Method for monitoring the height of buildings in an airport clearance area

Abstract

The present disclosure relates to an airport clearance area building height monitoring method, the method comprising: determining the error of a digital terrain model of an airport clearance area according to the digital terrain model of the airport clearance area obtained at two time points; obtaining a restricted height grid model of the airport clearance area according to the error; determining the type of each grid according to the digital terrain model and the restricted height 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 cluster, the digital terrain model and the restricted height grid model. The airport clearance area building height monitoring method according to the embodiments of the present disclosure can obtain the elevation data of each location of the airport clearance area based on the digital terrain model of the airport clearance area obtained at at least two time points, thereby improving the accuracy of data monitoring. Moreover, the grid cluster is formed based on the type of the grid to determine the time interval of each grid cluster, thereby reducing the monitoring cost and improving the monitoring efficiency.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to an airport clearance area building height monitoring method. BACKGROUND

[0002] The airport clearance area is a space area of limited topography and object height around the airport to ensure the safety of take-off, landing and go-around of the aircraft. In recent years, with the acceleration of urbanization and the development of the airport economic zone, the contradiction between the airport clearance safety and the expansion of urban building land has become increasingly serious. Since the clearance condition is directly related to flight safety, it is very important to accurately identify and dynamically monitor the over-height buildings / potential dangerous buildings in the airport clearance area.

[0003] In the related art, with the development of high-resolution sensors, artificial intelligence and other technologies, laser radar point clouds, satellite-borne high-resolution optical remote sensing images and synthetic aperture radar (SAR) images are widely used in the field of urban building identification and height detection, which can be roughly divided into shadow height measurement method and stereo image pair method. Although the shadow height measurement method can determine the linear relationship between the shadow length and the height of a certain building based on a single optical remote sensing image, a single SAR image, a single optical remote sensing image and a single SAR image, and then realize the inversion of the height of other buildings, it is difficult to automatically identify and extract the height of a large area of buildings by using the classification method, threshold classification method, edge detection method and other methods to determine the shadow length of each building in advance. The digital surface model generated based on the optical remote sensing satellite or SAR stereo image pair and combined with the existing geographic database can also realize the automatic identification and height extraction of a large area of buildings. The method based on SAR stereo image pair is often used for settlement monitoring of a single important building or infrastructure due to its high precision and cost, and it is difficult to be popularized in the dynamic monitoring of a large number of buildings. Moreover, the heights of the buildings in the clearance area may change (for example, buildings under construction), or may be constant. The indiscriminate monitoring of all buildings will increase the difficulty and cost of monitoring. SUMMARY

[0004] The present disclosure provides an airport clearance area building height monitoring method.

[0005] According to an aspect of the present disclosure, an airport clearance area building height monitoring method is provided, comprising: determining errors of digital terrain models of an airport clearance area at at least two time points, wherein the digital terrain models comprise elevation data of a plurality of locations of the airport clearance area monitored at the time points; obtaining a restricted height grid model of the airport clearance area according to the errors of the digital terrain models, wherein the restricted height grid model divides the airport clearance area into a plurality of grids and comprises height restriction information of locations represented by the grids; determining types of the grids according to the digital terrain models at the at least two time points and the restricted height grid model; determining grid clusters according to the types of the grids, wherein a type of a grid cluster is the same as a type of at least one grid included in the grid cluster; and determining monitoring time intervals of the grid clusters according to the grid clusters, the digital terrain models at the at least two time points and the restricted height grid model, wherein the monitoring time intervals represent intervals between time points at which elevation data is monitored.

[0006] In a possible implementation, the errors of the digital terrain models comprise plane errors in a horizontal plane direction and elevation errors in a vertical direction, and the determining of the errors of the digital terrain models according to the digital terrain models of the airport clearance area at the at least two time points comprises: determining the plane errors and the elevation errors according to position errors of a plurality of control points preset in the airport clearance area in the digital terrain models at the at least two time points.

[0007] In a possible implementation, the obtaining of the restricted height grid model of the airport clearance area according to the errors of the digital terrain models comprises: determining a grid width of the restricted height grid model according to the plane errors; determining height restriction information of the grids according to preset airport information and the grid width; and obtaining the restricted height grid model according to the grid width and the height restriction information of the grids.

[0008] In a possible implementation, the types of the grids comprise super-high grids and non-super-high grids, and the determining of the types of the grids according to the digital terrain models at the at least two time points and the restricted height grid model comprises: determining elevation data of the grids according to a last digital terrain model of the airport clearance area; and determining a type of a grid whose elevation data is greater than or equal to height restriction information as a super-high grid.

[0009] In a possible implementation, the non-super-high grid includes dynamic grids and static grids, the error of the digital terrain model includes an elevation error, the type of each grid is determined according to the digital terrain model obtained at the at least two time points and the limited height grid model, and the method further includes: determining an elevation difference threshold according to the elevation error; determining an elevation difference between elevation data of each grid at the at least two time points according to the digital terrain model obtained at the at least two time points; and determining, as the net state grid, a grid whose absolute value of the elevation difference is less than the elevation difference threshold.

[0010] In a possible implementation, the dynamic grid includes a dynamic increasing grid and a dynamic decreasing grid, and the type of each grid is determined according to the digital terrain model obtained at the at least two time points and the limited height grid model, and the method further includes: determining, as the dynamic increasing grid, a grid whose elevation difference between elevation data corresponding to a later time point and elevation data corresponding to an earlier time point is greater than 0; or determining, as the dynamic decreasing grid, a grid whose elevation difference between elevation data corresponding to a later time point and elevation data corresponding to an earlier time point is less than 0.

[0011] In a possible implementation, the grid cluster is determined according to the type of each grid, and the method includes: performing merging processing on adjacent grids of the same type according to the type of each grid, to obtain the grid cluster.

[0012] In a possible implementation, the monitoring time interval of each grid cluster is determined according to the grid cluster, the digital terrain model obtained at the at least two time points and the limited height grid model, and the method includes: determining a maximum change rate of elevation data of each grid cluster according to the digital terrain model obtained at the at least two time points; determining a minimum elevation difference of each grid cluster according to the limited height information and the elevation data of each grid in the last obtained digital terrain model; and determining the monitoring time interval of the grid cluster according to the maximum change rate, the minimum elevation difference and the type of the grid cluster.

[0013] In a possible implementation, the type of the grid cluster includes a dynamic increasing grid and a dynamic decreasing grid, and the monitoring time interval of the grid cluster is determined according to the maximum change rate, the minimum elevation difference and the type of the grid cluster, and the method includes: determining a first time interval of a dynamic increasing grid cluster according to the minimum elevation difference and the maximum change rate; determining a second time interval of a dynamic decreasing grid cluster according to the elevation data of each grid in the last obtained digital terrain model, the minimum elevation difference and the maximum change rate; and determining, as the monitoring time interval, a minimum value of the first time interval and the second time interval.

[0014] In a possible implementation, the types of the grid clusters include super-high grids and non-super-high grids, the non-super-high grids include dynamic grids and static grids, and the method further includes: determining attributes of buildings at corresponding positions of the electronic map for the grid clusters of the types of super-high grids and dynamic grids.

[0015] According to an aspect of the present disclosure, an airport clearance area building height monitoring device is provided, including: an error determination module configured to determine errors of digital terrain models of an airport clearance area at at least two time instants, wherein the digital terrain models include elevation data of a plurality of locations of the airport clearance area monitored at the time instants; a height limit determination module configured to obtain a limit height grid model of the airport clearance area according to the errors of the digital terrain models, wherein the limit height grid model divides the airport clearance area into a plurality of grids and includes limit height information of locations represented by the grids; a type determination module configured to determine types of the grids according to the digital terrain models at the at least two time instants and the limit height grid model; a grid cluster determination module configured to determine grid clusters according to the types of the grids, wherein the types of the grid clusters are the same as types of at least one grid included in the grid clusters; and a time interval determination module configured to determine monitoring time intervals of the grid clusters according to the grid clusters, the digital terrain models at the at least two time instants, and the limit height grid model, wherein the monitoring time intervals represent intervals between time instants at which elevation data is monitored.

[0016] In a possible implementation, the errors of the digital terrain models include plane errors in a horizontal plane direction and elevation errors in a vertical direction, and the error determination module is further configured to determine the plane errors and the elevation errors according to position errors of a plurality of control points preset in the airport clearance area in the digital terrain models at the at least two time instants.

[0017] In a possible implementation, the height limit determination module is further configured to: determine grid widths of the limit height grid model according to the plane errors; determine limit height information of the grids according to preset airport information and the grid widths; and obtain the limit height grid model according to the grid widths and the limit height information of the grids.

[0018] In a possible implementation, the types of the grids include super-high grids and non-super-high grids, and the type determination module is further configured to: determine elevation data of the grids according to a last obtained digital terrain model of the airport clearance area; and determine a type of a grid whose elevation data is greater than or equal to limit height information as a super-high grid.

[0019] In a possible implementation, the non-super-elevation grid includes a dynamic grid and a static grid, the error of the digital terrain model includes an elevation error, and the type determining module is further configured to: determine an elevation difference threshold according to the elevation error; determine an elevation difference between elevation data of each grid at the at least two time points according to the digital terrain model obtained at the at least two time points; and determine, as the net state grid, a grid whose absolute value of the elevation difference is less than the elevation difference threshold.

[0020] In a possible implementation, the dynamic grid includes a dynamic elevation-increasing grid and a dynamic elevation-decreasing grid, and the type determining module is further configured to: determine, as the dynamic elevation-increasing grid, a grid whose elevation difference between elevation data corresponding to a subsequent time point and elevation data corresponding to a previous time point is greater than 0; or determine, as the dynamic elevation-decreasing grid, a grid whose elevation difference between elevation data corresponding to a subsequent time point and elevation data corresponding to a previous time point is less than 0.

[0021] In a possible implementation, the grid cluster determining module is further configured to: according to the types of the grids, perform merging processing on adjacent grids of the same type, to obtain the grid clusters.

[0022] In a possible implementation, the time interval determining module is further configured to: determine a maximum change rate of elevation data of each grid cluster according to the digital terrain model obtained at the at least two time points; determine a minimum elevation difference of each grid cluster according to the height limit information and the elevation data of each grid in the last obtained digital terrain model; and determine the monitoring time interval of each grid cluster according to the maximum change rate, the minimum elevation difference, and the type of the grid cluster.

[0023] In a possible implementation, the type of the grid cluster includes a dynamic elevation-increasing grid and a dynamic elevation-decreasing grid, and the time interval determining module is further configured to: determine a first time interval of a dynamic elevation-increasing grid cluster according to the minimum elevation difference and the maximum change rate; determine a second time interval of a dynamic elevation-decreasing grid cluster according to the elevation data of each grid in the last obtained digital terrain model, the minimum elevation difference, and the maximum change rate; and determine, as the monitoring time interval, a minimum value of the first time interval and the second time interval.

[0024] In a possible implementation, the type of the grid cluster includes a super-elevation grid and a non-super-elevation grid, the non-super-elevation grid includes a dynamic grid and a static grid, and the apparatus further includes: an attribute determining module configured to determine an attribute of a building at a position corresponding to the grid cluster in the electronic map, the grid cluster being of the super-elevation grid and the dynamic grid.

[0025] According to an aspect of the present disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the above method.

[0026] According to an aspect of the present disclosure, a computer-readable storage medium is provided, having stored thereon computer program instructions, which when executed by a processor, implement the above method.

[0027] The airport clearance area building height monitoring method according to the embodiments of the present disclosure can obtain the elevation data of each location of the airport clearance area based on the digital terrain model of the airport clearance area obtained at at least two time points, thereby improving the accuracy of data monitoring. Moreover, the airport clearance area can be divided into multiple grids by using the height limitation grid model, and the grid clusters are formed based on the types of the grids, so as to determine the time interval of each grid cluster. The airport clearance area does not need to be monitored indiscriminately at each location, and the positions corresponding to each grid cluster can be flexibly and dynamically monitored, thereby reducing the monitoring cost and improving the monitoring efficiency. Furthermore, the monitoring time interval can be determined by the change rate of the height of the building, thereby improving the monitoring efficiency and flexibility and reducing the data cost of monitoring.

[0028] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, but not limiting the present disclosure. Other features and aspects of the present disclosure will become apparent according to the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0029] The accompanying drawings incorporated in and forming a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0030] Figure 1 A flowchart of an airport clearance area building height monitoring method according to an embodiment of the present disclosure is shown;

[0031] Figure 2 A schematic diagram of a height limitation grid model of an airport clearance area according to an embodiment of the present disclosure is shown;

[0032] Figure 3 A schematic diagram of obtaining a grid cluster according to an embodiment of the present disclosure is shown;

[0033] Figure 4 A schematic diagram of an electronic map according to an embodiment of the present disclosure is shown;

[0034] Figure 5 An application schematic diagram of an airport clearance area building height monitoring method according to an embodiment of the present disclosure is shown;

[0035] Figure 6 a block diagram of an airport clearance area building height monitoring device according to an embodiment of the present disclosure is shown;

[0036] Figure 7 a block diagram of an electronic device according to an embodiment of the present disclosure is shown;

[0037] Figure 8 a block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0038] Various exemplary embodiments, features and aspects of the present disclosure will be explained in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar elements. Although various aspects of the embodiments are illustrated in the drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0039] The term "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations.

[0040] The term "and / or", merely describes association relationship of associated objects, and means that three relationships can exist, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of multiple or any combination of at least two of multiple, for example, including at least one of A, B and C can mean including any one or more elements selected from the set consisting of A, B and C.

[0041] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the specific embodiments below. Those skilled in the art should understand that the present disclosure can also be implemented without some specific details. In some examples, methods, means, elements and circuits well known to those skilled in the art are not described in detail, in order to highlight the main idea of the present disclosure.

[0042] Figure 1 a flowchart of an airport clearance area building height monitoring method according to an embodiment of the present disclosure is shown, as shown in Figure 1 The method comprises:

[0043] In step S11, according to the digital terrain model of the airport clearance area acquired at at least two time instants, the error of the digital terrain model is determined, wherein the digital terrain model comprises elevation data of a plurality of locations of the airport clearance area monitored at the time instants;

[0044] In step S12, a restricted height grid model of the airport clearance area is obtained according to the error of the digital terrain model, wherein the restricted height grid model divides the airport clearance area into a plurality of grids and includes height limit information of a location represented by each grid;

[0045] In step S13, the type of each grid is determined according to the digital terrain model obtained at the at least two time points and the restricted height grid model.

[0046] In step S14, a grid cluster is determined 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.

[0047] In step S15, a monitoring time interval of each grid cluster is determined according to the grid cluster, the digital terrain model obtained at the at least two time points and the restricted height grid model, wherein the monitoring time interval represents an interval between time points for monitoring height data.

[0048] The airport clearance area building height monitoring method according to the embodiments of the present disclosure can obtain height data of each location in the airport clearance area based on the digital terrain model of the airport clearance area obtained at the at least two time points, thereby improving the accuracy of data monitoring. Moreover, the airport clearance area can be divided into a plurality of grids by using the restricted height grid model, and grid clusters can be formed based on the type of the grids, so as to determine the time interval of each grid cluster. In this way, each location corresponding to each grid cluster can be flexibly and dynamically monitored without indiscriminately monitoring each location in the airport clearance area, thereby reducing the monitoring cost and improving the monitoring efficiency.

[0049] The airport clearance area is a space region of restricted topography and object height drawn around an airport to ensure the safety of takeoff, landing and go-around of an aircraft. Although there can be buildings in the airport clearance area, each building has a corresponding restricted height according to the position of the building in the clearance area. If the building exceeds the restricted height, it may pose a hidden danger to flight safety. The restricted height is related to various factors, such as the grade of the airport, the parameters of the runway, the distance and angle from the runway, etc. The restricted height of each location in the clearance area can be different. Therefore, it is a large workload to monitor whether each building is overheight or has potential possibility of overheight.

[0050] In a possible implementation, to solve the above problems, a digital surface model including the height data of each location in the airport clearance area and a restricted height grid model including the height restriction information of each location in the airport clearance area can be used to monitor whether each location represented by a grid exceeds the restricted height, and based on this, the type of the grid can be classified, and then the same type of grids can be merged to obtain a grid cluster, for example, the grid cluster can be the minimum bounding rectangle of a plurality of same type of grids. Further, the monitoring time interval can be determined based on the height data and the height restriction information in each grid cluster, and the height of each location or each building can be flexibly monitored, without indiscriminately and comprehensively monitoring all locations in the airport clearance area, thereby reducing the workload and cost of monitoring.

[0051] In a possible implementation, in step S11, the digital surface model (DSM) is a model of data of a ground surface in a region, and the data of the ground surface includes height data of the ground surface. The region can include various terrains and a plurality of buildings. For a building, the digital surface model can include the height of the building.

[0052] In a possible implementation, the digital surface model can be established based on ground surface data provided by a remote sensing image data platform such as ENVI (environmental visualization image) or ERDAS (remote sensing image processing). For example, the digital surface model can be generated based on multi-view stereo pairs, or a large-scale digital surface model (for example, a data model provided by a remote sensing data platform) can be cropped to obtain a digital surface model of the airport clearance area.

[0053] In a possible implementation, at least two time instants of the digital surface model can be obtained. The height data of a plurality of locations in the airport clearance area provided by the digital surface model at different time instants can be inconsistent. For example, a building at a certain location is under construction, and the height of the building provided by the digital surface model obtained at a later time instant can be higher than the height of the building provided by the digital surface model obtained at an earlier time instant. For another example, a building at a certain location is being demolished, and the height of the building provided by the digital surface model obtained at an earlier time instant can be higher than the height of the building provided by the digital surface model obtained at a later time instant. Based on these characteristics, in addition to determining the height data of each location, the height change trend of each building can be determined based on the digital surface model at at least two time instants, to determine a building with a high potential of exceeding the height.

[0054] In a possible implementation, in the airport clearance area, a plurality of control points can be set, which are fixed points. The measurement error between the digital terrain models can be determined based on the error of the positions of the control points in the digital terrain models. The error of the digital terrain model includes a plane error in the horizontal plane direction and an elevation error in the vertical direction. The plane error and the elevation error are both relative errors between the digital terrain models obtained at different times. Step S11 can include: determining the plane error and the elevation error according to the position errors of the plurality of control points preset in the airport clearance area in the digital terrain models obtained at the at least two times.

[0055] In a possible implementation, the plane error can be determined based on the following formula (1):

[0056]

[0057] wherein SD xy is the plane error, n is the number of control points (n is a positive integer, for example, n = 5), is the plane distance of the i th (i ≤ n, and n is a positive integer) control point in the two digital terrain models, and ME xy can be determined by the following formula (2):

[0058]

[0059] In a possible implementation, the elevation error can be determined based on the following formula (3):

[0060]

[0061] wherein SD z is the elevation error, is the elevation distance of the i th control point in the two digital terrain models, and ME z can be determined by the following formula (3):

[0062]

[0063] In a possible implementation, to match the restricted height grid model, the digital terrain models obtained at the at least two time points can be resampled into grids, wherein the resolution of the grid, i.e., the width of the grid, is twice the plane error. By setting the width of the grid to be twice the plane error, the same grid can represent the position of the same building in the digital terrain models obtained at the at least two time points, or the position represented by the same grid belongs to the same building. The disclosure does not limit the width of the grid. In an example, the digital terrain model can be resampled into grids by using the "ArcToolBox--DataManagementTools--Raster--RasterProcessing--Resample" tool of the ArcMap software, and the disclosure does not limit the method or tool used for the resampling.

[0064] In a possible implementation, further, the digital terrain models obtained at the at least two time points can be aligned. For example, the range data of the airport clearance area boundary in the digital terrain model can be obtained, and the resolutions of the two digital terrain models are made the same (for example, both are the grid resolution described above), and then the digital terrain models obtained at the at least two time points are cropped to obtain at least two digital terrain models with the same resolution and range. In an example, the range data of the airport clearance area boundary can be determined by using the airport clearance area boundary KML data, and the cropping can be performed by using the "ArcToolBox--AnalysisTools--Extraction--Split" tool in the ArcMap software, and the disclosure does not limit the method or tool used for the alignment.

[0065] In a possible implementation, in step S12, the restricted height grid model of the airport clearance area can be obtained. The model can divide the airport clearance area into a plurality of grids, and include the height restriction information of the positions represented by the grids. As described above, the height restriction information of the positions in the airport clearance area can be different from each other, and the height restriction information of each position can be related to the level of the airport, the runway parameters, and the distance and / or angle between the runway. The height restriction information of the positions in the airport clearance area can be determined according to related technologies, for example, the height restriction information of the plane position (x, y) in the airport clearance area is H(x, y). Based on the height restriction information, the restricted height grid model of the airport clearance area can be obtained.

[0066] In a possible implementation, step S12 can include: determining the width of the grid of the restricted height grid model according to the plane error; determining the height restriction information of each grid according to the preset airport information and the width of the grid; and obtaining the restricted height grid model according to the width of the grid and the height restriction information of each grid.

[0067] In a possible implementation, the grid width of the restricted height grid model can be determined according to the planar error. Similar to the process of grid resampling of the digital terrain model, the width of the grid can be twice the planar error. The present disclosure does not limit the grid width of the restricted height grid model.

[0068] In a possible implementation, the restricted height information of each grid can be determined. As described above, the restricted height information of each position (x, y) in the airport clearance area can be determined as H(x, y). The restricted height information of each grid can be determined based on the restricted height information of each position and the width of the grid. For example, the restricted height information of the position of the center point of each grid can be determined as the restricted height information of the grid, or the average of the restricted height information of multiple positions in the grid can be determined as the restricted height information of the grid, and the present disclosure does not limit the determination manner of the restricted height information of the grid.

[0069] In a possible implementation, after the restricted height information of each grid is determined, the airport clearance area can be divided into multiple grids, and the restricted height grid model containing the restricted height information of the locations represented by each grid can be determined.

[0070] Figure 2 A schematic diagram of the restricted height grid model of the airport clearance area according to an embodiment of the present disclosure is shown as Figure 2 As shown, the airport clearance area can include multiple sub-areas, for example, a lift zone, an inner horizontal surface, a transition surface, an end clearance, a tapered surface, and an outer horizontal surface. Based on factors such as the level of the airport and the runway parameters, each sub-area has a corresponding size, for example, the length of the lift zone in the runway direction (x direction) is L, the length of the end of the lift zone to the end of the inner horizontal surface x1 in the x direction is l1, and the lengths of other landmark positions x2, x3, x4, x5, and x6 in the end clearance sub-area are l2, l3, l4, the total length of the end clearance in the x direction is D1, the total length of the inner horizontal surface in the y direction is D2, the total length of the tapered surface in the y direction is D3, the total length of the inner horizontal surface in the x direction is D4, the total length of the tapered surface in the x direction is D5, the total length of the outer horizontal surface in the y direction is D6, the total length of the outer horizontal surface in the x direction is D7, the 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.

[0071] In a possible implementation, as shown in Figure 2 The restricted height grid model divides the airport clearance area into multiple grids, and the width of each grid is d. In an example, d = 2SD xy For each grid, the restricted height information of the grid can be determined in the above manner, for example, the restricted height information of the position of the center point is taken as the restricted height information of the grid, or the average of the restricted height information of multiple positions in the grid is taken as the restricted height information of the grid, and the present disclosure does not limit the determination manner of the restricted height information of the grid.

[0072] In a possible implementation, in step S13, the type of each grid can be divided to identify the grid with the overheight or the potential possibility of overheight. As described above, the digital terrain model can include the elevation data of each location in the airport clearance area, and is divided into multiple grids in the same way, therefore, based on the digital terrain model, the elevation data of each grid can be obtained, and based on the relationship between the elevation data of each grid and the height limit information of each grid, the type of each grid can be divided, that is, the grid with the overheight or the potential possibility of overheight can be identified.

[0073] In a possible implementation, the type of the grid includes the overheight grid and the non-overheight grid, and step S13 can include: determining the elevation data of each grid according to the last acquired digital terrain model of the airport clearance area; and determining the type of the grid with the elevation data greater than or equal to the height limit information as the overheight grid.

[0074] In a possible implementation, the elevation data of each grid provided by the last acquired digital terrain model of the airport clearance area can 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 elevation data greater than or equal to the height limit information is determined as the overheight grid, and further, the category identifier of the overheight grid can be set, for example, the category identifier of the grid is set to -1. Conversely, the type of the grid with the elevation data less than the height limit information is determined as the non-overheight grid.

[0075] In a possible implementation, when judging the category of the grid, the error factor can also be considered, for example, the sum of the elevation data of each grid and the elevation error of the digital terrain model is taken as the maximum value of the elevation data of each grid, and the type of the grid with the maximum value of the elevation data greater than or equal to the height limit information is determined as the overheight grid, and conversely, the type of the grid is determined as the non-overheight grid.

[0076] In a possible implementation, for the overheight grid, the focus monitoring can be performed, for example, whether the building represented by the grid is demolished or not is periodically monitored. For the non-overheight grid, whether the grid has the potential risk of overheight can be further judged.

[0077] In a possible implementation, the non-overheight grid includes the dynamic grid and the static grid, and step S13 further includes: determining the elevation difference threshold value according to the elevation error; determining the elevation difference value between the elevation data of each grid at the at least two time instants according to the digital terrain model acquired at the at least two time instants; and determining the grid with the absolute value of the elevation difference value less than the elevation difference threshold value as the static grid.

[0078] In a possible implementation, whether the grid is a dynamic grid or a static grid can be determined based on whether the elevation data of the non-overheight grid changes and the magnitude of the change. If a non-overheight grid is a dynamic grid, the elevation data of the grid can exceed the height limit information in the future, i.e., there is a possibility of potential overheight. Conversely, if the non-overheight grid is a static grid, i.e., the elevation data of the grid does not change or changes little (e.g., the change in the elevation data is only caused by measurement error) during at least two time instants at which the digital terrain model is acquired, the grid does not have the possibility of potential overheight.

[0079] In a possible implementation, a threshold of elevation difference can be set to determine whether the change in the height of the non-overheight grid meets the dynamic criterion. For example, twice the elevation error of the digital terrain model can be determined as the threshold of elevation difference δ, δ = 2SD z The specific value of the threshold of elevation difference is not limited in the disclosure.

[0080] In a possible implementation, if the amount of change (i.e., the absolute value of the elevation difference) in the elevation data of the non-overheight grid provided by the digital terrain model acquired at at least two time instants is less than the threshold of elevation difference, the elevation data of the grid does not change or changes little, the building represented by the grid at the site can be a building that has been completed, and there is no possibility of potential overheight. The category of the grid can be determined as a static grid, and a category identifier can be determined for the grid, e.g., the category identifier is 0. Conversely, if the amount of change (i.e., the absolute value of the elevation difference) in the elevation data of the grid is greater than or equal to the threshold of elevation difference, the elevation data of the grid changes rapidly, and the building represented by the grid at the site can be a building that is under construction or is being demolished. In the future, the building can continue to be constructed to exceed the height limit information, or other overheight buildings can be built after the demolition, and therefore, there is a possibility of potential overheight. The category of the grid can be determined as a dynamic grid.

[0081] In a possible implementation, if the non-overheight grid is a dynamic grid, the dynamic grid can be further classified so as to more accurately monitor the building represented by each type of dynamic grid at the site. The dynamic grid includes a dynamic increasing grid and a dynamic decreasing grid. Step S13 can further include: determining a grid with an elevation difference between the elevation data corresponding to a subsequent time instant and the elevation data corresponding to a previous time instant greater than 0 as the dynamic increasing grid; or determining a grid with an elevation difference between the elevation data corresponding to the subsequent time instant and the elevation data corresponding to the previous time instant less than 0 as the dynamic decreasing grid.

[0082] In one possible implementation, for a dynamic grid that has not exceeded its height limit (the absolute value of the elevation difference is greater than or equal to the elevation difference threshold), if the difference between the elevation data of this grid provided in the digital surface model acquired at a later time step and the elevation data of this grid provided in the digital surface model acquired at a previous time step is greater than 0, then it can be considered that the height of the building at the location represented by this grid has increased between at least two time steps, and the increase is greater than or equal to the elevation difference threshold. In this case, the grid can be classified as a dynamically increasing grid, and a category identifier can be assigned to it, for example, a category identifier of 1. + Conversely, if the difference between the elevation data of the grid provided in the digital surface model acquired at a later time step and the elevation data of the grid provided in the digital surface model acquired at a previous time step is less than 0, then it can be considered that the height of the building at the location represented by the grid has decreased between at least two time steps, and the decrease is greater than or equal to the elevation difference threshold. In this case, the grid can be classified as a dynamically decreasing grid, and a category label can be assigned to it, for example, a category label of 1. - These two types of dynamic grids can be monitored separately. In the example, different monitoring time intervals can be determined for the two types of grids. For example, the elevation data of the dynamically increasing grid can be monitored again after one month to see if it exceeds the height limit information, and the elevation data of the dynamically decreasing grid can be monitored again after one year to see if it exceeds the height limit information, etc. This disclosure does not limit the time interval.

[0083] In one possible implementation, in step S14, to reduce monitoring costs, adjacent grids of the same type can be merged to monitor the merged grid cluster as a whole. During the merging process, grids of the same type can be merged based on their type to obtain a grid cluster. Step S14 may include: merging adjacent grids of the same type according to the type of each grid to obtain the grid cluster. For example, if a grid is a dynamically increasing grid and its adjacent grids (including its upper, lower, left, and right adjacent grids) are also dynamically increasing grids, then the two grids can be merged into a grid cluster, and the search continues to determine if other adjacent grids in the grid cluster include grids of the same type. After this merging process, multiple grid clusters can be obtained. The form of the grid cluster may include the minimum bounding matrix of multiple grids of the same type. This disclosure does not limit the form of the grid cluster.

[0084] Figure 3 A schematic diagram illustrating the acquisition of a grid cluster according to an embodiment of this disclosure is shown. Figure 3 As shown, t a Time and t b Digital surface models of the airport airspace are acquired at different times, where t b The time is a subsequent time. Figure 3 In step ①, it is possible to t bAmong multiple grids of the digital surface model acquired at any time, identify the superelevation grids (i.e., elevation data h) that exceed the height limit information. bij The grids that exceed the height limit information are identified and their category is set to -1; the remaining grids are not considered to exceed the height limit.

[0085] In one possible implementation, Figure 3 In step ②, it can be based on t a Time and t b The elevation data of each grid provided by the digital surface model of the airport airspace, acquired at two time points, is used to determine whether the elevation data change of each grid exceeds an elevation difference threshold. Grids that do not exceed the elevation difference threshold are classified as static grids and their category is assigned a value of 0. Otherwise, the grid is classified as a dynamic grid, and the value is assigned a value of t. b The elevation data at time t is greater than t a The grid for the elevation data at any given time is determined to be a dynamically increasing grid, and its category identifier is set to 1. + and t b The elevation data at time t is less than t a The grid for the elevation data at any given time is determined to be a dynamically decreasing grid, and its category identifier is set to 1. - .

[0086] In one possible implementation, Figure 3 In step ③, adjacent similar grids can be merged for grids with potentially very high probability (dynamically increasing grids and dynamically decreasing grids) to obtain a grid cluster. For example, the form of the grid cluster can be a minimum outer matrix.

[0087] In the example, merging can be done first along the column direction, for example, if two adjacent columns of grid MBR n and MBR n If there are grids of the same category, and the intersection of corresponding positions of grids of the same category in two adjacent columns is not empty, then the clusters of the two columns of grids can be obtained first. For example, Figure 3 In step ③, the grid on the left includes dynamically increasing grids in columns 2 and 3 (category labeled 1). + If the intersection of the dynamically increasing grids at corresponding positions is not empty (e.g., the grids in the first row of column 2 and the first row of column 3 are both dynamically increasing grids, ensuring the intersection of the dynamically increasing grids at corresponding positions is not empty), then columns 2 and 3 can be merged. Based on this method, multiple columns can be merged to obtain the grid clusters along the column direction, i.e., the minimum outer matrix along the column direction, such as the outer matrix of columns 2 and 3.

[0088] In an example, in the obtained cluster in the column direction (for example, the minimum circumscribed matrix), the merging can be continued in the direction of rows, for example, in the cluster in the column direction, if there are grids of the same category in the adjacent two rows, and the intersection of the same grids in the corresponding positions is not empty, then the grid cluster of the same grids can be obtained. For example, in the cluster composed of the 2nd column and the 3rd column, the grids in the 1st row of the 2nd column and the 1st row of the 3rd column are both dynamic increasing grids, and the grid cluster composed of the two grids can be obtained. In the merging in the row direction, in the 2nd row of the cluster, the grid in the 3rd column of the 2nd row is a dynamic increasing grid, so that in the cluster composed of the 2nd column and the 3rd column, the intersection of the dynamic increasing grids in the corresponding positions of the 1st row and the 2nd row is not an empty set (i.e., the position of the 3rd column), and the grid cluster composed of the above two grids can be used to merge the 2nd row in the cluster composed of the 2nd column and the 3rd column, to obtain a new grid cluster (a grid cluster including four grids of the 1st row of the 2nd column, the 1st row of the 3rd column, the 2nd row of the 2nd column, and the 2nd row of the 3rd column). Similarly, in the 3rd row, the grid in the 1st column of the 3rd row is a dynamic increasing grid, and the intersection of the dynamic increasing grids in the corresponding positions of the grid cluster is not an empty set (i.e., the position of the 2nd column), so that the grid cluster composed of the above four grids can be used to merge the 3rd row in the cluster composed of the 2nd column and the 3rd column, to obtain a new grid cluster (a grid cluster including six grids of the 1st row of the 2nd column, the 1st row of the 3rd column, the 2nd row of the 2nd column, the 2nd row of the 3rd column, the 3rd row of the 2nd column, and the 3rd row of the 3rd column). In this way, the grid cluster of the same grids, i.e., the minimum circumscribed matrix, can be obtained, as shown in the dashed box including six grids. Similarly, the grid cluster of the dynamic decreasing grids can also be obtained, as shown in the dashed box including one grid. The specific way of obtaining the grid cluster is not limited in the present disclosure, for example, the grid cluster can also be obtained by graph classification and the like.

[0089] In a possible implementation, after obtaining the grid cluster in step S15, the grid cluster can be used as a monitoring unit for monitoring, and the monitoring time interval of the next monitoring is determined, so that indiscriminate monitoring can be avoided, and only each grid cluster needs to be monitored when the corresponding monitoring time interval arrives.

[0090] In a possible implementation, step S15 can include: determining the maximum change rate of the elevation data of each grid cluster according to the digital terrain model obtained at the at least two time points; determining the minimum height limit information of each grid cluster according to the limited height grid model; determining the minimum elevation difference of each grid cluster according to the minimum height limit information and the elevation data of each grid in the last obtained digital terrain model; and determining the monitoring time interval of the grid cluster according to the maximum change rate, the minimum elevation difference, and the type of the grid cluster.

[0091] In a possible implementation, the grid cluster can include one or more grids, and the variation amount of the elevation data of each grid is not necessarily the same, and thus the variation rate of the elevation data of each grid is not necessarily the same. To maximize flight safety and reduce the possibility of potential overflight, a maximum variation rate among the variation rates of the grids can be determined. In an example, the maximum variation rate can be obtained by the following formula (5):

[0092]

[0093] where h bij is the elevation data of the grid in the i-th row and the j-th column of the digital terrain model obtained at the time t b , h aij is the elevation data of the grid in the i-th row and the j-th column of the digital terrain model obtained at the time t a , and V bij is the maximum variation rate.

[0094] In a possible implementation, similarly, since the grid cluster can include multiple grids, the height limit information of each grid can be inconsistent. To maximize flight safety and reduce the possibility of potential overflight, a minimum elevation difference of each grid can be determined, that is, the minimum value of the difference between the height represented by the last acquired elevation data of each grid and the height represented by the height limit information of the grid. The minimum elevation difference can be obtained according to the following formula (6):

[0095] H bij = min{H ij -h bij} (6)

[0096] where H bij is the minimum elevation difference, and H ij is the height limit information of the grid in the i-th row and the j-th column.

[0097] In a possible implementation, the monitoring time interval of each type of grid cluster can be determined according to the type of the grid cluster. This step can include: determining a first time interval of a dynamic increasing grid according to the minimum elevation difference and the maximum variation rate; determining a second time interval of a dynamic decreasing grid according to the last acquired elevation data of each grid in the digital terrain model, the minimum elevation difference, and the maximum variation rate; and determining the minimum value of the first time interval and the second time interval as the monitoring time interval.

[0098] In a possible implementation, for the dynamic height-increasing grid cluster, it is assumed that the height-increasing speed of the building at the location represented by the dynamic height-increasing grid cluster is uniform, that is, the building continues to increase at the maximum change rate, and the monitoring time interval for the next monitoring can be the time for the building to continue to increase at the maximum change rate to the height represented by the height limit information. For example, the first time interval can be represented by the following formula (7):

[0099] T ↑ =H bij / V bij (7)

[0100] wherein T ↑ is the first time interval. In an example, the first time interval of each dynamic height-increasing grid cluster can be determined, and the building at the location represented by each dynamic height-increasing grid cluster is monitored after the first time interval, without indiscriminate monitoring, improving monitoring efficiency and reducing monitoring cost.

[0101] In a possible implementation, for the dynamic height-decreasing grid cluster, it is assumed that the height-decreasing speed of the dynamic height-decreasing grid cluster is caused by the building being demolished, and a new building can be built at the original location after the building is demolished. It is also assumed that the speed of the demolition and the new building remains unchanged, that is, the building continues to decrease and increase at the maximum change rate, and the monitoring time interval for the next monitoring can be the time for the new building to reach the height represented by the height limit information after the demolition is completed. For example, the second time interval can be represented by the following formula (8):

[0102] T ↓ =(2h bij -H bij ) / V bij (8)

[0103] wherein T ↓ is the second time interval. In an example, the second time interval of each dynamic height-decreasing grid cluster can be determined, and the building at the location represented by each dynamic height-decreasing grid cluster is monitored after the second time interval, without indiscriminate monitoring, improving monitoring efficiency and reducing monitoring cost.

[0104] In a possible implementation, to further improve safety, the monitoring time interval for the next monitoring can be set for all grid clusters, for example, the minimum value of the first time interval and the second time interval can be determined as the monitoring time interval, so that all grid clusters are monitored when the monitoring time interval is reached. The monitoring time interval is shown in the following formula (9):

[0105] T=min(T ↑ ,T ↓ ) (9)

[0106] wherein T is the monitoring time interval. The next monitoring time is T+t b .

[0107] In a possible implementation, in the monitoring process, the attributes of the buildings in the grid cluster can also be retrieved from the electronic map, such as the name, area, current height, and the like of the buildings. The method further includes: determining the attributes of the buildings at the corresponding positions of the grid cluster in the electronic map.

[0108] In a possible implementation, the corresponding buildings can be retrieved from the electronic map according to the position of the grid cluster, for example, the retrieval can be performed from the electronic map according to the position of the center point of the grid cluster, the size of the grid cluster, and the like. For example, the position of the center point of the grid cluster is shown in the following formula (10):

[0109]

[0110] wherein j max is the column coordinate of the rightmost grid of the grid cluster, j min is the column coordinate of the leftmost grid of the grid cluster, i max is the row coordinate of the uppermost grid of the grid cluster, i min is the row coordinate of the lowermost grid of the grid cluster.

[0111] The size of the grid cluster is shown in the following formula (11):

[0112]

[0113] wherein L is the length of the grid cluster (the minimum outer package matrix), H is the width of the grid cluster, and d is the grid width.

[0114] In a possible implementation, the attributes of the buildings can be determined based on the retrieval from the electronic map based on the above information, for example, as shown in step ④ in Figure 3 the above information, it is determined that the building at the position of the dynamic increasing grid cluster is building A, and the building at the position of the dynamic decreasing grid cluster is building B. Of course, the attributes of the buildings in other grids, such as the super-high grid and the static grid, can also be retrieved by this method, and the present disclosure does not limit this.

[0115] In a possible implementation, the retrieval result can also be marked in the electronic map.

[0116] Figure 4 A schematic diagram of an electronic map according to an embodiment of the present disclosure is shown, as shown in Figure 4As shown, the above search results can be marked in the electronic map, for example, the location of the dynamic increasing grid cluster, the location of the dynamic decreasing grid cluster, the location of the super-high grid, etc. can be marked in the electronic map, and the present disclosure does not limit this.

[0117] The airport clearance area building height monitoring method according to the embodiments of the present disclosure can obtain the elevation data of each location of the airport clearance area based on the digital terrain models of the airport clearance area obtained at at least two time points, thereby improving the accuracy of data monitoring. Moreover, the airport clearance area can be divided into multiple grids by using the height limiting grid model, and the grid clusters are formed based on the types of the grids, so as to determine the time interval of each grid cluster. Without indiscriminate monitoring of each location of the airport clearance area, the positions corresponding to each grid cluster can be flexibly and dynamically monitored, thereby reducing the monitoring cost and improving the monitoring efficiency. Furthermore, the monitoring time interval can be determined by the change rate of the height of the building, thereby improving the monitoring efficiency and flexibility and reducing the data cost of monitoring.

[0118] Figure 5 The application schematic diagram of the airport clearance area building height monitoring method according to the embodiments of the present disclosure is shown as follows. Figure 5 As shown, five control points can be set in the airport clearance area, and the digital terrain models at t a time (presequence time) and t b time (subsequence time) are obtained respectively. The two digital terrain models can be preprocessed and accuracy evaluated, for example, the two digital terrain models can be cropped, and the plane error and elevation error are obtained based on the five control points. Then, the grid size can be determined based on 2 times of the plane error, so as to perform grid resampling on the two digital terrain models to obtain two digital terrain models with the same resolution and range.

[0119] In a possible implementation, the height limiting information of each location of the airport clearance area can be obtained based on the parameters of the runway and the airport clearance area, and the height limiting grid model is obtained according to the above grid size. Each grid in the model includes the height limiting information of the represented location.

[0120] In a possible implementation, each grid can be classified, for example, if the sum of the elevation data and the elevation error of a certain grid is greater than the height limiting information, the grid can be determined as a super-high grid, and the category identifier thereof is determined as -1.

[0121] In a possible implementation, in the other non-super-high grids, the change amount of the elevation data at t a time and t b time is less than a threshold (for example, 2SD z), it is determined as a non-exceeding high static grid, and its category identifier is determined as 0. Other grids, i.e. grids whose variation of elevation data exceeds the threshold, are determined as non-exceeding dynamic grids, wherein a dynamic grid with an increased height is a dynamic increasing grid, and its category identifier is determined as 1 + , and a dynamic grid with a decreased height is a dynamic decreasing grid, and its category identifier is determined as 1 - .

[0122] In a possible implementation, grids whose variation of elevation data exceeds the threshold can be determined as grids with potential exceeding high possibility, and these grids are merged into the same type of grids, and a minimum bounding rectangle is obtained, and the type of the minimum bounding rectangle is the same as the type of at least one grid included in the minimum bounding rectangle.

[0123] In a possible implementation, the monitoring time interval of each grid cluster can be determined according to the category of the grid cluster, and the minimum monitoring time interval T is determined, so that the next height monitoring is performed at T+t b , to ensure flight safety. Further, the area or location of the grid cluster in the electronic map, i.e. a POI (Point of Interest), can be determined, and the attribute information of the building can be determined based on the information provided by the electronic map.

[0124] In a possible implementation, the airport clearance area building height monitoring method can be used in height monitoring of buildings in the airport clearance area, and the monitoring time interval of each grid cluster can be adaptively determined to automatically monitor the height of the buildings in the airport clearance area, so as to reduce data cost and improve monitoring efficiency while ensuring flight safety.

[0125] It can be understood that the above-mentioned various method embodiments mentioned in the disclosure can be combined with each other to form combined embodiments without deviating from the principle logic. Limited by the length of the disclosure, the disclosure will not be described again. It can be understood by those skilled in the art that the specific execution order of each step in the above-mentioned method should be determined according to its function and possible internal logic.

[0126] In addition, the disclosure also provides an airport clearance area building height monitoring apparatus, an electronic device, a computer readable storage medium, and a program, which can be used to implement any one of the airport clearance area building height monitoring methods provided by the disclosure. The corresponding technical solutions and descriptions are described in the method part, and will not be described again.

[0127] Figure 6 A block diagram of an airport clearance area building height monitoring apparatus according to an embodiment of the disclosure is shown, as Figure 6As shown, the device comprises: an error determination module 11 configured to determine an error of a digital terrain model of an airport clearance area according to the digital terrain model of the airport clearance area acquired at at least two time points, wherein the digital terrain model comprises elevation data of a plurality of locations of the airport clearance area monitored at the time points; a height limit determination module 12 configured to obtain a height limit grid model of the airport clearance area according to the error of the digital terrain model, wherein the height limit grid model divides the airport clearance area into a plurality of grids and comprises height limit information of locations represented by each grid; a type determination module 13 configured to determine a type of each grid according to the digital terrain model acquired at the at least two time points and the height limit grid model; a grid cluster determination module 14 configured to determine 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 a time interval determination module 15 configured to determine a monitoring time interval of each grid cluster according to the grid cluster, the digital terrain model acquired at the at least two time points and the height limit grid model, wherein the monitoring time interval represents an interval between time points at which elevation data is monitored.

[0128] In a possible implementation, the error of the digital terrain model comprises 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 determine the plane error and the elevation error according to position errors of a plurality of control points preset in the airport clearance area in the digital terrain model acquired at the at least two time points.

[0129] In a possible implementation, the height limit determination module is further configured to determine a grid width of the height limit grid model according to the plane error, determine height limit information of each grid according to preset airport information and the grid width, and obtain the height limit grid model according to the grid width and the height limit information of each grid.

[0130] In a possible implementation, the type of the grid comprises an over-height grid and a non-over-height grid, and the type determination module is further configured to determine elevation data of each grid according to a last acquired digital terrain model of the airport clearance area, and determine the type of each grid as an over-height grid if the elevation data is greater than or equal to height limit information.

[0131] In a possible implementation, the non-super-elevation grid includes a dynamic grid and a static grid, the error of the digital terrain model includes an elevation error, and the type determining module is further configured to: determine an elevation difference threshold according to the elevation error; determine an elevation difference between elevation data of each grid at the at least two time instants according to the digital terrain model obtained at the at least two time instants; and determine, as the net state grid, a grid whose absolute value of the elevation difference is less than the elevation difference threshold.

[0132] In a possible implementation, the dynamic grid includes a dynamic elevation-increasing grid and a dynamic elevation-decreasing grid, and the type determining module is further configured to: determine, as the dynamic elevation-increasing grid, a grid whose elevation difference between elevation data corresponding to a subsequent time instant and elevation data corresponding to a previous time instant is greater than 0; or determine, as the dynamic elevation-decreasing grid, a grid whose elevation difference between elevation data corresponding to a subsequent time instant and elevation data corresponding to a previous time instant is less than 0.

[0133] In a possible implementation, the grid cluster determining module is further configured to: according to the types of the grids, perform merging processing on adjacent grids of the same type, to obtain the grid clusters.

[0134] In a possible implementation, the time interval determining module is further configured to: determine a maximum change rate of elevation data of each grid cluster according to the digital terrain model obtained at the at least two time instants; determine a minimum elevation difference of each grid cluster according to the height limit information and the elevation data of each grid in the last obtained digital terrain model; and determine the monitoring time interval of each grid cluster according to the maximum change rate, the minimum elevation difference, and the type of the grid cluster.

[0135] In a possible implementation, the type of the grid cluster includes a dynamic elevation-increasing grid and a dynamic elevation-decreasing grid, and the time interval determining module is further configured to: determine a first time interval of a dynamic elevation-increasing grid cluster according to the minimum elevation difference and the maximum change rate; determine a second time interval of a dynamic elevation-decreasing grid cluster according to the elevation data of each grid in the last obtained digital terrain model, the minimum elevation difference, and the maximum change rate; and determine, as the monitoring time interval, a minimum value of the first time interval and the second time interval.

[0136] In a possible implementation, the type of the grid cluster includes a super-elevation grid and a non-super-elevation grid, the non-super-elevation grid includes a dynamic grid and a static grid, and the apparatus further includes: an attribute determining module configured to determine an attribute of a building at a position corresponding to the grid cluster in the electronic map, the grid cluster being of the super-elevation grid and the dynamic grid.

[0137] In some embodiments, the apparatus provided by the embodiments of the present disclosure has functions or includes modules that can be used to perform the methods described in the above method embodiments, and specific implementation can be referred to the description of the above method embodiments. For brevity, details are not described here.

[0138] The embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, the computer program instructions being executed by a processor to implement the above method. The computer-readable storage medium can be a non-volatile computer-readable storage medium.

[0139] The embodiments of the present disclosure also provide an electronic device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored by the memory to execute the above method.

[0140] The embodiments of the present disclosure also provide a computer program product comprising computer readable code, which, when run on a device, causes a processor in the device to execute instructions for implementing the airport clearance area building height monitoring method provided by any one of the above embodiments.

[0141] The embodiments of the present disclosure also provide another computer program product for storing computer readable instructions, which, when executed, cause a computer to perform the operations of the airport clearance area building height monitoring method provided by any one of the above embodiments.

[0142] The electronic device can be provided as a terminal, a server or other forms of devices.

[0143] Figure 7 A block diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. The electronic device 800 can be, for example, a terminal such as 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, and the like.

[0144] Referring to Figure 7 The electronic device 800 can include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0145] The processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 can include one or more processors 820 to execute instructions to complete the steps of the methods described above, in whole or in part. Moreover, the processing component 802 can include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0146] The memory 804 is configured to store various types of data to support the operations of the electronic device 800. Examples of these data include instructions to operate any applications or methods on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and the like. The memory 804 can be realized by any type of volatile or non-volatile storage devices or a combination thereof, 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 disc, or optical disc.

[0147] The power component 806 provides power to the various components of the electronic device 800. The power component 806 can 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.

[0148] The multimedia component 808 includes a screen to provide an output interface between the electronic device 800 and a user. In some embodiments, the screen can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes the touch panel, the screen can 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 a touch, a slide, and a gesture on the touch panel. The touch sensors can not only sense a position of a touch or a slide, but also detect a duration and a pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a back camera. The front camera and / or the back camera can receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each of the front and back cameras can be a fixed optical lens system or have a focal length and optical zoom capability.

[0149] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC) that is configured to receive an external audio signal when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0150] The I / O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which can be a keypad, a click wheel, buttons, and the like. The buttons can include, but are not limited to, a home button, a volume button, a start button, and a lock button.

[0151] The sensor component 814 includes one or more sensors for providing status assessments of various aspects of the electronic device 800. For example, the sensor component 814 can detect an open / closed position of the electronic device 800, relative positioning of components, such as a display and a keypad of the electronic device 800, a change of position of the electronic device 800 or a component of the electronic device 800, presence or absence of user contact with the electronic device 800, orientation or acceleration / deceleration of the electronic device 800, and a temperature change of the electronic device 800. The sensor component 814 can include a proximity sensor configured to detect presence of a nearby object without any physical touch. The sensor component 814 can also include a light sensor, such as a CMOS or CCD image sensor, for use in an imaging application. In some embodiments, the sensor component 814 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0152] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an example embodiment, the communication component 816 receives broadcast signals or broadcast-related information from an external broadcasting management system via a broadcast channel. In an example embodiment, the communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technology.

[0153] In exemplary embodiments, the electronic device 800 can be implemented with 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.

[0154] In exemplary embodiments, a non-transitory computer readable storage medium, such as the memory 804 including computer program instructions, is also provided, which can be executed by the processor 820 of the electronic device 800 to complete the above-described methods.

[0155] Figure 8 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 can be provided as a server. Referring to Figure 8 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932, for storing instructions, such as application programs, executable by the processing component 1922. The application programs stored in the memory 1932 can include one or more than one module each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described methods.

[0156] The electronic device 1900 can also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows Server TM , Mac OS X TM , Unix TM , Linux TM , FreeBSD TM or the like.

[0157] In exemplary embodiments, a non-transitory computer readable storage medium, such as the memory 1932 including computer program instructions, is also provided, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above-described methods.

[0158] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0159] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media 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 disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0160] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A 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 within the respective computing / processing device.

[0161] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code 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 can 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 latter scenario, the remote computer can 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 can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0162] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0163] These computer readable program instructions can 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 can also be stored in a computer readable storage medium that can include random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other data storage device. When the computer readable program instructions are loaded into the computer and other programmable data processing apparatus, a series of operational steps are implemented that provide processes such that the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0164] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0165] The flow diagrams and the block diagrams in the accompanying drawings 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 flow diagrams and the block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logic functions. In some alternative implementations, the functions noted in the blocks can 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 can 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 flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0166] The computer program product can be embodied by a hardware, software or a combination thereof. In an optional embodiment, the computer program product is embodied by a computer storage medium. In another optional embodiment, the computer program product is embodied by a software product, such as a software development kit (SDK) or the like.

[0167] The above description has described various embodiments of the present disclosure. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles, practical application or improvement of the technology in the market of the embodiments, or to enable other ordinary skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method of monitoring the height of a building in an airport clearance area, characterized in that The method comprises: determining the error of the digital terrain model of the airport clearance area according to the digital terrain model of the airport clearance area acquired at at least two time points, wherein the digital terrain model comprises the elevation data of a plurality of locations of the airport clearance area monitored at the time points; obtaining a restricted height grid model of the airport clearance area according to the error of the digital terrain model, wherein the restricted height grid model divides the airport clearance area into a plurality of grids and comprises the height limit information of the locations represented by each grid; determining the type of each grid according to the digital terrain model acquired at the at least two time points and the restricted height grid model; 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; determining the monitoring time interval of each grid cluster according to the grid cluster, the digital terrain model acquired at the at least two time points and the restricted height grid model, wherein the monitoring time interval represents the interval between the time points at which the elevation data is monitored; wherein the error of the digital terrain model comprises a plane error in the horizontal direction and an elevation error in the vertical direction, determining the error of the digital terrain model of the airport clearance area according to the digital terrain model of the airport clearance area acquired at at least two time points, comprising: determining the plane error and the elevation error according to the position error of a plurality of control points preset in the airport clearance area in the digital terrain model acquired at the at least two time points; wherein obtaining a restricted height grid model of the airport clearance area according to the error of the digital terrain model comprises: determining the grid width of the restricted height grid model according to the plane error; determining the height limit information of each grid according to the preset airport information and the grid width; obtaining the restricted height grid model according to the grid width and the height limit information of each grid.

2. The method of claim 1, wherein, The type of the grid comprises an ultra-high grid and a non-ultra-high grid, determining the type of each grid according to the digital terrain model acquired at the at least two time points and the restricted height grid model comprises: determining the elevation data of each grid according to the digital terrain model of the airport clearance area acquired last; determining the type of the grid whose elevation data is greater than or equal to the height limit information as the ultra-high grid.

3. The method of claim 2, wherein, The non-ultra-high grid comprises a dynamic grid and a static grid, and the error of the digital terrain model comprises an elevation error, determining the type of each grid according to the digital terrain model acquired at the at least two time points and the restricted height grid model further comprises: determining an elevation difference threshold value according to the elevation error; determining the elevation difference value between the elevation data of each grid at the at least two time points according to the digital terrain model acquired at the at least two time points; determining the grid whose absolute value of the elevation difference value is less than the elevation difference threshold value as the static grid.

4. The method of claim 3, wherein, The dynamic grid comprises a dynamic increasing grid and a dynamic decreasing grid, determining the type of each grid according to the digital terrain model acquired at the at least two time points and the restricted height grid model further comprises: determine the dynamic increasing grid as a grid whose elevation difference between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time is greater than 0; or determine the dynamic decreasing grid as a grid whose elevation difference between the elevation data corresponding to the subsequent time and the elevation data corresponding to the previous time is less than 0.

5. The method of claim 1, wherein, determine the grid cluster according to the type of each grid, comprising: merge adjacent grids of the same type according to the type of each grid to obtain the grid cluster.

6. The method of claim 1, wherein, determine the monitoring time interval of each grid cluster according to the grid cluster, the digital terrain model obtained at the at least two times, and the height limit grid model, comprising: determine the maximum change rate of the elevation data of each grid cluster according to the digital terrain model obtained at the at least two times; determine the minimum elevation difference of each grid cluster according to the height limit information and the elevation data of each grid in the last obtained digital terrain model; determine the monitoring time interval of each grid cluster according to the maximum change rate, the minimum elevation difference, and the type of the grid cluster.

7. The method of claim 6, wherein, the type of the grid cluster comprises a dynamic increasing grid and a dynamic decreasing grid, determine the monitoring time interval of each grid cluster according to the maximum change rate, the minimum elevation difference, and the type of the grid cluster, comprising: determine the first time interval of the dynamic increasing grid cluster according to the minimum elevation difference and the maximum change rate; determine the second time interval of the dynamic decreasing grid cluster according to the elevation data of each grid in the last obtained digital terrain model, the minimum elevation difference, and the maximum change rate; determine the monitoring time interval as the minimum value of the first time interval and the second time interval.

8. The method of claim 1, wherein, the type of the grid cluster comprises an ultra-high grid and a non-ultra-high grid, the non-ultra-high grid comprises a dynamic grid and a static grid, the method further comprises: determine the attribute of the building at the corresponding position of the electronic map for the grid cluster whose type is the ultra-high grid and the dynamic grid.

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