A surveying and mapping geographic information data management method and system

Abstract

The present application relates to the field of data processing, in particular to a surveying and mapping geographic information data management method and system, comprising: obtaining the terrain complexity of the space where each point cloud data is located and the final adjacent point cloud data according to the distribution of other point cloud data around each point cloud data; obtaining the spatial anomaly factor of each point cloud data and the closed operation residual of each point cloud data according to the Euclidean distance between each point cloud data and its final adjacent point cloud data, the statistical trend direction, and the elevation value distribution of the surrounding point cloud data; obtaining a plurality of scanning lines when surveying and mapping the to-be-surveyed area; and obtaining the abnormal point cloud data according to the spatial anomaly factor of each point cloud data, the terrain complexity of the space where each point cloud data is located, and the closed operation residual of all point cloud data on the scanning line where each point cloud data is located. The present application aims to solve the problem that the accuracy of the abnormal point cloud data obtained in the current acquisition of the abnormal point cloud data in the point cloud data of the to-be-surveyed area is relatively low.

Description

Technical Field

[0001] This invention relates to the field of data processing, and specifically to a method and system for managing surveying and mapping geographic information data. Background Technology

[0002] Currently, when analyzing surveying and mapping geographic information data, LiDAR (LiDAR) technology is first used to acquire point cloud data of the area to be surveyed. However, due to anomalies such as signal interference during the acquisition process, abnormal data may be present in the acquired data. Therefore, it is necessary to extract the abnormal data from all point cloud data.

[0003] Because point cloud data, which reflects geographic information, exhibits a significant and complex spatial distribution, the collected point cloud data suffers from uneven distribution. This unevenness in point cloud density makes it prone to failure when using fixed-radius clustering methods to identify outliers. Graph structures, however, can adapt well to such density variations; therefore, graph diffusion algorithms are currently widely used to identify outliers in point cloud data.

[0004] However, when using the graph diffusion algorithm to obtain outlier point cloud data, only the influence of distance is considered. In geographic information, planar and elevation distances exhibit significant anisotropy, and different landforms have multi-scale characteristics, meaning terrain features are not taken into account. This means that when using the graph diffusion algorithm to obtain outlier data, the periodic systematic error of stripe drift in the point cloud data may be ignored, resulting in relatively low accuracy of the obtained outlier data. Summary of the Invention

[0005] This invention provides a method and system for managing surveying and mapping geographic information data, in order to solve the problem that the accuracy of abnormal data acquired when obtaining abnormal data in point cloud data is relatively low.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The area to be surveyed was scanned and mapped to obtain a number of point cloud data.

[0008] Based on the distribution of other point cloud data around each point cloud data, the terrain complexity and statistical trend direction of the space where each point cloud data is located are obtained;

[0009] The final diffusion scale of each point cloud data is obtained based on the terrain complexity of the space where each point cloud data is located.

[0010] Based on the final diffusion scale of each point cloud data, the final neighboring point cloud data of each point cloud data is obtained; based on the Euclidean distance between each point cloud data and its final neighboring point cloud data, the statistical trend direction, and the elevation value distribution of the surrounding point cloud data, the relation weight of each final neighboring point cloud data of each point cloud data is obtained.

[0011] Based on the terrain complexity and Euclidean distance of each point cloud data and its final neighboring point cloud data, the elevation value of each point cloud data and its final neighboring point cloud data after morphological operations on the point cloud data around it, the elevation value of each point cloud data and its final neighboring point cloud data, and the relation weight of each final neighboring point cloud data, the spatial anomaly factor of each point cloud data and the closing operation residual of each point cloud data are obtained.

[0012] Obtain several scan lines when surveying the area to be surveyed; based on the spatial anomaly factor of each point cloud data, the terrain complexity of the space, and the closed operation residual of all point cloud data on the scan line, obtain the abnormal point cloud data.

[0013] Furthermore, the specific calculation steps for obtaining the terrain complexity and statistical trend direction of the space where each point cloud data is located based on the distribution of other point cloud data around each point cloud data are as follows:

[0014] Among all point cloud data, those falling on the first Using point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The initial neighboring point cloud data of each point cloud data; among which... This indicates the preset radius value;

[0015] Taking the bottom left corner of the point cloud data as the origin, and extending horizontally to the right as... The positive direction of the axis, with vertical forward as the reference. The positive direction of the axis, with vertically upward as... Construct a three-dimensional coordinate system along the positive axis to obtain the coordinates of each point in the cloud data.

[0016] Based on the Euclidean distance between each point cloud data and its initial neighboring point cloud data, as well as the coordinate difference, the statistical trend direction of the space in which each point cloud data is located is obtained.

[0017] Based on the statistical trend direction of the space where each point cloud data is located, the Euclidean distance between each point cloud data and its initial neighboring point cloud data, and The differences on the axis give us the terrain complexity of the space where each point of cloud data is located.

[0018] Furthermore, the specific calculation steps for obtaining the statistical trend direction of the space in which each point cloud data is located based on the Euclidean distance between each point cloud data and each of its initial neighboring point cloud data, as well as the coordinate difference, are as follows:

[0019]

[0020] In the formula, Indicates the first The statistical trend direction of the space in which each point cloud data is located. Indicates the first The number of initial neighboring point cloud data for each point cloud data set. Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The point cloud data and its first The Euclidean distance between the initial neighboring point cloud data.

[0021] Furthermore, the statistical trend direction of the space where each point cloud data is located, the Euclidean distance between each point cloud data and its initial neighboring point cloud data, and The specific steps for calculating the terrain complexity of the space where each point of cloud data is located, based on the differences on the axis, are as follows:

[0022]

[0023] In the formula, Indicates the first The terrain complexity of the space in which each point cloud data resides. Indicates the first The statistical trend direction of the space in which each point cloud data is located. Indicates starting from the origin and... The vector obtained as the endpoint. This represents the vector originating from the origin and ending at a point with coordinates (0,0,1). The dot product symbol represents the vector. This represents the sigmoid function. Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The point cloud data and its first Euclidean distance of the initial neighboring point cloud data Indicates the first The number of initial neighboring point cloud data for each point cloud data.

[0024] Furthermore, the specific calculation steps for obtaining the final diffusion scale of each point cloud data based on the terrain complexity of the space where each point cloud data is located are as follows:

[0025] Obtain the terrain type of the area to be mapped and the sensitivity of the airborne LiDAR to terrain changes in the collected point cloud data;

[0026] The first adjustable coefficient is obtained based on the terrain type of the area to be surveyed;

[0027] The second adjustable coefficient is obtained based on the sensitivity of the airborne LiDAR that collects point cloud data to terrain changes;

[0028] Based on the first adjustable coefficient, the second adjustable coefficient, and the third The terrain complexity of the space where the point cloud data resides is obtained. The specific formula for calculating the final diffusion scale of point cloud data is as follows:

[0029]

[0030] In the formula, Indicates the first The final diffusion scale of point cloud data, This represents the preset radius value. Indicates the first The terrain complexity of the space in which each point cloud data resides. This represents the first adjustable coefficient. This represents the second adjustable coefficient. It is an exponential function with the natural constant as its base.

[0031] Furthermore, the specific calculation steps for obtaining the final neighboring point cloud data of each point cloud data based on the final diffusion scale of each point cloud data are as follows:

[0032] Among all point cloud data, those falling on the first Using point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The final neighboring point cloud data of each point cloud data; among which... Indicates the first The final diffusion scale of point cloud data.

[0033] Furthermore, the specific calculation steps for obtaining the relational weight of each final neighboring point cloud data based on the Euclidean distance, statistical trend direction, and elevation distribution of the surrounding point cloud data for each point cloud data are as follows:

[0034] Obtain the elevation value of each point of cloud data;

[0035] Among all point cloud data, those falling on the first The first point cloud data Using the final nearest point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The first point cloud data The final nearest neighbor point cloud data is located in the first Point cloud data within an initial local region; among which... Indicates the first The final diffusion scale of point cloud data;

[0036] Starting from the origin, and using the first... The first point cloud data The vector obtained by taking the statistical trend direction of the space where the final neighboring point cloud data resides as the endpoint is denoted as . ;

[0037] Get the The first point cloud data The specific formula for calculating the relation weights of the final neighbor point cloud data is as follows:

[0038]

[0039]

[0040]

[0041] In the formula, Indicates the first The first point cloud data The relational weights of the final neighboring point cloud data Indicates the first The first point cloud data The first weight of the final neighboring point cloud data, Indicates the first The first point cloud data The second weight of the final neighboring point cloud data, Indicates the first The point cloud data and its first The Euclidean distance between the final nearest point cloud data. Indicates the first The final diffusion scale of point cloud data, Indicates starting from the origin. The vector obtained as the endpoint. Indicates the first Starting with the first point cloud data, and taking the first point cloud data as the starting point... The first point cloud data The vector obtained by taking the final neighboring point cloud data as the endpoint This represents the result of the dot product of two vectors. This represents a function that takes the maximum value between two values. It is an exponential function with the natural constant as its base. Representing vectors with vector The included angle, Representing vectors with vector The included angle, Represents the sine function. Indicates the first The minimum elevation value among a point cloud data point and all its final neighboring point cloud data points. Indicates the first The first point cloud data The final nearest neighbor point cloud data and its position in the first nearest neighbor point cloud. The minimum elevation value among all point cloud data within an initial local region. This represents the absolute value function.

[0042] Furthermore, the specific calculation steps for obtaining the spatial anomaly factor of each point cloud data are as follows:

[0043]

[0044]

[0045]

[0046] In the formula, Indicates the first Spatial anomaly factors of point cloud data, Indicates the first The first outlier factor in point cloud data. Indicates the first The second anomaly factor in point cloud data. Indicates the first The point cloud data and its first The Euclidean distance between the final nearest point cloud data. Indicates the first The final number of neighboring point cloud data for each point cloud data set. Indicates the first The first point cloud data The relational weights of the final neighboring point cloud data Indicates the first The terrain complexity of the space in which each point cloud data resides. Indicates the first The first point cloud data The terrain complexity of the space where the final nearest point cloud data resides. Indicates the first Elevation values ​​of point cloud data. Indicates the first The first point cloud data The elevation values ​​of the final neighboring point cloud data. Indicates the first After performing a closing operation on the region formed by the i-th point cloud data and all its final neighboring point cloud data, the i-th point cloud data is... Elevation values ​​at each point cloud data point Indicates the first The first point cloud data The final nearest neighbor point cloud data and its position in the first nearest neighbor point cloud. After performing a closing operation on the region composed of all point cloud data within the initial local region, the th... The first point cloud data The elevation value at the final nearest point cloud data location. Represents the absolute value function;

[0047] Among all point cloud data, those falling on the first The first point cloud data Using the final nearest point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The first point cloud data The final nearest neighbor point cloud data is located in the first Point cloud data within an initial local region; among which... Indicates the first The final diffusion scale of point cloud data;

[0048] Will , denoted as the The closed-loop residual of point cloud data.

[0049] Furthermore, the specific calculation steps for obtaining anomalous point cloud data based on the spatial anomaly factor of each point cloud data, the terrain complexity of the space, and the closing operation residuals of all point cloud data on the scan line are as follows:

[0050] The first The scan line containing the point cloud data, and the scan lines adjacent to it, are denoted as the i-th scan line. The adjacent scan lines of the scan line containing the point cloud data;

[0051] The first The mean of the closed-loop residuals of all point cloud data along the scan line is denoted as the nth. The overall residual value of the closed operation of each scan line;

[0052] According to the The closed-loop residual of each point of cloud data on each scan line is used to obtain a sequence of closed-loop residuals, which is denoted as the i-th. The closed operation residual sequence of each scan line;

[0053] The first For each closed operation residual within the closed operation residual sequence of the scan line containing the point cloud data, subtract the first... The mean of the overall residual values ​​of the closed operation of all adjacent scan lines of the scan line containing the point cloud data is obtained. The update residual sequence of the scan line containing the point cloud data;

[0054] For the The update residual sequence of the scan line containing the point cloud data is subjected to Fourier transform to obtain several sine waves of different frequencies. The sum of the squares of the amplitude values ​​of all the obtained sine waves of different frequencies is denoted as the i-th sine wave. The final amplitude of each point cloud data;

[0055] Get the The specific formula for calculating the frequency domain anomaly factor of a point cloud data is as follows:

[0056]

[0057] In the formula, Indicates the first Frequency domain anomaly factors of point cloud data Indicates the first The terrain complexity of the space in which each point cloud data resides. Indicates the first The final amplitude of each point cloud data point. It is an exponential function with the natural constant as its base;

[0058] Abnormal point cloud data is obtained based on the frequency domain anomaly factor and spatial anomaly factor of each point cloud data.

[0059] Furthermore, the specific calculation steps for obtaining the anomalous point cloud data based on the frequency domain anomaly factor and spatial anomaly factor of each point cloud data are as follows:

[0060] Sort all point cloud data spatial anomaly factors from smallest to largest, take the maximum value of the absolute value of adjacent differences, and the minimum value of the corresponding two spatial anomaly factors as the first spatial anomaly threshold.

[0061] Point cloud data with spatial anomaly factors greater than the first spatial anomaly threshold are denoted as anomalous point cloud data.

[0062] Sort the frequency domain anomaly factors of all point cloud data from smallest to largest, take the maximum value of the absolute value of the difference between adjacent values, and the minimum value of the two corresponding frequency domain anomaly factors, as the frequency domain anomaly threshold.

[0063] Point cloud data with frequency domain anomaly factors greater than the frequency domain anomaly threshold are denoted as anomalous point cloud data.

[0064] The beneficial effects of the technical solution of the present invention are:

[0065] 1. This invention constructs a terrain complexity index reflecting the degree of local terrain undulation based on the distribution of point cloud data surrounding each point cloud data point, and adaptively adjusts the final diffusion scale of each point cloud data point based on this index. Compared with traditional graph diffusion methods with fixed radius neighborhoods or fixed scales, this invention can use larger neighborhoods in terrain-complex areas such as mountains and steep slopes to ensure sufficient statistical information, and smaller neighborhoods in terrain-flat areas such as plains to avoid smoothing out local details. This overcomes the interference of uneven point cloud density and multi-scale terrain changes on anomaly detection, significantly improving the accuracy of identifying anomalous point cloud data.

[0066] 2. In calculating the relational weights of the final neighboring point cloud data, this invention not only considers Euclidean distance but also introduces the local statistical trend direction, i.e., the downslope direction. It uses vector dot product to determine whether a neighboring point is located downslope-directed from the current point. Only points downslope-directed are retained; points upslope-directed are set to zero due to lack of physical continuity, effectively avoiding erroneous associations between points across terrain units. Furthermore, by combining the degree of abrupt changes in slope angle and the difference in local minimum elevation values, the weights are finely adjusted, making the weight allocation more consistent with the continuity of the actual terrain, reducing noise interference between discontinuous terrain units, and improving the reliability of spatial anomaly factors.

[0067] 3. This invention constructs a spatial anomaly factor, which includes two components: the first component utilizes the ratio of elevation difference to distance; the second component introduces morphological closing operation residuals. The synergistic effect of these two components enables this invention to simultaneously and effectively identify both convex and concave topographic anomalies, avoiding the problem of insufficient response to concave anomalies by a single elevation difference index, and comprehensively improving the coverage and sensitivity of anomaly detection.

[0068] 4. This invention utilizes the inherent structure of airborne LiDAR line scanning to assign each point cloud data point to its corresponding scan line, and performs centering and Fourier transform based on the closed-loop residual sequence of the scan line. By calculating the power spectrum amplitude, it effectively identifies strip offset systematic errors that are easily ignored by traditional methods, further improving the detection rate of anomalous point cloud data. Attached Figure Description

[0069] To more clearly illustrate the technical solutions 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.

[0070] Figure 1 This is a flowchart illustrating the steps of a surveying and mapping geographic information data management method and system according to an embodiment of the present invention. Detailed Implementation

[0071] 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 and mapping geographic information data management method and system 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.

[0072] 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.

[0073] Example 1:

[0074] This invention provides a method and system for managing surveying and mapping geographic information data, specifically as follows: Figure 1 As shown, it includes:

[0075] Step S001: Scan and map the area to be surveyed to obtain some point cloud data.

[0076] Specifically, an airborne LiDAR system mounted on an aerial platform such as an aircraft, helicopter, or drone is used to efficiently and extensively scan and map the area to be surveyed, obtaining a number of point cloud data points, i.e., all point cloud data points within the area to be surveyed. The method for acquiring point cloud data within a region is a well-known existing technology and will not be described in detail in this embodiment. Furthermore, the airborne LiDAR used in this embodiment is a conventional airborne LiDAR.

[0077] At this point, some point cloud data has been obtained.

[0078] Step S002: Based on the distribution of other point cloud data around each point cloud data, obtain the terrain complexity and statistical trend direction of the space where each point cloud data is located.

[0079] It should be noted that current anomaly detection methods for point cloud data often neglect random slope variations and height differences within the detection area, resulting in relatively low accuracy in identifying anomalies using graph diffusion algorithms. Furthermore, the distribution of surrounding point cloud data also varies depending on the spatial location of different point cloud datasets. Therefore, the terrain complexity of each point cloud dataset is determined by the distribution of surrounding point cloud data.

[0080] Specifically, among all point cloud data, those falling under the first... Using point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The initial neighboring point cloud data of each point cloud data. In this embodiment, a preset radius value is used. This is because the point cloud density obtained by conventional airborne LiDAR is about 1–20 pts / m², and the circular projection area with a radius of 3 meters is about 28m². In most scenarios, it can contain 30–300 point cloud data, which not only meets the sample size required for calculating the statistical trend direction, but also does not blur the local terrain details due to the large neighborhood.

[0081] Furthermore, taking the bottom left corner of the point cloud data as the origin, and extending horizontally to the right as... The positive direction of the axis, with vertical forward as the reference. The positive direction of the axis, with vertically upward as... Construct a three-dimensional coordinate system along the positive axis to obtain the coordinates of each point in the cloud data. Within this three-dimensional coordinate system, each unit distance corresponds to an actual length of 1 meter.

[0082] Furthermore, obtain the first The specific formula for calculating the terrain complexity of the space where a point cloud dataset is located is as follows:

[0083]

[0084] In the formula, Indicates the first The terrain complexity of the space in which each point cloud data resides. Indicates the first The statistical trend direction of the space in which each point cloud data is located. Indicates the first The number of initial neighboring point cloud data for each point cloud data set. Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The point cloud data and its first Euclidean distance of the initial neighboring point cloud data Indicates starting from the origin and... The vector obtained as the endpoint. This represents the vector originating from the origin and ending at a point with coordinates (0,0,1). The dot product symbol represents the vector. This represents the sigmoid function, used in this embodiment for normalization. The method for obtaining the Euclidean distance between two points is a well-known existing technique and will not be described in detail here. The point with coordinates (0,0,1) is... shaft and The value on the axis is 0. Points with a value of 1 on the axis.

[0085] It should be noted that, for In other words, This reflects that, with the first The point cloud data points to its first... Vectors of initial neighboring point cloud data, with Divide by This yields a unit vector of length 1, meaning the vector contains only directional information. It is the first The directional information of all initial neighboring point cloud data of a given point cloud is averaged. This average value represents the composite trend formed by the points from that point to its surrounding initial neighboring points, and is the ... The statistical trend direction of local terrain in point cloud data.

[0086] It should be further explained that when The vertical unit vector representing the elevation difference The smaller the angle between the two points, the greater the vertical drop in the surrounding area, meaning it is steeper. Therefore, the vector of the local slope direction and the angle between the two points are related. Dot product, i.e., symbolic multiplication The larger the result value, the more likely the two vectors are to be parallel, that is, the steeper the slope.

[0087] It should be further explained that, for Part of it reflects the height difference in space between the horizontal planes of the two points, while This reflects the overall distance between two points. In other words, even if two points have the same height difference, different Euclidean distances between them will result in different degrees of steepness. The smaller the slope, the steeper it is.

[0088] Thus, the terrain complexity of the space where each point of cloud data is located is obtained.

[0089] Step S003: Based on the terrain complexity of the space where each point cloud data is located, obtain the final diffusion scale of each point cloud data.

[0090] It should be noted that when performing anomaly detection on point cloud data, it is necessary to obtain the diffusion scale of each point cloud data point. The more complex the terrain of the local area where a point cloud data point is located, the more necessary it is to determine whether the point cloud data is anomaly over a larger area. Therefore, the final diffusion scale of each point cloud data point is obtained based on the terrain complexity of the space in which it is located.

[0091] Specifically, the terrain type of the area to be mapped and the sensitivity of the airborne LiDAR to terrain changes are obtained. Obtaining the terrain type of an area and the sensitivity of the airborne LiDAR to terrain changes are both well-known existing technologies and will not be elaborated upon in this embodiment.

[0092] Furthermore, obtain the first The specific formula for calculating the final diffusion scale of point cloud data is as follows:

[0093]

[0094] In the formula, Indicates the first The final diffusion scale of point cloud data, This represents the preset radius value. Indicates the first The terrain complexity of the space in which each point cloud data resides. This represents the first adjustable coefficient. This represents the second adjustable coefficient. This is an exponential function with the natural constant as its base. Wherein, The value is obtained based on the terrain type of the area to be surveyed. The terrain type can be plains, mountains, etc. The value of is determined by the sensitivity of the airborne LiDAR used to collect point cloud data to changes in the terrain complexity of the area to be mapped. Both are empirical coefficients, and those skilled in the art can determine suitable specific values ​​through limited experiments based on the terrain type of the area to be mapped and the airborne LiDAR used to collect the point cloud data. That is, to obtain... and The method for determining the value is a well-known existing technology, and will not be described in detail in this embodiment.

[0095] It should be noted that the first The final diffusion scale of the point cloud data is related to Related, but subject to adjustable coefficients and The impact.

[0096] It should be further explained that, The larger the value, the more significant the first... The more complex the terrain of the point cloud data, the larger the area needed to identify anomalies; conversely, the simpler the terrain, the more complex the terrain. The smaller the value, the more... The less complex the terrain of the space where the point cloud data is located, the more likely it is to be a plain or other gently sloping terrain. If there are any anomalies, they will be very obvious. Therefore, anomalies can be identified in a relatively small area.

[0097] It should be further explained that, regarding the two adjustable coefficients, This is a linear relationship coefficient, and its value will vary depending on the type of the area to be mapped. For example, if the area to be mapped is a plain, then the coefficient should be such that even... Changes in the value do not cause drastic fluctuations in the scale. The scale is too small; however, if the area to be mapped is a mountainous region, the terrain is more undulating, so the scale needs to respond more significantly to the complexity of the terrain. Too large. The coefficient is the exponential decay coefficient. The larger the coefficient, the flatter the decay curve, and the higher the complexity required to reach the decay inflection point. The smaller the coefficient, the more sensitive the decay is to the increase in complexity. Slightly complex terrain will quickly reduce the diffusion scale to prevent the scale from being over-diffused in steep areas.

[0098] Thus, the final diffusion scale of each point of cloud data is obtained.

[0099] Step S004: Based on the final diffusion scale of each point cloud data, obtain the final neighboring point cloud data of each point cloud data; based on the Euclidean distance between each point cloud data and its final neighboring point cloud data, the statistical trend direction, and the elevation value distribution of the surrounding point cloud data, obtain the relation weight of each final neighboring point cloud data of each point cloud data.

[0100] It should be noted that the final neighboring point cloud data for each point cloud data is obtained based on the final diffusion scale of each point cloud data. When determining whether a point cloud data is an anomalous point cloud data based on its final neighboring point cloud data, different weights need to be assigned to the neighboring point cloud data of each point cloud data. Thus, the relational weights of each final neighboring point cloud data for each point cloud data are obtained.

[0101] Specifically, among all point cloud data, those falling under the first... Using point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The final neighboring point cloud data of each point cloud data.

[0102] Furthermore, among all point cloud data, those falling within the first... The first point cloud data Using the final nearest point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The first point cloud data The final nearest neighbor point cloud data is located in the first Point cloud data within an initial local area. The elevation value of each point cloud data point is obtained. The method for obtaining the elevation value of each point cloud data point is a well-known existing technology and will not be described in detail in this embodiment.

[0103] Furthermore, starting from the origin, and using the first... The first point cloud data The vector obtained by taking the statistical trend direction of the space where the final neighboring point cloud data resides as the endpoint is denoted as . .

[0104] Furthermore, obtain the first The first point cloud data The specific formula for calculating the relation weights of the final neighbor point cloud data is as follows:

[0105]

[0106]

[0107]

[0108] In the formula, Indicates the first The first point cloud data The relational weights of the final neighboring point cloud data Indicates the first The first point cloud data The first weight of the final neighboring point cloud data, Indicates the first The first point cloud data The second weight of the final neighboring point cloud data, Indicates the first The point cloud data and its first The Euclidean distance between the final nearest point cloud data. Indicates the first The final diffusion scale of point cloud data, Indicates starting from the origin. The vector obtained as the endpoint. Indicates the first Starting with the first point cloud data, and taking the first point cloud data as the starting point... The first point cloud data The vector obtained by taking the final neighboring point cloud data as the endpoint This represents the result of the dot product of two vectors. This represents a function that takes the maximum value between two values. As an exponential function with the natural constant as its base, this embodiment is used to represent an inverse proportional relationship; Representing vectors with vector The included angle, Representing vectors with vector The included angle, Represents the sine function. Indicates the first The minimum elevation value among a point cloud data point and all its final neighboring point cloud data points. Indicates the first The first point cloud data The final nearest neighbor point cloud data and its position in the first nearest neighbor point cloud. The minimum elevation value among all point cloud data within an initial local region. Let represent the absolute value function. Where, the th... The minimum elevation value among the point cloud data and all its final neighboring point cloud data is the first point cloud data. The elevation value of the first point cloud data, and the first The minimum elevation value among all the final neighboring point cloud data of the first point cloud data; the first The first point cloud data The final nearest neighbor point cloud data and its position in the first nearest neighbor point cloud. The minimum elevation value among all point cloud data within the initial local region is the first... The first point cloud data The elevation value of the final neighboring point cloud data, and the elevation value of the first neighboring point cloud data. The first point cloud data The final nearest neighbor point cloud data is located in the first The minimum elevation value among all point cloud data within an initial local region.

[0109] It should be noted that, for the formula... Part, which is used to reflect if the first The point cloud data and its first The closer the distance between the final neighboring point cloud data, the better. The point cloud data and its first The closer the relationship between the final neighbor point cloud data, the more intimate the relationship becomes. The larger the value, the better.

[0110] It should be further explained that, for the formula... Part of this is based on the unidirectional nature of the terrain surface; only the first part... The point cloud data and its first The smaller the weight decay will be when the final neighboring point cloud data has a slope-like relationship.

[0111] It should be further explained that, It is the first in the area to be surveyed The distribution of a point cloud data point with its surrounding point cloud data is obtained. Due to the unidirectional nature of terrain, this direction naturally biases towards the downward slope, that is, from high to low. A value greater than 0 indicates that the first element in the area to be mapped is... The first point cloud data The final nearest neighbor point cloud data is located along its downhill direction, roughly downhill, and a value less than 0 indicates that the first nearest neighbor point cloud data point is located within the area to be mapped. The first point cloud data The final nearest neighbor point cloud data is located on its reverse slope, roughly uphill. Therefore... The purpose of this method is to retain downslope points and exclude upslope points, rather than to classify areas based on elevation values. Only downslope points are considered because of the unidirectional nature of topographic surfaces; that is, the migration and morphological evolution of surface materials are driven by gravity, and the direction is irreversible. When determining whether two points belong to the same continuous topographic unit, two points along the downslope direction are on the same gravity-driven physical path—that is, the directions of water flow, deposition, and slope evolution are consistent—and are more likely to be a continuous part of the same slope. Points along the upslope direction, however, cannot have materials migrating from lower to higher elevations. The point cloud and its first The final neighboring point clouds lack physical continuity, often belonging to different slope layers or different terrain units. Therefore, points on the opposite slope direction do not contribute to determining the same continuous terrain unit; instead, they introduce cross-unit noise interference, making it reasonable to set them to zero. Furthermore, when two point cloud data do not belong to the same terrain unit, the reference value between these two point cloud data is relatively weak.

[0112] It should be further explained that, for the formula... Part of it is considering the first The point cloud data and its first After determining the slope orientation of the final neighboring point cloud data, the angle morphology is verified. If the surface angle morphology undergoes a sudden change, it indicates a more pronounced trend of the terrain abruptly becoming steeper. For example, transitioning from a gentle slope to a steep slope or vice versa will cause... As the value increases, the reduction in weight becomes more pronounced. And... and The smaller the difference, the smaller the slope aspect difference between the two point cloud data. The larger the value, the better.

[0113] It should be further explained that, for the formula... Part, if the first The point cloud data and its first When the distance difference between the final neighboring point cloud data is small, while the difference in the minimum elevation values ​​of their respective regions is relatively large, it indicates that the first... The point cloud data and its first The final neighboring point cloud data do not belong to the same continuous terrain unit. In terrain features, if two points belong to different levels, there will be a height difference between the base layers of the two points. Therefore, this part of the formula considers quantifying the terrain feature using morphological methods. The smaller the result of this fraction, the more significant the reduction in weights will be.

[0114] At this point, the relational weights of each final neighboring point cloud data for each point cloud data are obtained.

[0115] Step S005: Based on the terrain complexity of the space where each point cloud data and each of its final neighboring point cloud data are located, the Euclidean distance, the elevation value after morphological operations on the point cloud data surrounding each point cloud data and its final neighboring point cloud data, the elevation value of each point cloud data and each of its final neighboring point cloud data, and the relational weight of each final neighboring point cloud data of each point cloud data, obtain the spatial anomaly factor of each point cloud data and the closing operation residual of each point cloud data.

[0116] It should be noted that after obtaining the relational weights of each final neighboring point cloud data for each point cloud data, the spatial anomaly factor of each point cloud data is obtained based on the difference between each point cloud data and its final neighboring point cloud data.

[0117] Specifically, to obtain the first The specific formula for calculating the spatial anomaly factor of point cloud data is as follows:

[0118]

[0119]

[0120]

[0121] In the formula, Indicates the first Spatial anomaly factors of point cloud data, Indicates the first The first outlier factor in point cloud data. Indicates the first The second anomaly factor in point cloud data. Indicates the first The point cloud data and its first The Euclidean distance between the final nearest point cloud data. Indicates the first The final number of neighboring point cloud data for each point cloud data set. Indicates the first The first point cloud data The relational weights of the final neighboring point cloud data Indicates the first The terrain complexity of the space in which each point cloud data resides. Indicates the first The first point cloud data The terrain complexity of the space where the final nearest point cloud data resides. Indicates the first Elevation values ​​of point cloud data. Indicates the first The first point cloud data The elevation values ​​of the final neighboring point cloud data. Indicates the first After performing a closing operation on the region formed by the i-th point cloud data and all its final neighboring point cloud data, the i-th point cloud data is... Elevation values ​​at each point cloud data point Indicates the first The first point cloud data The final nearest neighbor point cloud data and its position in the first nearest neighbor point cloud. After performing a closing operation on the region composed of all point cloud data within the initial local region, the th... The first point cloud data The elevation value at the final nearest point cloud data location. This represents an absolute value function. The method for obtaining the elevation value of each point cloud data point is a known prior art and will not be described in detail in this embodiment. Furthermore, the method for obtaining the elevation value by performing a closing operation on all point cloud data points within a region is a known prior art and will not be described in detail in this embodiment.

[0122] It should be noted that... This is considered as a part, which reflects the intensity of elevation abrupt changes in the local space.

[0123] It should be further explained that when the first The point cloud data and its first When comparing the final neighboring point cloud data, The larger the value, the stronger the connection between the two points; The larger the value, the more complex the local terrain where the two points are located. More complex areas are more forgiving in anomaly detection. For example, the abruptness of the same anomaly will be significantly different in a plain area and a steep, scree area; it will certainly be more abrupt in the former. Therefore, The smaller the value, The larger the value of a part, the stricter the judgment will be for relatively less complex regions.

[0124] It should be further explained that... Considered as a part, this part significantly amplifies concave anomalies and also helps with protruding anomalies, but its impact is relatively small. Since the closing operation involves first dilation and then erosion, the elevation value of a single 3D point cloud data point is derived by first finding the maximum elevation value in the surrounding area of ​​the 3D point cloud data, and then finding the minimum elevation value in the surrounding area. For a 3D point cloud and a point cloud surrounding it, the two closing operations correspond to the point cloud itself and another point in its vicinity, respectively. The search range for neighboring points is the space surrounding each point. First, a dilation operation is performed on the point to find the maximum elevation value in the space and replace its own elevation value. Then, an erosion operation is performed to find the minimum elevation value in the space and replace the already obtained elevation value to obtain the closing operation result. The difference between the original elevation values ​​of the two points and the elevation value after the closing operation is obtained in the formula. Partial results.

[0125] It should be further explained that, The difference in morphological closing operations is mainly enhanced in detecting low-concave anomalies, while the anisotropic diffusion term is more sensitive to local anomalies with prominent elevations. The two work together to achieve comprehensive and complementary capture of two typical anomalies, high and low, in point clouds.

[0126] Thus, the spatial anomaly factor for each point of cloud data is obtained.

[0127] Step S006: Obtain several scan lines when surveying the area to be surveyed; based on the spatial anomaly factor of each point cloud data, the terrain complexity of the space, and the closing operation residual of all point cloud data on the scan line, obtain the abnormal point cloud data.

[0128] It should be noted that after obtaining the spatial anomaly factor for each point cloud data, a systematic error due to strip offset occurs during point cloud data acquisition via airborne LiDAR. This manifests as an overall elevation shift between scan lines, forming a periodic pattern. Within the drift strip, discrete points have normal relative relationships with their neighbors on the same strip, and local features may not show obvious anomalies. However, between different scan lines, the drift causes abrupt elevation changes. Therefore, the error trend caused by this periodic systematic error needs to be quantified. Frequency domain analysis is required to separate the periodic systematic error from random noise and terrain signals, amplifying its weight in anomaly scoring, thereby identifying drifting areas and obtaining anomalous point cloud data. Therefore, the frequency domain anomaly factor for each point cloud data is obtained.

[0129] It should be further noted that, since this invention is carried out via airborne... When mapping the area to be surveyed, a line scan method is used, meaning the scan lines are perpendicular to the flight direction, and each scan line is a cross-section along the flight direction. In this case, strip drift typically manifests as a systematic elevation error between adjacent scan lines. Therefore, the direction perpendicular to the scan lines is chosen as the direction for constructing the sequence, as this direction represents the periodic direction where errors may exist. Based on the closing operation residual of each point cloud data point on the scan line sequence, the frequency domain anomaly factor for each point cloud data point is obtained. Based on the frequency domain anomaly factor of each point cloud data point, anomalous point cloud data is obtained.

[0130] Specifically, using airborne When surveying the area to be mapped, the scan lines are perpendicular to the flight direction. The direction perpendicular to the scan lines is used as the direction for constructing the scan line sequence, resulting in all scan lines and the order value of each scan line. In other words, the scan lines have already been sorted when they are acquired.

[0131] Furthermore, The value of is denoted as the first . The closed-loop residual of point cloud data.

[0132] Furthermore, the first The scan line containing the point cloud data, and the scan lines adjacent to it, are denoted as the i-th scan line. The adjacent scan lines of the scan line containing the point cloud data. That is, when the absolute difference between the order value of one scan line and the order value of another scan line is 1, these two scan lines are adjacent scan lines.

[0133] Furthermore, the first The mean of the closed-loop residuals of all point cloud data along the scan line is denoted as the nth. The overall residual value of the closed operation of each scan line.

[0134] Furthermore, according to the first The closed-loop residual of each point of cloud data on each scan line is used to obtain a sequence of closed-loop residuals, which is denoted as the i-th. The closed operation residual sequence of the scan lines. Among them, the first... The first scan line in the closed operation residual sequence The residual of the closing operation is the first one. The first scan line The closed-loop residual of point cloud data.

[0135] Furthermore, the first For each closed operation residual within the closed operation residual sequence of the scan line containing the point cloud data, subtract the first... The mean of the overall residual values ​​of the closed operation of all adjacent scan lines of the scan line containing the point cloud data is obtained. The update residual sequence of the scan line where the point cloud data is located.

[0136] Furthermore, regarding the first The update residual sequence of the scan line containing the point cloud data is subjected to Fourier transform to obtain several sine waves of different frequencies. The sum of the squares of the amplitude values ​​of all the sine waves of the frequencies obtained in this segment is denoted as the i-th sine wave. The final amplitude of the point cloud data. The methods for performing Fourier transform on a data sequence and obtaining the amplitude value of the sine wave at each frequency are existing well-known technologies and will not be described in detail in this embodiment.

[0137] Furthermore, obtain the first The specific formula for calculating the frequency domain anomaly factor of a point cloud data is as follows:

[0138]

[0139] In the formula, Indicates the first Frequency domain anomaly factors of point cloud data Indicates the first The terrain complexity of the space in which each point cloud data resides. Indicates the first The final amplitude of each point cloud data point. As an exponential function with the natural constant as its base, this embodiment uses it to represent an inverse proportional relationship.

[0140] It should be noted that since the stripe drift is more pronounced in flatter areas, the stringency of anomaly assessment should be appropriately reduced in complex areas to avoid misjudging normal terrain roughness as drift. The larger the value, the smaller the weight of that part.

[0141] It should be further explained that, since the mean of the closing operation residuals of all points in the scan line sequence is subtracted from the closing operation residual of each point in the scan line sequence, centering is completed and a residual sequence is obtained. For the residual sequence, the following steps are performed: That is, the Fourier transform, which can decompose any signal into a sum of sine waves of different frequencies. The output is the amplitude of each frequency component, and the square of the amplitude equals the power spectrum.

[0142] It should be further noted that, for normal points, the residual sequence... The results show a variety of frequencies, but none of them are particularly strong, therefore the power spectrum is flat. The value is relatively small. However, for points with stripe drift anomalies, the residual sequence is a strong periodic sine wave, which will produce extremely high peaks at specific low-frequency positions. Therefore, the larger the power spectrum amplitude of this part, the more obvious the corresponding anomaly.

[0143] Furthermore, the spatial anomaly factors of all point cloud data are sorted from smallest to largest, and the minimum value of the two spatial anomaly factors corresponding to the maximum absolute value of adjacent differences is taken as the first spatial anomaly threshold.

[0144] Furthermore, point cloud data with spatial anomaly factors greater than the first spatial anomaly threshold are denoted as anomalous point cloud data.

[0145] Furthermore, the frequency domain anomaly factors of all point cloud data are sorted from smallest to largest, and the minimum value of the two frequency domain anomaly factors corresponding to the maximum absolute value of adjacent differences is taken as the frequency domain anomaly threshold.

[0146] Furthermore, point cloud data with frequency domain anomaly factors greater than the frequency domain anomaly threshold are denoted as anomalous point cloud data.

[0147] At this point, all abnormal point cloud data have been obtained.

[0148] This concludes the embodiment.

[0149] Another embodiment of the present invention provides a surveying and mapping geographic information data management system, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the above method steps S001 to S006.

Claims

1. A method for managing surveying and mapping geographic information data, characterized in that, The method includes the following steps: The area to be surveyed was scanned and mapped to obtain a number of point cloud data. Based on the distribution of other point cloud data around each point cloud data, the terrain complexity and statistical trend direction of the space where each point cloud data is located are obtained; The final diffusion scale of each point cloud data is obtained based on the terrain complexity of the space where each point cloud data is located. Based on the final diffusion scale of each point cloud data, the final neighboring point cloud data of each point cloud data is obtained; based on the Euclidean distance between each point cloud data and its final neighboring point cloud data, the statistical trend direction, and the elevation value distribution of the surrounding point cloud data, the relation weight of each final neighboring point cloud data of each point cloud data is obtained. Based on the terrain complexity and Euclidean distance of each point cloud data and its final neighboring point cloud data, the elevation value of each point cloud data and its final neighboring point cloud data after morphological operations on the point cloud data around it, the elevation value of each point cloud data and its final neighboring point cloud data, and the relation weight of each final neighboring point cloud data, the spatial anomaly factor of each point cloud data and the closing operation residual of each point cloud data are obtained. Obtain several scan lines when surveying the area to be surveyed; based on the spatial anomaly factor of each point cloud data, the terrain complexity of the space, and the closed operation residual of all point cloud data on the scan line, obtain the abnormal point cloud data.

2. The method for managing surveying and mapping geographic information data according to claim 1, characterized in that, The specific calculation steps for obtaining the terrain complexity and statistical trend direction of the space where each point cloud data is located based on the distribution of other point cloud data around each point cloud data are as follows: Among all point cloud data, those falling on the first Using point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The initial neighboring point cloud data of each point cloud data; among which... This indicates the preset radius value; Taking the bottom left corner of the point cloud data as the origin, and extending horizontally to the right as... The positive direction of the axis, with vertical forward as the reference. The positive direction of the axis, with vertically upward as... Construct a three-dimensional coordinate system along the positive axis to obtain the coordinates of each point in the cloud data. Based on the Euclidean distance between each point cloud data and its initial neighboring point cloud data, as well as the coordinate difference, the statistical trend direction of the space in which each point cloud data is located is obtained. Based on the statistical trend direction of the space where each point cloud data is located, the Euclidean distance between each point cloud data and its initial neighboring point cloud data, and The differences on the axis give us the terrain complexity of the space where each point of cloud data is located.

3. The method for managing surveying and mapping geographic information data according to claim 2, characterized in that, The specific calculation steps for obtaining the statistical trend direction of the space in which each point cloud data is located based on the Euclidean distance between each point cloud data and its initial neighboring point cloud data, as well as the coordinate difference, are as follows: In the formula, Indicates the first The statistical trend direction of the space in which each point cloud data is located. Indicates the first The number of initial neighboring point cloud data for each point cloud data set. Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The point cloud data and its first The Euclidean distance between the initial neighboring point cloud data.

4. The method for managing surveying and mapping geographic information data according to claim 2, characterized in that, The statistical trend direction of the space where each point cloud data is located, the Euclidean distance between each point cloud data and each of its initial neighboring point cloud data, and The specific steps for calculating the terrain complexity of the space where each point of cloud data is located, based on the differences on the axis, are as follows: In the formula, Indicates the first The terrain complexity of the space in which each point cloud data resides. Indicates the first The statistical trend direction of the space in which each point cloud data is located. Indicates starting from the origin and... The vector obtained as the endpoint. This represents the vector originating from the origin and ending at a point with coordinates (0,0,1). The dot product symbol represents the vector. This represents the sigmoid function. Indicates the first The first point cloud data The initial neighboring point cloud data in Values ​​on the axis Indicates the first Point cloud data in Values ​​on the axis Indicates the first The point cloud data and its first Euclidean distance of the initial neighboring point cloud data Indicates the first The number of initial neighboring point cloud data for each point cloud data.

5. The method for managing surveying and mapping geographic information data according to claim 1, characterized in that, The specific calculation steps for obtaining the final diffusion scale of each point cloud data based on the terrain complexity of the space where each point cloud data is located are as follows: Obtain the terrain type of the area to be mapped and the sensitivity of the airborne LiDAR to terrain changes in the collected point cloud data; The first adjustable coefficient is obtained based on the terrain type of the area to be surveyed; The second adjustable coefficient is obtained based on the sensitivity of the airborne LiDAR that collects point cloud data to terrain changes; Based on the first adjustable coefficient, the second adjustable coefficient, and the third The terrain complexity of the space where the point cloud data resides is obtained. The specific formula for calculating the final diffusion scale of point cloud data is as follows: In the formula, Indicates the first The final diffusion scale of point cloud data, This represents the preset radius value. Indicates the first The terrain complexity of the space in which each point cloud data resides. This represents the first adjustable coefficient. This represents the second adjustable coefficient. It is an exponential function with the natural constant as its base.

6. The method for managing surveying and mapping geographic information data according to claim 1, characterized in that, The specific calculation steps for obtaining the final neighboring point cloud data of each point cloud data based on the final diffusion scale of each point cloud data are as follows: Among all point cloud data, those falling on the first Using point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The final neighboring point cloud data of each point cloud data; among which... Indicates the first The final diffusion scale of point cloud data.

7. The method for managing surveying and mapping geographic information data according to claim 3, characterized in that, The specific calculation steps for obtaining the relation weight of each final neighboring point cloud data based on the Euclidean distance, statistical trend direction, and elevation distribution of the surrounding point cloud data for each point cloud data are as follows: Obtain the elevation value of each point of cloud data; Among all point cloud data, those falling on the first The first point cloud data Using the final nearest point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The first point cloud data The final nearest neighbor point cloud data is located in the first Point cloud data within an initial local region; among which... Indicates the first The final diffusion scale of point cloud data; Starting from the origin, and using the first... The first point cloud data The vector obtained by taking the statistical trend direction of the space where the final neighboring point cloud data resides as the endpoint is denoted as . ; Get the The first point cloud data The specific formula for calculating the relation weights of the final neighbor point cloud data is as follows: In the formula, Indicates the first The first point cloud data The relational weights of the final neighboring point cloud data Indicates the first The first point cloud data The first weight of the final neighboring point cloud data, Indicates the first The first point cloud data The second weight of the final neighboring point cloud data, Indicates the first The point cloud data and its first The Euclidean distance between the final nearest point cloud data. Indicates the first The final diffusion scale of point cloud data, Indicates starting from the origin. The vector obtained as the endpoint. Indicates the first Starting with the first point cloud data, and taking the first point cloud data as the starting point... The first point cloud data The vector obtained by taking the final neighboring point cloud data as the endpoint This represents the result of the dot product of two vectors. This represents a function that takes the maximum value between two values. It is an exponential function with the natural constant as its base. Representing vectors with vector The included angle, Representing vectors with vector The included angle, Represents the sine function. Indicates the first The minimum elevation value among a point cloud data point and all its final neighboring point cloud data points. Indicates the first The first point cloud data The final nearest neighbor point cloud data and its position in the first nearest neighbor point cloud. The minimum elevation value among all point cloud data within an initial local region. This represents the absolute value function.

8. The method for managing surveying and mapping geographic information data according to claim 1, characterized in that, The specific calculation steps for obtaining the spatial anomaly factor of each point cloud data are as follows: In the formula, Indicates the first Spatial anomaly factors of point cloud data, Indicates the first The first outlier factor in point cloud data. Indicates the first The second anomaly factor in point cloud data. Indicates the first The point cloud data and its first The Euclidean distance between the final nearest point cloud data. Indicates the first The final number of neighboring point cloud data for each point cloud data set. Indicates the first The first point cloud data The relational weights of the final neighboring point cloud data Indicates the first The terrain complexity of the space in which each point cloud data resides. Indicates the first The first point cloud data The terrain complexity of the space where the final nearest point cloud data resides. Indicates the first Elevation values ​​of point cloud data. Indicates the first The first point cloud data The elevation values ​​of the final neighboring point cloud data. Indicates the first After performing a closing operation on the region formed by the i-th point cloud data and all its final neighboring point cloud data, the i-th point cloud data is... Elevation values ​​at each point cloud data point Indicates the first The first point cloud data The final nearest neighbor point cloud data and its position in the first nearest neighbor point cloud. After performing a closing operation on the region composed of all point cloud data within the initial local region, the th... The first point cloud data The elevation value at the final nearest point cloud data location. Represents the absolute value function; Among all point cloud data, those falling on the first The first point cloud data Using the final nearest point cloud data as the center and with a radius of 1, the circle is... The point cloud data within the circular plane containing the horizontal projection of the meter is denoted as the th. The first point cloud data The final nearest neighbor point cloud data is located in the first Point cloud data within an initial local region; among which... Indicates the first The final diffusion scale of point cloud data; Will , denoted as the The closed-loop residual of point cloud data.

9. The method for managing surveying and mapping geographic information data according to claim 1, characterized in that, The specific calculation steps for obtaining anomalous point cloud data based on the spatial anomaly factor of each point cloud data, the terrain complexity of the space, and the closing operation residuals of all point cloud data on the scan line are as follows: The first The scan line containing the point cloud data, and the scan lines adjacent to it, are denoted as the i-th scan line. The adjacent scan lines of the scan line containing the point cloud data; The first The mean of the closed-loop residuals of all point cloud data along the scan line is denoted as the nth. The overall residual value of the closed operation of each scan line; According to the The closed-loop residual of each point of cloud data on each scan line is used to obtain a sequence of closed-loop residuals, which is denoted as the i-th. The closed operation residual sequence of each scan line; The first For each closed operation residual within the closed operation residual sequence of the scan line containing the point cloud data, subtract the first... The mean of the overall residual values ​​of the closed operation of all adjacent scan lines of the scan line containing the point cloud data is obtained. The update residual sequence of the scan line containing the point cloud data; For the The update residual sequence of the scan line containing the point cloud data is subjected to Fourier transform to obtain several sine waves of different frequencies. The sum of the squares of the amplitude values ​​of all the obtained sine waves of different frequencies is denoted as the i-th sine wave. The final amplitude of each point cloud data; Get the The specific formula for calculating the frequency domain anomaly factor of a point cloud data is as follows: In the formula, Indicates the first Frequency domain anomaly factors of point cloud data Indicates the first The terrain complexity of the space in which each point cloud data resides. Indicates the first The final amplitude of each point cloud data point. It is an exponential function with the natural constant as its base; Abnormal point cloud data is obtained based on the frequency domain anomaly factor and spatial anomaly factor of each point cloud data.

10. The method for managing surveying and mapping geographic information data according to claim 9, characterized in that, The specific calculation steps for obtaining the abnormal point cloud data based on the frequency domain anomaly factor and spatial anomaly factor of each point cloud data are as follows: Sort all point cloud data spatial anomaly factors from smallest to largest, take the maximum value of the absolute value of adjacent differences, and the minimum value of the corresponding two spatial anomaly factors as the first spatial anomaly threshold. Point cloud data with spatial anomaly factors greater than the first spatial anomaly threshold are denoted as anomalous point cloud data. Sort the frequency domain anomaly factors of all point cloud data from smallest to largest, take the maximum value of the absolute value of the difference between adjacent values, and the minimum value of the two corresponding frequency domain anomaly factors, as the frequency domain anomaly threshold. Point cloud data with frequency domain anomaly factors greater than the frequency domain anomaly threshold are denoted as anomalous point cloud data.

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