Automatic overrun scanning method and system based on three-dimensional point cloud data

Abstract

The invention discloses an automatic overrun scanning method and system based on three-dimensional point cloud data, belongs to the technical field of three-dimensional geographic information systems and point cloud data processing, and particularly relates to the automatic overrun scanning method and system based on high-precision three-dimensional point cloud data. The problems that in the prior art, calculation is complex, the processing speed is low, efficiency is low, and the visualization effect is poor are solved. The method comprises the following steps: acquiring a limiting surface height threshold value corresponding to a single point projection coordinate position of each single point; and comparing the coordinates in the vertical direction in the actual geographic space coordinates of each single point with the corresponding height threshold value of the limiting surface. The automatic over-limit scanning method and system based on the three-dimensional point cloud data are suitable for over-limit object detection in a flight control area.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional geographic information systems and point cloud data processing, and in particular to an automated over-limit scanning method and system based on high-precision three-dimensional point cloud data. Background Art

[0002] With increasingly stringent aviation safety standards, detecting out-of-limit objects in flight control areas has become a critical task in aviation safety management. Traditional methods for detecting out-of-limit objects in flight control areas rely on radiographic inspection. However, when faced with massive point clouds (e.g., tens of millions of points), computational complexity increases exponentially, making it difficult to meet real-time requirements and presenting significant bottlenecks.

[0003] Currently, 3D point cloud data, due to its high accuracy and comprehensiveness in representing spatial information, has been gradually adopted in many industries, such as urban planning, architectural design, and geographic information systems (GIS). For out-of-limit object detection, the use of point cloud data can provide more comprehensive and intuitive 3D spatial information, improving detection efficiency and accuracy. However, existing point cloud data processing technologies often suffer from computational complexity, slow processing speeds, and a lack of effective visualization tools, making them incapable of quickly identifying out-of-limit objects in practical applications.

[0004] Therefore, an automated over-limit scanning method based on high-precision three-dimensional point cloud data is particularly urgent. Summary of the Invention

[0005] The present invention proposes an automated over-limit scanning method and system based on three-dimensional point cloud data, which solves the problems of complex calculation, slow processing speed, low efficiency and poor visualization effect existing in the prior art.

[0006] The automated over-limit scanning method based on three-dimensional point cloud data of the present invention comprises:

[0007] Step S1: Obtain the collected original point cloud data of the Fetion control area, perform data production and cleaning on it, and obtain a point cloud file in LAS format;

[0008] Step S2: Parse the file header of the LAS format point cloud file and extract the point cloud metadata;

[0009] Step S3: According to the point cloud metadata, the original integer coordinates stored in the LAS format point cloud file are converted into actual geographic space coordinates to obtain three-dimensional coordinate data;

[0010] Step S4: Use CesiumJS to build a 3D GIS scene, load geographic information data, and create a flight control area restriction surface;

[0011] Step S5: cyclically obtain the coordinates of each single point in the point cloud based on the three-dimensional coordinate data; for the coordinates of each single point in the point cloud, calculate its projection in the two-dimensional horizontal plane and extract the single point projection coordinates;

[0012] Step S6: Calculate the projection of the flight control area restriction surface on the two-dimensional horizontal plane as its influence range; determine whether each single point is within the influence range of the flight control area restriction surface based on the positional relationship between the single point projection coordinates and the influence range;

[0013] Step S7: Obtain the height threshold of the restriction surface corresponding to each single point at its single point projection coordinate position; compare the vertical coordinate of each single point in the actual geographic space coordinate with its corresponding restriction surface height threshold:

[0014] If the vertical coordinate is not less than the corresponding limit surface height threshold, the single point is an out-of-limit space point;

[0015] Otherwise, the single point is not an out-of-space point;

[0016] Step S8: Calculate the heat value of the out-of-limit space point and perform normalization calculation, generate a thermal distribution map based on the normalized heat value and superimpose it on the three-dimensional GIS scene.

[0017] Furthermore, a preferred embodiment is provided, wherein the point cloud file in LAS format includes a file header and a point record set, and its structure is represented as follows:

[0018] LAS format point cloud file = {file header H, point record set P}, where:

[0019] The file header H is used to record the point cloud metadata; the point cloud metadata includes version information, the number of single points n in the point cloud, coordinate offset and coordinate scaling factor;

[0020] The coordinate offset includes the x-axis coordinate offset x , y-axis coordinate offset y And the z-axis coordinate offset z ;

[0021] The coordinate scaling factor includes the x-axis coordinate scaling factor scale x , y-axis coordinate scaling factor scale y And the z-axis coordinate scaling factor scale z ;

[0022] The point record set P is used to record the data of n single points in the point cloud; let the i-th single point be p i , its storage structure is:

[0023] p i ={xi ,y i ,z i , other attributes...}, where x i ,y i ,z i Represents a single point p i The original integer coordinates of , where:

[0024] x i For a single point p i The original integer coordinate value in the x-axis direction; y i For a single point p i The original integer coordinate value in the y-axis direction; z i For a single point p i The value of the original integer coordinate in the z-axis direction.

[0025] Furthermore, a preferred embodiment is provided, in step S2:

[0026] Use the laspy library to import and read LAS format point cloud files, read the file header information through the API provided by the laspy library, and obtain the point cloud metadata.

[0027] Furthermore, a preferred embodiment is provided, in step S3:

[0028] The i-th single point p i The actual geospatial coordinates are:

[0029]

[0030] Furthermore, a preferred embodiment is provided, in step S4:

[0031] The underlying rendering engine of CesiumJS is called to generate and render the flight control area restriction surface based on the flight control area restriction surface data.

[0032] Furthermore, a preferred embodiment is provided, in step S8:

[0033] The heatmap.js plug-in is used to generate a heat distribution map based on the normalized heat value.

[0034] The present invention also proposes an automated over-limit scanning system based on three-dimensional point cloud data, the system comprising:

[0035] Module S1: Obtain the collected point cloud raw data of the Fetion control area, perform data production and cleaning on it, and obtain the point cloud file in LAS format;

[0036] Module S2: Parse the file header of the LAS format point cloud file and extract the point cloud metadata;

[0037] Module S3: According to the point cloud metadata, the original integer coordinates stored in the LAS format point cloud file are converted into actual geographic space coordinates to obtain three-dimensional coordinate data;

[0038] Module S4: Use CesiumJS to build a 3D GIS scene, load geographic information data, and create flight control area restriction surfaces;

[0039] Module S5: Based on the three-dimensional coordinate data, the coordinates of each single point in the point cloud are obtained cyclically; for the coordinates of each single point in the point cloud, its projection in the two-dimensional horizontal plane is calculated, and the single point projection coordinates are extracted;

[0040] Module S6: Calculate the projection of the flight control area restriction surface on a two-dimensional horizontal plane as its influence range; determine whether each single point is within the influence range of the flight control area restriction surface based on the positional relationship between the single point projection coordinates and the influence range;

[0041] Module S7: Obtain the height threshold of the restricted surface corresponding to each single point at its single point projection coordinate position; compare the vertical coordinate of each single point in the actual geographic space coordinate with the corresponding restricted surface height threshold:

[0042] If the vertical coordinate is not less than the corresponding limit surface height threshold, the single point is an out-of-limit space point;

[0043] Otherwise, the single point is not an out-of-space point;

[0044] Module S8: Calculate the heat value of the out-of-limit space point and perform normalization calculation, generate a thermal distribution map based on the normalized heat value and overlay it on the three-dimensional GIS scene.

[0045] The present invention also proposes a computer device comprising: a processor and a memory, wherein the memory is used to store executable instructions of the processor, and the processor is configured to execute any one of the above-mentioned automated over-limit scanning methods based on three-dimensional point cloud data by executing the executable instructions.

[0046] The present invention also proposes a computer storage medium, in which a computer program is stored. When the computer program is run, any one of the above-mentioned automated over-limit scanning methods based on three-dimensional point cloud data is executed.

[0047] The present invention also proposes a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of any one of the above-mentioned automated over-limit scanning methods based on three-dimensional point cloud data.

[0048] The present invention has the following beneficial effects:

[0049] 1. The automated out-of-limit scanning method based on 3D point cloud data described in the present invention can not only efficiently process and analyze point cloud data, but can also be combined with modern 3D visualization tools (such as CesiumJS) to achieve intuitive visualization, thereby improving the efficiency and accuracy of out-of-limit scanning in flight control areas and addressing the shortcomings of traditional methods.

[0050] 2. The automated over-limit scanning method based on three-dimensional point cloud data described in the present invention has a large amount of point cloud data and can quickly extract key over-limit information from massive data, which has significant effects in improving the safety of flight control areas, optimizing management, and supporting spatial planning.

[0051] 3. The automated out-of-limit scanning method based on three-dimensional point cloud data described in the present invention not only improves management efficiency but also provides important guarantees for flight safety through automated and accurate identification of out-of-limit objects.

[0052] 4. The automated over-limit scanning method based on 3D point cloud data, combined with the color mapping and transparency adjustment features of thermal maps, enables users to conduct timely analysis and decision-making on over-limit objects in controlled areas. With the continuous development of technology and optimization of algorithms, this technology will be widely applied in more scenarios in the future.

[0053] The automated out-of-limit scanning method and system based on three-dimensional point cloud data described in the present invention are suitable for detecting out-of-limit objects in flight control areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 The figure is a flow chart of an automated over-limit scanning method based on three-dimensional point cloud data in one embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the technical solutions and advantages of the present invention more clearly described, the specific embodiments of the present invention will be further described in detail and completely in conjunction with the accompanying drawings. The various embodiments described below are only part of the preferred embodiments of the present invention, rather than all implementation plans; the various embodiments described below are intended to explain the present invention and cannot be understood as limiting the present invention; the reasonable combination of the technical features defined in the various embodiments of the present invention, as well as all other implementation plans obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work, all fall within the scope of protection of the present invention.

[0057] Implementation 1: An automated over-limit scanning method based on three-dimensional point cloud data, the method comprising:

[0058] Step S1: Obtain the collected original point cloud data of the Fetion control area, perform data production and cleaning on it, and obtain a point cloud file in LAS format;

[0059] Step S2: Parse the file header of the LAS format point cloud file and extract the point cloud metadata;

[0060] Step S3: According to the point cloud metadata, the original integer coordinates stored in the LAS format point cloud file are converted into actual geographic space coordinates to obtain three-dimensional coordinate data;

[0061] Step S4: Use CesiumJS to build a 3D GIS scene, load geographic information data, and create a flight control area restriction surface;

[0062] Step S5: cyclically obtain the coordinates of each single point in the point cloud based on the three-dimensional coordinate data; for the coordinates of each single point in the point cloud, calculate its projection in the two-dimensional horizontal plane and extract the single point projection coordinates;

[0063] Step S6: Calculate the projection of the flight control area restriction surface on the two-dimensional horizontal plane as its influence range; determine whether each single point is within the influence range of the flight control area restriction surface based on the positional relationship between the single point projection coordinates and the influence range;

[0064] Step S7: Obtain the height threshold of the restriction surface corresponding to each single point at its single point projection coordinate position; compare the vertical coordinate of each single point in the actual geographic space coordinate with its corresponding restriction surface height threshold:

[0065] If the vertical coordinate is not less than the corresponding limit surface height threshold, the single point is an out-of-limit space point;

[0066] Otherwise, the single point is not an out-of-space point;

[0067] Step S8: Calculate the heat value of the out-of-limit space point and perform normalization calculation, generate a thermal distribution map based on the normalized heat value and superimpose it on the three-dimensional GIS scene.

[0068] In this embodiment, the automated over-limit scanning method based on three-dimensional point cloud data is a new type of over-limit identification technology for flight control areas based on multi-source data fusion.

[0069] In this embodiment, the (three-dimensional) point cloud original data of the Fetion control area describes in detail the spatial information of objects such as the ground, buildings, and trees in the Fetion control area.

[0070] In this embodiment, the (three-dimensional) point cloud raw data of the Fetion control area is obtained using a drone equipped with a (high-precision) laser radar.

[0071] In this embodiment, the LAS format is a standard storage format for LiDAR data.

[0072] In this embodiment, the data production cleaning includes pre-processing such as denoising and filtering to improve data quality and ensure the accuracy and reliability of subsequent processing.

[0073] In this embodiment, the actual geographic space coordinates include the actual x-axis coordinates, the actual y-axis coordinates, and the actual z-axis coordinates.

[0074] The x-axis and the y-axis are two horizontal directions perpendicular to each other. The two-dimensional horizontal plane is also called the x-plane, that is, the plane represented by the x-axis and the y-axis.

[0075] The z-axis is the vertical direction, and the actual z-axis coordinate is also called the vertical coordinate.

[0076] In this embodiment, in step S5, for the coordinates of each single point in the point cloud, its projection in the two-dimensional horizontal plane is calculated, and the single point projection coordinates are extracted:

[0077] For each single point in the point cloud, if its actual geographic space coordinates are (Px, Py, Pz), its projection in the two-dimensional horizontal plane (or called two-dimensional coordinate point, two-dimensional projection point) is obtained by removing its actual z-axis coordinate, that is, its single point projection coordinates are (Px, Py).

[0078] In this embodiment, in step S6, the projection of the flight control area restriction surface on the two-dimensional horizontal plane is calculated, that is, a polygon formed by the projection of the flight control area restriction surface on the two-dimensional horizontal plane, or called a projection polygon, i.e., the influence range, is obtained;

[0079] Determine whether each single point is within the influence range of the restricted surface of the flight control area, that is, determine whether the two-dimensional projection point of each single point in the two-dimensional horizontal plane is within the projection polygon. If it is within the polygon, it is considered that the single point is within the influence range of the restricted surface of the flight control area, that is, there is an obstacle restricted surface directly above the single point.

[0080] In this embodiment, for a single point within the influence range of the restricted surface of the flight control area, it is necessary to determine whether its height exceeds the limit; for a single point not within the influence range of the restricted surface of the flight control area, it is not necessary to determine whether its height exceeds the limit.

[0081] In this embodiment, the restricted surface height threshold corresponding to the projection coordinate position of each single point is as follows:

[0082] Let the single-point projection coordinates of each single point be (Px, Py), then the restricted surface height threshold represents a function of (Px, Py), that is, f(Px, Py);

[0083] The restricted surface height threshold is a given value, such as obtained from industry standards: establish a mapping relationship between the three-dimensional coordinates and the restricted surface, and query the corresponding restricted surface height threshold for this coordinate.

[0084] In this embodiment, compare the vertical coordinate in the actual geographical space coordinates of each single point with its corresponding restricted surface height threshold:

[0085] Let the vertical coordinate in the actual geographical space coordinates of each single point be Pz, then compare Pz with f(Px, Py) and perform a height compliance check:

[0086] If Pz < f(Px, Py), determine that this single point is a legal space point;

[0087] If Pz ≥ f(Px, Py), then this single point is an over-limit space point.

[0088] In this embodiment, the heat value is used to quantify the degree of over-limit, that is, the difference between the actual height of the over-limit space point and the restricted surface height threshold:

[0089] ΔPz = Pz - f(Px, Py).

[0090] In this embodiment, normalization calculation is used to standardize the heat value to a range (such as 0 to 1) to facilitate color mapping and ensure that the distribution of heat data adapts to display requirements.

[0091] Embodiment 2: The point cloud file in LAS format includes a file header and a set of point records, and its structure is represented as:

[0092] LAS format point cloud file = {file header H, set of point records P}, where:

[0093] The file header H is used to record the point cloud metadata; the point cloud metadata includes version information, the number of single points n in the point cloud, coordinate offset and coordinate scaling factor;

[0094] The coordinate offset includes the x-axis coordinate offset x , y-axis coordinate offset y And the z-axis coordinate offset z ;

[0095] The coordinate scaling factor includes the x-axis coordinate scaling factor scale x , y-axis coordinate scaling factor scale y And the z-axis coordinate scaling factor scale z ;

[0096] The point record set P is used to record the data of n single points in the point cloud; let the i-th single point be p i , its storage structure is:

[0097] p i ={x i ,y i ,z i , other attributes...}, where x i ,y i ,z i Represents a single point p i The original integer coordinates of , where:

[0098] x i For a single point p i The original integer coordinate value in the x-axis direction; y i For a single point p i The original integer coordinate value in the y-axis direction; z i For a single point p i The value of the original integer coordinate in the z-axis direction.

[0099] In this embodiment, in the LAS file format, the version information is key metadata in the file header, which is used to define the format version and compatibility rules of the LAS file.

[0100] Implementation 3: In step S2:

[0101] Use the laspy library to import and read LAS format point cloud files, read the file header information through the API provided by the laspy library, and obtain the point cloud metadata.

[0102] In this implementation, the API provided by the laspy library for LAS file processing refers to the predefined classes, methods, and properties used to manipulate LAS format data. These APIs simplify reading and writing binary LAS files, extracting metadata, and performing point cloud operations.

[0103] Implementation 4: In step S3:

[0104] The i-th single point p i The actual geospatial coordinates are:

[0105]

[0106] In this embodiment, the set of actual geographic space coordinates of each single point is used as three-dimensional coordinate data.

[0107] It should be noted that to save storage space, the original point cloud data converts floating-point coordinates into integers using a scale factor and an offset, resulting in a LAS-formatted point cloud file. The actual coordinates must match the geographic information data (such as DOM images and DEM elevations) in the CesiumJS 3D GIS scene for overrun detection and visualization. Coordinate conversion is used to convert the original integer coordinates stored in the LAS file into actual geospatial coordinates.

[0108] In this embodiment, the three-dimensional coordinate data is transmitted to the web front end in json format (input into CesiumJS for processing).

[0109] Implementation 5: In step S4:

[0110] The underlying rendering engine of CesiumJS is called to generate and render the flight control area restriction surface based on the flight control area restriction surface data.

[0111] In this implementation, CesiumJS is an open-source JavaScript library used to build a three-dimensional globe and GIS scene on the web.

[0112] In this embodiment, the core module of CesiumJS includes an underlying rendering engine (based on WebGL / WebGPU), which is responsible for real-time rendering of 3D models, terrain, point clouds and other data; the underlying rendering engine can be referred to as the Cesium engine.

[0113] In this embodiment, the geographic information data includes DOM image maps, DEM elevation data, etc.

[0114] In this embodiment, the flight control area restriction surface data includes attribute information such as the coordinates of the runway start and end points, azimuth, and starting elevation.

[0115] In this implementation, the underlying rendering engine of CesiumJS is called to generate and render the flight control zone restriction surface to facilitate subsequent over-limit identification.

[0116] Implementation 6: In step S8:

[0117] The heatmap.js plug-in is used to generate a heat distribution map based on the normalized heat value.

[0118] In this embodiment, heatmap.js (plug-in) is a lightweight JavaScript library for generating Canvas-based heat map designs (rendered using the Canvas element), supporting color gradients, transparency adjustments, and real-time updates.

[0119] The role of heatmap.js:

[0120] Color mapping: Map the normalized heat value to RGBA color;

[0121] Transparency control: Dynamically adjust layer transparency based on viewpoint distance.

[0122] According to different heat values, heatmap.js will automatically perform color gradient and transparency processing to display heat distribution of different intensities, that is, super-limit heat distribution.

[0123] In this embodiment, in step S8, the thermal distribution map is superimposed on the three-dimensional GIS scene:

[0124] The actual geographic coordinates of each single point are converted into the Cartesian coordinate system of CesiumJS to ensure that each data point of the heat distribution map can accurately fit the three-dimensional earth surface in the three-dimensional GIS scene.

[0125] The general process of overlaying (embedding) the heat distribution map into the 3D GIS scene is as follows:

[0126] (1) Data preparation and coordinate transformation:

[0127] Input data: latitude and longitude (lon, lat) and heat value (value) of the out-of-limit spatial point;

[0128] Convert coordinates: Use Cesium.Cartesian3.fromDegrees(lon,lat,height) to convert actual geographic space coordinates into Cartesian coordinates;

[0129] (2) Generate heat map (Canvas):

[0130] Use heatmap.js: Generate a color gradient canvas based on heat values;

[0131] (3) Embed Canvas into the Cesium scene:

[0132] Overlay as an ImageryLayer:

[0133] Convert the heat map Canvas to Cesium's ImageryProvider and add it to the scene.

[0134] Transparency and overlay: The Canvas background of the thermal layer is set to transparent, retaining only the color gradient part, which blends naturally with the Cesium base map (satellite imagery, terrain).

[0135] (4) Dynamic update (when viewpoint changes)

[0136] Monitor camera movement: Re-render the heatmap (i.e., heat distribution map) when the user rotates or zooms the globe.

[0137] Embodiment 7: An automated over-limit scanning system based on three-dimensional point cloud data, the system comprising:

[0138] Module S1: Obtain the collected point cloud raw data of the Fetion control area, perform data production and cleaning on it, and obtain the point cloud file in LAS format;

[0139] Module S2: Parse the file header of the LAS format point cloud file and extract the point cloud metadata;

[0140] Module S3: According to the point cloud metadata, the original integer coordinates stored in the LAS format point cloud file are converted into actual geographic space coordinates to obtain three-dimensional coordinate data;

[0141] Module S4: Use CesiumJS to build a 3D GIS scene, load geographic information data, and create flight control area restriction surfaces;

[0142] Module S5: Based on the three-dimensional coordinate data, the coordinates of each single point in the point cloud are obtained cyclically; for the coordinates of each single point in the point cloud, its projection in the two-dimensional horizontal plane is calculated, and the single point projection coordinates are extracted;

[0143] Module S6: Calculate the projection of the flight control area restriction surface on a two-dimensional horizontal plane as its influence range; determine whether each single point is within the influence range of the flight control area restriction surface based on the positional relationship between the single point projection coordinates and the influence range;

[0144] Module S7: Obtain the height threshold of the restricted surface corresponding to each single point at its single point projection coordinate position; compare the vertical coordinate of each single point in the actual geographic space coordinate with the corresponding restricted surface height threshold:

[0145] If the vertical coordinate is not less than the corresponding limit surface height threshold, the single point is an out-of-limit space point;

[0146] Otherwise, the single point is not an out-of-space point;

[0147] Module S8: Calculate the heat value of the out-of-limit space point and perform normalization calculation, generate a thermal distribution map based on the normalized heat value and overlay it on the three-dimensional GIS scene.

[0148] In this embodiment, in actual use, the automated over-limit scanning system based on three-dimensional point cloud data includes a server and a web terminal;

[0149] The server executes the functions of module S1, module S2, module S3 and part of module S8 (specifically, calculating the heat value of the out-of-limit space point and performing normalization calculation);

[0150] The web terminal is used to execute the functions of module S4, module S5, module S6 and module S7 and part of the functions of module S8 (specifically: generating a thermal distribution map based on the normalized heat value and superimposing it on a three-dimensional GIS scene).

[0151] Embodiment 8: A computer device comprises: a processor and a memory, wherein the memory is used to store executable instructions of the processor, and the processor is configured to execute any one of the above-mentioned automated over-limit scanning methods based on three-dimensional point cloud data by executing the executable instructions.

[0152] Implementation 9: A computer storage medium having a computer program stored therein, wherein when the computer program is run, any one of the above-mentioned automated over-limit scanning methods based on three-dimensional point cloud data is executed.

[0153] Embodiment 10: A computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of any one of the above-described automated over-limit scanning methods based on three-dimensional point cloud data.

[0154] This embodiment provides a computer device or system, the hardware device of this part is a general model and is not shown in the form of a diagram. The system includes a processor and a memory, wherein the processor and the memory can be connected through a bus or other means. The memory is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, and corresponding program instructions / modules. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory, so as to realize the data space entity resolution data quality enhancement method in the above method embodiment.

[0155] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, an intranet, a mobile communication network, and combinations thereof.

[0156] One or more modules are stored in the memory. When the processor executes, the method steps in the embodiment are executed. In this way, the purpose of the invention can be achieved through the method, device and process of the present invention. The specific details of the above-mentioned computer equipment can be understood by referring to the corresponding descriptions and effects in the embodiment, and will not be repeated here.

[0157] Those skilled in the art will appreciate that all or part of the processes in the above-described embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes of the above-described embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD). The storage medium can also include a combination of the above-mentioned types of memory.

[0158] The above further describes the technical solution provided by the present invention in detail through several specific embodiments in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific embodiments described above are not intended to limit the present invention. Any reasonable changes and improvements to the present invention, reasonable combinations of implementation methods and equivalent replacements based on the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An automated over-limit scanning method based on three-dimensional point cloud data, characterized in that: The method comprises: Step S1: Obtain the collected original point cloud data of the Fetion control area, perform data production and cleaning on it, and obtain a point cloud file in LAS format; Step S2: Parse the file header of the LAS format point cloud file and extract the point cloud metadata; Step S3: According to the point cloud metadata, the original integer coordinates stored in the LAS format point cloud file are converted into actual geographic space coordinates to obtain three-dimensional coordinate data; Step S4: Use CesiumJS to build a 3D GIS scene, load geographic information data, and create a flight control area restriction surface; Step S5: cyclically obtain the coordinates of each single point in the point cloud based on the three-dimensional coordinate data; for the coordinates of each single point in the point cloud, calculate its projection in the two-dimensional horizontal plane and extract the single point projection coordinates; Step S6: Calculate the projection of the flight control area restriction surface on the two-dimensional horizontal plane as its influence range; determine whether each single point is within the influence range of the flight control area restriction surface based on the positional relationship between the single point projection coordinates and the influence range; Step S7: Obtain the height threshold of the restriction surface corresponding to each single point at its single point projection coordinate position; compare the vertical coordinate of each single point in the actual geographic space coordinate with its corresponding restriction surface height threshold: If the vertical coordinate is not less than the corresponding limit surface height threshold, the single point is an out-of-limit space point; Otherwise, the single point is not an out-of-space point; Step S8: Calculate the heat value of the out-of-limit space point and perform normalization calculation, generate a thermal distribution map based on the normalized heat value and superimpose it on the three-dimensional GIS scene.

2. The automated over-limit scanning method based on three-dimensional point cloud data according to claim 1, characterized in that: The point cloud file in LAS format includes a file header and a point record set, and its structure is expressed as follows: LAS format point cloud file = {file header H, point record set P}, where: The file header H is used to record the point cloud metadata; the point cloud metadata includes version information, the number of single points n in the point cloud, coordinate offset and coordinate scaling factor; The coordinate offset includes the x-axis coordinate offset x , y-axis coordinate offset y And the z-axis coordinate offset z ; The coordinate scaling factor includes the x-axis coordinate scaling factor scale x , y-axis coordinate scaling factor scale y And the z-axis coordinate scaling factor scale z ; The point record set P is used to record the data of n single points in the point cloud; let the i-th single point be p i , its storage structure is: p i ={x i ,y i ,z i , other attributes...}, where x i ,y i ,z i Represents a single point p i The original integer coordinates of , where: x i For a single point p i The original integer coordinate value in the x-axis direction; y i For a single point p i The original integer coordinate value in the y-axis direction; z i For a single point p i The value of the original integer coordinate in the z-axis direction.

3. The automated over-limit scanning method based on three-dimensional point cloud data according to claim 1, characterized in that: In step S2: Use the laspy library to import and read LAS format point cloud files, read the file header information through the API provided by the laspy library, and obtain the point cloud metadata.

4. The automated over-limit scanning method based on three-dimensional point cloud data according to claim 2, characterized in that: In step S3: The i-th single point p i The actual geospatial coordinates are:

5. The automated over-limit scanning method based on three-dimensional point cloud data according to claim 2, characterized in that: In step S4: The underlying rendering engine of CesiumJS is called to generate and render the flight control area restriction surface based on the flight control area restriction surface data.

6. The automated over-limit scanning method based on three-dimensional point cloud data according to claim 2, characterized in that: In step S8: The heatmap.js plug-in is used to generate a heat distribution map based on the normalized heat value.

7. The automated over-limit scanning system based on 3D point cloud data is characterized by: The system comprises: Module S1: Obtain the collected point cloud raw data of the Fetion control area, perform data production and cleaning on it, and obtain the point cloud file in LAS format; Module S2: Parse the file header of the LAS format point cloud file and extract the point cloud metadata; Module S3: According to the point cloud metadata, the original integer coordinates stored in the LAS format point cloud file are converted into actual geographic space coordinates to obtain three-dimensional coordinate data; Module S4: Use CesiumJS to build a 3D GIS scene, load geographic information data, and create flight control area restriction surfaces; Module S5: Based on the three-dimensional coordinate data, the coordinates of each single point in the point cloud are obtained cyclically; for the coordinates of each single point in the point cloud, its projection in the two-dimensional horizontal plane is calculated, and the single point projection coordinates are extracted; Module S6: Calculate the projection of the flight control area restriction surface on a two-dimensional horizontal plane as its influence range; determine whether each single point is within the influence range of the flight control area restriction surface based on the positional relationship between the single point projection coordinates and the influence range; Module S7: Obtain the height threshold of the restricted surface corresponding to each single point at its single point projection coordinate position; compare the vertical coordinate of each single point in the actual geographic space coordinate with the corresponding restricted surface height threshold: If the vertical coordinate is not less than the corresponding limit surface height threshold, the single point is an out-of-limit space point; Otherwise, the single point is not an out-of-space point; Module S8: Calculate the heat value of the out-of-limit space point and perform normalization calculation, generate a thermal distribution map based on the normalized heat value and overlay it on the three-dimensional GIS scene.

8. A computer device comprising: A processor and a memory, characterized in that the memory is used to store executable instructions of the processor, and the processor is configured to execute the automated over-limit scanning method based on three-dimensional point cloud data as described in any one of claims 1 to 6 by executing the executable instructions.

9. A computer storage medium, characterized in that The storage medium stores a computer program, and when the computer program is run, the automatic over-limit scanning method based on three-dimensional point cloud data according to any one of claims 1 to 6 is executed.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the automated over-limit scanning method based on three-dimensional point cloud data according to any one of claims 1 to 6 are implemented.

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