Multi-source landslide monitoring data unified coordinate method, device and equipment and storage medium

Abstract

The application provides a multi-source landslide monitoring data unified coordinate method, device and equipment and a storage medium, relates to the technical field of data processing, and comprises the following steps: acquiring actual monitoring data of a landslide area and coordinates of target key points under a CGCS2000 coordinate datum by means of GNSS equipment, a laser radar measuring unmanned aerial vehicle, a synthetic aperture radar and engineering measuring instruments; constructing a key point coordinate mapping relationship by means of the coordinates under the original coordinate datum and the coordinates under the CGCS2000 coordinate datum; obtaining a seven-parameter coordinate conversion model by means of the key point coordinate mapping relationship and a geographic weighting coefficient based on neighborhood density normalization; filtering pixels outside the landslide area and pixels with low signal-to-noise ratios in all remaining point coordinates based on a mask rule; and inputting the filtered all remaining point coordinates into the seven-parameter coordinate conversion model to obtain target CGCS2000 coordinate data. The application improves the coordinate conversion precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a multi-source landslide monitoring data unified coordinate method, device, equipment and storage medium. BACKGROUND

[0002] Landslide is a common geological disaster, which poses a serious threat to human life and property safety. In order to effectively prevent and respond to landslide disasters, landslide monitoring technology becomes particularly important. At present, the displacement change of landslide area is mainly monitored to discover early signs of landslide in time and take appropriate preventive measures.

[0003] At present, the monitoring equipment used in landslide displacement field monitoring method is various, and each equipment has its own coordinate reference. Different monitoring equipment uses different coordinate reference. Because each monitoring method has its own coordinate system, there is no unified coordinate reference, which makes it difficult to directly superimpose and compare data of different coordinate systems, affecting the accuracy of comprehensive analysis. Manual coordinate conversion may have errors, and multiple conversions may cause error accumulation, reducing the reliability of data. SUMMARY

[0004] The present application aims to solve at least one of the above problems.

[0005] To solve the above problems, the present application provides a multi-source landslide monitoring data unified coordinate method, device, equipment and storage medium.

[0006] In a first aspect, the present application provides a multi-source landslide monitoring data unified coordinate method, comprising:

[0007] The actual monitoring data of the landslide area and the coordinates of the target key points under the CGCS2000 coordinate reference are obtained by GNSS equipment, laser radar measuring unmanned aerial vehicle, synthetic aperture radar and engineering measuring instrument, wherein the actual monitoring data includes the coordinates of the target key points under the original coordinate reference and the coordinates of all remaining points.

[0008] The key point coordinate mapping relationship is constructed by the coordinates of the target key points under the original coordinate reference and the coordinates of the target key points under the CGCS2000 coordinate reference.

[0009] Based on neighborhood density normalization, seven-parameter coordinate conversion model is obtained by the key point coordinate mapping relationship and geographic weighting coefficient.

[0010] Based on the mask rule, the pixels outside the landslide area and the pixels with low signal-to-noise ratio in the remaining all point coordinates are filtered to obtain the filtered remaining all point coordinates, wherein the pixels with low signal-to-noise ratio represent the pixels with signal-to-noise ratio less than a preset threshold.

[0011] The filtered coordinates of all remaining points are input into the seven-parameter coordinate transformation model to obtain the target CGCS2000 coordinate data.

[0012] Optionally, obtaining the seven-parameter coordinate transformation model through the key point coordinate mapping relationship and geographic weighting coefficients includes:

[0013] The geographic weighting coefficient is obtained by measuring the sparsity of the geographic distribution of key points;

[0014] The seven-parameter coordinate transformation model is obtained based on the key point coordinate mapping relationship and the geographic weighting coefficient.

[0015] The seven-parameter coordinate transformation model is as follows:

[0016]

[0017] Among them, X i Y i Z i Let w be the 3D coordinate value of the i-th keypoint in the CGCS2000 coordinate system. i Here, ΔX0, ΔY0, and ΔZ0 are the geographic weighting coefficients for the i-th key point, ΔX0, ΔY0, and ΔZ0 are the translation coefficients for transforming the coordinates from the original coordinate system to the CGCS2000 coordinate system, m is the scale coefficient, and ω is the weighting coefficient for the i-th key point. X ω Y ω Z X′ is the rotation factor used to transform the coordinates from the original coordinate system to the CGCS2000 coordinate system. i Y′ i Z′ i Let be the three-dimensional coordinates of the i-th key point in the original coordinate system.

[0018] Optionally, obtaining the geographic weighting coefficient based on the sparsity of the geographic distribution of key points includes:

[0019] The density of the neighborhood of a key point is obtained by measuring the sparsity of its geographical distribution.

[0020] The neighborhood density of the key point is:

[0021]

[0022] Among them, D i Let N be the neighborhood density of the i-th keypoint. i Let be the spatial Euclidean distance between the i-th keypoint and its nearest keypoint;

[0023] The geographic weighting coefficient is obtained based on the neighborhood density of the key points;

[0024] The geographical weighting coefficient is:

[0025]

[0026] Among them, w i D is the geographic weighting coefficient for the i-th key point. min D is the minimum of the neighborhood densities of all keypoints. max It is the maximum value among the neighborhood densities of all keypoints.

[0027] Optionally, the step of filtering out pixels outside the landslide area and pixels with low signal-to-noise ratio from all remaining point coordinates to obtain the filtered remaining point coordinates includes:

[0028] The matrix composed of the coordinates of all remaining points is multiplied by the corresponding region mask matrix to obtain the region mask processing data, wherein the region mask matrix is ​​used to mark the cells located in the landslide area;

[0029] The remaining coordinates of all points are obtained by filtering the area masking data using signal-to-noise ratio (SNR) masking rules, wherein the SNR masking rules are used to filter pixels with low SNR based on the SNR.

[0030] Optionally, the acquisition of actual monitoring data of the landslide area and the coordinates of key target points under the CGCS2000 coordinate datum through GNSS equipment, lidar measurement drones, synthetic aperture radar, and engineering surveying instruments includes:

[0031] The actual monitoring data were obtained by monitoring the landslide area using the lidar-based UAV, the synthetic aperture radar, and the engineering surveying instrument, respectively.

[0032] The coordinates of the target key points in the landslide area under the CGCS2000 coordinate datum are obtained by the GNSS equipment, the lidar measurement UAV, the synthetic aperture radar, and the engineering surveying instrument. The coordinates of the target key points under the CGCS2000 coordinate datum include the CGCS2000 key point coordinates of the lidar measurement UAV, the CGCS2000 key point coordinates of the synthetic aperture radar, and the CGCS2000 key point coordinates of the engineering surveying instrument.

[0033] Optionally, the GNSS equipment includes an RTK device connected to the BeiDou ground-based augmentation system. The step of obtaining the coordinates of the target key points in the landslide area under the CGCS2000 coordinate datum using the GNSS equipment, the lidar-based UAV, the synthetic aperture radar, and the engineering surveying instrument includes:

[0034] The coordinates of key points of the landslide area obtained by the RTK device connected to the BeiDou ground-based augmentation system, the lidar measurement drone, and the synthetic aperture radar are obtained by the lidar measurement drone CGCS2000 key point coordinates and the synthetic aperture radar CGCS2000 key point coordinates.

[0035] The coordinates of key points of the landslide area were obtained by jointly measuring with the engineering surveying instrument CGCS2000, which is used to set up forced centering observation piers.

[0036] Optionally, the engineering surveying instrument includes a total station and a level. The key point coordinates of the CGCS2000 engineering surveying instrument include the plane coordinates and elevation coordinates of the key points. The step of obtaining the key point coordinates of the CGCS2000 engineering surveying instrument in the landslide area by jointly measuring with the deployment of forced centering observation piers and the engineering surveying instrument includes:

[0037] The plane coordinates of the key points were obtained by setting up the forced centering observation pier at the entrance of the landslide tunnel and using the total station to perform traverse surveying.

[0038] The elevation coordinates of the key points are obtained by setting up the forced centering observation pier and the leveling instrument at the entrance of the landslide tunnel and conducting closed-loop leveling measurements.

[0039] Secondly, the present invention provides a unified coordinate device for multi-source landslide monitoring data, comprising:

[0040] The data acquisition module is used to acquire actual monitoring data of the landslide area and the coordinates of key target points under the CGCS2000 coordinate datum through GNSS equipment, lidar measurement drones, synthetic aperture radar and engineering surveying instruments. The actual monitoring data includes the coordinates of the key target points under the original coordinate datum and the coordinates of all remaining points.

[0041] The mapping relationship acquisition module is used to construct a key point coordinate mapping relationship by using the coordinates of the target key point under the original coordinate datum and the coordinates of the target key point under the CGCS2000 coordinate datum.

[0042] The coordinate transformation model acquisition module is used to obtain a seven-parameter coordinate transformation model based on neighborhood density normalization, through the key point coordinate mapping relationship and geographical weighting coefficients.

[0043] The data filtering module is used to filter the remaining coordinates of all points from the pixels outside the landslide area and the pixels with low signal-to-noise ratio based on masking rules, wherein the pixels with low signal-to-noise ratio are pixels with a signal-to-noise ratio less than a preset threshold.

[0044] The CGCS2000 coordinate acquisition module is used to input the filtered coordinates of all remaining points into the seven-parameter coordinate transformation model to obtain the target CGCS2000 coordinate data.

[0045] Thirdly, the present invention provides an electronic device, including a memory and a processor;

[0046] The memory is used to store computer programs;

[0047] The processor is configured to, when executing the computer program, implement the unified coordinate method for multi-source landslide monitoring data as described in the first aspect.

[0048] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the unified coordinate method for multi-source landslide monitoring data as described in the first aspect.

[0049] The beneficial effects of the multi-source landslide monitoring data unified coordinate method, device, equipment, and storage medium of the present invention are as follows: Actual monitoring data of the landslide area and the coordinates of target key points under the CGCS2000 coordinate datum are obtained through GNSS equipment, lidar measurement drones, synthetic aperture radar, and engineering surveying instruments. The actual monitoring data includes the coordinates of the target key points under the original coordinate datum and the coordinates of all remaining points. A key point coordinate mapping relationship is established through the correspondence between the coordinates of the target key points under the original coordinate datum and the coordinates of the target key points under the CGCS2000 coordinate datum. Based on neighborhood density normalization, considering that geographical distribution density may cause model bias, a geographical weighting coefficient is introduced to obtain a seven-parameter coordinate transformation model, making the model more consistent with the landslide coordinate transformation scenario. Based on masking rules, pixels outside the landslide area and pixels with low signal-to-noise ratios are filtered out from the remaining point coordinates to obtain the filtered coordinates of all remaining points, improving data quality and reducing subsequent data processing costs. The remaining coordinates of all points after filtering are input into the seven-parameter coordinate transformation model to obtain the target CGCS2000 coordinate data. The coordinate reference of the multi-source landslide monitoring is unified to the CGCS2000 coordinate reference, thereby improving the coordinate transformation accuracy. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a method for unifying coordinates of multi-source landslide monitoring data according to an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the seven-parameter coordinate transformation according to an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of the geographical weighting coefficients in an embodiment of the present invention;

[0053] Figure 4 This is a flowchart illustrating the process of filtering the coordinates of all remaining points according to an embodiment of the present invention.

[0054] Figure 5 This is a schematic diagram of a landslide displacement field monitoring scenario and instrumentation equipment according to an embodiment of the present invention;

[0055] Figure 6 This is a plan view of the image control markers for a lidar-based UAV, according to an embodiment of the present invention.

[0056] Figure 7 This is a side view of a ground-based synthetic aperture radar corner reflector according to an embodiment of the present invention;

[0057] Figure 8 This is a side view of a star-based synthetic aperture radar corner reflector according to an embodiment of the present invention;

[0058] Figure 9 This is a side view of a ground-based synthetic aperture radar according to an embodiment of the present invention;

[0059] Figure 10 This is a schematic diagram of the area near the landslide tunnel entrance according to an embodiment of the present invention;

[0060] Figure 11 This is a schematic diagram of the structure of a unified coordinate device for multi-source landslide monitoring data according to an embodiment of the present invention;

[0061] Figure 12 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0062] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0063] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0064] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0065] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0066] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0067] In related technologies, existing landslide displacement field monitoring through surveying methods is mostly limited to one of the surface or deep layers, and cannot provide a complete description of the displacement field of the entire landslide body. For some landslide displacement field monitoring that uses multiple monitoring methods to simultaneously monitor the surface and deep layers, the degree of multi-source data fusion and joint analysis is low due to unclear and inconsistent coordinate benchmarks.

[0068] To address the problems existing in the aforementioned related technologies, this embodiment provides a method, apparatus, equipment, and storage medium for unifying coordinates of multi-source landslide monitoring data.

[0069] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for unifying coordinates of multi-source landslide monitoring data, comprising:

[0070] Step 110: Obtain actual monitoring data of the landslide area and the coordinates of key target points under the CGCS2000 coordinate datum by using GNSS equipment, lidar measurement drone, synthetic aperture radar and engineering surveying instruments. The actual monitoring data includes the coordinates of the key target points under the original coordinate datum and the coordinates of all remaining points.

[0071] Specifically, GNSS (Global Navigation Satellite System) equipment is a type of device used to receive and process signals from satellites to determine position, velocity, and time. A lidar-based surveying drone is an unmanned aerial vehicle that integrates a LiDAR sensor for efficient and accurate collection of three-dimensional geographic information. Synthetic Aperture Radar (SAR) is an active remote sensing technology that images objects by emitting microwaves and receiving signals reflected from ground objects. Engineering surveying instruments are specialized equipment used to measure and determine the geometric position, distance, angle, elevation, and other parameters of ground points. The original coordinate datum includes, but is not limited to, regional independent coordinate datums, engineering independent coordinate datums, and radar coordinate datums. The number of target key points must be at least four; the coordinates of all remaining points represent the coordinates of other key points within the landslide area, excluding the target key points, under the original coordinate datum.

[0072] Step 120: Construct a key point coordinate mapping relationship by using the coordinates of the target key point under the original coordinate datum and the coordinates of the target key point under the CGCS2000 coordinate datum.

[0073] Specifically, based on the key point coordinates of the lidar measuring UAV, synthetic aperture radar and engineering measurement and monitoring methods under their respective original coordinate references, as well as the key point coordinates of these three monitoring methods under the CGCS2000 coordinate reference, key point coordinate mapping relationships are constructed respectively.

[0074] Step 130: Based on neighborhood density normalization, a seven-parameter coordinate transformation model is obtained through the key point coordinate mapping relationship and geographical weighting coefficient.

[0075] Specifically, based on these mapping relationships, a seven-parameter coordinate transformation model that takes into account geographical weighting is established under the premise of uneven geographical distribution of key points between the surface and underground. The influence of the geographical location of key points on the accuracy of the coordinate transformation is considered by assigning different weights to key points in different spatial locations.

[0076] Step 140: Based on the masking rules, filter out the pixels outside the landslide area and the pixels with low signal-to-noise ratio from the remaining coordinates of all points to obtain the filtered coordinates of all points, wherein the pixels with low signal-to-noise ratio represent pixels with a signal-to-noise ratio less than a preset threshold.

[0077] Specifically, a mask can be understood as a Boolean array that filters data based on specific conditions or patterns, where each element indicates whether the corresponding data point meets a certain condition. By applying a mask, data that meets the conditions can be filtered out efficiently. Cells with excessively low signal-to-noise ratios or not located in the landslide area are filtered out from the remaining point coordinates.

[0078] Step 150: Input the filtered coordinates of all remaining points into the seven-parameter coordinate transformation model to obtain the target CGCS2000 coordinate data.

[0079] Specifically, the filtered coordinates of all remaining points are input into the seven-parameter coordinate transformation model, which uniformly transforms the coordinates of the remaining points under different original coordinate datums to coordinates under the CGCS2000 coordinate datum. The spatial rectangular coordinates corresponding to the original coordinate datums of the multi-source monitoring methods are converted one by one to spatial rectangular coordinates under the CGCS2000 coordinate datum.

[0080] In this embodiment, actual monitoring data of the landslide area and the coordinates of key target points under the CGCS2000 coordinate datum are acquired using GNSS equipment, lidar-based UAVs, synthetic aperture radar, and engineering surveying instruments. The actual monitoring data includes the coordinates of the key target points under the original coordinate datum and the coordinates of all remaining points. A key point coordinate mapping relationship is established based on the correspondence between the coordinates of the key target points under the original coordinate datum and the coordinates of the key target points under the CGCS2000 coordinate datum. Based on neighborhood density normalization, and considering that geographical distribution density can cause model bias, a geographical weighting coefficient is introduced to obtain a seven-parameter coordinate transformation model, making the model more consistent with the landslide coordinate transformation scenario. Based on masking rules, pixels outside the landslide area and pixels with low signal-to-noise ratios are filtered out from the remaining point coordinates to obtain the filtered remaining point coordinates, improving data quality and reducing subsequent data processing costs. The remaining coordinates of all points after filtering are input into the seven-parameter coordinate transformation model to obtain the target CGCS2000 coordinate data. The coordinate reference of the multi-source landslide monitoring is unified to the CGCS2000 coordinate reference, thereby improving the coordinate transformation accuracy.

[0081] Optionally, obtaining the seven-parameter coordinate transformation model through the key point coordinate mapping relationship and geographic weighting coefficients includes:

[0082] The geographic weighting coefficient is obtained by measuring the sparsity of the geographic distribution of key points;

[0083] The seven-parameter coordinate transformation model is obtained based on the key point coordinate mapping relationship and the geographic weighting coefficient.

[0084] The seven-parameter coordinate transformation model is as follows:

[0085]

[0086] Among them, X i Y i Z i Let w be the 3D coordinate value of the i-th keypoint in the CGCS2000 coordinate system.i Here, ΔX0, ΔY0, and ΔZ0 are the geographic weighting coefficients for the i-th key point, ΔX0, ΔY0, and ΔZ0 are the translation coefficients for transforming the coordinates from the original coordinate system to the CGCS2000 coordinate system, m is the scale coefficient, and ω is the weighting coefficient for the i-th key point. X ω Y ω Z X′ is the rotation factor used to transform the coordinates from the original coordinate system to the CGCS2000 coordinate system. i Y′ i Z′ i Let be the three-dimensional coordinates of the i-th key point in the original coordinate system.

[0087] Specifically, in practical applications of landslide displacement field monitoring, due to the uneven geographical distribution of key points on the surface or underground, the conversion errors may differ between points in different regions due to factors such as data accuracy, terrain complexity, and acquisition methods. Therefore, the concept of geographical weighting is introduced, assigning a geographical weighting coefficient to each key point to reflect its impact on the overall conversion model during the conversion process.

[0088] In some more specific embodiments, combined with Figure 2 As shown, it includes the CGCS2000 coordinate system B and the original coordinate system A of each monitoring method. X b Y b Z b These are the three coordinate axes of the CGCS2000 coordinate system, X... a Y a Z a These are the three coordinate axes of the original coordinate system. ω X ω Y ω Z These are the rotation parameters for transforming the three coordinate axes from the original coordinate system to the CGCS2000 coordinate system, O A O B The resulting vector is the translation parameter for transforming from the original coordinate system to the CGCS2000 coordinate system.

[0089] In this optional embodiment, considering the impact of the geographical location of key points on the coordinate transformation accuracy, by assigning different weights to key points in different spatial locations, the spatial rectangular coordinates corresponding to the original coordinate reference of the multi-source monitoring method can be converted one by one into spatial rectangular coordinates under the CGCS2000 coordinate reference, and the transformation accuracy from the original coordinate reference to the CGCS2000 coordinate reference is improved compared with the traditional seven-parameter coordinate transformation model.

[0090] Optionally, obtaining the geographic weighting coefficient based on the sparsity of the geographic distribution of key points includes:

[0091] The density of the neighborhood of a key point is obtained by measuring the sparsity of its geographical distribution.

[0092] The neighborhood density of the key point is:

[0093]

[0094] Among them, D i Let N be the neighborhood density of the i-th keypoint. i Let be the spatial Euclidean distance between the i-th keypoint and its nearest keypoint;

[0095] The geographic weighting coefficient is obtained based on the neighborhood density of the key points;

[0096] The geographical weighting coefficient is:

[0097]

[0098] Among them, w i D is the geographic weighting coefficient for the i-th key point. min D is the minimum of the neighborhood densities of all keypoints. max It is the maximum value among the neighborhood densities of all keypoints.

[0099] Specifically, to quantify the geographical sparsity of key points, the neighborhood density of each point is calculated. Assuming a monitoring method has N key points, the density of each key point P can be calculated. i The distance to the specified nearest keypoint is used as the density metric. Keypoint P i Key point neighborhood density D i for:

[0100]

[0101] Among them, D i Let N be the neighborhood density of the i-th keypoint. i Let be the spatial Euclidean distance between the i-th keypoint and the nearest keypoint, where the nearest keypoint is the keypoint closest to the i-th keypoint.

[0102] The neighborhood density normalization formula is used to perform geographic weighting of keypoints in a seven-parameter coordinate transformation. The geographic weighting of all keypoints is standardized within the [0,1] interval. The geographic weighting coefficient is obtained based on the neighborhood density of the keypoints, where the geographic weighting coefficient is:

[0103]

[0104] Among them, w i D is the geographic weighting coefficient for the i-th key point. min D is the minimum neighborhood density of all keypoints.max This represents the maximum value of the neighborhood density of the keypoints among all keypoints.

[0105] Assume the spatial Euclidean distance between the sparsest key point and its nearest key point in the landslide area is N. a =300, the spatial Euclidean distance between the densest keypoint and its nearest neighbor keypoint is N. b =50, so w can be calculated. a =1, w b =0, and the geographical weighting coefficients of the remaining key points are all in the range of [0,1]. This formula can meet the actual needs of landslide displacement field monitoring application scenarios.

[0106] After resolving the geographical weighting principle and corresponding algorithm formula, in order to solve for the seven parameters of the coordinate transformation, the least squares method is needed to minimize the transformed CGCS2000 coordinates (X). i Y i Z i ) T Compared with the original coordinates (X′) i Y′ i Z′ i ) T The error between them needs to be minimized by the following objective function:

[0107]

[0108] in, This refers to minimizing the sum of the objective functions from the 1st to the nth keypoint, w i For key point P i The geographical weighting coefficient, m is the scale coefficient 1.00000, X i Y i Z i The key point is P. i The three-dimensional coordinate values ​​X′ in the original coordinate system. i Y′ i Z′ i is the 3D coordinate value of key point Pi in the CGCS2000 coordinate system, m is the scale factor, and the prior value is 1.00000.

[0109] Since the scaling coefficient has prior values, in fact only the three translation coefficients and three rotation coefficients in the formula need to be solved, requiring 6 observations, corresponding to 3D coordinate pairs of 2 key points. Each monitoring method requires at least 4 key points, and solving the formula can generate 6 redundant observations, which helps to improve the accuracy of solving coordinate transformation parameters and eliminate gross errors in individual coordinate data.

[0110] By differentiating the objective function through the corresponding interface in Matlab software, a model for the coordinate transformation parameter ω can be established.X ω Y ω Z The linear equations of ΔX0, ΔY0, and ΔZ0 are used to obtain the key parameters of the seven-parameter transformation model that takes into account geographical weighting.

[0111] In some more specific embodiments, combined with Figure 3 As shown, the parallelogram-shaped area represents the landslide area, and the circular points are key points, whose geographical distribution is uneven, with some areas sparse and others dense. The elliptical area represents a key point P. i The neighborhood of N, with radius N i This is its straight-line distance to the nearest key point. The arc above this point represents the geographic weighting function, and the height w of the arc directly above this point is... i This represents the geographical weighting coefficient. As shown in the figure, the larger the neighborhood (i.e., the sparser the points), the larger the geographical weighting coefficient; conversely, the smaller the neighborhood (i.e., the denser the points), the smaller the geographical weighting coefficient.

[0112] In this optional embodiment, due to non-ideal conditions such as landslide topography and vegetation cover, the geographical distribution of key points for various monitoring methods in the landslide displacement field is extremely uneven. While the planar and elevation distribution of surface monitoring methods in the multi-source data is relatively uniform, the overall number is low. Underground monitoring methods are mainly concentrated in landslide tunnels, with extremely weak uniformity in elevation and planar distribution of key points. Large gaps exist in the landslide body for both surface and underground monitoring methods. For key points with high geographical density, the small point spacing leads to significant spatial information redundancy. Including all key points equally in the coordinate transformation model may cause the model to overemphasize that area. Therefore, it is necessary to reduce the weight of these points to balance the impact of high-density data and avoid model bias during data fusion. Relatively speaking, from the perspective of landslide displacement field monitoring, areas with low key point density within the landslide region are often significantly affected by unstable factors such as vegetation cover and shallow topsoil retention. These factors are closely related to landslide disasters and need to be fully considered. Therefore, the geographical weighting coefficient for key points with low distribution density should be increased accordingly. By reasonably adjusting the geographical weighting coefficients of different key points, fully considering the distribution differences of key points in different areas within the landslide area, and reasonably participating in the solution of the seven unknown parameters in the coordinate transformation model.

[0113] Optionally, the step of filtering out pixels outside the landslide area and pixels with low signal-to-noise ratio from all remaining point coordinates to obtain the filtered remaining point coordinates includes:

[0114] The matrix composed of the coordinates of all remaining points is multiplied by the corresponding region mask matrix to obtain the region mask processing data, wherein the region mask matrix is ​​used to mark the cells located in the landslide area;

[0115] The remaining coordinates of all points are obtained by filtering the area masking data using signal-to-noise ratio (SNR) masking rules, wherein the SNR masking rules are used to filter pixels with low SNR based on the SNR.

[0116] Specifically, the remaining coordinates of all points include the remaining coordinates of the lidar-based UAV, the remaining coordinates of the synthetic aperture radar, and the remaining coordinates of the engineering surveying instruments. The filtered remaining coordinates, obtained by filtering out pixels outside the landslide area and pixels with low signal-to-noise ratios, include:

[0117] The filtered remaining coordinates of the lidar-measuring drone and the synthetic aperture radar are obtained by filtering out pixels outside the landslide area and pixels with low signal-to-noise ratio from the remaining coordinates of the lidar-measuring drone and the remaining coordinates of the synthetic aperture radar.

[0118] The remaining coordinates of the engineering surveying instrument, the filtered remaining coordinates of the lidar surveying UAV, and the remaining coordinates of the synthetic aperture radar are used as the filtered remaining coordinates of all points.

[0119] Unlike the "point-to-point" monitoring of GNSS and engineering surveying, the raw observation data of lidar surveying UAVs and synthetic aperture radar inevitably includes areas outside the landslide monitoring area, such as water bodies, towns, and farmland. The observation data from these areas is useless for landslide displacement field monitoring, increasing computation time and resources required for subsequent data processing. Furthermore, in subsequent data processing, the software program may incorrectly select reference points in these areas for atmospheric model correction, affecting the accuracy of the atmospheric model correction and reducing the overall solution accuracy. Therefore, it is necessary to filter out pixels outside the landslide area from the monitoring data acquired by lidar surveying UAVs and synthetic aperture radar. Since each pixel in the raw observation data of lidar and synthetic aperture radar contains its coordinates and radar echo signal-to-noise ratio (SNR) fields, it is feasible to filter out pixels outside the landslide area and pixels with low SNR by formulating masking rules. The interface provided by Matlab software, or the relevant data post-processing software of the corresponding monitoring method, allows for the creation of a region mask matrix and a signal-to-noise ratio (SNR) mask rule using an interactive interface. Each cell of the original observation data matrix is ​​multiplied one-to-one with the corresponding cell of the region mask matrix, and then, by applying the SNR mask rule, the data filtered by the mask matrix is ​​output.

[0120] In some more specific embodiments, combined with Figure 4As shown, the matrix of all remaining point coordinates is a 4x4 planar matrix, where each value represents the signal-to-noise ratio (SNR) of a pixel. Matrix A for all remaining point coordinates is (15, 12, 35, 22, 26, 36, 38, 24, 10, 42, 25, 17, 22, 43, 27, 19). The landslide area mask matrix is ​​exported using software, consisting of 0s and 1s, where cells with a value of 0 represent non-landslide areas and cells with a value of 1 represent landslide areas. Landslide area mask matrix B is (0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0). Multiplying the matrix of all remaining point coordinates and the landslide area mask matrix one by one filters out non-landslide pixels, resulting in non-landslide pixels having a value of 0. The SNR mask rule is that if the SNR value is less than 20, it is changed to 0; otherwise, the original SNR value is output. After filtering out non-landslide pixels, the matrix is ​​further processed using a signal-to-noise ratio mask rule to remove low-signal-noise ratio pixels. At this point, the observed data represented by the matrix has been filtered out from non-landslide pixels and low-signal-noise ratio pixels. The matrix C after filtering out non-landslide pixels is (0, 0, 35, 22, 0, 36, 38, 24, 0, 42, 0, 0, 22, 43, 0, 0).

[0121] In this optional embodiment, the masking rules for monitoring data acquired by lidar-based UAVs and synthetic aperture radar include filtering dimensions based on both signal-to-noise ratio and monitoring area. This improves data clarity and accuracy, avoids inaccurate phase calculations that could affect the detection and measurement of surface deformation, and reduces computation time and computational resource investment.

[0122] Optionally, the acquisition of actual monitoring data of the landslide area and the coordinates of key target points under the CGCS2000 coordinate datum through GNSS equipment, lidar measurement drones, synthetic aperture radar, and engineering surveying instruments includes:

[0123] The actual monitoring data were obtained by monitoring the landslide area using the lidar-based UAV, the synthetic aperture radar, and the engineering surveying instrument, respectively.

[0124] The coordinates of the target key points in the landslide area under the CGCS2000 coordinate datum are obtained by the GNSS equipment, the lidar measurement UAV, the synthetic aperture radar, and the engineering surveying instrument. The coordinates of the target key points under the CGCS2000 coordinate datum include the CGCS2000 key point coordinates of the lidar measurement UAV, the CGCS2000 key point coordinates of the synthetic aperture radar, and the CGCS2000 key point coordinates of the engineering surveying instrument.

[0125] Optionally, the GNSS equipment includes an RTK device connected to the BeiDou ground-based augmentation system. The step of obtaining the coordinates of the target key points in the landslide area under the CGCS2000 coordinate datum using the GNSS equipment, the lidar-based UAV, the synthetic aperture radar, and the engineering surveying instrument includes:

[0126] The coordinates of key points of the landslide area obtained by the RTK device connected to the BeiDou ground-based augmentation system, the lidar measurement drone, and the synthetic aperture radar are obtained by the lidar measurement drone CGCS2000 key point coordinates and the synthetic aperture radar CGCS2000 key point coordinates.

[0127] The coordinates of key points of the landslide area were obtained by jointly measuring with the engineering surveying instrument CGCS2000, which is used to set up forced centering observation piers.

[0128] Specifically, GNSS equipment includes, but is not limited to, GNSS continuous tracking station equipment, GNSS static observation equipment, and RTK equipment. GNSS continuous tracking station equipment is a fixed-installation receiver capable of receiving signals from multiple satellite systems continuously for extended periods to provide high-precision position information. GNSS static observation equipment refers to long-term observation of a specific location over a certain period to obtain high-precision position data. RTK equipment mainly consists of a base station and a rover. Synthetic aperture radar includes, but is not limited to, satellite-based synthetic aperture radar deployed on satellite platforms and ground-based synthetic aperture radar deployed on the ground. Engineering surveying instruments include, but are not limited to, total stations, levels, and 3D laser scanners.

[0129] Access to the BeiDou ground-based augmentation system is defined as the behavior that simultaneously meets the following three conditions: (1) using mobile communication signals or a self-provided radio for data transmission; (2) verifying through account password; (3) receiving the real-time differential signal of the fixed solution of the reference station of the BeiDou ground-based augmentation system, the key points of the laser radar measuring UAV are the key points of the image control marker, and the key points of the synthetic aperture radar include, but are not limited to, the points on both sides of the ground-based synthetic aperture radar slide rail, the phase center point of the satellite-based synthetic aperture radar corner reflector, and the phase center point of the ground-based synthetic aperture radar corner reflector.

[0130] In some more specific embodiments, combined with Figure 5As shown, the shaded area represents the landslide profile. The landslide tunnel passes under the landslide, and the gray dashed line indicates the tunnel's path. Key points and engineering surveying instruments are located inside the landslide tunnel. The white square sloping area of ​​the triangular prism represents the landslide surface area. The GNSS continuous tracking station, synthetic aperture radar corner reflector, and UAV image control marker are located on the landslide surface. The lidar surveying UAV is located at low altitude, while the GNSS satellite and synthetic aperture radar satellite are located in space. The ground-based synthetic aperture radar is located in an area far from the landslide and with a clear line of sight. Displacement field monitoring of the landslide was conducted using GNSS equipment, lidar-based UAVs, synthetic aperture radar (SAR), and engineering surveying instruments. Monitoring data, including the coordinates of key points and all remaining points, was acquired based on different original coordinate references. Data from the BeiDou ground-based augmentation system (BDA) reference station was used to calculate and obtain the CGCS2000 coordinates of the GNSS continuous tracking station on the landslide surface. RTK equipment connected to the BDA was used to acquire the CGCS2000 coordinates of key points on the landslide surface from the lidar-based UAV and the SAR. Forced-alignment observation piers were deployed near the landslide tunnel entrance for static GNSS observation. Data from the BDA reference station was used to calculate the GNSS static observation data, obtaining the CGCS2000 coordinates of the forced-alignment observation piers. Finally, engineering surveying instruments were used to jointly measure the monitoring points inside the landslide tunnel and the forced-alignment observation piers at the tunnel entrance, obtaining the CGCS2000 coordinates of the key points in the landslide tunnel.

[0131] In some more specific embodiments, combined with Figures 6-9 As shown, Figure 6 This is a plan view of the image control markers for a lidar-guided drone. The gray area represents the image control markers for the lidar-guided drone, which are painted red on the ground. The markers are 1m long at the bottom and 0.3m long at the top. The points indicated by the circles are key points where CGCS2000 coordinates need to be obtained using RTK equipment. Figure 7 This is a side view of a ground-based synthetic aperture radar (SAR) corner reflector. It is made entirely of stainless steel and is a single-sided, openwork, triangular-shaped SAR corner reflector with a side length of 1.5m. The triangular area to the left of the dashed line represents the openwork side facing away from the radar. The point indicated by the circle is a key point for obtaining CGCS2000 coordinates using RTK equipment. Figure 8 This is a side view of a satellite-based synthetic aperture radar (SAR) corner reflector. It is made entirely of stainless steel and is a single-sided, openwork, triangular-shaped SAR corner reflector with a side length of 1.5m. The white triangular area represents the openwork side facing away from the radar, while the other two gray triangles are shaded areas used to enhance the three-dimensional effect of the image. The circled point indicates a key point for obtaining CGCS2000 coordinates using RTK equipment. Figure 9This is a side view of a ground-based synthetic aperture radar. The truncated pyramid below represents the equipment's slide rail, which is 3.7m long. The slide rail supports the square main unit, and to the right (front) of the main unit is a horn-shaped radar wave transmitter and receiver. The points indicated by the circles on both sides of the slide rail are key points where CGCS2000 coordinates need to be obtained using RTK equipment.

[0132] Optionally, the engineering surveying instrument includes a total station and a level. The key point coordinates of the CGCS2000 engineering surveying instrument include the plane coordinates and elevation coordinates of the key points. The step of obtaining the key point coordinates of the CGCS2000 engineering surveying instrument in the landslide area by jointly measuring with the deployment of forced centering observation piers and the engineering surveying instrument includes:

[0133] The plane coordinates of the key points were obtained by setting up the forced centering observation pier at the entrance of the landslide tunnel and using the total station to perform traverse surveying.

[0134] The elevation coordinates of the key points are obtained by setting up the forced centering observation pier and the leveling instrument at the entrance of the landslide tunnel and conducting closed-loop leveling measurements.

[0135] Specifically, in combination Figure 10 As shown in the figure, the dotted line area represents the tunnel. The area near the landslide tunnel entrance is defined as the region that simultaneously meets the following three conditions: (1) the straight-line distance between the landslide tunnel entrance and the entrance is no more than 200 meters; (2) it forms a line of sight with the key point inside the landslide tunnel that is closest to the entrance, ensuring that it is within the line of sight area, rather than an area without line of sight; (3) there are no mountains or vegetation obstructing the view directly above. The key point has the following plane coordinates (x, y coordinates) and elevation coordinates (z coordinates).

[0136] like Figure 11 As shown in the figure, an embodiment of the present invention provides a unified coordinate device for multi-source landslide monitoring data, comprising:

[0137] The data acquisition module 10 is used to acquire actual monitoring data of the landslide area and the coordinates of key target points under the CGCS2000 coordinate datum through GNSS equipment, lidar measurement drone, synthetic aperture radar and engineering surveying instruments. The actual monitoring data includes the coordinates of the key target points under the original coordinate datum and the coordinates of all remaining points.

[0138] The mapping relationship acquisition module 20 is used to construct a key point coordinate mapping relationship by using the coordinates of the target key point under the original coordinate reference and the coordinates of the target key point under the CGCS2000 coordinate reference.

[0139] The coordinate transformation model acquisition module 30 is used to obtain a seven-parameter coordinate transformation model based on neighborhood density normalization, through the key point coordinate mapping relationship and geographical weighting coefficients.

[0140] The data filtering module 40 is used to filter the pixels outside the landslide area and the pixels with low signal-to-noise ratio in the remaining coordinates of all points based on masking rules to obtain the filtered remaining coordinates of all points, wherein the pixels with low signal-to-noise ratio represent pixels with a signal-to-noise ratio less than a preset threshold.

[0141] The CGCS2000 coordinate acquisition module 50 is used to input the filtered coordinates of all remaining points into the seven-parameter coordinate transformation model to obtain the target CGCS2000 coordinate data.

[0142] The unified coordinate device for multi-source landslide monitoring data in this embodiment is used to implement the unified coordinate method for multi-source landslide monitoring data as described above. Its advantages over the prior art are the same as those of the unified coordinate method for multi-source landslide monitoring data compared to the prior art, and will not be repeated here.

[0143] Optionally, the coordinate transformation model acquisition module 30 is specifically used to: obtain the geographic weighting coefficient by the sparsity of the geographic distribution of key points;

[0144] The seven-parameter coordinate transformation model is obtained based on the key point coordinate mapping relationship and the geographic weighting coefficient.

[0145] The seven-parameter coordinate transformation model is as follows:

[0146]

[0147] Among them, X i Y i Z i Let w be the 3D coordinate value of the i-th keypoint in the CGCS2000 coordinate system. i Here, ΔX0, ΔY0, and ΔZ0 are the geographic weighting coefficients for the i-th key point, ΔX0, ΔY0, and ΔZ0 are the translation coefficients for transforming the coordinates from the original coordinate system to the CGCS2000 coordinate system, m is the scale coefficient, and ω is the weighting coefficient for the i-th key point. X ω Y ω Z X′ is the rotation factor used to transform the coordinates from the original coordinate system to the CGCS2000 coordinate system. i Y′ i Z′ i Let be the three-dimensional coordinates of the i-th key point in the original coordinate system.

[0148] Optionally, the coordinate transformation model acquisition module 30 is specifically used to: obtain the neighborhood density of key points by the sparsity of the geographical distribution of key points;

[0149] The neighborhood density of the key point is:

[0150]

[0151] Among them, D i Let N be the neighborhood density of the i-th keypoint. i Let be the spatial Euclidean distance between the i-th keypoint and its nearest keypoint;

[0152] The geographic weighting coefficient is obtained based on the neighborhood density of the key points;

[0153] The geographical weighting coefficient is:

[0154]

[0155] Among them, w i D is the geographic weighting coefficient for the i-th key point. min D is the minimum of the neighborhood densities of all keypoints. max It is the maximum value among the neighborhood densities of all keypoints.

[0156] Optionally, the data filtering module 40 is specifically used to: multiply the matrix composed of the coordinates of all remaining points with the corresponding region mask matrix to obtain region mask processing data, wherein the region mask matrix is ​​used to mark the pixels located in the landslide area;

[0157] The remaining coordinates of all points are obtained by filtering the area masking data using signal-to-noise ratio (SNR) masking rules, wherein the SNR masking rules are used to filter pixels with low SNR based on the SNR.

[0158] Optionally, the data acquisition module 10 is specifically used to: monitor the landslide area using the lidar measuring drone, the synthetic aperture radar, and the engineering surveying instrument to obtain the actual monitoring data;

[0159] The coordinates of the target key points in the landslide area under the CGCS2000 coordinate datum are obtained by the GNSS equipment, the lidar measurement UAV, the synthetic aperture radar, and the engineering surveying instrument. The coordinates of the target key points under the CGCS2000 coordinate datum include the CGCS2000 key point coordinates of the lidar measurement UAV, the CGCS2000 key point coordinates of the synthetic aperture radar, and the CGCS2000 key point coordinates of the engineering surveying instrument.

[0160] Optionally, the data acquisition module 10 is specifically used to: acquire the coordinates of the key points of the CGCS2000 lidar measurement drone and the key points of the CGCS2000 synthetic aperture radar in the landslide area through the RTK device connected to the BeiDou ground-based augmentation system, the lidar measurement drone, and the synthetic aperture radar;

[0161] The coordinates of key points of the landslide area were obtained by jointly measuring with the engineering surveying instrument CGCS2000, which is used to set up forced centering observation piers.

[0162] Optionally, the data acquisition module 10 is specifically used to: obtain the plane coordinates of the key points by setting up the forced centering observation pier at the landslide tunnel entrance and using the total station to perform traverse surveying;

[0163] The elevation coordinates of the key points are obtained by setting up the forced centering observation pier and the leveling instrument at the entrance of the landslide tunnel and conducting closed-loop leveling measurements.

[0164] like Figure 12 As shown in the figure, an electronic device provided by an embodiment of the present invention includes a memory 10 and a processor 20; the memory 10 is used to store a computer program; the processor 20 is used to implement the multi-source landslide monitoring data unified coordinate method as described above when the computer program is executed.

[0165] Alternatively, an electronic device includes a memory 10 and a processor 20 coupled to the memory 10; the memory 10 is configured to store a computer program; and the processor 20 is configured to perform the following operations when the computer program is executed:

[0166] The actual monitoring data of the landslide area and the coordinates of the target key points under the CGCS2000 coordinate datum are obtained by using GNSS equipment, lidar measurement drones, synthetic aperture radar and engineering surveying instruments. The actual monitoring data includes the coordinates of the target key points under the original coordinate datum and the coordinates of all remaining points.

[0167] A key point coordinate mapping relationship is constructed by using the coordinates of the target key points under the original coordinate datum and the coordinates of the target key points under the CGCS2000 coordinate datum.

[0168] Based on neighborhood density normalization, a seven-parameter coordinate transformation model is obtained through the key point coordinate mapping relationship and geographical weighting coefficients.

[0169] Based on the masking rules, the remaining coordinates of all points are obtained by filtering out the pixels outside the landslide area and the pixels with low signal-to-noise ratio. The pixels with low signal-to-noise ratio are those with a signal-to-noise ratio less than a preset threshold.

[0170] The filtered coordinates of all remaining points are input into the seven-parameter coordinate transformation model to obtain the target CGCS2000 coordinate data.

[0171] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the method for unifying coordinates of multi-source landslide monitoring data as described above.

[0172] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations:

[0173] The actual monitoring data of the landslide area and the coordinates of the target key points under the CGCS2000 coordinate datum are obtained by using GNSS equipment, lidar measurement drones, synthetic aperture radar and engineering surveying instruments. The actual monitoring data includes the coordinates of the target key points under the original coordinate datum and the coordinates of all remaining points.

[0174] A key point coordinate mapping relationship is constructed by using the coordinates of the target key points under the original coordinate datum and the coordinates of the target key points under the CGCS2000 coordinate datum.

[0175] Based on neighborhood density normalization, a seven-parameter coordinate transformation model is obtained through the key point coordinate mapping relationship and geographical weighting coefficients.

[0176] Based on the masking rules, the remaining coordinates of all points are obtained by filtering out the pixels outside the landslide area and the pixels with low signal-to-noise ratio. The pixels with low signal-to-noise ratio are those with a signal-to-noise ratio less than a preset threshold.

[0177] The filtered coordinates of all remaining points are input into the seven-parameter coordinate transformation model to obtain the target CGCS2000 coordinate data.

[0178] The present invention will now describe electronic devices that can serve as servers or clients of the present invention, which are examples of hardware devices that can be applied to various aspects of the present invention. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0179] Electronic devices include a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0180] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0181] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A multi-source landslide monitoring data unified coordinate method, characterized in that, The method comprises the following steps: acquiring actual monitoring data of a landslide area and coordinates of target key points under a CGCS2000 coordinate datum by a GNSS device, a laser radar measuring unmanned aerial vehicle, a synthetic aperture radar, and an engineering surveying instrument, wherein the actual monitoring data comprises coordinates of the target key points under an original coordinate datum and coordinates of all remaining points; constructing a key point coordinate mapping relationship by the coordinates of the target key points under the original coordinate datum and the coordinates of the target key points under the CGCS2000 coordinate datum; obtaining a seven-parameter coordinate conversion model based on neighborhood density normalization by the key point coordinate mapping relationship and a geographic weighting coefficient, comprising: obtaining the geographic weighting coefficient by a geographic distribution sparsity degree of key points; obtaining the seven-parameter coordinate conversion model according to the key point coordinate mapping relationship and the geographic weighting coefficient; wherein the seven-parameter coordinate conversion model is: wherein X i , Y i , Z i are three-dimensional coordinate values of the i-th key point in the CGCS2000 coordinate system, w i is the geographical weighting coefficient of the i-th key point, ΔX0, ΔY0, ΔZ0 are translation coefficients for converting coordinates from the original coordinate system to the CGCS2000 coordinate system, m is a scale coefficient, ω X , ω Y , ω Z are rotation coefficients for converting coordinates from the original coordinate system to the CGCS2000 coordinate system, X′ i , Y′ i , Z′ i are three-dimensional coordinate values of the i-th key point in the original coordinate system; the obtaining of the geographic weighting coefficient by the geographic distribution sparsity degree of key points comprises: obtaining a key point neighborhood density by the geographic distribution sparsity degree of key points; wherein the key point neighborhood density is: wherein D i is the keypoint neighborhood density of the i-th keypoint, N i is the spatial Euclidean distance between the i-th keypoint and the nearest neighbor keypoint; obtaining the geographic weighting coefficient according to the key point neighborhood density; wherein the geographic weighting coefficient is: wherein w i is the geographical weighting coefficient for the i-th key point, D min is the minimum value among the key point neighborhood densities for all key points, D max is the maximum value among the key point neighborhood densities for all key points. filtering pixels outside the landslide area and pixels with low signal-to-noise ratio in the all remaining points to obtain filtered all remaining points based on a mask rule, wherein the pixels with low signal-to-noise ratio represent pixels with a signal-to-noise ratio less than a preset threshold; inputting the filtered all remaining points into the seven-parameter coordinate conversion model to obtain target CGCS2000 coordinate data.

2. The multi-source landslide monitoring data unified coordinate method according to claim 1, characterized in that, the filtering of pixels outside the landslide area and pixels with low signal-to-noise ratio in the all remaining points to obtain the filtered all remaining points comprises: multiplying a matrix composed of the all remaining points and a region mask matrix to obtain region mask processing data, wherein the region mask matrix is used to mark pixels located in the landslide area; filtering the region mask processing data by a signal-to-noise ratio mask rule to obtain the filtered all remaining points, wherein the signal-to-noise ratio mask rule is used to filter pixels with low signal-to-noise ratio according to signal-to-noise ratio.

3. The multi-source landslide monitoring data unified coordinate method of claim 1, wherein, the acquiring of actual monitoring data of a landslide area and coordinates of target key points under a CGCS2000 coordinate datum by a GNSS device, a laser radar measuring unmanned aerial vehicle, a synthetic aperture radar, and an engineering surveying instrument comprises: respectively monitoring the landslide area by the laser radar measuring unmanned aerial vehicle, the synthetic aperture radar, and the engineering surveying instrument to obtain the actual monitoring data; The GNSS device, the laser radar measurement unmanned aerial vehicle, the synthetic aperture radar, and the engineering surveying instrument are used to acquire coordinates of the target key points of the landslide area under the CGCS2000 coordinate reference, wherein the coordinates of the target key points under the CGCS2000 coordinate reference include laser radar measurement unmanned aerial vehicle CGCS2000 key point coordinates, synthetic aperture radar CGCS2000 key point coordinates, and engineering surveying instrument CGCS2000 key point coordinates.

4. The multi-source landslide monitoring data unified coordinate method according to claim 3, characterized in that, The GNSS device includes an RTK device accessing a Beidou ground-based augmentation system, and the coordinates of the target key points of the landslide area under the CGCS2000 coordinate reference are acquired by the GNSS device, the laser radar measurement unmanned aerial vehicle, the synthetic aperture radar, and the engineering surveying instrument, including: The laser radar measurement unmanned aerial vehicle CGCS2000 key point coordinates and the synthetic aperture radar CGCS2000 key point coordinates of the landslide area are acquired by the RTK device accessing the Beidou ground-based augmentation system, the laser radar measurement unmanned aerial vehicle, and the synthetic aperture radar; The engineering surveying instrument CGCS2000 key point coordinates of the landslide area are acquired by joint measurement of the forced centering observation pier and the engineering surveying instrument.

5. The multi-source landslide monitoring data unified coordinate method according to claim 4, characterized in that, The engineering surveying instrument includes a total station and a level, the engineering surveying instrument CGCS2000 key point coordinates include key point plane coordinates and key point elevation coordinates, and the engineering surveying instrument CGCS2000 key point coordinates of the landslide area are acquired by joint measurement of the forced centering observation pier and the engineering surveying instrument, including: The key point plane coordinates are obtained by joint traverse measurement of the forced centering observation pier and the total station at a landslide tunnel portal; The key point elevation coordinates are obtained by closed route leveling measurement of the forced centering observation pier and the level at the landslide tunnel portal.

6. A multi-source landslide monitoring data unified coordinate device, characterized in that, The method includes: A data acquisition module is configured to acquire actual monitoring data and coordinates of target key points of a landslide area under a CGCS2000 coordinate reference by a GNSS device, a laser radar measurement unmanned aerial vehicle, a synthetic aperture radar, and an engineering surveying instrument, wherein the actual monitoring data includes coordinates of the target key points under an original coordinate reference and coordinates of all remaining points; A mapping relationship acquisition module is configured to construct a key point coordinate mapping relationship based on the coordinates of the target key points under the original coordinate reference and the coordinates of the target key points under the CGCS2000 coordinate reference; A coordinate conversion model acquisition module is configured to obtain a seven-parameter coordinate conversion model based on neighborhood density normalization, the key point coordinate mapping relationship, and a geographic weighting coefficient, including: The geographic weighting coefficient is obtained based on a sparsity degree of key point geographic distribution; The seven-parameter coordinate conversion model is obtained based on the key point coordinate mapping relationship and the geographic weighting coefficient; The seven-parameter coordinate conversion model is: wherein X i , Y i , Z i are the three-dimensional coordinate values of the i-th key point in the CGCS2000 coordinate system, w i is the geographical weighting coefficient of the i-th key point, ΔX0, ΔY0, ΔZ0 are translation coefficients for converting coordinates from the original coordinate system to the CGCS2000 coordinate system, m is a scale coefficient, ω X , ω Y , ω Z are rotation coefficients for converting coordinates from the original coordinate system to the CGCS2000 coordinate system, X′ i , Y′ i , Z′ i are the three-dimensional coordinate values of the i-th key point in the original coordinate system. The geographic weighting coefficient is obtained based on a sparsity degree of key point geographic distribution, including: The key point neighborhood density is obtained according to the key point geographical distribution sparsity degree; The key point neighborhood density is: wherein D i is the keypoint neighborhood density of the i-th keypoint, N i is the spatial Euclidean distance between the i-th keypoint and the nearest keypoint. The geographical weighting coefficient is obtained according to the key point neighborhood density; The geographical weighting coefficient is: wherein w i is the geographical weighting coefficient for the i-th key point, D min is the minimum value among the key point neighborhood densities for all key points, D max is the maximum value among the key point neighborhood densities for all key points. The data filtering module is configured to filter, based on a mask rule, pixels outside the landslide area and low signal-to-noise ratio pixels from the remaining all point coordinates to obtain filtered remaining all point coordinates, wherein the low signal-to-noise ratio pixels represent pixels with a signal-to-noise ratio less than a preset threshold. The CGCS2000 coordinate acquisition module is configured to input the filtered remaining all point coordinates into the seven-parameter coordinate conversion model to obtain target CGCS2000 coordinate data.

7. An electronic device, comprising: comprising a memory and a processor; The memory is configured to store a computer program; The processor is configured to implement the multi-source landslide monitoring data unified coordinate method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that, The storage medium has a computer program stored thereon, and the computer program, when executed by a processor, implements the multi-source landslide monitoring data unified coordinate method according to any one of claims 1 to 5.

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