A survey data entry method and system for engineering construction

Abstract

The present application relates to surveying and mapping data analysis input technical field, specifically to a kind of surveying and mapping data input method and system for engineering construction, the present application is according to the screening of initial effective point cloud of multiple point cloud measurement deviation while obtaining the point to be interpolated;Further assess the lateral deviation performance of the layer and the adjacent layer on the two sides of the point to be interpolated, and the lateral deviation performance is corrected according to the flat disc tendency degree of the plane layer area of the point to be interpolated, obtain the steep performance degree at the point to be interpolated;According to the correlation law of steep performance degree and local point cloud density, the noise content presentation of each local area of the point to be interpolated is obtained, and the interpolation filling reliability is obtained by analyzing the intensity difference of point cloud, and the point cloud data is filled.The present application analyzes the wind instability factor of dust itself, and also analyzes the interference of the correlation between the steep performance of dust on mine terrain and point cloud measurement density, to obtain more accurate surveying and mapping input results.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of surveying data analysis and entry, and particularly relates to a surveying data entry method and system for engineering construction. BACKGROUND

[0002] With the development of science and technology in the field of surveying, airborne laser point clouds gradually replace traditional total station surveying and become widely used surveying and mapping application technology. Mine engineering is an engineering construction technology mainly for mine construction. In the process of mine engineering surveying, point cloud data of each mine terrain is often collected by airborne laser radar, and the mine terrain model is reconstructed through the collected point cloud data, so that the mine engineering can be dynamically supervised according to the terrain model in different periods.

[0003] In the process of reconstructing the mine terrain through laser point clouds, the initial data of the collected point clouds is directly used for terrain model reconstruction, which may result in poor reconstruction accuracy due to the small number of point clouds. Therefore, an interpolation method is often used to fill new point cloud data in the initial collected point cloud set to meet the model fitting accuracy. When laser point cloud surveying is performed, the position information of an object is measured by emitting a laser beam and analyzing the received reflection signal. However, due to the presence of a large amount of dust interference in the actual mine scene, large particles of dust may cause the laser point cloud to be reflected too early, that is, the dust in the surveying process is misidentified as a real mine topography point, resulting in a large number of dust noise points in the initial collected point cloud, which further reduces the accuracy of subsequent point cloud filling and mine terrain model reconstruction. SUMMARY

[0004] To solve the above technical problems, the purpose of the present application is to provide a surveying data entry method and system for engineering construction.

[0005] According to the first aspect of the embodiment of the present application, a surveying data entry method for engineering construction is provided, and the technical solution is as follows:

[0006] The point cloud data of the mine point position is collected repeatedly, and the point cloud data includes coordinate data and reflection intensity data;

[0007] The variation stability between the repeatedly collected point cloud data is analyzed, the effective point cloud is preliminarily screened out, and the position aggregation degree of the effective point cloud is analyzed to obtain a point position to be interpolated;

[0008] Based on the coordinate data, the lateral deviation degree between the layer where the point position to be interpolated is located and the upper and lower layers is analyzed, and the number distribution of the effective point cloud in the layer where the point position to be interpolated is located is analyzed to obtain the steepness degree of the local area of the point position to be interpolated;

[0009] Based on the coordinate data, a number aggregation degree of the effective point cloud in a local space region of the point to be interpolated is analyzed, and then a correlation degree between the steepness and the number aggregation degree is analyzed, to obtain a noise content presentation degree of the local region of the point to be interpolated;

[0010] Based on the reflection intensity data, a reflection intensity difference of all the effective point clouds in the local region of the point to be interpolated is analyzed, and the noise content presentation degree is combined to obtain an interpolation filling reliability of all the effective point clouds in the local region of the point to be interpolated.

[0011] According to the interpolation filling reliability, point cloud data filling processing is performed to complete mine surveying and mapping data input.

[0012] In some embodiments of the present application, the variation stability between the point cloud data collected repeatedly for multiple times is analyzed, and the effective point cloud is preliminarily screened out, including:

[0013] By comparing the point cloud data collected repeatedly for multiple times, if the point cloud data at the point position is not lost in multiple times, the point cloud data at the point position is retained, otherwise it is excluded, to obtain the effective point cloud.

[0014] In some embodiments of the present application, the position aggregation degree of the effective point cloud is analyzed to obtain the point to be interpolated, including:

[0015] Connecting the effective point clouds in space two by two in a straight line, a plurality of space triangles are obtained.

[0016] The area of each space triangle and the area average of all space triangles are calculated.

[0017] It is judged whether the area of each space triangle is greater than the area average.

[0018] If yes, the centroid position of the space triangle region is taken as the point to be interpolated.

[0019] In some embodiments of the present application, based on the coordinate data, the lateral deviation degree between the layer where the point to be interpolated is located and the upper and lower layers is analyzed, including:

[0020] Based on the coordinate data, the nearest n effective point clouds in the same height position as the point to be interpolated are extracted, to obtain a local region of the layer where the point to be interpolated is located.

[0021] A preset layer height is set.

[0022] The nearest m effective point clouds in the upper and lower layers of the preset layer height position of the point to be interpolated are extracted respectively, to obtain an upper layer local region and a lower layer local region of the point to be interpolated.

[0023] extracting the centroid of the local region of the layer where the point to be interpolated is located and the corresponding upper and lower local regions of the layer respectively, and calculating the Euclidean distance between the centroid of the local region of the layer and the centroid of the upper and lower local regions of the layer respectively, to obtain the lateral deviation degree between the layer where the point to be interpolated is located and the upper and lower layers.

[0024] In some embodiments of the application, based on the coordinate data, and analyzing the number distribution of the effective point cloud in the layer where the point to be interpolated is located, including:

[0025] Based on the coordinate data, analyzing the dispersion degree of the effective point cloud in the local region of the layer where the point to be interpolated is located, combining the number of all effective point clouds at the same height as the point to be interpolated and the maximum value of the number of all effective point clouds at the same height corresponding to all points to be interpolated, to obtain the number distribution of the effective point cloud in the layer where the point to be interpolated is located.

[0026] In some embodiments of the application, based on the coordinate data, analyzing the number aggregation degree of the effective point cloud in the local spatial region of the point to be interpolated, including:

[0027] Based on the coordinate data, determining the local spatial region of the point to be interpolated as the center of the sphere and R as the radius of the sphere space;

[0028] Obtaining the number of effective point clouds in the local spatial region of the point to be interpolated, calculating the mean value of the number of effective point clouds in all local spatial regions of the point to be interpolated, to obtain the number aggregation degree of the effective point cloud in the local spatial region of the point to be interpolated.

[0029] In some embodiments of the application, analyzing the correlation degree of the steepness and the number aggregation degree to obtain the noise content presentation degree of the local region of the point to be interpolated, including:

[0030] Taking the coordinate sequence of all points to be interpolated as the horizontal coordinate, and taking the steepness and the number aggregation degree of the effective point cloud corresponding to the point to be interpolated as the vertical coordinate respectively, constructing the steepness change curve and the point cloud aggregation change curve;

[0031] For any one of the points to be interpolated, retaining the correlation degree of the steepness change curve and the point cloud aggregation change curve calculated for the point to be interpolated, and eliminating the correlation degree of the steepness change curve and the point cloud aggregation change curve calculated for the point to be interpolated, to obtain the noise content presentation degree of the local region of the point to be interpolated.

[0032] In some embodiments of the application, according to the interpolation filling reliability, performing point cloud data filling processing, including:

[0033] a preset reliability threshold value;

[0034] screening the interpolation reference point cloud in the local region of the point to be interpolated, as an interpolation reference point cloud;

[0035] extracting the centroid of all the interpolation reference point clouds in the local region of the point to be interpolated, calculating the Euclidean distance between the coordinate data of the point to be interpolated and the centroid of all the interpolation reference point clouds, and obtaining a deviation distance;

[0036] calculating the interpolation filling reliability mean value of all the interpolation reference point clouds in the local region of the point to be interpolated;

[0037] obtaining the offset of the point to be interpolated according to the deviation distance and the interpolation filling reliability mean value;

[0038] adjusting the point to be interpolated to the real mine terrain point cloud according to the offset, to realize point cloud data filling processing.

[0039] According to a second aspect of an embodiment of the present application, a surveying and mapping data input system for engineering construction is provided, comprising a memory and a processor, wherein:

[0040] the memory is configured to store program code;

[0041] the processor is configured to read the program code stored in the memory and execute the method of the first aspect of the present application. In some embodiments of the present application, the processor comprises:

[0042] a point cloud data acquisition module configured to repeatedly acquire point cloud data of mine points, the point cloud data comprising coordinate data and reflection intensity data;

[0043] a point to be interpolated acquisition module configured to analyze the variation stability between the point cloud data acquired repeatedly, screen out effective point clouds, analyze the position aggregation degree of the effective point clouds, and acquire a point to be interpolated;

[0044] a noise content presentation degree analysis module configured to analyze the lateral deviation degree between the layer in which the point to be interpolated is located and the layers above and below based on the coordinate data, analyze the number distribution of the effective point clouds in the layer in which the point to be interpolated is located, obtain the steepness degree of the local region of the point to be interpolated, and analyze the number aggregation degree of the effective point clouds in the local spatial region of the point to be interpolated based on the coordinate data, further analyze the correlation degree between the steepness degree and the number aggregation degree, and obtain the noise content presentation degree of the local region of the point to be interpolated;

[0045] An interpolation filling reliability analysis module is configured to analyze the reflection intensity difference of all effective point clouds in the local area of the point to be interpolated based on the reflection intensity data, and obtain the interpolation filling reliability of all effective point clouds in the local area of the point to be interpolated in combination with the noise content presentation.

[0046] A data filling module is configured to perform point cloud data filling processing according to the interpolation filling reliability, and complete the mine surveying and mapping data entry.

[0047] Compared with the prior art, the mine surveying and mapping data entry method and system provided by the application has the following beneficial effects:

[0048] First, the application filters the initial effective point cloud according to the deviation of multiple point cloud measurements, and obtains the point to be interpolated; then, the lateral deviation performance of the layer where the point to be interpolated is located and the adjacent layers on both sides is evaluated, and the lateral deviation performance is corrected according to the flat plate trend degree of the planar layer area of the point to be interpolated to obtain the steepness performance of the point to be interpolated; then, the noise content presentation of the local area of each point to be interpolated is obtained according to the correlation law of the steepness performance and the local point cloud density; then, the interpolation filling reliability is obtained by analyzing the point cloud intensity difference based on the noise content presentation; and the data filling is performed according to the interpolation filling reliability of each point cloud point to the point to be interpolated, so as to reconstruct the mine terrain model from the point cloud data after the filling processing. Compared with the traditional interpolation method, the application can obtain more accurate surveying and mapping data entry results by combining the interference of dust in the actual scene on the point cloud collection process, and thus improves the entry accuracy of the mine surveying and mapping data. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0050] Figure 1 A basic flowchart of a mine surveying and mapping data entry method provided by an embodiment of the application;

[0051] Figure 2 A single platform structure diagram of an open pit concave mine provided by an embodiment of the application;

[0052] Figure 3 A steep change curve and point cloud aggregation change curve diagram provided by an embodiment of the application;

[0053] Figure 4A basic component diagram of a surveying data entry system for engineering construction provided by an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined object of the application, the following describes in detail the specific implementation, structure, features and effects of a surveying data entry method and system for engineering construction according to the present application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0055] 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 the present application belongs. Terms such as "comprise", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such article or device. Without more limitation, the element defined by the phrase "comprising one" does not exclude the presence of additional identical elements in the article or device comprising the element.

[0056] It should be noted that, in order to ensure that the calculation result is meaningful, when performing fractional operation, 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 is set by the implementer according to the actual situation, and the present application does not make special limitations.

[0057] The scenario addressed by the present application is that in the process of mine engineering surveying, point cloud data of each mine terrain is often collected by airborne laser radar, and the mine terrain model is reconstructed through the collected point cloud data, so that the mine engineering can be dynamically monitored according to the terrain model in different periods. Directly reconstructing the terrain model through the collected initial point cloud data will result in poor reconstruction accuracy due to the small number of point clouds, so interpolation is often used to fill new point cloud data in the initial point cloud set to meet the model fitting accuracy. However, there are many dust interferences in the actual mine scene, and large particles of dust will cause the laser point cloud to be reflected too early, resulting in many dust noise point clouds in the initial point cloud data, which will affect the accuracy of subsequent point cloud filling and mine terrain model reconstruction.

[0058] Therefore, the main purpose of the present application is to fill the initial collected mine terrain point cloud data by interpolation combined with dust interference characteristics, so as to reconstruct a more accurate mine terrain model, thereby improving the accuracy of mine surveying data entry.

[0059] The application provides a mine surveying data input method for engineering construction.

[0060] Please refer to Figure 1 , which shows the basic flow of a mine surveying data input method for engineering construction provided by an embodiment of the application.

[0061] As Figure 1 shown, the mine surveying data input method for engineering construction provided by an embodiment of the application specifically comprises the following steps.

[0062] S100: repeatedly collecting point cloud data of mine points, the point cloud data comprising coordinate data and reflection intensity data.

[0063] First, multiple unmanned aerial vehicles (UAVs) carrying laser emitters are deployed, and each UAV is responsible for surveying a different area of the mine. The flight route of the UAV is designed to ensure that each UAV can cover each mine point in the area it is responsible for. To ensure the surveying accuracy, each UAV measures the area it is responsible for multiple times, and in this embodiment, each UAV measures the same area three times. Then, the point cloud data of each mine point in the flight process is collected by the laser emitter module carried by the UAV, wherein the point cloud data comprises the coordinate data and the reflection intensity data of each mine point. Finally, the collected point cloud information is uploaded to a data collection system for subsequent analysis and use.

[0064] At this point, the pre-preparation and data collection work are completed.

[0065] S200: analyzing the variation stability between the point cloud data collected multiple times, preliminarily screening out effective point cloud, and analyzing the position aggregation degree of the effective point cloud to obtain a point to be interpolated.

[0066] The idea of the application is to exclude the interference of noise point cloud caused by dust in the process of interpolating and filling the point cloud initially collected in the mine, and then to reconstruct a more accurate three-dimensional model of the mine terrain. Therefore, in this step, the effective point cloud is preliminarily screened according to the variation of the point cloud data in the multiple measurements of the UAV, and the point to be interpolated is obtained, and then the point to be interpolated is interpolated and filled according to the mine terrain features reflected by the effective point cloud, so that a more accurate mine surveying data input result is obtained.

[0067] When the airborne laser radar emits a laser signal to the ground, if there is dust with large particles in the path, the dust will reflect the laser, and then generate invalid noise data in the point cloud. In the process of multiple measurements of the UAV on the mine area, the dust in the air above the mine is greatly affected by the wind, and shows less stability in the multiple measurements.

[0068] Therefore, in the embodiments of the present application, by analyzing the variation stability between the point cloud data collected repeatedly multiple times, the effective point cloud is preliminarily screened, and the position aggregation degree of the effective point cloud is analyzed to obtain the point position to be interpolated. Specifically, by comparing the point cloud data collected repeatedly multiple times by a single unmanned aerial vehicle on a single mine area, if the point cloud data at the point position is not lost in multiple measurements, the point cloud data at the point position is retained, otherwise it is excluded, and the noise point cloud obviously affected by wind force in multiple measurements is screened out to obtain the effective point cloud.

[0069] Further, the position aggregation degree of the effective point cloud is analyzed to obtain the point position to be interpolated. The specific implementation is: connecting the effective point clouds in space two by two to obtain a plurality of spatial triangles, i.e. dividing all the effective point clouds into a plurality of spatial triangles; calculating the area of each spatial triangle and the area average of all spatial triangles; determining whether the area of each spatial triangle is greater than the area average; if yes, it is considered that the point cloud data in the spatial triangle region is relatively dispersed, and the centroid position of the spatial triangle region is taken as the point position to be interpolated; if not, it is considered that the point cloud data in the spatial triangle region is relatively aggregated, and interpolation is not needed.

[0070] At this point, the preliminary screening of the point cloud data is completed, the effective point cloud is obtained, and the point position to be interpolated is determined.

[0071] In the effective point cloud set after preliminary screening, the non-landform point positions near the ground section of the mine may be in the case of large dust interference in multiple measurement processes, so further analysis of the preliminary screened effective point cloud set is needed when interpolating and filling.

[0072] Firstly, the topography of the open pit concave mine is composed of multiple platform structures, resulting in different steepness of different layer height slopes of the mine, and the reflection of laser point cloud on different steepness topography is different, so the noise content of the local region of each point position to be interpolated is obtained by further combining the correlation between point cloud density and steepness, and then the reflection intensity difference of each effective point cloud in the local region of the point position to be interpolated is analyzed to obtain the interpolation filling reliability of each effective point cloud on the point position to be interpolated, i.e. the interpolation reference value. Specifically, it includes steps S300 to S500.

[0073] S300: Based on the coordinate data, the lateral deviation degree between the layer to be interpolated and the upper and lower layers, and the number distribution of the effective point cloud in the layer to be interpolated are analyzed to obtain the steepness performance of the local region of the point position to be interpolated.

[0074] The laser point cloud has different reflection performances for different inclined slope surfaces. Therefore, in order to more accurately analyze the accuracy of the effective point cloud obtained in the subsequent initial screening, the steepness of each point to be interpolated is first evaluated to reflect the inclination of the local slope surface corresponding to the point to be interpolated.

[0075] Therefore, in the embodiments of the present application, based on the coordinate data, the lateral deviation degree between the layer where the point to be interpolated is located and the upper and lower layers is analyzed, and the number distribution of the effective point cloud in the layer where the point to be interpolated is located is analyzed, to obtain the steepness of the local region of the point to be interpolated.

[0076] First, the axial height feature layer of the mine is analyzed. When the local slope surface of the mine is more inclined, the lateral offset of the layer where the slope surface is located compared to the next layer is larger, and at the same time, the lateral offset of the previous layer compared to the layer where the current slope surface is located is larger in the offset direction. Conversely, if the lateral offset between the previous layer, the layer where the current slope surface is located, and the next layer is smaller, it is more likely that the layer where the current slope surface is located is low-tilted.

[0077] Therefore, based on the coordinate data, the lateral deviation degree between the layer where the point to be interpolated is located and the upper and lower layers is analyzed. Further including: based on the coordinate data, first, the nearest n (n can be 200) effective point clouds in the same height position as the point to be interpolated are extracted, the outermost circle point clouds of the 200 effective point clouds are connected to form a closed region by a boundary tracking algorithm, to obtain the local region of the layer where the point to be interpolated is located. Then, a preset layer height is taken, which can be 10 cm. Then, the nearest m (m can be 50) effective point clouds in the upper and lower layers of the preset layer height position of the point to be interpolated are extracted, the outermost circle point clouds of the 50 effective point clouds are connected to form a closed region by a boundary tracking algorithm, to obtain the local region of the upper layer and the local region of the lower layer of the point to be interpolated. It should be noted that the preset layer height of 10 cm and the number of the nearest 200 and 50 effective point clouds are all preset values, which can be adjusted according to the actual scene. Finally, the centroids of the local region of the layer where the point to be interpolated is located and the corresponding upper and lower local regions are extracted by centroid extraction technology, and the Euclidean distances between the centroids of the local region of the layer where the point to be interpolated is located and the centroids of the upper and lower local regions are calculated, to obtain the lateral deviation degree between the layer where the point to be interpolated is located and the upper and lower layers. The lateral deviation degree calculation formula between the layer where the first point to be interpolated is located and the upper and lower layers is constructed as follows:

[0078]

[0079] In the formula, d represents the lateral deviation degree between the layer where the point to be interpolated is located and the upper and lower layers, and x and y represent the Euclidean distances between the centroids of the local region of the layer where the point to be interpolated is located and the centroids of the upper and lower local regions, respectively. ​​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.

[0080] 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.

[0081] Similarly, the degree of lateral deviation between the layer containing all interpolation points and the layers above and below it is obtained.

[0082] 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.

[0083] 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.

[0084] 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:

[0085]

[0086] 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.

[0087] 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 it indicates the number of... 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.

[0088] Similarly, the degree of flat-panel tendency of the local area of ​​all points to be interpolated is obtained.

[0089] Finally, when the lateral deviation degree between the layer where the to-be-interpolated point is located and the upper and lower layers is higher, and the flat disc trend degree is lower, it indicates that the local steepness performance of the to-be-interpolated point is higher, and the steepness performance degree of the local region of the to-be-interpolated point is:

[0090]

[0091] indicates the steepness performance degree of the local region of the to-be-interpolated point; indicates the number distribution of the effective point cloud in the local region of the layer where the to-be-interpolated point is located, that is, the flat disc trend degree of the to-be-interpolated point; indicates the lateral deviation degree between the layer where the to-be-interpolated point is located and the upper and lower layers; indicates a linear normalization function.

[0092] Similarly, the steepness performance degrees of the local regions of all to-be-interpolated points are obtained.

[0093] S400: Based on the coordinate data, the number aggregation degree of the effective point cloud in the local spatial region of the to-be-interpolated point is analyzed, and then the correlation degree between the steepness performance degree and the number aggregation degree is analyzed, to obtain the noise content presentation degree of the local region of the to-be-interpolated point.

[0094] It is considered that, in an ideal case without dust noise, when the laser point cloud is vertically hit on a terrain surface, the reflection effect of the laser is good, and most of the laser is reflected back to the airborne laser receiving device, and when the laser point cloud is hit on a terrain surface with a certain steepness, part of the laser is scattered on the inclined steep surface, so that less laser is reflected back to the laser receiving device, so that the density of the point cloud at the steep surface is reduced, that is, according to the above analysis, in the ideal case without dust noise, the steepness degree of the terrain surface is inversely proportional to the point cloud density in the region, and the noise destroys this correlation rule.

[0095] Based on the above analysis, in the embodiment of the present application, based on the coordinate data, the number aggregation degree of the effective point cloud in the local spatial region of the to-be-interpolated point is analyzed, and then the correlation degree between the steepness performance degree and the number aggregation degree is analyzed, to obtain the noise content presentation degree of the local region of the to-be-interpolated point.

[0096] ​​​​​​​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:

[0097]

[0098] 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.

[0099] 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.

[0100] Similarly, the degree of aggregation of effective point clouds in the local spatial region of each point to be interpolated is obtained.

[0101] 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.

[0102] 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 3As 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:

[0103]

[0104] 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.

[0105] 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.

[0106] Similarly, obtain the noise content presentation of the local area of ​​all points to be interpolated.

[0107] 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.

[0108] 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 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 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.

[0109] 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:

[0110]

[0111] 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.

[0112] 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.

[0113] Similarly, the interpolation filling confidence of all valid point clouds in the local region of each point to be interpolated is obtained.

[0114] S600: Based on the interpolation fill confidence level, point cloud data fill processing is performed to complete the mine surveying data entry.

[0115] According to the interpolation filling reliability, the point cloud data filling processing is carried out, and the mine surveying and mapping data input is completed. The specific implementation is as follows: first, a preset reliability threshold is set, and the preset reliability threshold is 0.88 in the application; and the interpolation filling reliability of the effective point cloud in the local area of the to-be-interpolated point position is greater than the reliability threshold, which reduces the influence of dust and is used as an interpolation reference point cloud; then, for a single to-be-interpolated point position, the centroid of all interpolation reference point clouds in the local area of the to-be-interpolated point position is extracted, the Euclidean distance between the coordinate data of the to-be-interpolated point position and the centroid of all interpolation reference point clouds is calculated, and the deviation distance is obtained; at the same time, the interpolation filling reliability average of all interpolation reference point clouds in the local area of the to-be-interpolated point position is calculated; then, according to the deviation distance and the interpolation filling reliability average, the offset of the to-be-interpolated point position is obtained; wherein the offset calculation formula of the first to-be-interpolated point position is as follows:

[0116]

[0117] In the formula, d represents the offset of the first to-be-interpolated point position; d represents the interpolation filling reliability average of all interpolation reference point clouds in the local area of the first to-be-interpolated point position; and d represents the Euclidean distance between the coordinate data of the first to-be-interpolated point position and the centroid of all interpolation reference point clouds, that is, the deviation distance.

[0118] Finally, the to-be-interpolated point position is offset from the original position to the centroid position of the interpolation reference point cloud, and the offset is d; and then the initial position of each to-be-interpolated reference point cloud is adjusted to the real mine terrain point cloud, so that the point cloud data filling processing is realized.

[0119] After the point cloud data filling processing is completed, the mine surveying and mapping data input process is perfected according to the point cloud data after the filling processing. The specific implementation is as follows: first, the mine terrain interpolation filling point cloud set obtained by the plurality of unmanned aerial vehicles is registered by an ICP point cloud registration algorithm; then, the registered mine point cloud set is reconstructed in three dimensions by Ashape technology to obtain a mine terrain reconstruction model; finally, the mine terrain reconstruction model is input into a mine surveying and mapping management system, and then the system center can dynamically supervise the mine by analyzing the model changes at different times.

[0120] Based on the same inventive concept as the above method, the embodiment also provides a surveying and mapping data input system for engineering construction.

[0121] Please refer to​​​​​​​​Figure 4 which shows the basic components of a surveying data entry system for engineering construction provided by one embodiment of the present application.

[0122] As shown in Figure 4 , a surveying data entry system for engineering construction comprises a memory 10 and a processor 20, wherein:

[0123] The memory 10 is configured to store program codes.

[0124] The processor 20 is configured to read the program codes stored in the memory 10 and perform the following operations: repeatedly collecting point cloud data of mine point positions, the point cloud data comprising coordinate data and reflection intensity data; analyzing the variation stability between the point cloud data collected repeatedly, preliminarily screening out effective point clouds, and analyzing the position aggregation degree of the effective point clouds to obtain a point position to be interpolated; based on the coordinate data, analyzing the lateral deviation degree between the layer where the point position to be interpolated is located and the upper and lower layers, and analyzing the number distribution of the effective point clouds in the layer where the point position to be interpolated is located to obtain the steepness degree of the local region of the point position to be interpolated; based on the coordinate data, analyzing the number aggregation degree of the effective point clouds in the local spatial region of the point position to be interpolated, and then analyzing the correlation degree between the steepness degree and the number aggregation degree to obtain the noise content degree of the local region of the point position to be interpolated; based on the reflection intensity data, analyzing the reflection intensity difference of all the effective point clouds in the local region of the point position to be interpolated, combining the noise content degree to obtain the interpolation filling reliability of all the effective point clouds in the local region of the point position to be interpolated; and performing point cloud data filling processing according to the interpolation filling reliability to complete the mine surveying data entry.

[0125] Further, the processor 20 comprises a point cloud data collection module 21, a point position to be interpolated obtaining module 22, a noise content degree analysis module 23, an interpolation filling reliability analysis module 24 and a data filling module 25. Wherein:

[0126] The point cloud data collection module 21 is configured to repeatedly collect point cloud data of mine point positions, the point cloud data comprising coordinate data and reflection intensity data.

[0127] The point position to be interpolated obtaining module 22 is configured to analyze the variation stability between the point cloud data collected repeatedly, preliminarily screen out effective point clouds, and analyze the position aggregation degree of the effective point clouds to obtain a point position to be interpolated.

[0128] The noise content presentation degree analysis module 23 is configured to analyze the lateral deviation degree between the layer where the point to be interpolated is located and the upper and lower layers based on the coordinate data, analyze the number distribution of the effective point cloud in the layer where the point to be interpolated is located, and obtain the steepness presentation degree of the local region of the point to be interpolated; and analyze the number aggregation degree of the effective point cloud in the local spatial region of the point to be interpolated based on the coordinate data, and further analyze the correlation degree between the steepness presentation degree and the number aggregation degree, to obtain the noise content presentation degree of the local region of the point to be interpolated.

[0129] The interpolation filling reliability analysis module 24 is configured to analyze the reflection intensity difference of all the effective point clouds in the local region of the point to be interpolated based on the reflection intensity data, and obtain the interpolation filling reliability of all the effective point clouds in the local region of the point to be interpolated in combination with the noise content presentation degree.

[0130] The data filling module 25 is configured to perform point cloud data filling processing according to the interpolation filling reliability, and complete the mine surveying and mapping data input.

[0131] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0132] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference 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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