Engineering construction-oriented surveying and mapping data entry method and system

Abstract

The invention relates to the technical field of surveying and mapping data analysis and entry, in particular to a surveying and mapping data entry method and system for engineering construction, and the method comprises the steps: carrying out the screening of initial effective point clouds according to multiple times of point cloud measurement deviation, and obtaining a to-be-interpolated point location; evaluating the transverse deviation performance of the layer where the to-be-interpolated point location is located and adjacent layers on the two sides, and correcting the transverse deviation performance according to the flat plate trend degree of the plane layer area of the to-be-interpolated point location to obtain the steep performance degree of the to-be-interpolated point location; and obtaining the noise content presentation degree of the local area of each point location to be interpolated according to the correlation rule of the steep presentation degree and the local point cloud density, then analyzing the point cloud intensity difference to obtain the interpolation filling reliability, and carrying out point cloud data filling. According to the method, the pneumatic unstable factors of the dust are analyzed, auxiliary analysis is carried out in combination with the interference condition of the dust on the association regularity between the steep performance of the mine terrain and the point cloud measurement density, and a more accurate surveying and mapping input result is obtained.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping data analysis and entry technology, specifically to a surveying and mapping data entry method and system for engineering construction. Background Technology

[0002] With the development of science and technology in the surveying and mapping field, airborne laser point cloud technology has gradually replaced traditional total station surveying and mapping, becoming a widely used surveying and mapping application technology. Mining engineering is an engineering construction technology primarily focused on mine construction. During the surveying and mapping process in mining engineering, airborne laser radar is often used to collect point cloud data of various mine terrain features. This collected point cloud data is then used to reconstruct the mine terrain model, enabling dynamic monitoring of the mining project based on terrain models from different periods.

[0003] In the process of reconstructing mine terrain using laser point clouds, directly reconstructing the terrain model using the initial collected point cloud data results in poor reconstruction accuracy due to the limited number of point clouds. Therefore, interpolation is often used to fill in new point cloud data from the initial collected point cloud set to meet the model fitting accuracy requirements. Laser point cloud mapping requires emitting a laser beam and analyzing the received reflected signals to measure the position information of objects. However, due to the significant dust interference in actual mine scenes, large dust particles can cause the laser point cloud to be reflected prematurely. This means that dust during the mapping process is misidentified as real mine terrain points, resulting in a large number of dust-noise point clouds in the initial collected point cloud, further reducing the accuracy of subsequent point cloud filling and mine terrain model reconstruction. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this invention is to provide a surveying data entry method and system for engineering construction.

[0005] According to a first aspect of the present invention, a method for inputting surveying and mapping data for engineering construction is provided, and the specific technical solution adopted is as follows: Point cloud data of the mine locations are collected repeatedly, and the point cloud data includes coordinate data and reflection intensity data; The stability of changes among repeatedly collected point cloud data is analyzed to initially screen out valid point clouds, and the degree of positional clustering of the valid point clouds is analyzed to obtain the points to be interpolated. Based on the coordinate data, the degree of lateral deviation between the layer where the interpolation point is located and the layers above and below it is analyzed, and the distribution of the number of effective point clouds within the layer where the interpolation point is located is analyzed to obtain the steepness of the local area of ​​the interpolation point. Based on the coordinate data, the degree of aggregation of the effective point cloud in the local spatial region of the point to be interpolated is analyzed, and then the correlation between the steepness performance and the degree of aggregation is analyzed to obtain the noise content presentation degree of the local region of the point to be interpolated. Based on the reflection intensity data, the difference in reflection intensity of all effective point clouds in the local area of ​​the point to be interpolated is analyzed, and combined with the noise content presentation degree, the interpolation filling confidence of all effective point clouds in the local area of ​​the point to be interpolated is obtained. Based on the interpolation fill confidence level, point cloud data filling processing is performed to complete the mine surveying data entry.

[0006] In some embodiments of the present invention, analyzing the stability of changes among repeatedly collected point cloud data to initially screen out valid point clouds includes: By comparing point cloud data collected repeatedly, if the point cloud data at a given point is not lost in multiple collections, the point cloud data at that point is retained; otherwise, it is discarded to obtain a valid point cloud.

[0007] In some embodiments of the present invention, analyzing the positional aggregation degree of the effective point cloud to obtain the points to be interpolated includes: Connect each pair of valid point clouds in space with a straight line to obtain several spatial triangles; Calculate the area of ​​each of the spatial triangles and the average area of ​​all the spatial triangles; Determine whether the area of ​​each of the spatial triangles is greater than the average area; If so, the centroid of the spatial triangular region is taken as the interpolation point.

[0008] In some embodiments of the present invention, based on the coordinate data, the degree of lateral deviation between the layer containing the point to be interpolated and the layers above and below is analyzed, including: Based on the coordinate data, extract the n nearest valid point clouds at the same height as the point to be interpolated to obtain the layered local region where the point to be interpolated is located; Preset layer height; Extract the m nearest valid point clouds of the point to be interpolated within the upper and lower layers at the preset layer height position to obtain the upper layer local region and the lower layer local region of the point to be interpolated. Extract the centroids of the local regions of the layer where the interpolation point is located, as well as the corresponding local regions of the upper and lower layers. Calculate the Euclidean distances between the centroids of the local regions of the layer and the centroids of the local regions of the upper and lower layers, respectively, to obtain the degree of lateral deviation between the layer where the interpolation point is located and the upper and lower layers.

[0009] In some embodiments of the present invention, based on the coordinate data and analyzing the distribution of the number of effective point clouds within the layer where the point to be interpolated is located, the method includes: Based on the coordinate data, the dispersion of the effective point cloud in the local layer where the interpolation point is located is analyzed. Combining the number of all effective point clouds at the same height as the interpolation point, and the maximum value of the number of all effective point clouds at the same height corresponding to all interpolation points, the distribution of the number of effective point clouds in the layer where the interpolation point is located is obtained.

[0010] In some embodiments of the present invention, based on the coordinate data, the degree of aggregation of the number of effective point clouds in the local spatial region of the point to be interpolated is analyzed, including: Based on the coordinate data, a spherical space with the point to be interpolated as the center and R as the radius is determined as the local spatial region of the point to be interpolated. The number of effective point clouds in the local spatial region of the point to be interpolated is obtained, and the average number of effective point clouds in the local spatial region of all the points to be interpolated is calculated to obtain the degree of aggregation of the number of effective point clouds in the local spatial region of the point to be interpolated.

[0011] In some embodiments of the present invention, the correlation between the steepness of the representation and the degree of clustering of quantities is analyzed to obtain the noise content representation of the local region of the point to be interpolated, including: Using the coordinate order of all the points to be interpolated as the x-axis, and the steepness of the corresponding points and the degree of aggregation of the effective point cloud as the y-axis, respectively, a steepness variation curve and a point cloud aggregation variation curve are constructed. For any given interpolation point, retain the correlation between the calculated steep change curve and the point cloud aggregation change curve of the interpolation point, and remove the correlation between the calculated steep change curve and the point cloud aggregation change curve of the interpolation point to obtain the noise content presentation degree of the local area of ​​the interpolation point.

[0012] In some embodiments of the present invention, point cloud data imputation processing is performed based on the interpolation imputation confidence level, including: Preset trust threshold; The effective point clouds in the local area of ​​the point to be interpolated, where the interpolation filling confidence level is greater than the confidence threshold, are initially screened and used as interpolation reference point clouds. Extract the centroids of all the interpolation reference point clouds in the local region of the point to be interpolated, calculate the Euclidean distance between the coordinate data of the point to be interpolated and the centroids of all the interpolation reference point clouds, and obtain the deviation distance; Calculate the mean interpolation fill confidence score of all interpolation reference point clouds in the local region of the point to be interpolated; The offset of the point to be interpolated is obtained based on the deviation distance and the mean interpolation fill confidence level. Based on the offset, the points to be interpolated are adjusted to the actual mine terrain point cloud to achieve point cloud data filling processing.

[0013] According to a second aspect of the present invention, a surveying data entry system for engineering construction is provided, comprising: a memory and a processor, wherein: The memory is used to store program code; The processor is configured to read program code stored in the memory and execute the method described in the first aspect of the embodiments of the present invention. In some embodiments of the present invention, the processor includes: The point cloud data acquisition module is used to repeatedly collect point cloud data of mine locations. The point cloud data includes coordinate data and reflection intensity data. The interpolation point acquisition module is used to analyze the stability of changes between point cloud data collected repeatedly, initially screen out effective point clouds, analyze the degree of positional aggregation of the effective point clouds, and acquire the interpolation points. The noise content presentation analysis module is used to analyze the degree of lateral deviation between the layer where the interpolation point is located and the layers above and below it based on the coordinate data, and to analyze the distribution of the number of effective point clouds within the layer where the interpolation point is located, thereby obtaining the steepness presentation of the local area of ​​the interpolation point; and to analyze the degree of aggregation of the number of effective point clouds in the local spatial area of ​​the interpolation point based on the coordinate data, and further analyze the correlation between the steepness presentation and the degree of aggregation, thereby obtaining the noise content presentation of the local area of ​​the interpolation point. The interpolation filling reliability analysis module is used to analyze the difference in reflection intensity of all effective point clouds in the local area of ​​the point to be interpolated based on the reflection intensity data, and to obtain the interpolation filling reliability of all effective point clouds in the local area of ​​the point to be interpolated by combining the noise content presentation degree. The data filling module is used to perform point cloud data filling processing based on the interpolation filling confidence level, and complete the mine surveying data entry.

[0014] Compared with existing technologies, the surveying data entry method and system for engineering construction provided by this invention have the following advantages: This invention first filters initial valid point clouds based on the deviation of multiple point cloud measurements, and simultaneously obtains the points to be interpolated. Then, it evaluates the lateral deviation between the layer containing the point and its adjacent layers, and corrects the lateral deviation based on the flatness tendency of the planar layer region of the point, obtaining the steepness at the point. Next, it obtains the noise content presentation of the local area of ​​each point based on the correlation between steepness and local point cloud density. Then, it analyzes the point cloud intensity differences based on the noise content presentation to obtain the interpolation filling reliability. Finally, it performs data filling based on the interpolation filling reliability of each point, thereby reconstructing the mine terrain model from the filled point cloud data. Compared to traditional interpolation methods, this invention, by incorporating the interference of dust on the point cloud acquisition process in real-world scenarios, can obtain more accurate surveying data entry results, thus improving the accuracy of mine surveying data entry. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the basic process of a surveying data entry method for engineering construction provided in one embodiment of the present invention; Figure 2 This is a schematic diagram of a single platform structure in an open-pit mine, provided as an embodiment of the present invention. Figure 3 A schematic diagram of a steep change curve and a point cloud aggregation change curve provided in one embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the basic components of a surveying data entry system for engineering construction, provided in one embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a surveying data entry method and system for engineering construction proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of additional identical elements in the article or device that includes the element.

[0019] It should be noted that, in order to ensure that the calculation results are meaningful, when performing fractional operations, if the denominator is 0, a parameter adjustment factor greater than 0 needs to be added to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor shall be set by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0020] The scenario addressed by this invention is as follows: In the process of mine engineering surveying, airborne lidar is often used to collect point cloud data of various mine terrains, and the collected point cloud data is used to reconstruct the mine terrain model, thereby enabling dynamic monitoring of the mine project based on the terrain model at different times. Directly reconstructing the terrain model using the collected initial point cloud data results in poor reconstruction accuracy due to the small number of point clouds. Therefore, interpolation methods are often used to fill in new point cloud data in the initial point cloud set to meet the model fitting accuracy. However, in actual mine scenarios, there is a lot of dust interference. Large dust particles can cause the lidar point cloud to be reflected prematurely, resulting in a lot of dust noise point clouds in the initial point cloud data, affecting the accuracy of subsequent point cloud filling and mine terrain model reconstruction.

[0021] Therefore, the main objective of this invention is to interpolate and fill in the initially collected mine terrain point cloud data with dust interference characteristics in order to reconstruct a more accurate mine terrain model, thereby improving the accuracy of mine surveying and mapping data entry.

[0022] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a surveying data entry method for engineering construction provided by the present invention.

[0023] Please see Figure 1 This illustrates the basic flow of a surveying data entry method for engineering construction provided by an embodiment of the present invention.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a method for inputting surveying and mapping data for engineering construction, specifically including: S100: Repeatedly collect point cloud data of mine locations. The point cloud data includes coordinate data and reflection intensity data.

[0025] First, multiple drones equipped with laser emitters are deployed, each responsible for surveying different areas of the mine. The drone flight routes are designed to ensure each drone covers all mine locations within its assigned area. To guarantee surveying accuracy, each drone performs multiple measurements of its assigned mining area; in this embodiment, a single drone performs three measurements of the same area. Then, the laser emitter modules on the drones collect point cloud data of each mine location during the flight. This point cloud data includes the coordinates and reflection intensity of each location. Finally, the collected point cloud information is uploaded to a data acquisition system for subsequent analysis.

[0026] This completes the preliminary preparations and data collection.

[0027] S200: Analyze the stability of changes between point cloud data collected repeatedly, initially screen out valid point clouds, analyze the degree of clustering of valid point clouds, and obtain the points to be interpolated.

[0028] The core idea of ​​this invention is to eliminate interference from noise-generated point clouds caused by dust during the interpolation and filling process of the initial point cloud data collected in the mine, thereby reconstructing a more accurate 3D model of the mine terrain. Therefore, this step first performs an initial screening of effective point clouds based on changes in the point cloud data during multiple UAV measurements, and obtains the points to be interpolated. Then, based on the mine terrain features reflected by the effective point cloud set, the interpolation and filling process is performed on the points to be interpolated, resulting in more accurate mine surveying data entry results.

[0029] When an airborne lidar emits a laser signal to the ground, if there are large dust particles in the path, the dust will reflect the laser, thus generating invalid noise data in the point cloud. In the process of multiple measurements of the mining area by the UAV, unlike the stable characteristics of the point cloud of the mine's own terrain, the dust in the air above the mine is greatly affected by the wind and is less stable in multiple measurements.

[0030] Therefore, in the embodiments of the present invention, by analyzing the stability of changes among repeatedly collected point cloud data, effective point clouds are initially screened out, and the degree of clustering of the effective point clouds is analyzed to obtain the points to be interpolated. Specifically, by comparing the point cloud data repeatedly collected by a single UAV on a single mining area, if the point cloud data at a point is not lost in multiple collections, the point cloud data at that point is retained; otherwise, it is discarded. Noisy point clouds that are significantly affected by wind in multiple measurements are screened out to obtain effective point clouds.

[0031] Then, the clustering degree of the effective point cloud is analyzed to obtain the interpolation points. Specifically, the effective point cloud in space is connected pairwise by straight lines to obtain several spatial triangles, thus dividing all effective point clouds into several spatial triangles; the area of ​​each spatial triangle and the average area of ​​all spatial triangles are calculated; it is determined whether the area of ​​each spatial triangle is greater than the average area; if so, the point cloud data distribution in that spatial triangle region is considered relatively dispersed, and the centroid of that spatial triangle region is taken as the interpolation point; if not, the point cloud data distribution in that spatial triangle region is considered relatively clustered, and no interpolation is needed.

[0032] At this point, the initial screening of point cloud data is complete, valid point clouds are obtained, and the points to be interpolated are determined.

[0033] In the effective point cloud set after initial screening, non-geomorphic points near the mine surface may experience significant dust interference during multiple measurements. Therefore, further analysis of the effective point cloud set after initial screening is required during interpolation.

[0034] Firstly, because the terrain of open-pit mines consists of multi-level platform structures, the steepness of slopes at different levels varies. Since laser point clouds reflect differently on terrains with varying steepness, the correlation between point cloud density and steepness is further analyzed to determine the noise content in local areas of each interpolation point. This analysis then examines the differences in reflection intensity of individual effective point clouds within the local area of ​​each interpolation point, yielding the interpolation reliability of each effective point cloud at the interpolation point, i.e., its interpolation reference value. Specifically, this includes steps S300 to S500.

[0035] S300: Based on coordinate data, analyze the degree of lateral deviation between the layer where the point to be interpolated is located and the layers above and below it, and analyze the distribution of the number of effective point clouds within the layer where the point to be interpolated is located, so as to obtain the steepness of the local area of ​​the point to be interpolated.

[0036] Laser point clouds exhibit different reflection characteristics on slopes with varying inclinations. Therefore, in order to more accurately analyze the effectiveness of the point clouds obtained from the initial screening, it is necessary to first evaluate the steepness of each interpolation point to reflect the inclination of the local slope corresponding to that point.

[0037] Therefore, in the embodiments of the present invention, based on coordinate data, the degree of lateral deviation between the layer where the point to be interpolated is located and the upper and lower layers are analyzed, and the number distribution of effective point clouds within the layer where the point to be interpolated is located is analyzed to obtain the steepness of the local area of ​​the point to be interpolated.

[0038] First, by analyzing the axial height characteristics of the mine, the more inclined the local slope of the mine is, the greater the lateral offset of the layer where the slope is located compared with the next layer. At the same time, in the direction of offset, the greater the lateral offset of the previous layer compared with the current slope layer. Conversely, if the lateral offset between the previous layer, the current slope layer, and the next layer is smaller, it indicates that the current slope layer is more likely to be low-inclination.

[0039] Therefore, based on coordinate data, the degree of lateral deviation between the layer containing the interpolation point and the layers above and below is analyzed. Further steps include: First, based on coordinate data, extract the nearest n (n can be 200) valid point clouds at the same height as the interpolation point. Connect the outermost points of these 200 valid point clouds using a boundary tracing algorithm to form a closed region, obtaining the local region of the layer containing the interpolation point. Then, preset the layer height, which can be 10cm. Next, extract the nearest m (m can be 50) valid point clouds within the layers above and below the interpolation point at the preset layer height. Connect the outermost points of these 50 valid point clouds using a boundary tracing algorithm to form a closed region, obtaining the upper and lower local regions of the interpolation point. It should be noted that the preset layer height of 10cm and the values ​​for the nearest 200 and 50 valid point clouds are preset values ​​and can be adjusted according to the actual scenario. Finally, centroid extraction techniques are used to extract the centroids of the local regions within the layer containing the interpolation point, as well as the corresponding upper and lower local regions. The Euclidean distances between the centroids of the local regions within each layer and the centroids of the upper and lower local regions are calculated to obtain the degree of lateral deviation between the layer containing the interpolation point and the layers above and below. This process is then used to construct the first... The formula for calculating the lateral deviation between the layer containing the interpolation point and the layers above and below it is: In the formula, Indicates the first The degree of lateral deviation between the layer containing the interpolation point and the layers above and below it; Indicates the first The Euclidean distance between the centroid of the local region in the layer where the interpolation point is located and the centroid of the local region in the upper layer; No. The Euclidean distance between the centroid of the local region in the layer where the interpolation point is located and the centroid of the local region in the next layer; This represents the linear normalization function.

[0040] When the The larger the Euclidean distance between the local slope layer and the upper and lower layers corresponding to the first interpolation point, the more it indicates that the first interpolation point... The greater the likelihood that a local area of ​​a point to be interpolated will exhibit a high degree of inclination.

[0041] Similarly, the degree of lateral deviation between the layer containing all interpolation points and the layers above and below it is obtained.

[0042] Then, because open-pit mines often exhibit a multi-level platform topographic structure, where a single platform structure is like... Figure 2 As shown, the core components of the platform structure are the central slope and the upper and lower platforms. Compared to the relatively flat plateau area, the slope exhibits a steep inclination. However, judging the local inclination of the interpolation point by only analyzing the comparison between the points at the same height layer and the adjacent height layers above and below may mistakenly identify the interpolation point located in the plateau area as having a local high inclination. Therefore, it is necessary to combine the characteristics of the plateau and slope in the mine composition to correct the degree of lateral deviation.

[0043] Because the flat surface is the direct working area of ​​mining equipment, it has been repeatedly rolled over by excavators and transport vehicles over a long period of time, resulting in a relatively uniform surface. In addition, the flat surface has a relatively wide design range for mine safety buffer and slope stability. In contrast, the slope has more rock layers in its local slope that are difficult to be rolled and ground. Therefore, the flat surface has a more uniform distribution of internal point clouds and a relatively wider area covered compared to the slope.

[0044] Therefore, based on coordinate data, the distribution of the number of effective point clouds within the layer where the interpolation point is located is analyzed to obtain the degree of flat-panel orientation at the interpolation point. Furthermore, based on coordinate data, the dispersion of the effective point clouds within the local region of the layer where the interpolation point is located is analyzed. Combining the number of all effective point clouds at the same height as the interpolation point, and the maximum value of the number of all effective point clouds at the same height corresponding to all interpolation points, the distribution of the number of effective point clouds within the layer where the interpolation point is located is obtained. The specific implementation method is as follows: Taking the first... Taking the nth interpolation point as an example, the nth... The local area containing the first interpolation point is gridded, with each grid cell having a preset coverage area of ​​3x3 cm; the statistics are then compiled. The number of valid point clouds in each grid within the hierarchical local region where each point to be interpolated is located, and then the standard deviation among the number of valid point clouds in all grids within that hierarchical local region is calculated. ; Obtain all valid point clouds obtained from the initial screening that are consistent with the first Number of all valid point clouds at the same height as the point to be interpolated And obtain the maximum value of the number of all valid point clouds at the same height corresponding to all points to be interpolated. Constructing the first The quantification formula for the distribution of the number of effective point clouds within the layer containing the nth interpolation point, i.e., the nth The formula for calculating the flat trend at each interpolation point is: In the formula, Indicates the first The distribution of the number of effective point clouds within the local layered region where the point to be interpolated is located, i.e., the number of points in the local layered region. The degree of flat trend at each interpolation point; Indicates the first The standard deviation between the number of valid point clouds in all grids within the local region where the nth interpolation point is located, i.e. the nth The degree of dispersion of the effective point cloud within the hierarchical local region where each point to be interpolated is located; This represents the maximum number of valid point clouds at the same height corresponding to all points to be interpolated; This indicates that among all valid point clouds obtained from the initial screening, the one that matches the first... The number of all valid point clouds at the same height as the point to be interpolated.

[0045] Among them, the first The standard deviation between the number of valid point clouds in the local mesh within the layered local region where each interpolation point is located. The smaller the value, the more likely it is to be the first... The more uniform the distribution of the effective point cloud within the local layer where the interpolation point is located, the better it is compared with the first interpolation point. The more effective point clouds at the same height for each interpolation point, the better. The greater the likelihood that the region of the points to be interpolated is wide, the more it illustrates the significance of the first interpolation point. The higher the degree of flattening tendency of each interpolation point.

[0046] Similarly, the degree of flat-panel tendency of the local area of ​​all points to be interpolated is obtained.

[0047] Finally, the higher the lateral deviation between the layer containing the interpolation point and the layers above and below it, and the lower the degree of plateau tendency, the higher the local steepness at the interpolation point. Therefore, the first... The steepness of a local area at each interpolation point for: In the formula, Indicates the first The steepness of a local area at each interpolation point; Indicates the first The distribution of the number of effective point clouds within the local layered region where the point to be interpolated is located, i.e., the number of points in the local layered region. The degree of flat trend at each interpolation point; Indicates the first The degree of lateral deviation between the layer containing the interpolation point and the layers above and below it; This represents the linear normalization function.

[0048] Similarly, the steepness of the local area of ​​all points to be interpolated is obtained.

[0049] S400: Based on coordinate data, analyze the degree of aggregation of effective point clouds in the local spatial region of the point to be interpolated, and then analyze the correlation between steepness and aggregation to obtain the noise content presentation degree of the local region of the point to be interpolated.

[0050] Considering the ideal situation of no dust or noise, when a laser point cloud strikes a terrain surface perpendicularly, the laser reflection effect is good, and most of the laser light will be reflected back to the airborne laser receiver. However, when the laser point cloud strikes a terrain surface with a certain steepness, some of the laser light will be scattered on the steep surface, resulting in less laser light being reflected back to the laser receiver. This leads to a decrease in the density of the point cloud at the steep surface. In other words, the above analysis shows that under ideal dust-free and noise-free conditions, the steepness of the terrain surface is inversely proportional to the point cloud density in that area, and noise will disrupt this correlation.

[0051] Based on the above analysis, in the embodiments of the present invention, based on coordinate data, the degree of aggregation of the number of effective point clouds in the local spatial region of the point to be interpolated is analyzed, and then the correlation between steepness and aggregation is analyzed to obtain the noise content presentation degree of the local region of the point to be interpolated.

[0052] First, based on coordinate data, the degree of aggregation of effective point clouds in the local spatial region of the point to be interpolated is analyzed. Specifically, based on coordinate data, a spherical space with the point to be interpolated as its center and radius R (which can be 15cm) is defined as the local spatial region of the point to be interpolated; the number of effective point clouds in the local spatial region of the point to be interpolated is obtained, and the average number of effective point clouds in all local spatial regions of the points to be interpolated is calculated to obtain the degree of aggregation of effective point clouds in the local spatial region of the point to be interpolated. This process is then used to construct the... The formula for calculating the degree of aggregation of the effective point cloud in the local spatial region of each interpolation point is: In the formula, Indicates the first The degree of aggregation of the number of effective point clouds in the local spatial region of each point to be interpolated; Indicates the first The number of effective point clouds in the local spatial region of each point to be interpolated; This represents the average number of valid point clouds in the local spatial region of all points to be interpolated; This represents the linear normalization function.

[0053] Among them, the first The greater the number of effective point clouds in the local spatial region of a given point to be interpolated compared to the overall number of effective point clouds in the local spatial regions of all points to be interpolated, the higher the level of the number of effective point clouds. The higher the point cloud density of a local spatial region at a given interpolation point, the better.

[0054] Similarly, the degree of aggregation of effective point clouds in the local spatial region of each point to be interpolated is obtained.

[0055] Furthermore, the stronger the scattering of laser point clouds in steeper and more inclined regions, the less reflected light is reflected back to the laser receiving device, resulting in a lower density of effective point clouds detected in the region. Therefore, the steepness of the local region of the interpolation point is negatively correlated with the degree of aggregation of the number of effective point clouds. Consequently, the noise content in the local region of the interpolation point is directly proportional to the violation of this correlation law within the region.

[0056] Therefore, by analyzing the correlation between steepness and density of point clouds, the noise content presentation of the local area at the interpolation point is obtained. Specifically, the implementation method is as follows: using the coordinate order of all interpolation points as the x-axis, and the steepness and density of effective point clouds corresponding to each interpolation point as the y-axis, steepness variation curves and point cloud density variation curves are constructed, such as... Figure 3 As shown; for any point to be interpolated, the correlation between the calculated steep change curve and the point cloud aggregation change curve of that point is retained, and the correlation between the calculated steep change curve and the point cloud aggregation change curve of that point is removed, to obtain the noise content presentation degree of the local area of ​​the point to be interpolated. Calculate the... The noise content of a local area at each interpolation point is presented in degree. for: In the formula, Indicates the first The presentation degree of noise content in a local area of ​​each interpolation point; Indicates that the first [number] is reserved. Calculate the correlation between the steep change curve and the point cloud aggregation change curve for each interpolation point; Indicates the removal of the first The correlation between the steep change curve and the point cloud aggregation change curve is calculated for each interpolation point.

[0057] If we retain the first one, we can eliminate the second one. The larger the correlation coefficient ratio between the two sets of change curves obtained from the first interpolation point, the more it indicates that the first curve should be retained compared to the second curve that should be removed. The more interpolation points there are, the weaker the negative correlation between the two sets of change curves becomes; that is, the more points are retained compared to the number of points removed. The more the interpolation point deviates from the negative correlation between the two sets of change curves, the more it indicates that the first interpolation point... The higher the noise content within the local area of ​​each interpolation point, the better.

[0058] Similarly, obtain the noise content presentation of the local area of ​​all points to be interpolated.

[0059] S500: Based on reflection intensity data, analyze the difference in reflection intensity of all effective point clouds in the local area of ​​the point to be interpolated, and combine the noise content presentation degree to obtain the interpolation filling confidence of all effective point clouds in the local area of ​​the point to be interpolated.

[0060] The reflection intensity of laser light is strongly correlated with the material properties of the irradiated terrain. Since the rock materials in the same mine terrain are similar, their reflection intensity is relatively consistent. However, the reflection intensity of dust noise is relatively random. Therefore, for a single point to be interpolated, if the convergence of the reflection intensity of a certain point cloud point in its local area is worse, it indicates that the dust noise performance of the point cloud is stronger. Then, the interpolation filling reliability of each effective point cloud to be interpolated can be obtained by combining the noise content presentation degree.

[0061] Therefore, in the embodiments of the present invention, based on reflection intensity data, the differences in reflection intensity of all effective point clouds in the local region of the point to be interpolated are analyzed, and combined with the noise content presentation degree, the interpolation filling confidence of all effective point clouds in the local region of the point to be interpolated is obtained. Specifically, let the first... The first interpolation point in the local area of ​​the local region The reflection intensity of each effective point cloud is Simultaneously calculate the first The average reflection intensity of all valid point clouds in the local region of the point to be interpolated is Then the first The first interpolation point in the local area of ​​the local region The effective point cloud pair Interpolation fill confidence level of each interpolation point for: In the formula, Indicates the first The first interpolation point in the local area of ​​the local region The effective point cloud pair Interpolation fill confidence level of each interpolation point; Indicates the first The presentation degree of noise content in a local area of ​​each interpolation point; Indicates the first The first interpolation point in the local area of ​​the local region The reflection intensity of an effective point cloud; Indicates the first The mean reflection intensity of all valid point clouds in a local region of a point to be interpolated; This represents the linear normalization function.

[0062] If the first The first interpolation point in the local area of ​​the local region The greater the difference in reflection intensity between a single effective point cloud and the overall effective point cloud in that region, the more it indicates that the single effective point cloud... The greater the noise performance of the effective point cloud, and using the noise content of a local region of the point cloud to be interpolated as a validity parameter, further proves that the effective point cloud has the highest noise performance. The first interpolation point in the local area of ​​the local region The greater the noise performance of an effective point cloud, the lower the reliability of the interpolation.

[0063] Similarly, the interpolation filling confidence of all valid point clouds in the local region of each point to be interpolated is obtained.

[0064] S600: Based on the interpolation fill confidence level, point cloud data fill processing is performed to complete the mine surveying data entry.

[0065] Based on the interpolation completion confidence level, point cloud data completion processing is performed to complete the mine surveying data entry. The specific implementation method is as follows: First, a confidence level threshold is preset; this invention selects a preset confidence level threshold of 0.88. Then, effective point clouds with interpolation completion confidence levels greater than the confidence level threshold in the local area of ​​the point to be interpolated are initially screened to reduce the impact of dust and serve as interpolation reference point clouds. Next, for a single point to be interpolated, the centroids of all interpolation reference point clouds in the local area of ​​the point to be interpolated are extracted, and the Euclidean distance between the coordinate data of the point to be interpolated and the centroids of all interpolation reference point clouds is calculated to obtain the deviation distance. Simultaneously, the mean interpolation completion confidence level of all interpolation reference point clouds in the local area of ​​the point to be interpolated is calculated. Finally, based on the deviation distance and the mean interpolation completion confidence level, the offset of the point to be interpolated is obtained. The first step involves constructing... The formula for calculating the offset of each interpolation point is: In the formula, Indicates the first The offset of each interpolation point; Indicates the first The mean interpolation fill confidence of all interpolation reference point clouds in a local region of a point to be interpolated; Indicates the first The Euclidean distance between the coordinate data of the point to be interpolated and the centroid of all interpolation reference point clouds is called the deviation distance.

[0066] Finally, the point to be interpolated is offset from its original position to the centroid of the interpolation reference point cloud by an offset amount of [missing information]. Then, the initial positions of each reference point cloud to be interpolated are adjusted to the actual mine terrain point cloud to achieve point cloud data filling processing.

[0067] After completing the point cloud data filling process, the mine surveying and mapping data entry procedure is improved based on the filled point cloud data. The specific implementation method is as follows: First, the point cloud sets obtained by multiple UAVs after interpolation and filling are registered using the ICP point cloud registration algorithm to register the point cloud sets of different areas of the mine obtained by multiple UAVs; then, the registered mine point cloud sets are reconstructed in 3D using Ashape technology to obtain a mine terrain reconstruction model; finally, the mine terrain reconstruction model is entered into the mine surveying and mapping management system, allowing the system center to dynamically monitor the mine by analyzing model changes over different periods.

[0068] Based on the same inventive concept as the above method, this embodiment also provides a surveying data entry system for engineering construction.

[0069] Please see Figure 4 This illustrates the basic components of a surveying data entry system for engineering construction provided by an embodiment of the present invention.

[0070] like Figure 4 As shown, a surveying data entry system for engineering construction includes: a memory 10 and a processor 20, wherein: Memory 10 is used to store program code; The processor 20 is used to read the program code stored in the memory 10 and execute repeated collection of point cloud data of mine locations. The point cloud data includes coordinate data and reflection intensity data. It analyzes the stability of the changes between the repeatedly collected point cloud data, initially screens out valid point clouds, and analyzes the degree of clustering of valid point clouds to obtain the points to be interpolated. Based on the coordinate data, it analyzes the degree of lateral deviation between the layer where the point to be interpolated is located and the layers above and below it, and analyzes the distribution of the number of valid point clouds within the layer where the point to be interpolated is located, obtaining the steepness of the local area of ​​the point to be interpolated. Based on the coordinate data, it analyzes the degree of clustering of the number of valid point clouds in the local spatial area of ​​the point to be interpolated, and further analyzes the correlation between steepness and clustering, obtaining the noise content presentation of the local area of ​​the point to be interpolated. Based on the reflection intensity data, it analyzes the difference in reflection intensity of all valid point clouds in the local area of ​​the point to be interpolated, and combines this with the noise content presentation to obtain the interpolation filling reliability of all valid point clouds in the local area of ​​the point to be interpolated. Based on the interpolation filling reliability, it performs point cloud data filling processing to complete the mine surveying data entry.

[0071] Furthermore, the processor 20 includes: a point cloud data acquisition module 21, a point acquisition module 22, a noise content presentation analysis module 23, an interpolation completion reliability analysis module 24, and a data completion module 25. Wherein: The point cloud data acquisition module 21 is used to repeatedly acquire point cloud data of the mine location. The point cloud data includes coordinate data and reflection intensity data. The interpolation point acquisition module 22 is used to analyze the stability of changes between point cloud data collected repeatedly, initially screen out effective point clouds, analyze the degree of positional aggregation of effective point clouds, and acquire the interpolation points. The noise content presentation analysis module 23 is used to analyze the degree of lateral deviation between the layer where the interpolation point is located and the layers above and below it based on coordinate data, and to analyze the distribution of the number of effective point clouds within the layer where the interpolation point is located, so as to obtain the steepness presentation of the local area of ​​the interpolation point; and to analyze the degree of aggregation of the number of effective point clouds in the local spatial area of ​​the interpolation point based on coordinate data, and then analyze the correlation between steepness presentation and aggregation, so as to obtain the noise content presentation of the local area of ​​the interpolation point. The interpolation filling reliability analysis module 24 is used to analyze the difference in reflection intensity of all effective point clouds in the local area of ​​the point to be interpolated based on the reflection intensity data, and combined with the noise content presentation degree, to obtain the interpolation filling reliability of all effective point clouds in the local area of ​​the point to be interpolated. The data filling module 25 is used to perform point cloud data filling processing based on the interpolation filling confidence level, and complete the mine surveying data entry.

[0072] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for inputting surveying and mapping data for engineering construction, characterized in that, The method includes: Point cloud data of the mine locations are collected repeatedly, and the point cloud data includes coordinate data and reflection intensity data; The stability of changes among repeatedly collected point cloud data is analyzed to initially screen out valid point clouds, and the degree of positional clustering of the valid point clouds is analyzed to obtain the points to be interpolated. Based on the coordinate data, the degree of lateral deviation between the layer where the interpolation point is located and the layers above and below it is analyzed, and the distribution of the number of effective point clouds within the layer where the interpolation point is located is analyzed to obtain the steepness of the local area of ​​the interpolation point. Based on the coordinate data, the degree of aggregation of the effective point cloud in the local spatial region of the point to be interpolated is analyzed, and then the correlation between the steepness performance and the degree of aggregation is analyzed to obtain the noise content presentation degree of the local region of the point to be interpolated. Based on the reflection intensity data, the difference in reflection intensity of all effective point clouds in the local area of ​​the point to be interpolated is analyzed, and combined with the noise content presentation degree, the interpolation filling confidence of all effective point clouds in the local area of ​​the point to be interpolated is obtained. Based on the interpolation fill confidence level, point cloud data filling processing is performed to complete the mine surveying data entry.

2. The surveying data entry method for engineering construction according to claim 1, characterized in that, Analyze the stability of changes in point cloud data collected repeatedly to initially screen out valid point clouds, including: By comparing point cloud data collected repeatedly, if the point cloud data at a given point is not lost in multiple collections, the point cloud data at that point is retained; otherwise, it is discarded to obtain a valid point cloud.

3. The surveying data entry method for engineering construction according to claim 2, characterized in that, Analyze the location clustering of the effective point cloud to obtain the points to be interpolated, including: Connect each pair of valid point clouds in space with a straight line to obtain several spatial triangles; Calculate the area of ​​each of the spatial triangles and the average area of ​​all the spatial triangles; Determine whether the area of ​​each of the spatial triangles is greater than the average area; If so, the centroid of the spatial triangular region is taken as the interpolation point.

4. The surveying data entry method for engineering construction according to claim 1, characterized in that, Based on the coordinate data, the degree of lateral deviation between the layer containing the point to be interpolated and the layers above and below it is analyzed, including: Based on the coordinate data, extract the n nearest valid point clouds at the same height as the point to be interpolated to obtain the layered local region where the point to be interpolated is located; Preset layer height; Extract the m nearest valid point clouds of the point to be interpolated within the upper and lower layers at the preset layer height position to obtain the upper layer local region and the lower layer local region of the point to be interpolated. Extract the centroids of the local regions of the layer where the interpolation point is located, as well as the corresponding local regions of the upper and lower layers. Calculate the Euclidean distances between the centroids of the local regions of the layer and the centroids of the local regions of the upper and lower layers, respectively, to obtain the degree of lateral deviation between the layer where the interpolation point is located and the upper and lower layers.

5. The surveying data entry method for engineering construction according to claim 4, characterized in that, Based on the coordinate data, and by analyzing the distribution of the number of valid point clouds within the layer where the point to be interpolated is located, including: Based on the coordinate data, the dispersion of the effective point cloud in the local layer where the interpolation point is located is analyzed. Combining the number of all effective point clouds at the same height as the interpolation point, and the maximum value of the number of all effective point clouds at the same height corresponding to all interpolation points, the distribution of the number of effective point clouds in the layer where the interpolation point is located is obtained.

6. The surveying data entry method for engineering construction according to claim 1, characterized in that, Based on the coordinate data, the degree of aggregation of effective point clouds in the local spatial region of the point to be interpolated is analyzed, including: Based on the coordinate data, a spherical space with the point to be interpolated as the center and R as the radius is determined as the local spatial region of the point to be interpolated. The number of effective point clouds in the local spatial region of the point to be interpolated is obtained, and the average number of effective point clouds in the local spatial region of all the points to be interpolated is calculated to obtain the degree of aggregation of the number of effective point clouds in the local spatial region of the point to be interpolated.

7. The surveying data entry method for engineering construction according to claim 6, characterized in that, Analyzing the correlation between the steepness and the degree of clustering, the noise content presentation of the local region of the interpolation point is obtained, including: Using the coordinate order of all the points to be interpolated as the x-axis, and the steepness of the corresponding points and the degree of aggregation of the effective point cloud as the y-axis, respectively, a steepness variation curve and a point cloud aggregation variation curve are constructed. For any given interpolation point, retain the correlation between the calculated steep change curve and the point cloud aggregation change curve of the interpolation point, and remove the correlation between the calculated steep change curve and the point cloud aggregation change curve of the interpolation point to obtain the noise content presentation degree of the local area of ​​the interpolation point.

8. The surveying data entry method for engineering construction according to claim 1, characterized in that, Based on the interpolation fill confidence level, point cloud data fill processing is performed, including: Preset trust threshold; The effective point clouds in the local area of ​​the point to be interpolated, where the interpolation filling confidence level is greater than the confidence threshold, are initially screened and used as interpolation reference point clouds. Extract the centroids of all the interpolation reference point clouds in the local region of the point to be interpolated, calculate the Euclidean distance between the coordinate data of the point to be interpolated and the centroids of all the interpolation reference point clouds, and obtain the deviation distance; Calculate the mean interpolation fill confidence score of all interpolation reference point clouds in the local region of the point to be interpolated; The offset of the point to be interpolated is obtained based on the deviation distance and the mean interpolation fill confidence level. Based on the offset, the points to be interpolated are adjusted to the actual mine terrain point cloud to achieve point cloud data filling processing.

9. A surveying data entry system for engineering construction, characterized in that, The system includes: a memory and a processor, wherein: The memory is used to store program code; The processor is configured to read program code stored in the memory and execute the method as described in any one of claims 1 to 8.

10. The surveying data entry system for engineering construction according to claim 9, characterized in that, The processor includes: The point cloud data acquisition module is used to repeatedly collect point cloud data of mine locations. The point cloud data includes coordinate data and reflection intensity data. The interpolation point acquisition module is used to analyze the stability of changes between point cloud data collected repeatedly, initially screen out effective point clouds, analyze the degree of positional aggregation of the effective point clouds, and acquire the interpolation points. The noise content presentation analysis module is used to analyze the degree of lateral deviation between the layer where the interpolation point is located and the layers above and below it based on the coordinate data, and to analyze the distribution of the number of effective point clouds within the layer where the interpolation point is located, thereby obtaining the steepness presentation of the local area of ​​the interpolation point; and to analyze the degree of aggregation of the number of effective point clouds in the local spatial area of ​​the interpolation point based on the coordinate data, and further analyze the correlation between the steepness presentation and the degree of aggregation, thereby obtaining the noise content presentation of the local area of ​​the interpolation point. The interpolation filling reliability analysis module is used to analyze the difference in reflection intensity of all effective point clouds in the local area of ​​the point to be interpolated based on the reflection intensity data, and to obtain the interpolation filling reliability of all effective point clouds in the local area of ​​the point to be interpolated by combining the noise content presentation degree. The data filling module is used to perform point cloud data filling processing based on the interpolation filling confidence level, and complete the mine surveying data entry.

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