CN114241145A

CN114241145A - Method for monitoring height of airport clearance area building

Info

Publication number: CN114241145A
Application number: CN202111505752.XA
Authority: CN
Inventors: 马莉; 童矿; 宋洋; 董可; 曾微波
Original assignee: China Academy of Civil Aviation Science and Technology
Current assignee: China Academy of Civil Aviation Science and Technology
Priority date: 2021-12-10
Filing date: 2021-12-10
Publication date: 2022-03-25

Abstract

The disclosure relates to a method for monitoring the height of a building in an airport clearance area, which comprises the following steps: determining the error of the digital earth surface model according to the digital earth surface model of the airport clearance area obtained at two moments; obtaining a height-limited grid model of the airport clearance area according to the error; determining the type of each grid according to the digital earth surface model and the height-limited grid model; determining a grid cluster according to the type of each grid; and determining the monitoring time interval of each grid cluster according to the grid clusters, the digital earth surface model and the height-limiting grid model. According to the method for monitoring the height of the airport clearance area building, the height data of each place of the airport clearance area can be obtained based on the digital earth surface model of the airport clearance area obtained at least two moments, and the precision of data monitoring is improved. And grid clusters are formed based on the types of the grids so as to determine the time interval of each grid cluster, so that the monitoring cost can be reduced, and the monitoring efficiency is improved.

Description

Method for monitoring height of airport clearance area building

Technical Field

The disclosure relates to the technical field of computers, in particular to a method for monitoring the height of a building in an airport clearance area.

Background

The airport clearance area is used as a space area which is defined around the airport and used for ensuring the safety of take-off, landing and re-flying of the airplane and limiting the landform and the height of ground objects. In recent years, with the acceleration of urbanization and the development of the temporary free economic area, the contradiction between airport clearance safety and urban building land expansion is increasingly intensified. Since the clearance condition is directly related to flight safety, accurate identification and dynamic monitoring of the airport clearance area super high buildings/potentially dangerous buildings is very important.

In the related art, the development of technologies such as a high-resolution sensor and artificial intelligence, and laser Radar point cloud, satellite-borne high-resolution optical remote sensing image and Synthetic Aperture Radar (SAR) image are widely applied to the fields of urban building identification and height detection, and the methods can be roughly divided into a shadow height measurement method and a stereopair method. Although the shadow height measurement method can realize the inversion of the heights of other buildings by determining the linear relation between the shadow length and the height of a certain building based on the combination of a single optical remote sensing image, a single SAR image, a single optical remote sensing image and a single SAR image, the method needs to determine the shadow length of each building in advance by a classification method, a threshold classification method, an edge detection method and other methods, so that the automatic identification and height extraction of the buildings in a large area are difficult. The digital earth surface model generated based on the optical remote sensing satellite or the SAR stereopair can be combined with the existing geographic database to realize the automatic identification and height extraction of the large-area buildings, wherein the SAR stereopair-based mode is usually used for settlement monitoring of a single important building or infrastructure due to higher precision and cost, and is difficult to popularize in the aspect of large-range building dynamic monitoring. Also, the height of each building in the clearance area may be variable (e.g., a building under construction) or may be constant, and indiscriminate monitoring of all buildings increases the difficulty and cost of monitoring.

Disclosure of Invention

The disclosure provides a method for monitoring the height of a building in an airport clearance area.

According to an aspect of the present disclosure, there is provided a method for monitoring the height of an airport clearance area building, including: determining an error of a digital surface model from digital surface models of airport clearance areas acquired at least two moments in time, wherein the digital surface model comprises elevation data of a plurality of locations of the airport clearance areas monitored at the moments in time; obtaining a height-limited grid model of an airport headroom according to the error of the digital surface model, wherein the height-limited grid model divides the airport headroom into a plurality of grids and comprises height limit information of a place represented by each grid; determining the type of each grid according to the digital earth surface model obtained at the at least two moments and the grid model with the limited height; determining a grid cluster according to the type of each grid, wherein the type of the grid cluster is the same as the type of at least one grid included in the grid cluster; and determining the monitoring time interval of each grid cluster according to the grid cluster, the digital earth surface model acquired at the at least two moments and the height-limiting grid model, wherein the monitoring time interval represents the interval between the moments of monitoring the elevation data.

In one possible implementation, the determining the error of the digital surface model according to the digital surface model of the airport clearance area acquired at least two moments includes: and determining the plane error and the elevation error according to the position errors in the digital earth surface model acquired at the at least two moments by a plurality of control points preset in the airport clearance area.

In one possible implementation, obtaining a limited-height mesh model of an airport clearance area from errors of the digital surface model includes: determining the grid width of the height-limited grid model according to the plane error; determining height limit information of each grid according to preset airport information and the grid width; and obtaining the height-limited grid model according to the grid width and the height limit information of each grid.

In a possible implementation manner, the determining the type of each mesh according to the digital surface model obtained at the at least two moments and the limited-height mesh model includes: determining elevation data of each grid according to the finally obtained digital earth surface model of the airport clearance area; and determining the type of the grid with the elevation data larger than or equal to the height limit information as the ultrahigh grid.

In one possible implementation, the non-ultra-high grids include dynamic grids and static grids, the error of the digital surface model includes an elevation error, and the determining the type of each grid according to the digital surface model obtained at the at least two time instants and the height-limited grid model further includes: determining an elevation difference threshold according to the elevation error; determining an elevation difference value between the elevation data of each grid at the at least two moments according to the digital earth surface model obtained at the at least two moments; and determining the grids with the absolute values of the elevation difference values smaller than the elevation difference value threshold value as the net state grids.

In a possible implementation manner, the dynamic mesh includes a dynamic height increasing mesh and a dynamic height decreasing mesh, and the type of each mesh is determined according to the digital surface model obtained at the at least two time instants and the height-limited mesh model, further including: determining the grids with the elevation difference value larger than 0 between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time as the dynamic heightening grids; or determining the grid with the elevation difference value smaller than 0 between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time as the dynamic reduction grid.

In a possible implementation manner, determining a grid cluster according to the type of each grid includes: and merging the adjacent grids of the same type according to the types of the grids to obtain the grid cluster.

In a possible implementation manner, determining a monitoring time interval of each grid cluster according to the grid cluster, the digital surface model obtained at the at least two moments, and the limited-height grid model includes: determining the maximum change rate of the elevation data of each grid cluster according to the digital earth surface model acquired at the at least two moments; determining the minimum elevation difference value of each grid cluster according to the height limit information and the finally obtained elevation data of each grid in the digital earth surface model; and determining the monitoring time interval of the grid cluster according to the maximum change rate, the minimum elevation difference value and the type of the grid cluster.

In one possible implementation, the determining the monitoring time interval of the grid cluster according to the maximum change rate, the minimum elevation difference value and the type of the grid cluster includes: determining a first time interval of the dynamic heightening grid cluster according to the minimum elevation difference value and the maximum change rate; determining a second time interval for dynamically reducing the grid cluster according to the finally obtained elevation data of each grid in the digital earth surface model, the minimum elevation difference value and the maximum change rate; determining a minimum of the first time interval and the second time interval as the monitoring time interval.

In one possible implementation, the types of the mesh cluster include an ultra-high mesh and a non-ultra-high mesh, the non-ultra-high mesh includes a dynamic mesh and a static mesh, and the method further includes: and determining the attributes of buildings at the corresponding positions of the grid clusters of the ultrahigh grids and the dynamic grids in the electronic map.

According to an aspect of the present disclosure, there is provided an airport clearance area building height monitoring device, including: an error determination module for determining an error of a digital surface model based on digital surface models of airport clearance areas acquired at least two moments, wherein the digital surface model includes elevation data of a plurality of locations of the airport clearance areas monitored at the moments; the height limit determining module is used for obtaining a height limit grid model of the airport headroom according to the error of the digital earth surface model, wherein the height limit grid model divides the airport headroom into a plurality of grids and comprises height limit information of the position represented by each grid; the type determining module is used for determining the type of each grid according to the digital earth surface model obtained at the at least two moments and the grid model with the limited height; a grid cluster determining module, configured to determine a grid cluster according to the type of each grid, where the type of the grid cluster is the same as the type of at least one grid included in the grid cluster; and the time interval determining module is used for determining the monitoring time interval of each grid cluster according to the grid cluster, the digital earth surface model acquired at the at least two moments and the height-limiting grid model, wherein the monitoring time interval represents the interval between the moments of monitoring the elevation data.

In one possible implementation, the errors of the digital surface model include a plane error in a horizontal plane direction and an elevation error in a vertical direction, and the error determination module is further configured to: and determining the plane error and the elevation error according to the position errors in the digital earth surface model acquired at the at least two moments by a plurality of control points preset in the airport clearance area.

In one possible implementation, the height limit determining module is further configured to: determining the grid width of the height-limited grid model according to the plane error; determining height limit information of each grid according to preset airport information and the grid width; and obtaining the height-limited grid model according to the grid width and the height limit information of each grid.

In one possible implementation, the types of the mesh include an ultra-high mesh and a non-ultra-high mesh, and the type determination module is further configured to: determining elevation data of each grid according to the finally obtained digital earth surface model of the airport clearance area; and determining the type of the grid with the elevation data larger than or equal to the height limit information as the ultrahigh grid.

In one possible implementation, the non-super grid includes a dynamic grid and a static grid, the error of the digital surface model includes an elevation error, and the type determination module is further configured to: determining an elevation difference threshold according to the elevation error; determining an elevation difference value between the elevation data of each grid at the at least two moments according to the digital earth surface model obtained at the at least two moments; and determining the grids with the absolute values of the elevation difference values smaller than the elevation difference value threshold value as the net state grids.

In one possible implementation, the dynamic mesh includes a dynamic increasing mesh and a dynamic decreasing mesh, and the type determining module is further configured to: determining the grids with the elevation difference value larger than 0 between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time as the dynamic heightening grids; or determining the grid with the elevation difference value smaller than 0 between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time as the dynamic reduction grid.

In one possible implementation, the grid cluster determining module is further configured to: and merging the adjacent grids of the same type according to the types of the grids to obtain the grid cluster.

In one possible implementation, the time interval determining module is further configured to: determining the maximum change rate of the elevation data of each grid cluster according to the digital earth surface model acquired at the at least two moments; determining the minimum elevation difference value of each grid cluster according to the height limit information and the finally obtained elevation data of each grid in the digital earth surface model; and determining the monitoring time interval of the grid cluster according to the maximum change rate, the minimum elevation difference value and the type of the grid cluster.

In one possible implementation, the types of the mesh cluster include a dynamic increasing mesh and a dynamic decreasing mesh, and the time interval determination module is further configured to: determining a first time interval of the dynamic heightening grid cluster according to the minimum elevation difference value and the maximum change rate; determining a second time interval for dynamically reducing the grid cluster according to the finally obtained elevation data of each grid in the digital earth surface model, the minimum elevation difference value and the maximum change rate; determining a minimum of the first time interval and the second time interval as the monitoring time interval.

In one possible implementation, the types of the mesh cluster include an ultra-high mesh and a non-ultra-high mesh, the non-ultra-high mesh includes a dynamic mesh and a static mesh, and the apparatus further includes: and the attribute determining module is used for determining the attributes of buildings at the corresponding positions of the grid clusters of the ultrahigh grids and the dynamic grids in the electronic map.

According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.

According to the method for monitoring the height of the airport clearance area building, the height data of each place of the airport clearance area can be obtained based on the digital earth surface model of the airport clearance area obtained at least two moments, and the precision of data monitoring is improved. In addition, the airport clearance area can be divided into a plurality of grids by utilizing a height-limiting grid model, grid clusters are formed based on the types of the grids so as to determine the time intervals of the grid clusters, each place of the airport clearance area does not need to be monitored indiscriminately, the position corresponding to each grid cluster can be monitored flexibly and dynamically, the monitoring cost is reduced, and the monitoring efficiency is improved. Furthermore, the monitoring time interval can be determined through the change rate of the height of the building, the monitoring efficiency and flexibility are improved, and the monitoring data cost is reduced.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

FIG. 1 illustrates a flow diagram of an airport clearance area building height monitoring method in accordance with an embodiment of the present disclosure;

FIG. 2 illustrates a schematic diagram of a limited-height mesh model of an airport clearance zone, in accordance with an embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of obtaining a grid cluster in accordance with an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of an electronic map according to an embodiment of the present disclosure;

FIG. 5 illustrates an application schematic diagram of an airport clearance area building height monitoring method according to an embodiment of the present disclosure;

FIG. 6 illustrates a block diagram of an airport clearance area building height monitoring apparatus, in accordance with an embodiment of the present disclosure;

FIG. 7 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure;

FIG. 8 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

Fig. 1 illustrates a flow chart of an airport clearance area building height monitoring method according to an embodiment of the present disclosure, as shown in fig. 1, the method comprising:

in step S11, determining an error of a digital surface model from digital surface models of airport clearance areas acquired at least two times, wherein the digital surface model includes elevation data of a plurality of locations of the airport clearance areas monitored at the times;

in step S12, obtaining a height-limited mesh model of the airport headroom according to the error of the digital surface model, wherein the height-limited mesh model divides the airport headroom into a plurality of meshes and includes height-limited information of the location represented by each mesh;

in step S13, determining the type of each grid according to the digital earth surface model obtained at the at least two moments and the grid model with limited height;

in step S14, determining a mesh cluster according to the type of each mesh, where the type of the mesh cluster is the same as the type of at least one mesh included in the mesh cluster;

in step S15, a monitoring time interval for each grid cluster is determined according to the grid cluster, the digital surface model obtained at the at least two times, and the height-limited grid model, where the monitoring time interval represents an interval between times of monitoring elevation data.

According to the method for monitoring the height of the airport clearance area building, the height data of each place of the airport clearance area can be obtained based on the digital earth surface model of the airport clearance area obtained at least two moments, and the precision of data monitoring is improved. In addition, the airport clearance area can be divided into a plurality of grids by utilizing a height-limiting grid model, grid clusters are formed based on the types of the grids so as to determine the time intervals of the grid clusters, each place of the airport clearance area does not need to be monitored indiscriminately, the position corresponding to each grid cluster can be monitored flexibly and dynamically, the monitoring cost is reduced, and the monitoring efficiency is improved.

In the airport clearance area, although buildings can exist, each building has a corresponding limited height according to the position of the building in the clearance area, and if the building exceeds the limited height, hidden danger can be brought to flight safety. The limit height is related to various factors, such as the grade of the airport, the parameters of the runway, the distance and angle between the runway, etc., and the limit heights of the various locations in the clearance area may be different from each other. Therefore, it is a heavy task to monitor each building for the potential of being super tall or super tall.

In a possible implementation manner, in view of the above problem, whether the location represented by each grid exceeds the limit height may be monitored through a digital surface model including elevation data of each location of an airport headroom area and a height-limit grid model including height-limit information of each location of the airport headroom area, and based on the monitoring, the types of the grids are divided, and then the grids of the same type may be merged to obtain a grid cluster, for example, the grid cluster may be a minimum outsourcing rectangle of a plurality of grids of the same type. Furthermore, the monitoring time interval can be determined based on the elevation data and the height limit information in each grid cluster, the height of each place or each building can be flexibly monitored, all places in the airport clearance area do not need to be monitored indiscriminately and comprehensively, and the monitoring workload and the monitoring cost are reduced.

In one possible implementation, in step S11, a Digital Surface Model (DSM) is a Model that monitors data of the Surface of the earth in a region, the data of the Surface including elevation data of the Surface, the region may include various terrains and may also include a plurality of buildings, and the Digital Surface Model may include heights of the buildings for the buildings.

In one possible implementation, the digital surface model may be created from surface data provided by a remote sensing image data platform such as enii (visualized environment image) or ERDAS (remote sensing image processing), for example, generated by multi-view stereopair, or may be obtained by cropping a large range of digital surface models (e.g., data models provided by a remote sensing image platform).

In one possible implementation, a digital surface model may be obtained for at least two instances of time that may provide elevation data for a plurality of locations of airport clearance area that are inconsistent. For example, if a building is being constructed at some location, the later-obtained digital terrain model may provide a height of the building that is higher than the height of the building provided by the earlier-obtained digital terrain model. For another example, if a building is being demolished at some location, the earlier acquired digital terrain model may provide a height of the building that is higher than the height of the building provided by the later acquired digital terrain model. Based on these characteristics, the digital earth surface model at least two times can determine the altitude change trend of each building in addition to the altitude data of each site so as to determine the building with ultrahigh potential.

In one possible implementation, at the airport clearance area, a plurality of control points may be provided, which are fixed points, and the measurement error between the digital surface models may be determined based on the error in the digital surface models for their positions. The errors of the digital earth surface model comprise plane errors in the horizontal plane direction and elevation errors in the vertical direction. And the plane error and the elevation error are relative errors between the digital earth surface models obtained at all times. Step S11 may include: and determining the plane error and the elevation error according to the position errors in the digital earth surface model acquired at the at least two moments by a plurality of control points preset in the airport clearance area.

In one possible implementation, the plane error may be determined based on the following equation (1):

wherein, SD_xyFor plane errors, n is the number of control points (n is a positive integer, e.g., n is 5),

is the plane distance of the ith (i is less than or equal to n and is a positive integer) control point in the two digital surface models, ME_xyCan be determined by the following equation (2):

in one possible implementation, the elevation error may be determined based on the following equation (3):

wherein, SD_zIn order to be said elevation error,

for the elevation distance of the ith control point in the two digital surface models, ME_zCan be determined by the following equation (3):

in one possible implementation, the digital earth model obtained at least two moments in time may be grid resampled for matching with the height-limited grid model, wherein the resolution of the grid, i.e. the width of the grid, is twice the plane error. Setting the width of the grid to twice the plane error may allow the same grid to represent the location of the same building or the location represented by the same grid to belong to the same building in the digital surface model obtained at least twice. The present disclosure does not limit the width of the grid. In an example, the digital surface model may be grid resampled by an "ArcToolBox-datamanagement tools-rater processing-repeat" tool of ArcMap software, and the present disclosure does not limit the method or tool used for grid resampling.

In a possible implementation manner, further, the digital surface models acquired at least two times may be aligned, for example, range data of the airport clearance area boundary in the digital surface model may be acquired, and the resolutions of the two digital surface models are the same (for example, both the grid resolutions described above), and then the digital surface models acquired at least two times are clipped, so that at least two digital surface models with the same resolution and range may be acquired. In an example, the extent data of the airport headroom boundary may be determined using the airport headroom boundary KML data and clipped using the "ArcToolBox-AnalysisTools-Extraction-Split" tool in the ArcMap software, the present disclosure is not limited to the method or tool used by the alignment process described above.

In one possible implementation, in step S12, a restricted height mesh model of the airport clearance area may be obtained. The model may divide the airport headroom into multiple meshes and include height limit information for the locations represented by each mesh. As described above, the height limit information for each location in the airport clearance area may be different from each other, and the height limit information for each location may be related to the grade of the airport, runway parameters, and distance and/or angle to the runway. Height limit information for a location in the airport clearance area may be determined according to related techniques, e.g., the height limit information for a planar location (x, y) in the airport clearance area is H (x, y). Based on the height-limited information, a height-limited mesh model of the airport clearance area can be obtained.

In one possible implementation, step S12 may include: determining the grid width of the height-limited grid model according to the plane error; determining height limit information of each grid according to preset airport information and the grid width; and obtaining the height-limited grid model according to the grid width and the height limit information of each grid.

In one possible implementation, the mesh width of the height-constrained mesh model may be determined from the plane error. Similar to the above digital surface model process when performing grid resampling, the width of the grid may be doubled compared to the plane error. The present disclosure does not limit the mesh width of the height-limited mesh model.

In one possible implementation, the height limit information for each grid may be determined. As described above, the height limit information for each location (x, y) in the airport clearance area may be determined to be H (x, y). The height limit information for each grid may be determined based on the height limit information for each location and the width of the grid. For example, height limit information of a position of a center point of each mesh may be determined and used as the height limit information of the mesh, or an average value of the height limit information of a plurality of positions in the mesh may be used as the height limit information of the mesh, and the determination manner of the height limit information of the mesh is not limited by the present disclosure.

In one possible implementation, after determining the height limit information for each grid, a height-limited grid model may be determined that divides the airport headroom into multiple grids and contains the height limit information for the location represented by each grid.

Fig. 2 illustrates a schematic diagram of a limited-height mesh model of an airport clearance area, which may include multiple zones, e.g., a lift zone, an inner horizontal plane, a transition surface, an end clearance, a conical surface, an outer horizontal plane, as illustrated in fig. 2, in accordance with an embodiment of the present disclosure. Each zone has a corresponding dimension based on factors such as the grade of the airport, runway parameters, etc., e.g., length of the lift belt in the runway direction (x-direction) is L, end of the lift belt to end of the inner horizontal plane x₁Length in x direction is l₁And other landmark locations x within the end headroom partition₂、x₃、x₄、x₅、x₆Length l of₂、l₃、l₄Total length D of terminal clearance in x direction₁Total length D of the inner horizontal plane in the y direction₂Total length D of the tapered surface in the y direction₃Total length D of the inner horizontal plane in the x direction₄Total length D of the tapered surface in the x direction₅Total length D of the outer horizontal plane in the y-direction₆Total length D of the outer horizontal plane in the x-direction₇The length of the airport clearance area in the x direction is a, and the length of the airport clearance area in the y direction is b.

In one possible implementation, as shown in fig. 2, the limited-height grid model divides the airport headroom into multiple grids, each grid having a width d, in the example, d ═ 2SD_xy. For each mesh, the height limit information of the mesh may be determined in the above manner, for example, the height limit information of the position of the central point is used as the height limit information of the mesh, or the average value of the height limit information of a plurality of positions in the mesh is used as the height limit information of the mesh, and the like.

In one possible implementation, in step S13, each mesh may be classified to identify meshes that are ultra-high, or have an ultra-high potential. As described above, the digital surface model may include elevation data of each location of the airport headroom and be divided into a plurality of meshes in the same manner, and thus, based on the digital surface model, elevation data of each mesh may be obtained, and based on a relationship between the elevation data of each mesh and height limit information of each mesh, the type of each mesh may be divided, that is, a mesh in which an ultrahigh potential or a potential of an ultrahigh is identifiable may be divided.

In one possible implementation, the types of the mesh include an ultra-high mesh and a non-ultra-high mesh, and the step S13 may include: determining elevation data of each grid according to the finally obtained digital earth surface model of the airport clearance area; and determining the type of the grid with the elevation data larger than or equal to the height limit information as the ultrahigh grid.

In one possible implementation, the altitude data of each grid provided by the digital surface model of the airport clearance area obtained last may be compared with the height limit information of each grid provided by the height limit grid model, and the type of the grid with the altitude data greater than or equal to the height limit information may be determined as the ultra-high grid, and further, a category identifier of the ultra-high grid may be set, for example, the category identifier of the grid is set to-1. Conversely, a type of mesh for which the elevation data is less than the height limit information may be determined to be a non-ultra mesh.

In one possible implementation, when determining the category of the grids, an error factor may be further considered, for example, a sum of the elevation data of each grid and the elevation error of the digital surface model may be used as a maximum value of the elevation data of each grid, and a type of the grid of which the maximum value of the elevation data is greater than or equal to the height limit information may be determined as an ultra-high grid, otherwise, the type of the grid may be determined as a non-ultra-high grid.

In one possible implementation, for an ultra-high grid, then, an emphasis monitoring may be performed, such as periodically monitoring whether a building at the site represented by the grid is removed, etc. For a grid that is not super-tall, it can be further determined whether the grid has a potential risk of super-tall.

In one possible implementation, the non-ultra-high mesh includes a dynamic mesh and a static mesh, and step S13 further includes: determining an elevation difference threshold according to the elevation error; determining an elevation difference value between the elevation data of each grid at the at least two moments according to the digital earth surface model obtained at the at least two moments; and determining the grids with the absolute values of the elevation difference values smaller than the elevation difference value threshold value as the net state grids.

In one possible implementation, whether the grid is a dynamic grid or a static grid may be determined based on whether elevation data for the non-supertall grid has changed and the magnitude of the change. If a non-superelevation grid is a dynamic grid, its elevation data may exceed the height limit information in the future, i.e., have a potential for being superelevation. Conversely, if the non-superelevation grid is a static grid, i.e., the elevation data for the grid has not changed, or changed very little, during at least two of the times that the digital surface model was acquired (e.g., only the elevation data has changed for the reason of measurement error), then the grid has no potential for being superelevation.

In one possible implementation, an elevation difference threshold may be set to determine whether the height change of the non-supertall mesh meets dynamic criteria. For example, twice the elevation error of the digital surface model may be determined as the elevation difference threshold δ, δ being 2SD_z. The present disclosure does not limit the specific values of the elevation difference threshold.

In one possible implementation, if the amount of change (i.e., the absolute value of the elevation difference) in the elevation data of the grid that is not ultra-high provided by the digital surface model acquired at least two times is less than the elevation difference threshold, the elevation data of the grid is not changed or is changed very little, the building of the place represented by the grid may be a finished building and has no potential for being ultra-high, the category of the grid may be determined as a static grid, and a category identifier, for example, the category identifier is 0, may be determined for the static grid. Conversely, if the amount of change in elevation data (i.e., the absolute value of the elevation difference) for the grid is greater than or equal to the elevation difference threshold, the elevation data for the grid changes more quickly, and the building at the location represented by the grid may be an unfinished building or a building being demolished, future construction may continue resulting in height exceeding height limit information, or other super-tall buildings may be constructed after demolition, and therefore, there is a potential for super-height, and the category of the grid may be determined to be a dynamic grid.

In one possible implementation, if the non-super grid is a dynamic grid, the classification of the dynamic grid may continue to facilitate more accurate monitoring of the buildings at the locations represented by the various types of dynamic grids. The dynamic mesh includes a dynamic increasing mesh and a dynamic decreasing mesh, and step S13 may further include: determining the grids with the elevation difference value larger than 0 between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time as the dynamic heightening grids; or determining the grid with the elevation difference value smaller than 0 between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time as the dynamic reduction grid.

In one possible implementation, for a dynamic grid that is not super high (the absolute value of the elevation difference is greater than or equal to an elevation difference threshold), if the difference between the elevation data for the grid provided in the digital surface model obtained at a subsequent time and the elevation data for the grid provided in the digital surface model obtained at a previous time is greater than 0, the height of the building at the location represented by the grid may be considered to have increased between at least two times, and the increase is greater than or equal to the elevation difference threshold, the grid may be classified as a dynamic increase grid, and a category identifier may be determined for the dynamic increase grid, e.g., a category identifier of 1⁺. Conversely, if the difference between the elevation data of the grid provided in the digital surface model obtained at the subsequent time and the elevation data of the grid provided in the digital surface model obtained at the previous time is less than 0, the height of the building at the place represented by the grid may be considered to have decreased between at least two times, and the decrease magnitude is greater than or equal to the elevation difference threshold, the grid may be classified as a dynamic decrease grid, and a category identifier, for example, a category identifier of 1, may also be determined for the grid^-. The two dynamic grids may be monitored separately, and in an example, different monitoring intervals may be determined for the two grids, e.g., a dynamic increasing grid may monitor again after one month if its elevation data exceeds the height limit information, a dynamic decreasing grid may monitor again after one year if its elevation data exceeds the height limit information, etc., and the disclosure is not limited to the intervals.

In one possible implementation manner, in step S14, to reduce the monitoring cost, adjacent grids of the same type may be merged to perform overall monitoring on the merged grid cluster. In the merging process, grids of the same type can be merged based on the types of the grids to obtain a grid cluster. Step S14 may include: and merging the adjacent grids of the same type according to the types of the grids to obtain the grid cluster. For example, if a certain mesh is of a dynamically raised type and its neighboring meshes (including its upper, lower, left, and right neighboring meshes) are also dynamically raised meshes, two meshes may be merged into a mesh cluster, and a search may be continued for whether other neighboring meshes of the mesh cluster include homogeneous meshes. After the merging process is performed, a plurality of grid clusters can be obtained, the form of the grid cluster can include a minimum outsourcing matrix of a plurality of grids of the same type, and the form of the grid cluster is not limited by the disclosure.

FIG. 3 shows a schematic diagram of obtaining a grid cluster according to an embodiment of the present disclosure. As shown in FIG. 3, t_aTime t and_brespectively acquiring digital earth surface models of airport clearance areas at all times, wherein t_bThe time is the subsequent time. In step (r) of FIG. 3, at t_bDetermining an ultra-high grid (i.e., elevation data h) that exceeds height limit information among a plurality of grids of a digital surface model acquired at a time_bijThe grid higher than the height-limiting information) and determines that the category label is-1 and the rest grids are not super-high grids.

In one possible implementation, in step two of FIG. 3, t may be based on_aTime t and_band determining whether the elevation data change of each grid exceeds an elevation difference threshold value or not by using the elevation data of each grid provided by the digital earth surface model of the airport clearance area acquired at two moments, determining the grid which does not exceed the elevation difference threshold value as a static grid, and determining the category identifier of the static grid as 0. Otherwise, determining the grid as the dynamic grid and t_bElevation data at time greater than t_aThe grid of elevation data at a time is determined as a dynamically heightened grid, and its category identification is determined as 1⁺And will t_bElevation data at time less than t_aThe grid of elevation data at a time is determined as a dynamic lowering grid and its category identification is determined as 1^-。

In one possible implementation, in step three of fig. 3, merging of adjacent homogeneous grids may be performed for a grid with a potentially very high probability (a dynamically increased grid and a dynamically decreased grid), to obtain a grid cluster, which may be in the form of a minimum outer-wrapping matrix, for example.

In an example, the merging may be done first in the column direction, e.g., if two adjacent columns of the mesh MBR_nAnd MBR_n' there are grids of the same category, and the intersection of the homogeneous grids at the corresponding positions in two adjacent columns of grids is not empty, then a cluster of two columns of grids can be obtained first. For example, in the grid on the left side of step c in fig. 3, the 2 nd and 3 rd columns both include a dynamic height increasing grid (the category is identified as 1)⁺) And the intersection of the dynamic boosting grids of the corresponding positions is not an empty set, for example, the grids of the 1 st column and the 1 st row of the 2 nd column and the 3 rd column are both dynamic boosting grids, so that the intersection of the dynamic boosting grids of the corresponding positions is not an empty set, then the 2 nd column and the 3 rd column can be merged. The columns may be combined based on this way, obtaining a column-wise clustering of the grid, i.e. a minimum outsourcing matrix in the column direction, e.g. the outsourcing matrices of

column

2 and 3.

In an example, in the obtained cluster in the column direction (e.g., the minimum outsourcing matrix), merging may be continued in the row direction, for example, within the cluster in the column direction, if there are meshes in two adjacent rows with the same category and the intersection of the homogeneous meshes in the corresponding positions is not empty, a mesh cluster of the homogeneous meshes may be obtained. For example, in the cluster composed of the 2 nd column and the 3 rd column, the grids in the 1 st column and the 1 st row in the 2 nd column and the 3 rd column are both dynamic height grids, a grid cluster composed of the two grids may be obtained, in the merging in the row direction, in the 2 nd row of the cluster, the grid in the 2 nd row and the 3 rd column of the grid in the 2 nd column, so that the intersection of the dynamic height grids in the corresponding positions of the 1 st row and the 2 nd row in the cluster composed of the 2 nd column and the 3 rd column is not an empty set (i.e., the position of the 3 rd column), and the grid cluster composed of the two grids may be merged with the 2 nd row in the cluster composed of the 2 nd column and the 3 rd column to obtain a new grid cluster (a grid cluster including four grids in the 1 st row, the 3 rd column, the 1 st row, the 2 nd column, the 2 nd row and the 3 rd column, the 2 nd row). Similarly, in row 3, the grid in row 3 and column 1 is a dynamic raised grid, and the intersection of the dynamic raised grids at the corresponding positions in the grid cluster is not an empty set (i.e., the position of column 2), so that the grid cluster composed of the above four grids can be made to merge row 3 in the cluster composed of column 2 and column 3, and a new grid cluster (grid cluster including six grids in column 2, row 1, column 3, row 2, column 2, row 3, and column 3) … … can be obtained in this way, and a grid cluster of the same type of grid, that is, a minimum outsourcing matrix, is shown by a dashed box including six grids. Similarly, a grid cluster may also be obtained that dynamically lowers the grid, as indicated by the dashed box that includes one grid. The present disclosure does not limit the specific manner of obtaining the grid cluster, and for example, the grid cluster may also be obtained by a graph classification or the like.

In a possible implementation manner, in step S15, after obtaining the grid clusters, the grid clusters are used as monitoring units to perform monitoring, and the monitoring time interval of the next monitoring is determined, so that it is only necessary to perform monitoring on each grid cluster when the corresponding monitoring time interval reaches, instead of performing indiscriminate monitoring every moment.

In one possible implementation, step S15 may include: determining the maximum change rate of the elevation data of each grid cluster according to the digital earth surface model acquired at the at least two moments; determining minimum height limit information of each grid cluster according to the height limit grid model; determining the minimum elevation difference value of each grid cluster according to the minimum height limit information and the finally obtained elevation data of each grid in the digital earth surface model; and determining the monitoring time interval of the grid cluster according to the maximum change rate, the minimum elevation difference value and the type of the grid cluster.

In one possible implementation, one or more grids may be included in the grid cluster, and the elevation data for each grid may not necessarily vary by the same amount, and therefore, the elevation data for each grid may not necessarily vary by the same rate, and in order to maximize flight safety and reduce the potential for superelevation, the maximum rate of change among the rates of change for each grid may be determined, and in an example, may be obtained by equation (5) below:

wherein h is_bijIs t_bObtaining elevation data of ith row and jth column grids in the digital earth surface model at time (subsequent time) (wherein each grid corresponds to a row and column coordinate (i, j)), h_aijIs t_aAltitude data, V, of the ith row and jth column grid in the digital surface model obtained at time (preceding time)_bijIs the maximum rate of change.

In one possible implementation, similarly, since multiple grids may be included in a grid cluster, the height limit information for each grid may not be consistent, and in order to maximize flight safety and reduce the potential for superelevation, a minimum elevation difference value for each grid may be determined, i.e., a minimum value of the difference between the height represented by the last acquired elevation data for each grid and the height represented by the height limit information for that grid. The minimum elevation difference value may be obtained according to the following equation (6):

H_bij＝min{H_ij-h_bij} (6)

wherein H_bijIs the minimum elevation difference, H_ijThe height limit information of the grid of the ith row and the jth column.

In one possible implementation, the monitoring time interval of each type of grid cluster may be determined according to the type of the grid cluster, respectively. This step may include: determining a first time interval of the dynamic heightening grid according to the minimum elevation difference value and the maximum change rate; determining a second time interval for dynamically reducing the grids according to the finally obtained elevation data of each grid in the digital earth surface model, the minimum elevation difference value and the maximum change rate; determining a minimum of the first time interval and the second time interval as the monitoring time interval.

In one possible implementation, for a dynamically-boosted grid cluster, it may be assumed that the boosting speed of the buildings at the site represented by the dynamically-boosted grid cluster is uniform, i.e., the maximum rate of change is kept increasing, and the monitoring interval for which the next monitoring is performed may be the time during which the building continues to increase at the maximum rate of change to reach the altitude represented by the height-limiting information. For example, the first time interval may be represented by the following equation (7):

T^↑＝H_bij/V_bij (7)

wherein, T^↑Is the first time interval. In an example, a first time interval for each dynamic raised grid cluster can be determined, and a building at a location represented by each dynamic raised grid cluster can be monitored after the first time interval without indiscriminate monitoring, thereby improving monitoring efficiency and reducing monitoring cost.

In one possible implementation, for a dynamically lowered grid cluster, it may be assumed that the reason for the elevation data reduction is because the building is being demolished, and that a new building may also be built in place after demolition. And assuming that the removal and new building speed is kept unchanged, i.e. decreased and increased according to the maximum change rate, the monitoring time interval for the next monitoring can be the time for the new building to reach the height indicated by the height limit information after the removal is completed. For example, the second time interval may be expressed by the following equation (8):

T^↓＝(2h_bij-H_bij)/V_bij (8)

wherein, T^↓Is the second time interval. In an example, a second time interval of each dynamic reduction grid cluster may be determined, and the building at the location represented by each dynamic reduction grid cluster may be monitored after the second time interval without indiscriminate monitoring, improving monitoring efficiency, and reducing monitoring cost.

In one possible implementation, to further improve the security, the monitoring time interval of the next monitoring may be set for all the grid clusters, for example, the minimum of the first time interval and the second time interval may be determined as the monitoring time interval, so as to monitor all the grid clusters when the monitoring time interval is reached, where the monitoring time interval is as shown in the following formula (9):

T＝min(T^↑,T^↓) (9)

wherein T is the monitoring time interval. The next monitoring time is T + T_b。

In one possible implementation, during the monitoring process, attributes of the buildings in the grid cluster, such as names, areas, current heights, and the like of the buildings, may also be retrieved through the electronic map. The method further comprises the following steps: and determining the attributes of buildings at the corresponding positions of the grid clusters of the ultrahigh grids and the dynamic grids in the electronic map.

In one possible implementation manner, the corresponding building may be retrieved in the electronic map according to the position of the grid cluster, for example, the location of the center point of the grid cluster, the size of the grid cluster, and the like, and the retrieval may be performed in the electronic map, for example, the location of the center point of the grid cluster is shown in the following formula (10):

wherein j is_maxColumn coordinate, j, of the rightmost grid of the grid cluster_minColumn coordinate, i, of the leftmost grid of the grid cluster_maxLine coordinate, i, of the uppermost grid of the grid cluster_minIs the row coordinate of the lowermost grid of the grid cluster.

The size of the grid cluster is shown in the following equation (11):

where L is the length of the grid cluster (minimum outsourcing matrix), H is the width of the grid cluster, and d is the grid width.

In one possible implementation, the search may be performed in an electronic map based on the information to determine the attributes of the buildings, for example, as shown in step (iv) in fig. 3, the search may be performed in the electronic map based on the information to determine that the building at the position of the dynamic height grid cluster is building a and the building at the position of the dynamic height grid cluster is building B. Of course, attributes of buildings in other grids, such as ultra-high grids and static grids, may also be retrieved in this manner, which is not limited by this disclosure.

In a possible implementation manner, the search result can be further marked in an electronic map.

Fig. 4 is a schematic diagram of an electronic map according to an embodiment of the present disclosure, and as shown in fig. 4, the above search results may be marked in the electronic map, for example, the position of the dynamic height grid cluster, the position of the ultra-high grid, and the like may be marked in the electronic map, which is not limited by the present disclosure.

Fig. 5 is a schematic diagram illustrating an application of the airport clearance area building height monitoring method according to an embodiment of the present disclosure, and as shown in fig. 5, 5 control points may be set in the airport clearance area and t is obtained respectively_aTime (preamble time) and t_bA digital earth model of time of day (subsequent time of day). The two digital earth surface models can be preprocessed and evaluated in precision, for example, the two digital earth surface models can be cut, plane errors and elevation errors can be obtained based on 5 control points, and then grids can be determined based on 2 times of the plane errorsAnd (4) size to perform grid resampling on the two digital earth surface models to obtain two digital earth surface models with the same resolution and the same range.

In one possible implementation, height limit information for each location of the airport headroom may be obtained based on parameters of the runway and the airport headroom, and a height-limited mesh model may be obtained according to the mesh sizes, where each mesh in the model includes height limit information for the indicated location.

In one possible implementation, the grids may be classified, for example, if the sum of the elevation data and the elevation error of a grid is greater than the height limit information, the grid may be determined to be an ultra-high grid, and the category identifier of the grid may be determined to be-1.

In one possible implementation, in other non-supertall grids, at t_aTime t and_bthe amount of change in elevation data at a time is less than a threshold (e.g., 2 SD)_z) It is determined as not being an ultra-high static grid and its category identification is determined to be 0. The other grids, i.e., the grids for which the amount of change in the elevation data exceeds the threshold, are classified as not-exceeding dynamic grids, wherein the dynamic grids for which the height increases are dynamically heightened grids, and the classification thereof is 1⁺The height-reduced dynamic mesh is a dynamic reduction mesh whose category is identified as 1^-。

In one possible implementation, the grids in which the amount of change in the elevation data exceeds the threshold may be determined as grids having a potentially very high probability, and the grids may be merged into homogeneous grids, resulting in a minimum outsourced rectangle of the same type as the grid of which at least one category is included.

In one possible implementation, the monitoring time interval of each grid cluster may be determined according to the category of the grid cluster, and the minimum monitoring time interval T may be determined to be at T + T_bAnd carrying out next height monitoring at any moment to ensure the flight safety. Further, an area or a location where the grid cluster is located, i.e., poi (point of interest), may also be determined in the electronic map, and attribute information of the building may be determined based on information provided by the electronic map.

In a possible implementation manner, the airport clearance area building height monitoring method can be used for height monitoring of buildings in the airport clearance area, and can adaptively determine the monitoring time interval of each grid cluster so as to automatically monitor the height of the buildings in the airport clearance area, thereby ensuring flight safety, reducing data cost and improving monitoring efficiency.

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.

In addition, the present disclosure also provides an airport clearance area building height monitoring device, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any airport clearance area building height monitoring method provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are omitted for brevity.

Fig. 6 illustrates a block diagram of an airport clearance area building height monitoring apparatus, as shown in fig. 6, comprising: an error determination module 11, configured to determine an error of a digital earth surface model according to the digital earth surface model of the airport clearance area acquired at least two moments, where the digital earth surface model includes elevation data of a plurality of locations of the airport clearance area monitored at the moments; a height limit determining module 12, configured to obtain a height limit grid model of an airport headroom according to an error of the digital surface model, where the height limit grid model divides the airport headroom into multiple grids and includes height limit information of a location represented by each grid; a type determining module 13, configured to determine a type of each grid according to the digital earth surface model obtained at the at least two moments and the grid model with the limited height; a grid cluster determining module 14, configured to determine a grid cluster according to the type of each grid, where the type of the grid cluster is the same as the type of at least one grid included in the grid cluster; and a time interval determining module 15, configured to determine a monitoring time interval of each grid cluster according to the grid cluster, the digital earth surface model obtained at the at least two moments, and the height-limited grid model, where the monitoring time interval represents an interval between moments of monitoring the elevation data.

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

Embodiments of the present disclosure also provide a computer program product comprising computer readable code which, when run on a device, executes instructions for implementing the airport clearance area building height monitoring method as provided in any of the above embodiments.

Embodiments of the present disclosure also provide another computer program product for storing computer readable instructions that, when executed, cause a computer to perform the operations of the airport clearance area building height monitoring method provided by any of the above embodiments.

The electronic device may be provided as a terminal, server, or other form of device.

Fig. 7 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.

Referring to fig. 7, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense an edge of a touch or slide action, but also detect a duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

Fig. 8 illustrates a block diagram of an electronic device 1900 in accordance with an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 8, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The electronic device 1900 may further include a power component 1926 configured to perform power management of the electronic device 1900, aA wired or wireless network interface 1950 is configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as Windows Server, stored in memory 1932^TM，Mac OS X^TM，Unix^TM,Linux^TM，FreeBSD^TMOr the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for monitoring the height of a building in an airport clearance area is characterized by comprising the following steps:

determining an error of a digital surface model from digital surface models of airport clearance areas acquired at least two moments in time, wherein the digital surface model comprises elevation data of a plurality of locations of the airport clearance areas monitored at the moments in time;

obtaining a height-limited grid model of an airport headroom according to the error of the digital surface model, wherein the height-limited grid model divides the airport headroom into a plurality of grids and comprises height limit information of a place represented by each grid;

determining the type of each grid according to the digital earth surface model obtained at the at least two moments and the grid model with the limited height;

determining a grid cluster according to the type of each grid, wherein the type of the grid cluster is the same as the type of at least one grid included in the grid cluster;

and determining the monitoring time interval of each grid cluster according to the grid cluster, the digital earth surface model acquired at the at least two moments and the height-limiting grid model, wherein the monitoring time interval represents the interval between the moments of monitoring the elevation data.

2. The method of claim 1, wherein the errors of the digital surface model include planar errors in a horizontal plane direction and elevation errors in a vertical direction,

determining an error of the digital surface model based on the digital surface model of the airport clearance area acquired at least two times, comprising:

and determining the plane error and the elevation error according to the position errors in the digital earth surface model acquired at the at least two moments by a plurality of control points preset in the airport clearance area.

3. The method of claim 2, wherein obtaining a limited-height mesh model of airport clearance area from errors of the digital terrain surface model comprises:

determining the grid width of the height-limited grid model according to the plane error;

determining height limit information of each grid according to preset airport information and the grid width;

and obtaining the height-limited grid model according to the grid width and the height limit information of each grid.

4. The method of claim 1, wherein the types of meshes include ultra-high meshes and non-ultra-high meshes,

determining the type of each grid according to the digital earth surface model obtained at the at least two moments and the grid model with the limited height, wherein the determining comprises the following steps:

determining elevation data of each grid according to the finally obtained digital earth surface model of the airport clearance area;

and determining the type of the grid with the elevation data larger than or equal to the height limit information as the ultrahigh grid.

5. The method of claim 4, wherein the non-super grids include dynamic grids and static grids, the errors of the digital surface model include elevation errors,

determining the type of each grid according to the digital earth surface model obtained at the at least two moments and the grid model with the limited height, and further comprising:

determining an elevation difference threshold according to the elevation error;

determining an elevation difference value between the elevation data of each grid at the at least two moments according to the digital earth surface model obtained at the at least two moments;

and determining the grids with the absolute values of the elevation difference values smaller than the elevation difference value threshold value as the net state grids.

6. The method of claim 5, wherein the dynamic mesh comprises a dynamic increasing mesh and a dynamic decreasing mesh,

determining the grids with the elevation difference value larger than 0 between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time as the dynamic heightening grids; or

And determining the grids with the elevation difference value smaller than 0 between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time as the dynamic reduction grids.

7. The method of claim 1, wherein determining a grid cluster according to the type of each grid comprises:

and merging the adjacent grids of the same type according to the types of the grids to obtain the grid cluster.

8. The method of claim 1, wherein determining a monitoring interval for each grid cluster based on the grid cluster, the digital surface model obtained at the at least two time instants, and the limited-height grid model comprises:

determining the maximum change rate of the elevation data of each grid cluster according to the digital earth surface model acquired at the at least two moments;

determining the minimum elevation difference value of each grid cluster according to the height limit information and the finally obtained elevation data of each grid in the digital earth surface model;

and determining the monitoring time interval of the grid cluster according to the maximum change rate, the minimum elevation difference value and the type of the grid cluster.

9. The method of claim 8, wherein the types of mesh clusters include a dynamic up mesh and a dynamic down mesh,

determining a monitoring time interval of the grid cluster according to the maximum rate of change, the minimum elevation difference value and the type of the grid cluster, including:

determining a first time interval of the dynamic heightening grid cluster according to the minimum elevation difference value and the maximum change rate;

determining a second time interval for dynamically reducing the grid cluster according to the finally obtained elevation data of each grid in the digital earth surface model, the minimum elevation difference value and the maximum change rate;

determining a minimum of the first time interval and the second time interval as the monitoring time interval.

10. The method of claim 1, wherein the types of mesh clusters comprise ultra-high meshes and non-ultra-high meshes, the non-ultra-high meshes comprising dynamic meshes and static meshes,

the method further comprises the following steps:

and determining the attributes of buildings at the corresponding positions of the grid clusters of the ultrahigh grids and the dynamic grids in the electronic map.