according to K neighbor query and a region growing algorithm to obtain the type of the single obstacle.
Fig. 6 is a schematic diagram of an airport clearance safety detection apparatus according to another embodiment of the present application, as shown in fig. 6, the apparatus further includes: a drawing unit 503;
and a drawing unit 503 for drawing the contour line and the superelevation line of the single obstacle according to the corresponding point of the single obstacle.
Fig. 7 is a schematic diagram of an airport clearance safety detection apparatus according to another embodiment of the present application, as shown in fig. 7, the apparatus further includes: a generating unit 504;
and the generating unit 504 is configured to generate a visualized three-dimensional model corresponding to the monomer obstacle according to the point corresponding to the monomer obstacle.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application, including: processor 710, storage medium 720 and bus 730, storage medium 720 storing machine-readable instructions executable by processor 710, processor 710 executing machine-readable instructions to perform steps of the above-described method embodiments when the electronic device is operating, processor 710 communicating with storage medium 720 via bus 730. The specific implementation manner and the technical effect are similar, and are not repeated here.
The present embodiments provide a storage medium having a computer program stored thereon, which when executed by a processor performs the above method.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (english: processor) to perform part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes or substitutions are covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. An airport clearance safety detection method, comprising:
acquiring point cloud data and airport information data;
classifying the point cloud data to obtain at least 1 type of point cloud data; at least the class 1 point cloud data comprises: ground points and non-ground points, the non-ground points comprising: buildings, trees, and towers;
acquiring an airport clearance model according to airport data;
acquiring overrun points from the point cloud data according to the airport clearance model, wherein the overrun points indicate points with heights exceeding a preset limit in the point cloud data;
performing cluster analysis on the overrun points to obtain the types of the monomer barriers; the cluster analysis mode comprises the following steps: a partition-based manner, a hierarchy-based manner, a density-based manner, and a grid-based manner;
the obtaining, according to the airport clearance model, an overrun point from the point cloud data includes:
acquiring a plurality of clearance model sub-surfaces according to the airport clearance model;
intersection point cloud data with intersection points of the clearance model subplane and the external bounding box of the point cloud data are obtained;
judging whether the airport clearance model subplane is a plane or not;
when the airport clearance model subplane is a plane, acquiring the overrun point by using a height difference method, the airport clearance model subplane and the intersection point cloud data;
and when the airport clearance model subplane is non-planar, acquiring the overrun point by using a collision detection method, the airport clearance model subplane and the intersection point cloud data.
2. The method of claim 1, wherein the data classifying the point cloud data to obtain at least 1 type of point cloud data comprises:
dividing the point cloud data into ground points and non-ground points through an improved asymptotic triangle network encryption filtering algorithm;
the non-ground points are separated into buildings, trees and towers by a building extraction algorithm based on planar segmentation.
3. The method of claim 1, wherein the clustering the overrun points to obtain a type of monomer obstacle comprises:
storing the overrun points according to a k-d tree structure to obtain target overrun point data;
and carrying out cluster analysis on the target overrun point data according to K neighbor query and a region growing algorithm to obtain the type of the single obstacle.
4. The method of claim 1, wherein after clustering the overrun points to obtain the type of monomer obstacle, the method further comprises:
and drawing the contour line and the ultrahigh line of the single obstacle according to the corresponding points of the single obstacle.
5. The method according to claim 1 or 4, wherein after the clustering analysis of the overrun points to obtain the type of the monomer obstacle, the method further comprises:
and generating a visual three-dimensional model corresponding to the single obstacle according to the point corresponding to the single obstacle.
6. An airport clearance safety inspection device, comprising: an acquisition unit and a classification unit;
the acquisition unit is used for acquiring point cloud data and airport information data;
the classifying unit is used for classifying the point cloud data to obtain at least 1 type of point cloud data; at least the class 1 point cloud data comprises: ground points and non-ground points, the non-ground points comprising: buildings, trees, and towers;
the acquisition unit is also used for acquiring an airport clearance model according to airport data;
acquiring overrun points from the point cloud data according to the airport clearance model, wherein the overrun points indicate points with heights exceeding a preset limit in the point cloud data;
performing cluster analysis on the overrun points to obtain the types of the monomer barriers; the cluster analysis mode comprises the following steps: a partition-based manner, a hierarchy-based manner, a density-based manner, and a grid-based manner;
the acquisition unit is specifically configured to acquire a plurality of clearance model subplanes according to the airport clearance model;
intersection point cloud data with intersection points of the clearance model subplane and the external bounding box of the point cloud data are obtained;
judging whether the airport clearance model subplane is a plane or not;
when the airport clearance model subplane is a plane, acquiring the overrun point by using a height difference method, the airport clearance model subplane and the intersection point cloud data;
and when the airport clearance model subplane is non-planar, acquiring the overrun point by using a collision detection method, the airport clearance model subplane and the intersection point cloud data.
7. An electronic device, comprising: a processor, a storage medium, and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium in communication over the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the method of any one of claims 1-5.
8. A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method according to any of claims 1-5.
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